gamma 1 (CACNG1) were identified in the turquoise module which was negatively correlated with tissue and had higher expression levels in the breast muscle. Some immune-related genes such as RAD52 motif containing 1 (RMD1), class I histocompatibility antigen, F10 alpha chain-like (HA1F), and MHC B-G antigen genes were identified as key driver genes in the blue module and highly expressed in the Leghorn line. The apoptosis inducing factor, mitochondria associated 2 (AIFM2) gene was identified as a driver gene for both pH and sO₂ levels. Only 1 driver gene, NADH-ubiquinone oxidoreductase chain 2 (MT-ND2) was identified associated with glucose levels. There were 245 genes in the blue module, in which 145 genes were identified as driver genes correlated with line, 11 driver genes with pH levels, 20 driver genes with PO₂, and 40 driver genes with sO₂.

GO terms from the biological process and KEGG pathways were further analyzed to understand the biological functions of driver genes in significantly correlated gene modules. Genes in the black module, which had higher expression levels in the hypothalamus, enriched 1 GO term: inter-kinetic nuclear migration. Seventy-seven GO terms and 15 KEGG pathways were enriched by the top-3,000 genes in the turquoise module that are highly expressed in the breast muscle. The top-5 enriched GO terms and KEGG pathways are listed in Figure 22. Biological functions enriched by most of these genes were related to muscle contraction and the glycolytic process in the metabolism function, which was not a surprise. Two GO terms, DNA-templated transcription, and initiation and nucleosome assembly were enriched by genes in the blue module and highly expressed in the Leghorn line. No GO terms and KEGG pathways were enriched by driver genes correlated with other traits which may be due to the lower number of genes.

The blue module was the most interesting one because it correlated with the genetic line as well as multiple phenotypic traits. A gene network of all genes in this module was generated (Fig. 23). More metabolic genes served as node genes in the network. Metabolic genes and immune-related genes were relatively clustered in the module, respectively.

The reported study is a part of the Innovation Lab for Genomics to Improve Poultry (GIP: http://gip.ucdavis.edu), which aims to genetically enhance resistance to heat stress and NDV infection in African poultry. Effects of the combination of both biotic (NDV inoculation) and abiotic (heat stress) stressors on the same two inbred lines (Leghorn: relatively susceptible and Fayoumi: relatively resistant used as experimental lines) have been investigated in previous studies [Deist et al., 2017, 2018; Deist and Lamont, 2018; Saelao et al., 2018; Wang Y et al., 2018a, 2020; Zhang J et al., 2018] as part of this program, with the Fayoumi line (originating in Egypt) as a representative of local African type chickens.

<table><caption>**Table 6.** Significantly correlated traits and gene modules</caption><thead><tr><th rowspan="2">Traits</th><th colspan="4">Module</th></tr><tr><th>Turquoise</th><th>Blue</th><th>Black</th><th>Yellow</th></tr></thead><tbody><tr><td>Tissue</td><td><strong>-1</strong></td><td>0.15</td><td><strong>0.60</strong></td><td>-0.14</td></tr><tr><td>Line</td><td>0.01</td><td><strong>0.96</strong></td><td>-0.03</td><td>-0</td></tr><tr><td>CH&amp;NDV</td><td>-0</td><td>-0</td><td>-0.2</td><td><strong>-0.29</strong></td></tr><tr><td>pH</td><td>-0.01</td><td><strong>-0.46</strong></td><td>0.2</td><td>0.23</td></tr><tr><td>PCO<sub>2</sub></td><td>-0.01</td><td>0.16</td><td>0.09</td><td><strong>-0.29</strong></td></tr><tr><td>HCO<sub>3</sub></td><td>-0.01</td><td><strong>-0.31</strong></td><td>-0.07</td><td>-0.1</td></tr><tr><td>TCO<sub>2</sub></td><td>-0.01</td><td><strong>-0.28</strong></td><td>-0.05</td><td>-0.08</td></tr><tr><td>BE</td><td>-0.01</td><td><strong>-0.40</strong></td><td>0.01</td><td>-0</td></tr><tr><td>PO<sub>2</sub></td><td>-0.01</td><td><strong>-0.50</strong></td><td>0.09</td><td>0.08</td></tr><tr><td>sO<sub>2</sub></td><td>-0.01</td><td><strong>-0.53</strong></td><td>0.13</td><td>0.13</td></tr><tr><td>Na<sup>+</sup></td><td>-0.01</td><td><strong>0.25</strong></td><td>-0.06</td><td>-0.08</td></tr><tr><td>Glu</td><td>0</td><td><strong>-0.45</strong></td><td>-0.17</td><td>-0.22</td></tr></tbody></table>

As demonstrated by the physiological responses of these two lines with the AH and CH&NDV treatments, the relatively heat tolerant Fayoumi birds were able to maintain electrolyte levels, respiratory alkalosis, and metabolic acidosis. Lower levels of TCO₂, HCO₃, and BE, higher levels of PO₂ and sO₂ in Fayoumi birds demonstrated heat resistance characteristics, while higher levels of iCa²⁺ and glucose and lower levels of sO₂ in Leghorn birds indicated heat stress susceptibility [Wang Y et al., 2018a]. Global transcriptome profile surveys of the host response to heat stress under NDV infection, or NDV infection alone, have been studied not only in organs where viral replication occurs: Harderian gland, lung, and trachea [Deist et al., 2017, 2018; Deist and Lamont, 2018; Saelao et al., 2018, 2021] but also of the liver, as a representative of a highly metabolic organ [Wang Y et al., 2020]. In the liver transcriptome study, Leghorn birds responded to heat stress and NDV infection mostly by regulating metabolism function. However, Fayoumi birds recruited many immune-related genes, which may regulate both metabolism and immune function to respond to heat stress and viral infection. Results from the liver transcriptome analysis provided insights into how Fayoumi birds are heat and viral infection resilient by activating immune functions even during acute heat stress without viral infection. The surveillance of the Fayoumi immune system was much more sensitive and active than in the
HYP&BME AH&CH module-trait relationships

<figure><img src="image_23.png" /></figure>

(For legend see next page.)
Leghorns, which could be one of the reasons why Fayoumi birds are more resilient to both heat stress and viral infection.

When birds are exposed to heat stress, the neuroendocrine system is the first responder, in which the hypothalamus is one of the key regulators for temperature regulation [Chen et al., 2015]. The hypothalamus belongs to the hypothalamic-pituitary-adrenal (HPA) axis, which is one of the two stress axes and can release glucocorticoids to circulate in the peripheral system. In circulation, glucocorticoids interact with a wide variety of cells to regulate both metabolic and immune functions [Nawaz et al., 2021]. Importantly in poultry, glucocorticoids promote proteolysis by damaging myofibrils in skeletal muscles through the ubiquitin-proteasome system which has negative effects on muscle metabolism and meat quality [Bell et al., 2016]. We observed gene expression regulation differences in the liver of heat resistant or susceptible lines. A more comprehensive understanding of the heat stress response from the upstream sensing by the hypothalamus to the downstream effectors such as the skeletal muscle can be obtained by profiling the transcriptomes of both hypothalamus and breast muscle from the same birds. The current study is the first to investigate transcriptome response in hypothalamus and breast muscle, two important organs related to heat stress, under both acute heat stress and chronic heat stress combined with NDV infection in the two inbred lines, which can further elucidate the specific molecular mechanism for heat stress resilience in poultry.

## Relatively Mild Response in the Hypothalamus

In general, the hypothalamus had a mild response under both AH and CH&NDV treatments in the two genetic lines with very limited DEGs. Fewer DEGs (41 in LTLCAH, 35 in FTFCAH, 10 in LTLCCH, and 45 in FT-FCCH) was what we expected based on other chicken hypothalamus transcriptome studies [Sun et al., 2015; Tu et al., 2016]. The two genetic lines showed minor differences in the hypothalamic gene regulation with the treatments, which suggests gene regulation in the hypothalamus during heat stress may undergo only fine-tuning or that the action of the hypothalamus is in a non-genetic role, with effects seen in other target tissues.

As a thermo-regulator, the hypothalamus regulates metabolic and immunological functions by secreting glucocorticoids through the HPA axis. Target genes would participate in the amino acid, glycerol, lipo-biogenesis, muscle biogenesis, and glucose metabolism in the metabolic category [Goel et al., 2021]. Meanwhile, they would also involve in inflammatory expression and homeostasis of T lymphocytes [Honda et al., 2015]. Even with small numbers of DEGs, many heat shock protein genes were identified under both treatments, and a few immune-re-

<table><caption>**Table 7.** Top 5 driver genes in each significant trait-correlated gene module</caption><thead><tr><th>Factor</th><th>Correlation</th><th>Module color</th><th>Gene name (GS<sup>a</sup>, MM<sup>b</sup>)</th></tr></thead><tbody><tr><td rowspan="2">Tissue</td><td>Positive</td><td>Black (hypothalamus)</td><td>Novel (0.82, 0.92), ELP1 (0.76, 0.91), HINT2 (0.75, 0.94), BRIX1 (0.72, 0.97), RPP25L (0.71, 0.093)</td></tr><tr><td>Negative</td><td>Turquoise (breast muscle)</td><td>GRIN1 (-1, 1), TPM (-1, 1), VSTM2A (-1, 1), GABRE (-1, 1), GRIK2 (-1, 1)</td></tr><tr><td>Line</td><td>Positive</td><td>Blue (Leghorn)</td><td>KPFL (0.98, 0.94), Novel (0.98, 0.92), RMD1 (0.97, 0.89), KIFC1 (0.97, 0.92), HA1F (0.97, 0.91)</td></tr><tr><td>CH&amp;NDV</td><td>Negative</td><td>Yellow</td><td>AUH (-0.55, 0.68), SKP2 (-0.53, 0.74)</td></tr><tr><td>pH</td><td>Negative</td><td>Blue</td><td>TNFRSF8 (-0.61, 0.69), MRERF2 (0.60, -0.71), AIFM2 (0.55, -0.85), MOG (0.51, -0.73), HA1F (-0.53, 0.91)</td></tr><tr><td>PO<sub>2</sub></td><td>Negative</td><td>Blue</td><td>BLEC3 (-0.59, 0.78), KIFC1 (-0.56, 0.92), PYROXD2 (0.55, -0.71), ENSGALG00000039964 (-0.54, 0.94), ENSGALG00000054582 (-0.54, 0.92)</td></tr><tr><td>sO<sub>2</sub></td><td>Negative</td><td>Blue</td><td>AIFM2 (0.57, -0.85), BLEC2 (0.57, -0.88), ENSGALG00000049158 (0.56, -0.90), ENSGALG00000033498 (0.55, -0.92), Protein Manbal-like (0.54, -0.94)</td></tr><tr><td>Glu</td><td>Negative</td><td>Blue</td><td>MT-ND2 (0.50, -0.82)</td></tr></tbody></table>

**Fig. 21.** Module-trait relationships from WGCNA. Each module (y axis) is correlated with each phenotype (x axis); the correlation and p values were reported for each comparison. Strong positive correlations are coloured in red, and strong negative correlations are coloured in green. TrtAH, acute heat stress; TrtCH, chronic heat stress and 2 dpi NDV infection. <sup>a</sup> Correlation coefficients. <sup>b</sup> p values.

<footnote><sup>a</sup> Gene significance. <sup>b</sup> Module membership.</footnote>
<figure><img src="image_24.png" /><figcaption>**Fig. 22.** Top GO terms and KEGG pathways enriched by genes highly expressed in the turquoise module in the breast muscle.</figcaption></figure>

<figure><img src="image_25.png" /><figcaption>**Fig. 23.** The blue module gene network. Red highlighted dots indicate immune-related genes, blue highlighted dots metabolic genes. Gray highlighted dots show all other genes in the blue module.</figcaption></figure>
lated genes were identified in the two lines (Tables 4, 5). This is consistent with previous studies that heat shock family genes are mainly upregulated during heat stress [Hasan Siddiqui et al., 2020; Shehata et al., 2020]. For the genetic line-specific DEGs, immune-related genes were identified in both lines with the AH treatment. *MX1*, *IFIT5*, and *RSAD2* genes were downregulated in the Fayoumi line and *NR1D1* was downregulated in the Leghorn line. With the combination of heat stress and NDV infection, two immune-related genes, *TLX3* and *IFI6*, were upregulated in Leghorn birds (Table 4). These findings contrast with our earlier liver transcriptome results for Fayoumi birds, which had an earlier immune response to heat stress than Leghorns [Wang Y et al., 2020]. That both Fayoumi and Leghorn birds' hypothalamus regulates immune gene expression to deliver an immunological response is further supported by the GO analysis.

GO terms enriched by the upregulated DEGs in the two lines were still quite similar with acute heat stress, which contributed to protein folding and protein processing functions. Leghorn downregulated DEGs were involved in more aspects of metabolic functions, such as cholesterol, cellular glucose, circadian temperature homeostasis, glycogen biosynthesis process, and iron transportation, than Fayoumi downregulated DEGs. Interestingly, downregulated DEGs from both lines enriched immune-related functions such as the negative regulation of NF-κB signaling and negative regulation of toll-like receptor 4 signaling in the Leghorn line and defense response to the virus in the Fayoumi line. The *NR1D1* gene, downregulated in the Leghorn birds, was a contributor to the two functions. NR1D1 is a ligand-sensitive transcription factor that negatively regulates gene expression in metabolic and inflammatory processes [Pivovarova et al., 2016]. Downregulation of *NR1D1* may be one of the regulatory mechanisms used by the Leghorn birds to respond to heat stress. Three immune-related genes, *MX1*, *IFIT5*, and *RSAD2*, a part of biological function “defense response to virus,” were identified in Fayoumi birds when they were exposed to acute heat stress. The immune response triggered in the Fayoumi birds is more specific than in Leghorns.

## Breast Muscle Responds to Heat Stress under NDV Infection Differently in the Two Genetic Lines

For differential gene regulation, the breast muscle had more dramatic responses to both treatments, especially with chronic heat under NDV infection. Many heat shock protein family genes were upregulated in breast muscle. In breast muscle, upregulation of heat shock protein

genes can result in skeletal muscle remodeling to protect the muscle cell from damage [Abdelnour et al., 2019]. Functional GO terms, which were metabolically related and enriched by DEGs at the AH stage, were similar between the two lines, except for one immune-related GO term: positive regulation of T cell activation in the Fayoumi line. Two upregulated DEGs in Fayoumi with the AH treatment, Thy-1 cell surface antigen (*THY1*) and heat shock protein family D member 1 (*HSPD1*), contributed the most to this function. Therefore, Fayoumi birds had earlier and stronger immune responses than Leghorn birds, mediated by activating T cells.

More metabolic and immune functions were activated with the CH&NDV treatment in breast muscle of both genetic lines. Thermoregulation from the hypothalamus to downstream organs is modulated by glucocorticoids. Negative regulation of glucocorticoid receptor signaling pathway was enriched by DEGs in the Leghorn line. Two DEGs, cryptochrome circadian regulators 1 and 2 (*CRY1* and -2), play important roles in the activation of this pathway and both of them were upregulated with the CH&NDV infection in Leghorns. We speculate that Leghorn birds may increase the gene expression levels of *CRY1* and -2, subsequently reduce glucocorticoid receptor signaling [Lamia et al., 2011]. Meanwhile, the innate immune response was enriched by the upregulated Leghorn DEGs with both heat and viral stressors. Five DEGs, catelicidin-B1-like (*CATHB1*), tyrosine kinase non receptor 2 (*TKNR2*), joining chain of multimeric IgA and IgM (*JCHAIN*), tripartite motif containing 25 (*TRIM25*), and *MX1* worked together to activate this innate immune response.

The canonical pathway analysis by IPA provided additional insight about the differential response of these two genetic lines. The phospholipase C signaling was activated in Leghorn birds and inhibited in the Fayoumi line with the CH&NDV treatment (Fig. 20). This pathway belongs to the intracellular and second messenger signaling and is associated with cell signaling, molecular transport, and vitamin and mineral metabolism [Putney and Tomita, 2012]. In Leghorn birds, upregulation of the protein kinase C genes (*PKCs*) activated many downstream genes such as nuclear factor of activated T cell family (*NFAT*), histon deacetylase 3 (*HDAC*), cAMP responsive element binding protein 1 (*CREB*) and nuclear factor kappa-B (*NFkB*) complex and then promoted downstream gene expression. With the inhibition of this pathway, Fayoumi birds decreased the expression levels of above genes and then suppressed gene expression (Fig. 24). Calcium is an important secondary messenger
in the phospholipase C signaling pathway, and calcium signaling plays a critical role in muscle contraction [Zhu Y et al., 2019]. The calcium signaling pathway was also activated in Leghorns and inhibited in Fayoumi birds with the CH&NDV treatment (Fig. 25). Downregulation of Fayoumi DEGs may prevent Ca²⁺ transportation and then slow down cell growth and development. On the other hand, without the inhibition of Ca²⁺ transportation, Leghorn birds might be focusing on rapid cell growth, development, and inflammations. This is consistent with the GO term, potassium ion transport, enriched by the upregulated Fayoumi DEGs (Fig. 19f). Collectively, regulating mineral transportation might be one of the key differences between the two genetic lines in responding to chronic heat stress and NDV infection in the breast muscle.

## WGCNA Revealed Gene Modules and Driver Genes Important in the Response to Heat Stress

WGCNA has been applied to many transcriptome studies, especially for complex traits such as diseases in farm animals [Kogelman et al., 2014; Deist et al., 2017; Monson et al., 2018; Fan et al., 2021; Farhadian et al., 2021]. Genes that share a similar function are clustered in gene modules and genes associated with interesting traits can be identified [Langfelder and Horvath, 2008]. In the current study, tissue, line, treatment, and physiological parameters were used to identify potential important driver genes.

Four gene modules showed significant correlation with traits, with the blue module having the most correlated traits (9 traits). Genes highly expressed in the blue module are Leghorn-favored genes. These genes were also correlated with higher levels of Na⁺ and lower levels of pH, HCO₃, TCO₂, BE, PO₂, sO₂, and the glucose level in the blood. Heat resilient Fayoumi birds had higher oxygen-related parameters and susceptible Leghorn birds had higher glucose, iron, and lower sO₂ levels [Wang Y et al., 2018a]. Gene expression patterns in the blue module partially explained these physiological phenotypes with transcriptome data, in which genes highly expressed in Leghorn birds correlated with lower sO₂, and higher Na⁺ levels.

Potentially important genes were selected from the top driver genes for each significantly correlated gene module with different traits. TNF receptor superfamily member 8 (TNFRSF8) gene was the top driver gene negatively correlated with the pH levels. As a member of the TNF-receptor superfamily, the TNFRSF8 gene is expressed by active T and B cells and leads to the activation of NFκB [Lee et al., 1996; Morais-Perdigao et al., 2022]. Subsequently,

the lower pH level could be due to the activation of apoptosis by the *TNFRSF8* gene [Wang M et al., 2008]. The pH level is an important parameter for heat stress treatments, which affects several other blood gas parameters. Further investigation of *TNFRSF8*'s effect on heat tolerance is desired. *RDM1*, identified by our previous liver transcriptome study, was also identified in this study in these two tissues that are highly expressed in Leghorn birds. It is potentially one of the Leghorn signature genes for response to heat stress. The *AIFM2* gene was negatively correlated with both pH and sO₂. This gene can be induced by cold stress and contributes to apoptosis in the presence of bacterial and viral DNA with oxidoreductase and NADH dehydrogenase activities [Nguyen et al., 2020]. It correlates with thermoregulation and needs further validation for its multiple effects on both antiviral and metabolic functions. Only one driver gene, the *MT-ND2* gene, was correlated with glucose. MT-ND2 is the core subunit of the mitochondrial membrane respiratory chain NADH dehydrogenase which can catalyze electron transfer from NADH through the respiratory chain and is essential for the catalytic activity [Rhooms et al., 2020]. The variant on this gene was reported to be associated with glucose metabolism in skeletal muscle in rats [Houstek et al., 2012]. This gene requires further investigation into the molecular mechanisms involved in glucose metabolism during heat stress in chickens. Most of these driver genes were also the node genes on the gene network generated by the blue module genes (Fig. 23). *TNFRSF8* is on the boundary of the immune and metabolic gene clusters. *MT-ND2* is distant from the immune gene cluster and close to the solute carrier family 9 member B2 (*SLC9B2*) gene which contributes to the regulation of intracellular pH and sodium homeostasis [Anderegg et al., 2022]. Gene network analysis here demonstrated the interactions of genes of interest and provided more hypotheses for potential future studies.

To understand the distinct physiological responses during acute heat stress or chronic heat stress combined with NDV infection, transcriptome profiles of two metabolically associated organs, the hypothalamus and the breast muscle, were surveyed in the current study to elucidate the molecular mechanisms of host responses in relatively heat stress-resistant Fayoumi and susceptible Leghorn birds.

<caption>**Fig. 24.** Phospholipase C signaling pathway and gene heat map in within-line comparisons in the breast muscle. **a** Molecule activity prediction of the pathway in Leghorn birds with CH&NDV treatment. **b** Molecule activity prediction of the pathway in Fayoumi birds with AH. **c** DEG heatmap matching the pathway.</caption>

*(For figure see next page.)*
<figure><img src="image_26.png" /></figure>

a

<figure><img src="image_27.png" /></figure>

b
a

<figure><img src="image_28.png" /></figure>

(For legend see next page.)
<figure><img src="image_29.png" /></figure>

<figure><img src="image_30.png" /><figcaption>**Fig. 25.** Calcium signaling pathway and gene heat map in within-line comparisons in the breast muscle. **a** Molecule activity prediction of the pathway in Leghorn birds with CH&NDV treatment. **b** Molecule activity prediction of the pathway in Fayoumi birds with AH. **c** DEG heat map matching the pathway.</figcaption></figure>
horn chicken lines. Both lines responded to heat stress and NDV infection by stimulating metabolic and immune functions. The heat and NDV-resistant Fayoumi line had earlier, more active, and specific immune regulation in the breast muscle than the Leghorn line with both treatments. Genes highly expressed in Leghorns correlated with heat-susceptible physiological phenotypes. Important driver genes, gene modules, and interactive networks identified in the current study provide valuable information for future validation of molecular mechanisms of resistance and for developing novel breeding programs to improve heat and disease resistance in chickens.

*Statement of Ethics*

The animal experiment was performed according to the guidelines approved by the Institutional Animal Care and Use Committee, University of California, Davis (IACUC #17853).

*Conflict of Interest Statement*

The authors have no conflicts of interest to declare.

*Funding Sources*

This study was funded by the US Agency for International Development Feed the Future Innovation Lab for Genomics to Improve Poultry AID-OAAA-13-00080. Partial support was provided by the United States Department of Agriculture, National Institute of Food and Agriculture, Multistate Research Project NRSP8 and NE1834 (H.Z. and S.L.) and the California Agricultural Experimental Station (H.Z.).

*Data Availability Statement*

Sequencing data are available in the Short Read Archive (SRA) under accession number PRJNA896699.

**Chicken Repeatome**

(Prepared by Y. Bigot and P. Arensburger)

The repeatome gathers all repeated sequences found either in tandem or as interspersed sequences in the genome. Studies of the repeatome in bird genomes have so far been limited, despite its demonstrated importance in other vertebrate genomes. Indeed, the repeatome is a source of regulation and signalling for gene transcription as well as for the networks controlling some epigenetic marks, and for drivers of ectopic recombination events such as deletions, origination of new genes, functioning of telomeres, etc. This lack of interest in the bird repeatome was likely due to the suggestion that the reduced size of bird genomes was the result of depletions of useless sequences (i.e., non-genic repeated sequences) and the absence of activity of those repeats that were able to amplify by transposition [Wicker et al., 2005; Ellengren 2010;

Gao et al., 2017]. Since publication of the Third Report on Chicken Genes and Chromosomes in 2015 [Schmid et al., 2015] new data and analyses have dramatically changed our view of bird genomes, specifically with respect to transposable elements (TEs), GC-rich tandem repeats and their connections to the organization of bird genomes. Unfortunately, repeat annotation of bird genomes continues to be an understudied field. For example, of the four chicken genome models available in 2022 (galGal4-6, Ogye1.0, bGalGal1 GRCg7b and GRCg7w), only galGal4 and 5 have had both interspersed and tandem repeats annotated.

*Annotation of Repeats in Avian Genomes*

For most bird genomes repeats have been annotated automatically using a repeat database (most often Repbase [Kojima, 2020]) and a library-based annotation tool (usually RepeatMasker). The resulting annotations include both full-length and fragmented copies of interspersed repeats. These consist mainly of TEs, tandemly repeated units of simple repeats ((RY)n and homonucleotidic motifs), low complexity repeats (minisatellite/variable number tandem repeat and microsatellite/short tandem repeat), and telomeric and centromeric macrosatellite DNA (large stretches, from thousand to millions of base pairs, of repeated units ranging in size from ~10 bp to several hundred bp). Some repeated genes encoding small noncoding RNAs such as tRNAs, 7SL RNAs, and ribosomal RNA are also annotated.

The main limitation of such library-based approaches is that their annotations depend heavily on the quality of the reference database used, including completeness and accuracy of consensus sequences. Furthermore, because these methods are primarily based on sequence similarity, they tend to overestimate the diversity of interspersed repeats by flagging very small non-overlapping hits that often display low sequence complexity. Finally, these methods are inappropriate for annotating satellite DNAs in newly sequenced species because those repeated units are absent from reference databases.

Some studies use signature-based methods instead, focusing on traits that are unique to certain TEs or repeats. For example, to detect retrotransposons with large terminal repeats (LTR) at both ends, programs such as LTR Finder, LocaTR, LtrHarvest, or ReroTector can detect specific DNA organization patterns and signatures (motifs) that are specific to retroviruses [Bolisetty et al., 2012; Mason et al., 2016; Ji and DeWoody, 2016]. Tandem repeat finder (TRF) [Benson, 1999], another signature method tool, is dedicated to detecting all types of uncom-
plicated tandem repeats such as simple repeats, microsat-
ellites, minisatellites and satellite DNAs.

The final method for repeat annotation are DNA de
novo consensus methods that combine a range of detec-
tion tools. The REPET pipeline [Permal et al., 2012] in-
cludes the TEdenovo module which uses both de novo
(RECON, GROUPER, and PILER) and signature-based
tools such as TRF (it can also be set to include a library-
based step). A second REPET module, TEannot, uses the
output of the first module to annotate the genome. Such
de novo consensus methods have historically been lim-
ited by the need for powerful calculation and storage re-
sources, which has restricted their application to small
eukaryotic genomes (~10 Mb to 500 Mb). However, ad-
vances in computing clusters and a recent REPET update
have opened the way for the use of this software package
with larger genomes such as those of vertebrates. A sec-
ond de novo package, the RepeatModeler2 pipeline [Flyn
et al., 2020], has an architecture that is similar, but sim-
pler than that of REPET. It employs two discovery algo-
rithms, RepeatScout and RECON, followed by consensus
building and classification steps. In addition, Repeat-
Modeler2 includes two signature-based tools LTRhar-
vest and LTR_retriever. The de novo repeat library pro-
duced by RepeatModeler2 is then used to annotate ge-
nomes using the RepeatMasker program. The main
weakness of these de novo methods is that they are un-
able to detect repeats with very few copies, such as DNA
transposable elements or endogenous viral elements
(EVEs).

The method(s) used to estimate the amounts of repeats
in a chicken genome model has a strong influence on the
final result. Physico-chemical approaches, such as reas-
sociation kinetics, indicate that the chicken genome is
composed of ~30% repeats. However, annotations of gal-
Gal4 through 6 report repeat content of 8–21%, depend-
ing on the bioinformatic pipeline used [for review, see
Guizard et al., 2016]. Compared to other vertebrate spe-
cies the TE diversity of chickens is lower (33 TE species)
[Guizard et al., 2016].

Interspersed Repeat

Interspersed repeats are primarily composed of TEs.
These are DNA segments with the potential to move and/
or duplicate from one chromosomal location to another
(i.e., transposition). The most common way to classify
TEs is based on whether or not they use an RNA molecule
as a transposition intermediate. Class I elements (a.k.a.
retrotransposons) reverse transcribe into a DNA mole-
cule in the process of integration, while class II elements,

DNA transposons, do not [Kojima, 2020]. The advantage
of classifying elements this way is that it is relatively sim-
ple to understand and widely used. However, a significant
failing of this classification method is that it gathers many
sequences into subclasses and families that are evolution-
arily unrelated [for a more in-depth discussion, see Arens-
burger et al., 2016]. An alternative classification scheme
divides TEs into at least 10 classes based on the enzymat-
ic machinery used to transpose between loci [Arensburg-
er et al., 2016]. There are 2 primary advantages to this
alternative classification. First, it is easily amenable to the
addition of new classes, as new TE transposition mecha-
nisms are discovered. Second, its organization is compat-
ible with the classification scheme adopted by the Inter-
national Committee for the Taxonomy of Viruses (ICTV)
with which it shares a number of retrotransposon, virus-
es [Katzourakis and Gifford, 2010], and DNA transposon
families [Koonin and Krupovic, 2017].

The chicken genome repeatome consists mostly of 4
kinds of interspersed repeats, making up 15.5% of the ge-
nome. Most abundant are non-LTR elements (a.k.a. long
interspersed elements or LINEs) belonging to the CR1
group of retrotransposons (>410,000 annotated frag-
ments, 11.8% in the galGal4 and galGal5 model chromo-
somes). CR1 elements contain 2 open reading frames
(ORFs) coding for the ORF1 protein that binds to CR1
mRNA to assemble a ribonucleic particle, and a reverse
transcriptase (RT) protein with an endonuclease domain
(Fig. 26a). In bird genomes at least 22 CR1 subfamilies
have been described [Liu et al., 2009], 8 of which are cur-
rently annotated in chicken genomes. These do not show
signs of recent mobility, but full-length intact CR1s are
present (fewer than 20) [Galbraith et al., 2021], suggesting
that these might be mobilized in vitro under the right ex-
perimental conditions. In addition to the CR1s, 7 other
LINE group elements have been described [Kojima,
2020], only two of which are found in bird genomes, R2
and RTE. Short interspersed elements (SINEs) are non-
autonomous non-LTR retrotransposons derived from
nuclear RNA (tRNA, rRNA, 7SL RNA, snRNA, etc.) that
parasitize the enzymatic machinery of LINEs (Fig. 26b).
There is some controversy regarding the presence of
AmnSINE1 elements in the chicken genome [Nishihara
et al., 2006]. This is an element that uses the transposition
machinery of specific L1 LINE elements for its own trans-
position, but L1 elements are absent from all Sauropsida
except Lepidopsauria. We have failed to find these ele-
ments in the chicken genome [Guizard et al., 2016]. Oth-
er SINEs, AviRTE [Suh et al., 2016] and TguSINE1 [Suh
et al., 2017], that use the transposition machinery of spe-
<figure><img src="image_31.png" /><figcaption>**Fig. 26.** Sequence organization of transposable elements in avian genomes. **a** CR1 elements are composed of segments that resemble long retro-inserted messenger RNA (mRNA) with an A-rich tail at their 3' end. Within a "species" of CR1s many copies are truncated at their 5' ends. Full-length elements contain 2 open reading frames (ORFs) with ORF2 encoding a protein containing an apurinic endonuclease domain fused to a reverse transcriptase. **b** SINEs using the CR1 machinery are present in some bird species, but not in chickens. They consist of the fusion of a former tRNA to a 3' CR1 end. **c** LTR retrotransposons have all the signatures of an endogenous retrovirus-like element, including long terminal repeats (LTR) at both ends and ORFs coding for a group antigen (Gag), a reverse transcriptase (RT), and in some case an envelope protein (Env). **d** DNA transposons that transpose directly from DNA to DNA have short terminal inverted repeats (arrows) at both ends. When these elements are intact, they may contain a gene encoding a transposase, an enzyme required for their own transposition. There are also internally deleted forms such as *Galluhop* and chimeric elements such as *Charlie/Galluhop*.</figcaption></figure>

cific CR1 and RTE elements, respectively, were found in other bird lineages.

The second most abundant interspersed repeats found in the chicken genome are LTR retrotransposons. These TEs have LTRs directly repeated at both ends of the element and contain ORFs encoding a group-specific antigen (Gag), a reverse transcriptase (RT), and in some cases a retroviral envelope protein (Env) (Fig. 26c). From an evolutionary and enzymatic standpoint, their mobility mechanism is strikingly different from that of LINEs and SINEs. At least 21 "species" of LTR-retrotransposons were found in galGal4 and galGal5 [Guizard et al., 2016]. These have either 2 LTRs or are annotated as solo LTRs, likely resulting from the loss of the inner part of the LTR retrotransposon by recombination between the LTRs of each inserted element. We found no copies corresponding to complete, internally deleted, or partly truncated

elements of 6 models of solo LTRs (Birddawg, putative_LTR_group 4, 9, 12, 22, 28, and 30). Among the 10 elements that could reliably be classified as LTR retrotransposons, 8 belonged to the endogenous retrovirus (ERV) superfamily (EAV, EAV-HP, ERV2, ERv7, ERv11, Krios, Soprano, and RetroTux) and 2 were related to the *Gypsy-Ty3* superfamily (retroCalimero, retroSaturnin). No element was found to belong to the *Copia-Ty1* superfamily. A peculiarity of the LTR retrotransposons is that they are dramatically enriched on the chicken W chromosome.

DNA transposons, the third kind of interspersed repeat, cover about 1.8% of chromosome sequences in the chicken genome. These TEs typically contain a single ORF encoding a transposase enzyme that is generally sufficient to catalyze all the steps of its mobility. This ORF is flanked at both ends by terminal inverted repeats that are
used as binding sites by the transposase to excise and re-
insert the transposon. The diversity of DNA transposons
in the chicken genome is extremely low, with only one
member of the following superfamilies: *IS630/Tc1/mari-
ner*, *Mariner1_GG*, *hobo/Ac/Tam*, and *Charlie*. Two oth-
er DNA transposons are amplified in the chicken ge-
nome, but one is an internally deleted derivative of *Mari-
ner1_GG* (*Galluhop*) and one a chimeric element
consisting of a *Charlie* copy with a *Galluhop* copy insert-
ed (Fig. 26d). This DNA transposon profile is shared by
most bird genome model species, even by the woodpeck-
er genomes which display elevated TE abundances (17–
30% of the genome) [Manthey et al., 2018]. Sequences
related to DNA transposon superfamilies *Ginger1*, *Gin-
ger2*, *hAT*, *IS630/Tc1/mariner*, *P*, *piggyBac*, *Polinton*,
*Transib*, *Crypton*, and *Zisupton* are also present as single
or as a few repeated copies in various bird genomes. Most
of these sequences are domesticated genes, derived from
ORFs encoding transposases [for an inventory, see addi-
tional file 12 in Guizard et al., 2016].

Finally, there are also 7 interspersed repeat sequence
types called Hitchcock, and undetermined_group_1
through 6 that do not appear to correspond to any exist-
ing classifications [Arensburger et al., 2016; Kojima,
2020]. Their annotation covers about 0.8% of chicken
chromosomes. So far, their main outstanding feature is
that they are enriched in the microchromosomes [Guiz-
ard et al., 2016].

Tandem Repeats

Noncoding tandem repeats are mainly composed of DNA minisatellites, microsatellites, and macrosatellites and account for approximately 4% of the chicken genome. In the case of DNA mini- and microsatellites, the percent coverage of the genome is unlikely to be severely biased by issues related to genome completion. These sequences were the subject of few studies in chickens because single-nucleotide polymorphisms (SNPs) were long used as genetic markers. Telomeric repeats, which contain large numbers of macro- and microsatellites, are actively maintained by the telomerase enzyme. These telomere ends display very different numbers of repeats, depending on which chromosome was examined, animal age, and whether tissues or cell lines were examined [Taylor and Delany, 2000; Rodrigue et al., 2005; O'Hare and Delany, 2009].

Unlike the mini- and microsatellites above, macrosat-
elites, which are located in centromeres, telomeres, and
in non-coding regions of sex chromosomes, are likely un-
derestimated. The most studied macrosatellites are those

located in the Z chromosome (Z_rep in Repbase and
Guizard et al. [2016]) and in the W chromosome [for re-
view, see Komissarov et al., 2018], and between the MHC
and rRNA genes repeated in tandem on chromosome 16
[Miller et al., 2014]. Those present in the inner regions
juxtaposed to telomeres of autosomes remain unknown.
In chickens, and other bird species, these regions are
highly GC-rich (Fig. 27) [Federico et al., 2005], G-qua-
druplex (G4) rich, and contain genes with flanking re-
gions and introns mainly composed of satellite DNA se-
quences. There are differences in the estimates of repeat
content between those based on genome assemblies and
those based on cytological and physico-chemical ap-
proaches. Resolving these discrepancies may require elu-
cidating the sequence composition of centromeric and
telomeric regions.

In addition to the sequences above, tandemly repeated
genes represent between 1.5 and 2% of the chicken ge-
nome. They encode large ribosomal RNAs (18S, 5.2S,
28S) [Piégu et al., 2020], and account for a number of gene
duplications in some bird genomes [Warren et al., 2010].
Unfortunately, when genome models are annotated, du-
plicated genes with sequences that are more than 95–97%
identical are not reported as separate genes. In some cas-
es, such as the *PHF7* genes, annotations between genome
models vary from 0 to 68 gene copies (likely paralogs plus
segmental duplication). In the galGal6 release 105 model,
these genes are distributed among 4 loci located on 2 dif-
ferent chromosomes [Fouchécourt et al., 2022]; while in
the white leghorn breed, 39 PHF7-like gene copies are
annotated at 10 different loci and distributed among 5
chromosomes. Therefore, much work remains to be done
to fix the number of repeated genes present in wild fowls
and domesticated lines.

Balance between Genome Size and Repeats

To our knowledge only 2 publications have examined
the issue genome and repeats in birds. The first one [Pié-
gu et al., 2020] showed that the genome of numerous do-
mesticated chicken lines was smaller than that of the red
jungle fowl. Both bioinformatics and molecular investi-
gations showed that the genome coverage of various tan-
dem repeat types found in the red jungle fowl genome
(rDNA, telomeric repeats, macrosatellite DNA, and seg-
mental duplications) were lower in domesticated lines,
but that novel segmental duplications were present in
these lines. This supports the hypothesis that domestic
lines have been significantly reshaped during domestica-
tion and subsequently by human-mediated selection.
<figure><img src="image_32.png" /><figcaption>**Fig. 27.** Hybridization of GC-rich and GC-poor DNA probes on chicken chromosomes. Chicken DNA fractions characterized as having the lowest and the highest GC levels were hybridized to chicken chromosomes. **a** The DNA fraction with the highest GC level (red signals) was localized to the microchromosomes and to telomeric bands of the macrochromosomes (see white arrows as examples). Some internal bands of the macrochromosomes also hybridized (see yellow arrows as examples). In contrast, the DNA fraction with the lowest GC levels (green signals) localized to the internal bands of the macrochromosomes. **b** The same metaphase shown in the panel is DAPI stained in order to better show micro-chromosomes. The bar in the upper right is 5 µm long. Figure taken from Federico et al. [2005].</figcaption></figure>

The second publication [Kapusta et al., 2017], focused on genome size variations on the scale of bird evolution. Prior to this publication, it was thought that bird genomes were small due to selection pressure related to metabolic constraints linked to flight, and to a dearth of active TEs able to expand their numbers. This view was supported by research showing that piwi RNA (piRNA) did not control TE activity in birds and which therefore would not be committed to an arm race with TEs [Lee et al., 2009]. However, Kapusta et al. [2017] showed that TE expansions in birds were counteracted by DNA losses, mainly through large segmental deletions (>10 kb). This new

view, where TEs have remained active in bird genomes, has been strengthened by reports that (1) the chicken genome contains full-length and intact CR1 elements that are putatively active in transposition [Nishihara et al., 2006], (2) it contains recently active LTR retrotransposons [Wang Z et al., 2013], and (3) that W chromosomes may act as refugia for active ERVs [Peona et al., 2021]. This new view was further supported by reports that the chicken genome uses the piRNA system to control CR1 elements and at least some ERVs (Sun et al., 2017; Chang et al., 2018]. Together, these findings support the “accordion” model of genome size evolution [Kapusta et al., 2017] and have changed our understanding of genome plasticity in birds, from a static [Wicker et al., 2005; Ellegren, 2010; Gao et al., 2017] to a dynamic view where all forms of recombination are involved. However, it is important to note that the analyses above all used genome assemblies made from somatic DNA. We currently do not know the organization of the germinal genome and therefore are unable to determine if variations in genome organization observed in somatic cells are either due to differences in recombination events occurring in germ cells over generations or to differences in programmed DNA rearrangements or aging during development at each generation, or both.

## Future Challenges and Technical Perspectives about Repeats in Avian Genomes

Thanks to the latest versions of Pacific Biosciences and Oxford Nanopore MinION sequencing technologies, the Telomere-to-Telomere (T2T) consortium was able to resolve telomeric and centromeric regions of the human genome, adding another 8% of new sequences (~200 million bp) including 1,956 new genes, 99 of which are predicted to be protein-coding [Nurk et al., 2022]. Such a T2T project for avian genomes would be very helpful for resolving highly GC-rich genomes such as those of ratites, but also the GC-rich telomeric regions of macro- and microchromosomes of numerous bird species. However, one should not be too optimistic about the ability of these new reads to resolve the telomeric and subtelomeric regions of birds. Indeed, studies of Pacific Biosciences sequences of mRNA and genomic DNA have demonstrated that these sequences are high in G4 motifs that may lead to formation of G-quadruplexes [Beauclair et al., 2019]. These structures interfere with sequence library fabrication for both Pacific Biosciences and Oxford Nanopore MinION technologies, as well as with the sequencing of these libraries. HiFi reads from Pacific Biosciences do not solve this issue because G4 motifs block
DNA polymerization and no sequences are produced
[Zhu et al., 2016].

In theory there are technical solutions to circumvent
these problems. Indeed, the formation of G-quadruplexes
requires the presence of Na⁺ or K⁺ cations [Guiblet et al.,
2021]. While these cations are normally used during li-
brary preparation by reverse transcriptases and by DNA
polymerase enzymes, it is possible to substitute the Na⁺
or K⁺ cations with Li⁺ or Cs⁺ cations using alkali chloride
salts in the buffers [Ramos-Alemán et al., 2018]. How-
ever, as Pacific Biosciences and Oxford Nanopore do not
make the content of their solutions accessible to the pub-
lic, users cannot modify them to replace the salts in the
buffers [Flores-Juárez et al., 2016].

The elucidation of these enigmatic GC-rich telomeric, subtelomeric, and centromeric regions of bird genomes is an exciting challenge. Indeed, these regions contain genes where all noncoding segments are filled with GC-DNA satellites, they are G4 motifs repeated in tandem [Beauclair et al., 2019]. In mammalian genomes G-quadruplexes are detected in vivo [Zheng et al., 2020] and have been shown to be important as transcription factor binding hubs [Bochman et al., 2012; Hall et al., 2017; Spiegel et al., 2021] and as epigenetic modulators of chromatin [Guilbaud et al., 2017]. Birds have significantly expanded these motifs in telomeric, subtelomeric, and centromeric regions likely to control the expression. From an evolutionary standpoint, it would be important to determine if this characteristic is specific to birds or evolved early in the Sauropsida and expanded in bird genomes.

*Conflict of Interest Statement*

The authors report no competing interests.

Ribosomal DNA Repeats in Chicken and Guinea Fowl
Genomes

(Prepared by A. Dyomin, S. Galkina, A. Davidian, and
E. Gaginskaya)

The ribosomal DNA (rDNA) is one of the key ele-
ments in the cellular genome. It consists of multiple tan-
demly arranged repeats that bear coding sequences for
ribosomal RNAs (rRNAs). rRNA molecules play an es-
sential role in the ribosome functioning. They define the
size and shape of ribosomal subunits forming a structur-
al scaffold for the specific placement of proteins inside the
ribosome. rRNAs are involved in all events of translation,
including mRNA initial binding [Martin et al., 2016], co-
don recognition by aminoacyl-tRNAs [Ogle et al., 2001;

Demeshkina et al., 2012], peptide bond formation [Nissen et al., 2000], and tRNA/mRNA translocation [Mohan and Noller, 2017; Noller et al., 2017; Djumagulov et al., 2021]. These processes are based on conformational rearrangements of the rRNA molecules which constitute 2 ribosomal subunits. In the cytoplasmic ribosome of a eukaryotic cell, 28S, 5.8S, and 5S rRNA make up the large subunit, while 18S rRNA is the core molecule of the small one. Due to the role of rRNA in the mechanism of protein synthesis in the cell, rDNA is sometimes referred to as a separate subgenome (rDNAome) [Symonová [2019].

Most eukaryotes feature 2 main types of rDNA loci. The first type includes the 5S rRNA genes. The other type, nucleolus organiser regions (NOR), is specific for encoding the 18S, 5.8S, and 28S rRNAs. rDNA repeating units are organised the same way in both types of loci: a transcriptional unit (gene) is followed by a spacer sequence. The 5S rRNA gene (~120 bp) is followed by the so-called non-transcribed spacer (NTS), which can be of varying length. 5S rDNA is transcribed by RNA-pol III, regulator sites being located within the coding sequence [Pieler et al., 1987; Paule and White, 2000; Hall, 2005]. In the eukaryotic NOR, rDNA repeats are organised in a more complex way [Singer and Berg, 1991; Shaw and Brown, 2012; Hori et al., 2021]. Each of them consists of a cluster of 18S, 5.8S, and 28S rRNA genes followed by an intergenic spacer (IGS) sequence. The 18S, 5.8S, and 28S rRNAs coding sequences are separated by 2 internal transcribed spacers (ITS1 and ITS2) and flanked by 2 external transcribed spacers (5'ETS and 3'ETS). The rDNA cluster is transcribed by RNA-pol I into the primary 45/47S rRNA molecule (pre-rRNA) as a single transcriptional unit. The coding sequences for each rRNA are highly conserved in length and nucleotide composition across taxa. However, rRNA inter- and intra-individual genomic polymorphisms have been described in several species [Pillet et al., 2012; Locati et al., 2017; Kim JH et al., 2018, 2021; Parks et al., 2018; Ding et al., 2022]. The spacer regions containing splicing cleavage sites evolve more rapidly. Their high variability can be detected even within the same genome [Kim JH et al., 2018, 2021].

The IGS, which plays important regulatory roles in the
cell function, is one of the most interesting regions in the
rDNA repeat. In many animals, IGS have been found to
contain such functional elements as pre-rRNA promoters
[Haltiner et al., 1986; Caudy and Pikaard, 2002; Massin et
al., 2005; Agrawal and Ganley, 2018], several transcrip-
tion termination sites (Sal box) [Pfleiderer et al., 1990;
Agrawal and Ganley, 2018], noncoding RNA binding
sites involved in cell stress response and regulation of
rDNA transcription [Audas et al., 2012; Agrawal and Ganley, 2018], cdc27 pseudogene [Grandori et al., 2005; Agrawal and Ganley, 2018], and putative c-Myc and p53 binding sites [Gonzalez et al., 1993; Zentner et al. 2011; Agrawal and Ganley, 2018]. Moreover, the formation of R loops (RNA-DNA duplex) at certain IGS loci prevents RNA-pol I from reading sense ncRNAs, which can disrupt rRNA expression in the human nucleolus [Abraham et al., 2020]. Recently, we have shown a localization of the functional 5S gene within the IGS in turtles and crocodiles that is unique for the vertebrates [Davidian et al., 2022]. This NOR-5S rRNA gene is only active in oocytes and apparently plays a role in producing a maternal pool of extra ribosomes during NOR amplification in oogenesis. All this supports the importance of studying the IGS structure. However, the number of complete ribosomal repeats annotated with their IGS constituents remains extremely low among the available genome assemblies. So far few publications on the identification of structural and functional blocks within IGS in *Xenopus* [Caudy and Pihaard, 2002], mice [Grozdanov et al., 2003], human and other Apes [Gonzalez and Sylvester, 1995; Agrawal and Ganley, 2018], chicken [Dyomin et al., 2019], and some reptiles [Davidian et al., 2022] exist. This under-investigation of spacer regions is a significant obstacle to understanding their function and evolution.

## NOR rDNA Repeat Sequences

Information on the genomic location and cytogenetic features of NOR rRNA genes, the rDNA copy number and repeat size variability in chicken has long been available [Delany and Krupkin, 1999; Schmid et al., 2000, 2005]. However, complete sequence details and organisation of the rDNA repeat unit remained unknown until very recently. The 45S rDNA cluster sequence of *Gallus gallus* was completely assembled in 2016 [Dyomin et al., 2016]. A complete sequence of the rDNA repeat including IGS was annotated in 2019 [Dyomin et al., 2019]. We also introduce new data on the rDNA repeat sequence of the guinea fowl *Numida meleagris* so that we can compare complete rDNA repeats in representatives of 2 galliform families, Phasianidae and Numididae.

Integrated data on the chicken demonstrate the single NOR location on microchromosome 16 [Bloom and Bacon, 1985; Delany et al., 2009; Solinhac et al., 2010], the rDNA repeat copy number intra- and inter-individual variability to be 150–250 per haploid genome, and average rDNA array sizes in various chicken populations having a range from 5 to 7 Mb [Delany and Krupkin, 1999; Delany, 2000; Schmid et al., 2005]. The latter authors also

showed that rDNA repetitive units in the chicken NORs vary from 11 to 50 kb. This difference depends on the IGS size, which is significantly larger in broiler breeds [Delany and Krupkin, 1999; Schmid et al., 2005]. Four chicken rDNA repeats identified to date are of 34,497 bp, 26,999 bp, 27,055 bp, and 25,865 bp, the latter detected in the red jungle fowl [Dyomin et al., 2019]. A sample of the complete chicken rRNA gene cluster sequence was first assembled from raw reads and annotated as being 11,863 bp long (NCBI accession number KT445934) [Dyomin et al., 2016]. This was later verified by data from sequencing BAC clone WAG137G04 containing 3 complete rDNA clusters using PacBio RSII [Dyomin et al., 2019]. The results obtained were very similar to the previously assembled sequence, both in sequence homology and cluster size (11,871 bp, 11,830 bp, and 11,855 bp).

The recently released *N. meleagris* genome assembly [Vignal et al., 2019] lacks rDNA data. We identified 2 complete rDNA clusters and 3 IGSs of the guinea fowl in NCBI JABXER010000123 contig (online suppl. Material 11, Table S1). The sequence lengths and GC content in the elements that make up the rRNA gene clusters have been determined for both chicken and guinea fowl (Fig. 28; Table 8; online suppl. Material 11, Table S1). Their 18S, 5.8S, and 28S rRNA gene sequences are typical of vertebrates but ITS1 and ITS2 sequences are more extended in size, and show higher GC content compared to the majority of other Deuterostomia [Dyomin et al., 2017]. The secondary structure of rDNA cluster sequences should therefore be complicated and infusible while containing multiple hairpins, as was shown for the chicken ITS1 [Dyomin et al., 2016]. The ITSs may be the source of species-specific microRNAs, as was shown for human ITS1 (miRNA-663) [Chak et al., 2015] and for mouse ITS2 (miRNA-712) [Son et al., 2013].

## Sequence Complexity and GC Content in Intergenic Spacers

When deciphering the guinea fowl NOR rDNA repeats, we also found that the repeat length difference between chicken (~27–34 kb) and guinea fowl (~18–20 kb) is due to the great difference in the IGS lengths (Fig. 28; Table 9). Even within a single NOR, IGS sequences may differ in length (Table 9) and, as a consequence, in their nucleotide composition. The difference in IGS lengths correlates with the difference in the amount of internal repeats (Fig. 29), which may be caused by unequal crossing-over [Erickson and Schmickel, 1985; Smirnov et al., 2016]. It is noteworthy that the red jungle fowl IGS is the shortest, which suggests the possibility of the repeat num-
<table><caption>**Table 8.** Length and GC content in the rDNA cluster and cluster elements in chicken and guinea fowl</caption><thead><tr><th rowspan="2">Species</th><th rowspan="2">Cluster</th><th colspan="7">Cluster elements</th></tr><tr><th>5'ETS</th><th>18S</th><th>ITS1</th><th>5.8S</th><th>ITS2</th><th>28S</th><th>3'ETS</th></tr></thead><tbody><tr><td rowspan="2">Gallus g. domesticus (KT445934)</td><td>length, bp</td><td>1,836</td><td>1,823</td><td>2,530</td><td>157</td><td>733</td><td>4,441</td><td>343</td></tr><tr><td>GC, %</td><td>75</td><td>54.5</td><td>82.2</td><td>57.3</td><td>82</td><td>68</td><td>79.9</td></tr><tr><td rowspan="2">Numida meleagris (JABXER010000123)</td><td>length, bp</td><td>1,792</td><td>1,824</td><td>2,837</td><td>157</td><td>761</td><td>4,422</td><td>340</td></tr><tr><td>GC, %</td><td>75.7</td><td>54.4</td><td>82.7</td><td>57.3</td><td>82.3</td><td>68</td><td>83.2</td></tr></tbody></table>

<figure><img src="image_33.png" /><figcaption>**Fig. 28.** Comparison of the chicken *Gallus g. domesticus* and guinea fowl *N. meleagris* rDNA repeat structure. The structures of chicken rDNA repeat II from the WAG137G04 BAC clone (a) and guinea fowl rDNA repeat II from the NCBI JABXER010000123 contig (b). 18S, 5.8S, and 28S rRNA genes are indicated by red</figcaption></figure>

blocks, external (5' and 3'ETS) and internal (ITS1 and ITS2) transcribed spacers are indicated by yellow blocks, and intergenic transcribed spacers (IGS) by green blocks. GC pair distribution is shown in the graphs as "GC%".

ber increasing in the course of domestication [Dyomin et al., 2019]. We should remember the splendid data by M. Delany and coworkers [Delany and Krupkin, 1999; Delany, 2000; Schmid et al., 2005] of broiler chicken breeds having the longest rDNA repeats (up to 50 kb) due to the longer IGS sequences. Interestingly, despite the strongly differing lengths of IGS, the GC content in IGS remains particularly high in chicken and guinea fowl (Fig. 28; Table 9).

Compared to mammals [Agrawal and Ganley, 2018], the IGS of Galliformes representatives feature a strict hierarchy and order: they contain internal tandem repeats conserved in the unit lengths and are arranged in long blocks and oriented in the same direction. According to Dyomin et al. [2019], the chicken IGS contains 3 internal repeat blocks: 5' SV-AL block, central EL block, and 3' VAL block (Fig. 29a). The 5' block consists of GC-rich AL repeats (~250 bp) alternating with AT-rich SV repeats (~150 bp). The central block is the longest (9,297–14,414 bp) and consists of short GC-enriched tandem EL repeats (~93 bp). The 3' block is separated from the central block

by a poly-A motif and consists of VAL repeats (~85 bp). The guinea fowl IGS also contains internal repeat blocks of 3 types: the conserved SV-AL and VAL blocks and apparently a species-specific Nme block (Fig. 29b). Both chicken and guinea fowl IGS have a poly-T motif (~10–49 bp) at the 5' end of the IGS (Fig. 29), which is usually considered the transcription termination motif [Mason et al., 1997]. However, according to the results of transcriptome analysis, the SV-AL repeat block following the poly-T motif is transcribed in all analysed chicken tissues [Dyomin et al., 2019]. Chicken IGS contains one long unique highly conserved sequence (~1,940 bp) between SV-AL and EL repeat blocks and one short unique region (~191 bp) after the 3' VAL repeat block. At the same time, the guinea fowl IGS has at least 3 unique regions: between Nme and VAL repeat block (~1,350 bp), between VAL blocks (~735 bp), and after the 3' VAL block (169 bp) (Fig. 29; online suppl. Material 11, Data S1). Each unique region contains a single sequence of interspersed repeat (IR, 93 bp), which is enriched with adenine (Fig. 29). (CT)n repeats (32–36 bp) have also been found in both
<figure><img src="image_34.png" /><figcaption>**Fig. 29.** Chicken and guinea fowl IGS structure. **a** Four aligned chicken IGS sequences. IGS_I, IGS_II, and IGS_III are from the *Gallus g. domesticus* BAC-clone containing rDNA (WAG137G04). IGS_IV belongs to a red jungle fowl (AADN04001305.1). All four IGS have different sizes caused by the difference in repeat blocks of each type (SV-AL, EL, VAL). The unique regions are almost of the same length in all analysed IGS (see also online suppl. Material 11, Table S1). **b** Three aligned IGS sequences from the guinea</figcaption></figure>

fowl JABXER010000123 contig. Two of them are completely identical, IGS_III has an insertion at 5,500 bp (dotted rectangle). Each of the IGS contains no EL repeats, one SV-AL repeat block and at least two VAL repeat blocks, differentiated into 6 repeat variants (see also online suppl. Material 11, Fig. S1, S2, Data S1). A species-specific Nme repeat block following SV-AL block is marked in black. Sequence gaps are designated with fine black lines.

<table><caption>**Table 9.** Intraindividual variability of IGS lengths in chicken and guinea fowl</caption><thead><tr><th></th><th></th><th>IGS_I</th><th>IGS_II</th><th>IGS_III</th></tr></thead><tbody><tr><td rowspan="2">Gallus g. domesticus (WAG137G4)</td><td>Length, bp</td><td>22,627</td><td>15,169</td><td>15,241</td></tr><tr><td>GC, %</td><td>68.1</td><td>69.2</td><td>68.1</td></tr><tr><td>Numida meleagris (JABXER010000123)</td><td>Length, bp</td><td>6,530</td><td>6,562</td><td>8,223</td></tr></tbody></table>

chicken and guinea fowl unique sequence regions (Fig. 29). It is noteworthy that the size of the third guinea fowl IGS is enlarged due to an insertion into the VAL block between VAL_C and VAL_B units (Fig. 29b, dotted rectangle). This insertion duplicates the IGS fragment containing the unique area and a part of the VAL block. Thus, the longest IGS in the contig contains 3 VAL repeat blocks and 4 unique regions, with 3 of these regions carrying a copy of the IR.

Neither within chicken IGS [Dyomin et al., 2019] nor within the IGS of guinea fowl (this research) were functionally significant sites detected.

As it turned out, the variants of EL and VAL repeats are non-randomly scattered over the corresponding blocks. They are strictly organised into groups and form high-or-

der repeats (HORs) (Fig. 30). This pattern is common to domestic and red jungle chicken in case of EL repeats (Fig. 29a, 30) and to guinea fowl in case of VAL repeats (Fig. 29b, 30). A comparison of different IGS copies demonstrates that internal repeats play the key role in the variability of IGS lengths at the individual and species levels (Fig. 29). In both chicken and guinea fowl, the IGSs are very rich in GC and CpG. This brings them closer to the turtle IGS [Dyomin et al., 2019; Davidian et al., 2022]. However, it is still unclear whether the IGS internal repeats perform any function. The IGS internal repeats and unique sequence regions described herein are not homologous to mammalian and amphibian IGS elements. This may reflect a separate evolutionary pathway for avian rDNA regulatory sequences, which are still to be discovered.
<figure><img src="image_35.png" /><figcaption>**Fig. 30.** Internal IGS repeats demonstrate a HOR (high order repeat) organisation. Contracted IGS_I figure from WAG137G04 contig of *G. g. domesticus* (a) and IGS_I figure from JABXER010000123 contig of *N. meleagris* (b).</figcaption></figure>

## 5S rDNA in Galliformes

The previous report on chicken genes [Schmid et al., 2005] has estimated the copy number of 5S rRNA genes in chicken as 35–41 copies with a predominant repeat (5Sα) of 2.2 kb [Daniels and Delany, 2003; Schmid et al., 2005]. Genetic linkage analysis and cytogenetic localization assigned the 5S rDNA to chromosome 9 [Daniels and Delany, 2003]. This is fully consistent with what can be found in the latest chicken genome assembly GRCg7w: one 5S rDNA locus (partly annotated) is situated at ~0.5 Mb on chromosome 9 (NC_052540.1). It comprises 37 full and 1 incomplete tandemly repeated unit made up of a 5S rRNA gene followed by a spacer sequence (NTS) that can be of 2,038–2,268 bp. Such a polymorphism in the length is mainly due to the (G)n microsatellite present in the middle of each NTS. Traces of endogenous retroviruses (ERV-LTR) are also present in the NTSs. Two 5S rRNA gene copies seem to be nonfunctional; they comprise 117 and 118 bp due to nucleotide deletions. Some copies contain 1 or 2 nucleotide substitutions, allowing insights into the intraindividual sequence variability (online suppl. Material 11, Fig. S3a). A variant 5Sβ rRNA gene repeat of 0.6 kb [Daniels and Delany, 2003] is not present in the Red Jungle fowl genome, but can be found in many WGS contigs of the Rhode Island, Liyang, Houdan, White Leghorn, Cornish, and Naked Neck chicken genomes. The GRCg7w assembly contains 3 single 5S rRNA gene copies not followed by the NTS (online suppl. Material 11, Fig. S3b). Two of them at chr2:44,384,176 bp (LOC112531980 at NC_052533.1) and at chr6:9,273,471 bp (NC_052537.1) are slightly degenerated (7 and 8 SNPs correspondingly), whereas the gene situated at chr9:3,007,209 bp is completely identical to the reference 5S rRNA gene

sequence (NCBI X01,309.1). We could assume the existence of a single functional 5S rDNA site in the chicken genome on chromosome 9. However, this assumption would conflict with the earlier descriptions of 2 [Krol et al., 1981; Lazar et al., 1983] and even 3 [Keith et al., 1986] 5S rRNA types from various somatic tissues of the chicken. The alignment of 5S rDNA sequences with the available 5S rRNA sequences of the two types [Lazar et al., 1983] shows that 5S rRNA genes located on chromosome 9 correspond to 5S rRNA type I (NCBI M13920, online suppl. Material 11, Fig. S3b). A variant gene on chromosome 2 is identical to 5S rRNA type II (NCBI M13919, online suppl. Material 11, Fig. S3b). Both types of 5S rRNAs were found in the cytoplasm polysome fractions of chicken liver and brain [Lazar et al., 1983]. The 5S rRNA type II coding sequence is not associated with a common NTS.

In the *N. meleagris* genome assembly no 5S rRNA genes have been annotated so far. The corresponding array consisting of 18 full repeats is present in NCBI WGS contig JABXER010000004. The predicted NTSs are much shorter (937–961 bp) than in chicken. In general, the guinea fowl 5S rDNA repeats are highly homogeneous as only 1 nucleotide substitution in the genic regions (online suppl. Material 11, Fig. S3b) and a dozen mutations in the NTS were found. Neither guinea fowl NTSs nor chicken repeats show the complex organisation as described above for the IGS, but they both feature a high GC content (~70%). When aligned, NTSs of both species share no related significant features. Only simple oligonucleotide motifs can be found (e.g., CCCGC, GGGTCG, GCGTG, GGAGCAG etc.). The biological meaning of these features is still to be elucidated.
## Conclusion

Structural features and variability of chicken rDNA arrays are well studied [Delany and Krupkin, 1999; Schmid et al., 2000, 2005; Daniels and Delany, 2003; Dyomin et al., 2016, 2017, 2019]. This report additionally introduces the first decoding and analysis of the guinea fowl rDNA repeat. The 5S rRNA genes are variable in their structure, copy number, and location in the chicken genome but appear to be homogeneous in the guinea fowl. Both studied species, and presumably all representatives of Galliformes, do not have a special oocyte type of 5S rRNA genes, in contrast to fish, amphibians, and archelosaur reptiles. This perfectly coincides with the absence of rRNA gene amplification in avian oogenesis.

The complete sequences of the ribosomal repeat from the NORs in chicken and guinea fowl, representatives of 2 closely related galliform families, Phasianidae and Numinidae, are compared. On the whole, the rDNA clusters were found to be quite similar in both species. The 18S, 5.8S, and 28S rRNA coding sequences are typical of higher eukaryotes. The ITSs are longer and more enriched in GC than in other Deuterostomia [Dyomin et al., 2017]. The similarity on high GC and CpG content in the ITS1 and ITS2 sequences of both species may indicate the existence of a general evolutionary mechanism that maintains the same proportions of nucleotides in avian rDNA spacers. Avian IGS seem to differ essentially from the IGS in Mammalia. In both studied species of birds, the IGS sequences separating rRNA gene clusters contain several blocks of internal tandem repeats of the same type. The internal tandem repeats of chicken IGS are HORs enriched in GC and CpG. This brings the IGS closer to turtles and distinguishes them from fish, amphibians, and mammals [Dyomin et al., 2019]. The same is true for the guinea fowl IGS. Some of the internal repeats, such as SV-AL, (CT)n, VAL, were found to be common to the IGS of both species and perhaps to IGS of all Galliformes. At the same time, specific internal IGS repeats exist, such as the Nme repeats for the guinea fowl IGS and the EL repeats for the chicken IGS. The existence of specific repeat types within relatively close taxa of birds, the high degree of degeneracy of SV-AL and VAL, and the low interspecies homology of unique sequences indicate a high rate of evolution of both the entire IGS structure and of its elements. No functionally significant sites associated with RNA-polymerase activity were found in the IGS sequences of the chicken and guinea fowl using conventional transcription databases. In the identification of internal regions of IGS with high functional significance, it could be quite useful to study the IGS structure in representatives

of all major taxa of birds. Such a study could reveal the main evolutionarily conserved sequences in the genomes of Galliformes. It could also clarify the mechanisms that determine the structural diversity and rapid evolution of IGS sequences compared to other spacer sequences of the avian rDNA arrays, even at the intraspecific level.

## Acknowledgments

We are sincerely grateful to Alissa Gousseva for her kind help in English. The analytical facilities were provided by "Chromas" Resource Center of the Saint Petersburg State University Scientific Park. The study was supported by Russian Science Foundation grant #22-24-00538.

## Conflict of Interest Statement

The authors declare that they have no competing interests.

## Funding Sources

This work was supported by the grant from the Russian Science Foundation #22-24-00538.

## Data Availability Statement

One full rDNA repeat annotation of *N. meleagris* in the JABX-ER010000123 contig, the alignments of the 5S rRNA sequences found in the chicken genome assembly bGalGal1.mat.broiler. GRCg7b and the guinea fowl JABXER010000004 NCBI WGS contig are available in the Supplementary material.

# Endogenous Avian Leukosis Viruses: The Chicken-Specific Endogenous Retrovirus

(Prepared by A.S. Mason)

As with all birds, the chicken genome is repeat sparse, extending across all repetitive element classes (comprehensively described by Bigot and Arensburger in this report). In mammals, approximately 10% of the genome consists of endogenous retroviruses (ERVs), a subclass of long terminal repeat (LTR) retrotransposons which reflect ancestral retroviral infections and integrations into the germline [Bromham, 2002]. However, and consistent with other Neognathae birds, ERVs only constitute approximately 3% of the chicken genome, though this may change with greater assembly contiguity [Mason et al., 2016; Kapusta and Suh, 2017; Warren et al., 2017]. Chicken ERVs include full-length and degraded examples of spumaviruses, beta- and gamma-retroviruses, but the endogenous alpha-retroviruses are of particular interest as this retroviral group is endemic to birds. Chicken genomes consist of 2 major groups of endogenous alpha-retroviruses: endogenous avian viruses (EAVs) and avian leukosis virus subgroup E (ALV-E) integrations, previously known as "ev" loci [Payne and Nair, 2012; Sacco and Nair, 2014].
## ALV-E Structure, Retrotransposition, and Expression

Intact ALV-E integrations have a typical length of 7,524 bp. This includes LTRs of 274 bp which are identical upon integration, terminally flanked by 6-bp target site duplications. Due to their recent integration, most ALV-E LTRs remain identical, and many ALV-Es retain full structural integrity (e.g., ALVE21, ALVE-TYR). De-graded ALV-Es, including terminal (e.g., ALVE6, ALVE9) or internal (e.g., ALVE3) truncations, and solo LTRs (e.g., ALVE15, ALVE_ros005) are common, but less frequent than in more ancient ERV groups [Stoye, 2001].

ALV-E LTRs are shorter than exogenous ALV, limiting endogenous promoter activity and transformation capability [Ruddell, 1995; Benachenhou et al., 2013]. The phenotypic impact of ALV-E integrations is therefore dependent on the specific integration site. Like all alpha-retroviruses and lentiviruses, the presence of a nuclear localisation signal in the retroviral integrase leads to an enrichment of ALV-E integrations within open chromatin encompassing protein-coding genes [Narezkina et al., 2004; Justice and Beemon, 2013]. This distribution holds across all identified ALV-E loci, even after the impact of selection [Mason et al., 2020c].

ALV-Es exhibit the canonical retroviral structure without accessory genes, expressed by host RNA polymerase II from 2 frame-separated open reading frames: *gag-pol* and *env*. Like other retroviruses, translation of the *gag-pol* transcript is regulated by ribosomal frameshifting. Frameshifting is successful 5-10% of the time, resulting in far lower abundance of *pol* proteins compared to those encoded by *gag*, including alpha-retrovirus-specific inclusion of the *protease* domain [Arad et al., 1995]. *env* translation is inhibited by host miR-155 binding within the surface receptor gp85 domain [Hu et al., 2016], although some integrations, such as ALVE6 [Mason et al., 2020a], have escaped regulation by mutation, resulting in high envelope titres [Robinson et al., 1981].

Novel horizontal transmission of ALV-Es within flocks, and between cells of the host, is dependent on the cell entry receptor genotype [Hunt et al., 2008]. ALV-E, as well as exogenous ALV subgroups B and D, use tumour virus cell entry receptor B (TVB), encoded by *tumour necrosis factor receptor superfamily member 10b (TNFRSF10B)*. Wildtype TVB (*TVB*S1) is susceptible to infection, but alleles which result in either truncation before the transmembrane domain (e.g., Q58*, Q100*), or direct or indirect disruption of disulfide bridges in cysteine-rich domains (e.g., P61L, C62S, C101R, C125S), have been observed to provide resistance to ALV-E infection in commercial flocks [Adkins et al., 2000, 2001; Klucking and

EAVs are ancestral, found broadly across Galliformes with element divergence matching host co-speciation patterns. EAVs are further subdivided into 3 phylogenetically distinct clades (EAV-0, EAV-HP, and EAV-E51/ E33), although inter-clade recombination has been described, resulting in ART-CH elements (avian retrotransposon in chicken; EAV-HP/EAV-51 recombinant) [Gudkov et al., 1992; Sacco and Nair, 2014]. Most chicken EAVs are replication-incompetent, but many are polymorphic with some retaining transcriptional or regulatory potential, associated with phenotypic traits such as blue-green eggshell colour [Wang Z et al., 2013; Wragg et al., 2013]. Most prominently, recombination between an intact EAV-HP *envelope* and exogenous ALV-A produced the emergent ALV-J, with the recombinant virion enabling altered haemopoietic tropism, inducing myelocytomas rather than the typical B cell lymphoma [Payne et al., 1991; Benson et al., 1998; Sacco et al., 2004]. Even these limited examples highlight the potential impact of ERVs when they retain high structural integrity.

ALV-E integrations are evolutionarily recent and recurrent additions to the chicken genome, as these ERVs are endemic only to *Gallus gallus* and exemplify an evolutionarily brief period in retroviral life history where exogenous and endogenous forms co-exist [Payne and Nair, 2012; Kanda et al., 2013]. Consequently, ALV-Es are highly polymorphic and are present at low copy number, but typically retain high structural integrity. Replication-competent and transcriptionally active ALV-Es are common, even in commercial flocks, where *gag* expression has been associated with reductions in muscle mass and egg number, size, and shell thickness [Crittenden et al., 1984; Fox and Smyth, 1985; Kuhnlein et al., 1989; Gavora et al., 1991]. Furthermore, ALV-E expression has complex impacts on the immunological status of flocks. ALV-Es can be shed and transmitted horizontally through a flock, high antigen titre can lead to persistent viremia and immune exhaustion, and yet expression of endogenous *envelope* can provide protection against exogenous ALV by receptor interference [Robinson et al., 1981; Smith et al., 1990]; a form of ERV-derived immunity (EDI) [Aswad and Katzourakis, 2012; Hurst and Magiorkinis, 2015]. Modulated infection dynamics have also been observed with other viruses [Mays et al., 2019]. Efforts to eradicate ALV-Es in commercial flocks have been hindered by their close association with desirable traits such as recessive white [Chang et al., 2006], henny feathering [Li J et al., 2019], and sex-linked slow feathering [Bacon et al., 1988; Elferink et al., 2008].
Young, 2004; Reinisová et al., 2008]. As mentioned above, production of ALV-E envelope gp85 protein can also infer resistance by receptor interference, as gp85 and TVB complex together in the Golgi apparatus before presentation on the cell surface [Cosset and Lavillette, 2011].

## ALV-E Representation in Reference Genomes

Early work characterising ALV-E diversity focused on White Leghorn lines (typically 1–3 loci) due to the well-described detrimental effects on egg-laying success [Gavora et al., 1991]. Expansion into the more genetically diverse brown-egg layers (typically 5–10 loci) and broilers (genotypes rarely published from commercially relevant lines) was inhibited by available technologies [Iraqi et al., 1991; Sabour et al., 1992; Grunder et al., 1995; Muir et al., 2008b]. Restriction fragment length polymorphisms (RFLPs) became harder to interpret with a greater number of ALV-Es, especially following the discovery that some RFLPs varied between breeds for the same loci [Aarts et al., 1991; Boulliou et al., 1991]. Taken together, these studies showed that whilst some ALV-Es were shared between white- and brown-egg layers and broilers (notably ALVE3 and ALVE6), many loci were novel, and there was no clear indication of an ancestral ALV-E complement.

By the time of the publication of the draft red jungle-fowl (RJF) reference genome [International Chicken Genome Sequencing Consortium, 2004], almost 50 different ALVE loci had been identified, many with diagnostic and commercially-utilised PCR genotyping assays [Benkel, 1998]. It was therefore surprising that the RJF individual used for the reference contained only 2 ALVEs: ALVE6 (ALVE-JFevA), widespread but polymorphic in commercial layers and broilers yet not found in other RJF, and the intact ALVE-JFevB, which is, so far, unique to the reference genome individual [International Chicken Genome Sequencing Consortium, 2004; Benkel and Rutherford, 2014; Mason et al., 2020a]. Even the study of just these 2 ALV-E elements has not been straightforward, as ALVE6 is near the chromosome 1 p-arm telomere, and was only fully assembled in GRCg6a [Mason et al., 2020a]. High-throughput sequencing studies, first with bait-capture enrichment [Rutherford et al., 2016] and more recently utilising unenriched short-read whole-genome sequencing data [Mason et al., 2020b, c], have now characterised almost 1,300 different ALV-E loci. This better reflects the vast diversity across non-commercial chicken populations, including “wild-caught” RJF, which often have over 20 individual-specific loci.

Not only does the reference RJF assembly poorly represent the diversity of ALV-Es in commercially relevant chickens, it also poorly represents RJF ALV-E diversity [Mason, 2021]. This corresponds with the well-described White Leghorn introgression into this RJF individual, as well as the general issues studying polymorphic repetitive elements [Ulfah et al., 2016]. Unfortunately, despite the literature on ALV-E polymorphic diversity, and the clear absence of a conserved, ancestral ALV-E complement, the RJF genome is still used to represent the pre-domesticated state [Hu et al., 2017; Sun YH et al., 2017; Chen S et al., 2019], leading to ambiguous or overreaching results, both with ALV-Es and more broadly.

Pangenomes will more comprehensively document ALV-E diversity. The recently derived Chinese and South-East Asian chicken pangenome consisting of 664 individuals [Wang K et al., 2021], many of which were previously analysed for their ALV-E content [Mason et al., 2020b], was certainly a good start, however even these resources can never be “complete.” Researchers studying ALV-Es need to be sure which specific integrations are present in their study system. As high-throughput sequencing is the only unambiguous method for novel integration detection, the reality of having a highly contiguous but nearly ALV-E-blank genome, such as GRCg6a, is actually quite appealing, as the first step of many detection algorithms is to mask homologous repeats.

Whilst no individual- or pangenome can fully represent ALV-E diversity, the newly derived, haplotype-phased layer (GRCg7w; paternal) and broiler (GRCg7b; maternal) references are good representatives of their breeds, and highly informative for the study of Western commercial stock [Warren et al., this report]. GRCg7w contains 6 ALVEs which, while more than usual for a White Leghorn, contain the common ALVE1, ALVE3, ALVE15, and ALVE21. GRCg7b has 5 ALVEs commonly observed in brown-egg layers and broilers, including ALVE-TYR, responsible for the recessive white phenotype of the maternal bird. Both haplotypes contain TVB resistance alleles.

The direct benefit of long read-scaffolded assemblies was the full characterisation of ALV-E integrity in these haplotype references. This remains an outstanding issue with ALV-E identification from short-read technologies alone, which can optimally identify integration sites, but are unable to uniquely resolve internal integrity; crucial for predicting expression and retrotransposition potential.
## Future Relevance of ALV-Es in Disease

Much of the impetus for studying ALV-Es was based on the detrimental impact of these ERVs on productivity traits [Crittenden et al., 1984; Fox and Smyth, 1985; Kuhnlein et al., 1989; Gavora et al., 1991]. However, direct and indirect selection against these elements, particularly in commercial layers, has largely eradicated these effects. A recent association analysis suggested only linkage disequilibrium in ALV-E/trait associations, even for integrations such as ALVE3 [Fulton et al., 2021]. The full picture is less simple, as ALV-Es fixed in a population cannot be measured in such studies. ALVE-TYR, for example, is structurally intact, common in layers and broilers, and its impact on growth rate has been appreciated since the 1980s [Fox and Smyth, 1985]. Further work is needed to characterise the phenotypic effects of individual ALV-Es to prioritise their eradication from flocks, particularly given the poorly understood role of ALV-E loci in spontaneous lymphoid leukosis [Cao et al., 2015; Mays et al., 2019].

Outside intensive selection, ALV-E diversity and abundance appears to support a more natural role through ERV-derived immunity [Mason et al., 2020c]. Whilst this may improve the host response to novel ALV infections, the deleterious productivity associations impact the food and economic security of subsistence and small-holder poultry farmers, and will introduce unwanted deleterious loci in the generation of “localised” commercial birds in areas such as sub-Saharan Africa.

Unregulated flocks also enable the more nebulous possibility of novel, emergent recombinant retroviruses. This has been documented extensively in China with exogenous ALV subgroups A, J, and K [Chesters et al., 2001; Liu et al., 2011; Dong et al., 2015; Přikryl et al., 2019], but co-infection could facilitate recombination with other retroviruses, leading to novel tropisms or host expansion.

## Conflict of Interest Statement

The author has no conflicts of interest to declare.

## Funding Sources

A.S.M. is supported by a Research Fellowship in Cancer Informatics by York Against Cancer. This work was supported by a Biotechnology and Biological Sciences Research Council pump priming award (BB/W510737/1) and a researcher mobility award from The Houghton Trust (HT/RMG/22/01).

## Recombination Frequency and Distribution along the Chicken Chromosomes

(Prepared by P.M. Borodin, L.P. Malinovskaya, and A.A. Torgasheva)

## What Is the Recombination Rate and Why Is It Important?

Recombination is essential to orderly chromosome segregation and generation of new allele combinations. The efficiency of artificial selection is critically dependent on the recombination rate; that is, the number of recombination events per whole genome, chromosome, and chromosome region. Populations with higher recombination rates demonstrate a higher response to selection [Martin et al., 2006; Dapper and Payseur, 2017; Gonen et al., 2017]. The distribution of recombination events along chromosomes is another important variable affecting the efficiency of selection. A position of 2 crossing overs too close to each other does not affect the linkage phase [Gorlov and Gorlova, 2001; Berchowitz and Copenhaver, 2010]. Similarly, crossing overs located too close to the centromere of an acrocentric chromosome or to the telomere do not produce new allele combinations. Thus, estimates of genome-wide and chromosome- and region-specific recombination rates in livestock are important for breeding programs.

Recombination is a stochastic but tightly controlled process occurring in the prophase of the first meiotic division [Zickler and Kleckner, 2015; Gray and Cohen, 2016]. It starts with chromatin remodeling and the scheduled generation of multiple DNA double-strand breaks (DSBs), followed by a RAD51-mediated search for homologous DNA sequences and formation of heteroduplexes involving DNA strands of homologous chromosomes. Polymerization of the synaptonemal complex, a meiosis-specific proteinaceous structure, stabilizes homologous chromosome synapsis. A small percentage of DSBs are repaired in a crossover manner, while the majority are repaired in a non-crossover manner. Most of the crossover sites are located in recombination hotspots, regions 1-2 kb long, usually flanked by longer cold regions with lower than average recombination frequency [Paul et al., 2016]. The sites of crossing over can be visualized at the pachytene stage as recombination nodules containing MLH1 (mismatch repair protein), and at the diplotene-diakinesis stage as chiasmata. Sister chromatid cohesion beyond the chiasmata holds homologues together at metaphase I, ensuring proper orientation and orderly segregation. Crossover and non-crossover chromatids segregate at the second meiotic division.
<figure><img src="image_36.png" /><figcaption>**Fig. 31.** Chicken spermatocyte (a) and oocyte (b) after immunolocalization of SYCP3 (red), centromeric proteins (blue), and MLH1 (green). Arrows point to the synaptonemal complexes of the macrochromosomes identified by their lengths and centromeric indices. Arrowheads indicate MLH1 signals at ZW bivalent. Scale bar, 5 µm. (a From Malinovskaya et al. [2019], licensed under Creative Commons Attribution 4.0 License. b From Torgasheva et al. [2021], licensed under Creative Commons Attribution 4.0 License).</figcaption></figure>

## How to Measure the Recombination Rate

There are genetic and cytological methods of assessing the recombination rate. Genetic assessment is based on linkage analysis. It requires large sets of well-controlled crosses or well-characterized pedigree records. This approach provides precise estimates of recombination rate even between closely linked markers. However, it is expensive and time- and labor-consuming. Its efficiency is critically dependent on the number and distribution of the markers. Chiasma count at diplotene and diakinesis provides an unbiased estimate of the genome-wide and chromosome-specific rate of recombination. However, the efficiency of this approach is restricted by difficulties in obtaining the cells at the particular stages of meiosis and by the accuracy of ascertaining chiasma position. The cytological methods of recombination mapping based on the electron microscopic visualization of the recombination nodules or immunolocalization of MLH1 protein in pachytene spermatocytes and oocytes provide highly reliable estimates of the total recombination rate, as well as the frequency and distribution of recombination events in individual chromosomes [Anderson et al., 1999]. The material for this analysis is easily available in the testis of adult males during the breeding season and in the ovaries of juvenile females during the first week after hatching [Pigozzi, 2016]. Figure 31 shows examples of the application of this method to chicken spermatocytes and oocytes.

## Genetic and Cytological Estimates of the Chicken Recombination Rate

The first genetic map of the chicken chromosomes was constructed by Serebrovsky and Petrov [1930]. It contained 12 markers at 4 linkage groups and 4 more un-

linked markers. The total length of the map was 252 cM. The most recent high-density consensus linkage map is based on the analysis of the segregation of 9,268 SNPs and other markers in 3 different mapping populations. The total length of the linkage map is 3,098 cM for female meiosis and 3,145 cM for male meiosis [Groenen et al., 2009].

The first cytological estimate of the recombination rate has been carried out by chiasmata count in the cockerel spermatocytes at the diakinesis-metaphase I stage [Pollock and Fechheimer, 1978]. There was a significant interindividual variation: from 56 to 66 chiasmata per spermatocyte. Analysis of the number and distribution of chiasmata along the lampbrush chromosomes in chicken diplotene oocytes gave a similar interval of variation (59–64) [Rodionov et al., 1992]. The average numbers of recombination nodules estimated by electron microscopic visualization [Rahn and Solari, 1986] and immunolocalization of MLH1 protein in pachytene oocytes [Pigozzi, 2001] were rather close to each other (57.5 and 65.0, correspondingly) and to the chiasma count in the spermatocytes. Because each chiasma or recombination nodule represents one crossingover (50 cM), the total length of the sex averaged chicken genetic map is estimated to be 2,800–3,300 cM. There is a reasonably good correspondence between the genetic and cytological estimates of the recombination rate in chicken. It has, for a long time, been considered the highest among birds. It was suggested that such a high recombination rate could have resulted from domestication and strong artificial selection [Groenen et al., 2009; Backström et al., 2010]. Further studies clarified the intermediate position of the chicken recombination rate between those of the white wagtail (3,805 cM) and the black tern (2,155 cM) [Semenov et al., 2018].
## The Distribution of the Recombination Nodules along the Macrochromosomes

Mapping of recombination nodules at the synaptone- mal complexes made it possible to visualize the recombi- nation landscapes of chicken macrochromosomes. They showed a highly positive correlation between the length of synaptonemal complex and the number of recombi na- tion nodules [Pigozzi, 2001; Rahn and Solari, 1986], which is typical for vertebrate chromosomes. The macro- chromosomes of most birds examined show a highly po- larized distribution of the recombination nodules with steep peaks near the telomeres and deep valleys near the centromeres [Pigozzi and del Priore, 2016]. The recom- bination landscapes of the chicken macrochromosomes are slightly more flatter than those of other Galloanseræ [Calderón and Pigozzi, 2006; del Priore and Pigozzi, 2015; Pigozzi and del Priore, 2016].

The recombination landscape at ZW bivalent is of spe- cial interest. In all Neoaves examined, it contains a single crossover located in a very small pseudoautosomal region (PAR). Using the limits of MLH1 foci distribution, Tor- gasheva et al. [2021] estimated the size of PAR in domes- tic chicken as 5% of the completely paired ZW bivalent. Interestingly, in one oocyte they detected the second MLH1 focus located at 21% of the ZW length from the telomere, far beyond the most distant MLH1 foci found in other chicken oocytes. Although this exceptional MLH1 focus could be an artifact, it might indicate that rare recombination events are possible in the dispersed regions of residual homology between Z and W detected by Zhou Q et al. [2014].

## Interbreed Variation in the Recombination Rate

The recombination rate shows substantial interbreed and individual variation. Malinovskaya et al. [2019] ana- lyzed the number and distribution of MLH1 foci in sper- matocytes of the roosters of 6 chicken breeds. They re- vealed significant effects of breed (R² = 0.17; *p* < 0.001) and individual (R² = 0.28; *p* < 0.001) on variation in this trait, determined mainly by variation in recombination density on macrochromosomes. There was an interesting correspondence between the age of the breed and its re- combination rate. Those with high recombination rates were the breeds created during the last century by cross- ing several local breeds. The breeds with a low recombi- nation rate were the ancient local breeds.

The linkage experiment also revealed significant inter- population differences in recombination rates [Groenen et al., 2009]. F1 male and female hybrids between Red Jungle fowl and White Leghorn showed significantly low-

er recombination rates than purebred broiler popula- tions. The authors suggested that a higher recombination rate in purebred domestic animals was the result of strong artificial selection. Alternatively, a lower recombination rate in the F1 hybrids might be the result of negative het- erosis. Malinovskaya et al. [2021] found that F1 hybrids between 2 purebred breeds had a significantly lower re- combination rate (2,950 cM) than the cockerels of both parental breeds, Russian Crested and Pervomai (3,150 cM and 3,350 cM, respectively). The authors explained the negative heterosis for the recombination rate by difficul- ties in homology matching between the DNA sequences of genetically divergent breeds.

## Future Prospects

Controlling recombination frequency and, more im- portantly, its distribution, is a necessary future step in or- der to increase the efficiency of selection and overcome its limitations. Identification of the key molecular regula- tors of crossing-over in plants leads to new technologies for increasing recombination rate, which could potential- ly benefit plant breeding [Blary and Jenczewski, 2019]. The first group of methods relies on suppressing the genes limiting meiotic recombination, such as topoisom- erase TOP3α, DNA-helicases FANCM and RECQ4, and AAA-ATPase FIGL1 [Girard et al., 2015; Séguéla-Arnaud et al., 2015, 2016; Mieulet et al., 2018]. Combinations of mutations in these genes resulted in up to an 8-fold in- crease in recombination frequency in *Arabidopsis thali- ana* hybrids [Fernandes et al., 2018].

This promising result, however, leaves doubts about the feasibility of developing such methods and their ap- plicability for livestock. Although simulation studies show that an increase in genome-wide recombination would indeed result in an increased response to selection, a significant effect can only be achieved with a 10–20-fold increase in recombination rate [Battagin et al., 2016; Gonen et al., 2017]. Such a huge increase in genome-wide recombination rate, if ever possible, will break up existing beneficial allele combinations and end up in decreased genomic selection accuracy [Battagin et al., 2016]. This might outweigh the benefits of reshuffling genetic mate- rial and a potential increase of genetic gain [Blary and Jenczewski, 2019]. In addition, due to the relatively high stability of recombination hotspots in birds [Singhal et al., 2015], a genome-wide increase in recombination may not lead to the appearance of new crossover sites. Indeed, a 19% interbreed variation in recombination rate did not affect pattern of crossover localization along the chicken macrochromosomes [Malinovskaya et al., 2019].
By manipulating the distribution of crossover events (for example, by stimulating recombination in cold regions), the negative aspects of increased recombination might be avoided. Another approach for controlling crossing-over tested in *Saccharomyces cerevisiae* operates by targeting SPO11, a protein which induces DSBs, to specific regions [Peciña et al., 2002; Sarno et al., 2017]. Simulation study predicts that inducing crossing-over in non-recombining regions decreases the loss of genetic variability and increases genetic gain, especially in cases when polymorphisms associated with the trait are clustered [Gonen et al., 2017]. Thus, the development of a technology for local modifying recombination patterns, taking into account the genetic architecture of a trait, might have the potential to improve breeding programs in the future. Considering the relatively high stability of recombination hotspots in birds [Singhal et al., 2015], their redistribution could have potential for chicken breeding.

## Conclusion

Being an important trait for artificial selection, recombination rate is actively studied in the domestic chicken by various cytological and molecular genetics methods. Recent estimates are consistent and indicate that chicken is characterized by a high level of recombination rate, which is typical for birds. It also shows substantial interbreed variation and a relatively stable pattern of distribution along chromosomes. Methods for controlling the position of crossovers could potentially be useful for chicken breeding in the future.

## Conflict of Interest Statement

The authors have no conflicts of interest to declare.

## Funding Sources

The work was supported by the Ministry of Science and Higher Education of the Russian Federation, grant numbers FWNR-2022-2015 and 2019-0546 (FSUS-2020-0040).

# The Broad-Scale Recombination Landscape of Chicken Macrochromosomes Inferred by in situ Mapping of Crossovers

(Prepared by M.I. Pigozzi)

Meiotic crossovers (CO) serve to maintain and generate genetic variability in genomes by breaking associations between alleles at linked loci, resulting in new haplotypes. This has implications for the effect of selection on molecular evolution, as non-recombining genomic re-

gions of sexually reproducing organisms accumulate deleterious mutations and deteriorate [Reeve et al., 2016]. Also, the correct number and placement of meiotic CO have a vital role to ensure faithful segregation, with failures often resulting in aneuploidy and infertility. Recombination rates, or the number of recombination events per Mb every generation, may be explained by both differences in CO positions within a genome (recombination landscape) and genome-wide recombination (how many CO events occur per meiosis). A direct method to record both features in individual meiosis is the immunodetection of the MLH1 protein, a component of mature recombination nodules in pachytene (Fig. 32a). The length of the recombination map in centimorgans (cM) can be obtained multiplying the average number of MLH1 foci per cell by 50 map units, as one recombination event is equivalent to 50 cM. MLH1 focus data from chicken oocytes established that the average female genetic map is between 3,150 and 3,250 cM, in white layer and broiler lines, respectively [Pigozzi, 2001; del Priore and Pigozzi, 2020]. The use of the same methodology in spermatocytes from 5 different breeds established similar genetic lengths in males with interbreed variations of up to 19% in overall genomic recombination rates [Malinovskaya et al., 2019]. These independent studies also demonstrated a close match to the existing chicken linkage maps [Groenen et al., 2009].

From the MLH1 count in a number of domestic and wild bird species, global recombination rates and recombination patterns for macrochromosomes have been determined, offering a source for comparative analysis [Semenov et al., 2018; Pigozzi, 2022]. The usage of meiotic chromosomes at pachytene is particularly favorable to investigate the distribution of CO along chromosome arms, because at this stage of meiosis genomic distances are proportional to physical distances. This relationship is based on the relatively uniform size of the DNA loops attached to the synaptonemal complexes (SCs) of pachytene bivalents [Veller et al., 2019]. The theory of the regular spacing of DNA loops along the meiotic chromosomal axis can also be extended to the chicken where SC/DNA ratios exhibit little fluctuation [Pigozzi, 2007; del Priore and Pigozzi, 2021]. Thus, physical distances measured in micrometers or percentages along pachytene bivalents can be converted into genomic positions (in base pairs) along chromosomes. The resolution of the method has the limitation of the fluorescence microscopy. Figure 32b, c shows that the FISH signal of a BAC located at 1.2 Mb from the sequence start of the assembly of GGA1 can be clearly separated from the end of the synaptonemal com-
<figure><img src="image_37.png" /><figcaption>**Fig. 32.** Immunolocalization of recombination and FISH mapping of single-copy sequences on synaptonemal complexes of the chick- en. **a** Immunostained chicken oocyte showing the complete set of synaptonemal complexes labeled with anti-SMC3 and the cross- overs detected with anti-MLH1. The 8 largest autosomal bivalents have a number next to the centromere signal (red protruding marks). The ZW pair has a single MLH1 focus located near the</figcaption></figure>

homologous end of the bivalent. Scale bar, 10 µm. **b** Localization of BAC clone 40N14 on the synaptonemal complex of GGA1 (SC1). The BAC insert is at 1.2 Mb from the sequence start. Scale bar, 1 µm. **c** Graph showing the distribution of distances from the end of SC1 to the FISH signal in 8 pachytene nuclei. (Data from del Priore and Pigozzi [2021]).

plex, indicating that accurate measurements could be made for sequences located in pachytene oocyte spreads that were separated by 2 Mb [Pigozzi, 2001; del Priore and Pigozzi, 2021].

Taking this resolution into account, the recombination rates along the 8 largest autosomal bivalents were calculated here for 2.5-Mb intervals based on MLH1 focus data in 138 chicken oocytes from previous work of our laboratory [del Priore and Pigozzi, 2020]. The fluctuation of the recombination rates along the macrobivalents is shown in the form of a heatmap where the colour of the cells represents the range of recombination rates (Fig. 33). The mean recombination rate in these intervals was 2 cM/Mb, with 54% of the intervals showing rates between 1.5 and 2.5 cM/Mb. Even though crossovers can be found anywhere along chromosome arms, recombination rates are higher near chromosome ends and lower in the center. Regions without foci were rare (5 in 283 intervals) and they were limited to the 0.5-µm intervals closest to the centromeres of metacentric/submetacentric chromosomes.

An MLH1 recombination map can be used when only the genetic (cM) positions of markers are known to predict their physical positions on a chromosome [Anderson et al., 2004]. One of the initial scans for potential quantitative trait loci (QTL) for production-related traits in the chicken identified a region on GGA1 spanning the area of 263 and 285 cM [Hansen et al., 2005]. The cumulative cM frequency along GGA1 calculated from the MLH1 recombination map predicts that this region comprises about 20 Mb between 145 and 165 Mb from the end of the short arm (Fig. 34a). This estimate could have been done even with the approximate size of GGA1 obtained by image cytometry [Smith and Burt, 1998; Mendonça et al., 2016]. Although this particular prediction is now outdated, it is an example of how cM maps based on MLH1 foci can help integrate physical and genetic maps at their initial stages. Identification of genomic loci governing complex traits involves time-consuming and expensive procedures such as QTL mapping or genome-wide association studies (GWAS). Linkage disequilibrium (LD) between QTL or markers plays a central role in gene lo-
<figure><img src="image_38.png" /><figcaption>**Fig. 33.** Recombination rates along GGA macrochromosomes 1 to 8. Each chromosome is divided in cells of 2.5 Mb. The recombination rates are represented by the different colours as indicated in the legend. The arrowheads point at the centromere positions calculated from the centromeric indexes in synaptonemal complex spreads.</figcaption></figure>

calization [Pritchard and Przeworski, 2001]. As recombination is known to shuffle genetic material, leading to decay of LD, prior knowledge of the broad recombination landscape in regions of interest could be valuable before embarking on more complex genetic analyses. The MLH1 recombination maps can be overlaid onto assembled chromosomes to obtain cM distances between markers of interest. For instance, a region of GGA5 contains the majority of QTL for woody breast and white striping myopathies which are located in an 8-Mb-long area on GGA5, between 8 and 16.5 Mb of the chromosome sequence [Lake et al., 2021]. The MLH1-cM map shows that this segment is located in an area with recombination rates higher than the average for that chromosome and is bordered by regions with low recombination rates (Fig. 34b).

The recombination rates obtained by MLH1 mapping have been applied directly or indirectly to analyze macroevolutionary processes, the variation in crossing over between populations or species, the existence of sex-specific recombination landscapes or the evolution of genome-wide recombination rates [Segura et al., 2013; Semenov et al., 2018; Guo X et al., 2020; Peterson and Payseur, 2021]. The wide range of biological issues analyzed by the immunocytological location of crossovers highlights the importance of this approach to studying meiotic recombination as well as its versatility to comparing data from independent studies.

**Statement of Ethics**

Handling and euthanasia of birds were conducted in accordance with Argentinian and international guidelines for the use of farm and laboratory animals and were approved by the Animal Care and Use Committee of the University of Buenos Aires School of Medicine (EXP-UBA 0047533/16, Res 2116/16).

**Conflict of Interest Statement**

The author declares no conflict of interest.

**Funding Sources**

Current research at the author's laboratory is supported by AN-PCyT, FONCYT BID-PICT 2016 #2302.

**Data Availability Statement**

The data presented in this study are openly available at Mendel Data, V2, 10.17632/w3n9xp5dnp.2.

**The BF-BL and BG Regions of the Chicken B Locus Differ in Genome Dynamics**

(Prepared by J. Kaufman)

Genomes do not evolve homogeneously, and different regions may evince divergent properties. A classic example is the presence of isochores that separate by Giemsa staining and by density centrifugation. In mammals, GC-rich isochores are considered to replicate earlier in the cell cycle than AT-rich isochores, and also have a greater density of genes which are more compact [Constantini and Musto, 2017; Bernardi, 2021]. Analyses of the chicken B locus illustrate regions that differ in another way: the BF-BL region (which corresponds to the major histocompatibility complex, MHC) is relatively compact and stable with little obvious recombination, whereas the BG region undergoes frequent expansion and contraction leading to copy number variation (CNV). These two regions are organised differently, with the structure of the BF-BL region differing significantly from the MHC of mammals and having a profound effect on function, while the pheno-
<figure><img src="image_39.png" /><figcaption>**Fig. 34.** MLH1 recombination maps of GGA1 and GGA5 integrat-
ed to physical positions. In both graphs the *x* axis is the length of
the chromosome in Mb from the sequence start on the short arm
(p) to the end on the long arm (q) with *c* indicating the centromere.
A schematic representation of the chromosomes is shown below.
**a** The shaded area between the lines represents a chromosome re-</figcaption></figure>

gion localized between 263 and 285 cM in a QTL analysis [Hansen
et al., 2005]. The physical location of this region can be predicted
from distribution of the cumulative cM distances (blue line). **b** The
shaded area near the centromere spans over 8 Mb and contains
multiple QTL for 2 myopathies [Lake et al., 2021]. The MLH1-cM
map shows the recombination pattern in this segment.

typic effects of recombination and deletion on the BG
genes are at present still a mystery [Kaufman et al., 1999a;
Salomonsen et al., 2014].

The long history of the B locus has been extensively
reviewed [with many more citations: Miller and Taylor,
2016; Afrache et al., 2020; Kaufman, 2021]. It was discov-
ered as a polymorphic alloantigen system on chicken red
blood cells, then found to co-segregate with a variety of
functions associated with the MHC in mammals, and lat-
er found to co-segregate with the nucleolar organiser re-
gion (NOR) on a microchromosome, now numbered
chromosome 16 (Fig. 35). Two regions became apparent
by immunoprecipitation of the antigens recognised by
the alloantisera: the BG region encoding large erythrocyte
membrane proteins without obvious glycans, and the BF-
BL region encoding membrane glycoproteins corre-
sponding to MHC classical class I molecules (BF antigens
with wide tissue distribution) and class II molecules (lym-
phocyte BL antigens). Later, it became clear that both BG
and BL molecules are specifically expressed on various
other cell types. Different B locus haplotypes were found
to determine decisive resistance and susceptibility to var-
ious economically important pathogens, later narrowed
down to the BF-BL region [Briles et al., 1983; Miller and
Taylor, 2016; reviewed in Kaufman, 2021].

More recently (Fig. 35), a variety of approaches have
shown that the BF-BL and BG regions flank a region pri-
marily encoding tripartite motif-containing (TRIM)
genes, and are separated by high levels of recombination
from non-classical MHC-like genes in the Rfp-Y region,
the ribosomal RNA genes of the NOR, olfactory recep-
tors, and scavenger receptors [Ruby et al., 2005; Shiina et
al., 2007; Delany et al., 2009; Miller et al., 2014]. More-
over, the BF-BL region was discovered to extend towards
the telomere with a few genes found in the MHC class III
region and with some non-polymorphic non-classical
class I genes encoding CD1 molecules, which in mam-
mals are found on other chromosomes in so-called MHC-
paralogous regions [Maruoka et al., 2005; Miller et al.,
2005; Salomonsen et al., 2005; reviewed in Kaufman,
2021]. The non-classical class I-like YF genes in the Rfp-Y
region are apparently polymorphic and likely bind hy-
drophobic ligands, but they are not found in mammals
nor do they determine rapid graft rejection or other clas-
sical MHC functions [Thoraval et al., 2003; Goto et al.,
2022].

An early description of the BF-BL region was a “mini-
mal essential MHC” [Kaufman et al., 1999a, b], based on
the fact that most genes expected from the MHC of typi-
cal mammals are missing (including genes encoding the
complement components C2 and factor B, and the class
<figure><img src="image_40.png" /><figcaption>**Fig. 35.** Organisation of regions on chicken chromosome 16, as currently published. **a** Depiction of chromosome 16, based on analysis by FISH, radiation hybrids, genetics, southern blotting, and sequencing. **B**, **B locus**; **GC**, G+C-rich region of PO1 repeats; **Y**, Rfp-Y region; **NOR**, nucleolar organiser region; **BLA**, class II A gene; **fB**, factor B gene; **ORs**, olfactory receptor genes; **SRCRs**, scavenger receptor with cysteine repeat genes. Double-headed arrows indicate recombination frequencies between **B** and **BLA**, **fB** and **Rfp-Y**, and **B** and **Rfp-Y**. **b** Region of the **B** locus currently sequenced, including the BF-BL region, the TRIM region and the BG region. Genes</figcaption></figure>

represented by boxes. Rising and falling stripes indicate genes of the classical class I and class II presentation system, respectively; stippled indicate class II region genes; black indicates lectin-like genes and pseudogenes; horizontal stripes indicate TRIM family genes; vertical stripes indicate BG genes. Names of genes above indicate transcription from left to right, below indicate transcription from right to left; note the homologous genes in opposite transcriptional orientation in the BF-BL region but in the same transcriptional orientation in the BG region, strongly affecting the dynamics of evolution based on recombination. Figure from Kaufman [2021].
II A chain BLA), with those few genes present being critical to classical MHC function, including the genes encoding the TAP1 and TAP2 chains forming the peptide transporter, the class I-dedicated chaperone tapasin, and the class II-dedicated chaperones formed by DMA, DMB1, and DMB2. Also present is the *BRD2* (or *RING3*) gene encoding a serine-threonine kinase, somewhat mysteriously present in the MHC of all jawed vertebrates examined down to cartilaginous fish. However, the organisation of the BF-BL region is significantly different from the MHC of typical mammals, particularly with the class III region on the outside of the class I and class II regions, and with the two class I (*BF*) genes flanking the *TAP* genes. Moreover, at one end of the BF-BL region there is an unexpected pair of genes encoding lectin-like membrane glycoproteins, likely a natural killer (NK) receptor and ligand which in mammals are found on a different chromosome in the natural killer complex (NKC) [Rogers et al., 2005; Rogers and Kaufman, 2008], and a single gene resembling the many genes in the BG region, now called *BG1* [Goto et al., 2009; Salomonsen et al., 2014].

Compared to mammalian MHCs, the BF-BL region is notably compact and overall there has been little evidence for ongoing recombination, so that the BF-BL region exists as relatively stable haplotypes with potential co-evolution between the closely linked polymorphic genes. Unlike typical mammals, the peptide-loading genes (*TAP1*, *TAP2*, *tapasin*, *DMA*, *DMB1*, and *DMB2*) are polymorphic and, as an example, the peptide-translocation specificity of the TAP protein allele from a particular BF-BL haplotype correlates with the peptide-binding specificity of the dominantly expressed class I molecule encoded by the *BF2* gene. A few examples of apparent recombination within the BF-BL region have been described, including one between B15 and B12 haplotypes giving rise to the famous B19 haplotype. In fact, out of 242 haplotypes recently identified by sequence (PCR-NGS) typing of commercial, fancy, and African chickens [Tregaskes et al., this volume], 53% could be related by potential recombination between the *BF* and *BLB* genes, suggesting that even low levels of recombination could lead to new combinations given sufficient time. Thus far, deletions have only been identified due to short direct repeats in the *BF1* gene, leading to disabling of the promoter or to loss of the gene locus.

The stability of the BF-BL region may also depend on the presence of many pairs of sequence-related genes in opposite transcriptional orientation: *BNK* and *Blec*, *BLB1* and *BLB2*, *BF1* and *BF2*, and *TAP1* and *TAP2* [Kaufman et al., 1999b]. Homologous recombination between the members of such pairs should lead to inversion rather than deletion

or unequal crossing-over, thus preserving the essential genes in a putative “minimal essential MHC.” The presence of BLB1-like sequences in the *BLB2* locus adjacent to the *BRD2* gene might be a result of such inversions, although gene conversion or some other form of segmental exchange might also be responsible [Afrache et al., 2020]. The orientations of TAP and tapasin genes in closely related galliform birds are consistent with historic inversions [He et al., 2021].

In contrast to the several different kinds of genes in the BF region that exist in relatively stable haplotypes, the BG region is relatively homogeneous but very dynamic. Almost all the genes in the BG region have similar structures, with exons encoding a signal peptide, a single extracellular immunoglobulin-like (Ig) domain, a hydrophobic transmembrane (TM) region, and a long cytoplasmic tail composed of heptad repeats [Miller et al., 1991; Salomonsen et al., 2014]. BG molecules are dimers, so the two chains interact through the Ig and TM regions, and the cytoplasmic tails wind around each other in a coiled coil. All these genes are in the same transcriptional orientation, which is well-suited for unequal crossing-over and deletion, leading to CNV which has been observed by sequence comparisons and by fibre fluorescent in situ hybridisation (FISH) [Salomonsen et al., 2014].

Further evidence for the dynamic recombinational landscape of the BG region comes from sequence comparisons [Salomonsen et al., 2014]. The *BG* genes in the BG region are expressed either in haemopoietic cells (such as erythrocytes, lymphocytes, and myeloid cells) or in non-haemopoietic cells (generally epithelial cells), and this expression correlates with 2 kinds of promoter and 5'UTR sequences. The exons encoding the cytoplasmic tail and the 3'UTR also fall into 2 classes. Overall, the *BG* genes represent the 4 combinations of the 2 classes of promoter/5'UTR and the 2 classes of cytoplasmic tail/3'UTR, with the exons for the signal peptide and the Ig-like region not following any particular pattern, as through recombination in the middle of the genes creates chimeric genes.

This kind of recombination leads to a conundrum. All the exons of *BG* genes are polymorphic, but determining whether the variation is selected has depended on being able to identify alleles, which is difficult in an expanding and contracting multigene family. A comparison of BG transcripts from different BG haplotypes that are expressed in the same cell type (so-called “functional alleles”) showed that selection is apparent only in the cytoplasmic tails, despite the fact that the B locus was originally identified by serological reactions with the extracellular Ig-like domain [Chattaway et al., 2016; Chen et al., 2018]. The conundrum is that recombination will
lead to a particular cytoplasmic tail being switched to expression in different cell types, and the rationale for the same sequence functioning in different cell types is unclear. However, the function of *BG* genes is currently a mystery, with the first clue being sequence similarities of the extracellular Ig-like domain to mammalian butyrophilins, which are thought primarily to be involved in negative regulation of αβ T cells and with localisation and function of γδ T cells [Henry et al., 1999; Rhodes et al., 2016; Herrmann and Karunakaran, 2022]. However, there is evidence that the cytoplasmic tail is important, forming coiled-coils similar to tropomyosin and affecting actin-myosin interactions [Bikle et al., 1996]. Moreover, there is some evidence that suggests that the cytoplasmic tail is important for viral resistance [Goto et al., 2009]. Perhaps the apparent conundrum will be resolved when the mysteries of BG function are solved.

This report summarises evidence for the divergent properties of 2 closely linked regions of the B locus, the relatively stable BF-BL region and the highly dynamic BG region. In between is the TRIM region, whose properties have not been explored. On the centromeric side of the BG region are various genes which, along with the NOR, olfactory genes, and scavenger receptor genes, largely remain to be analysed [Delany et al., 2009; Miller et al., 2014]. However, evidence for the dynamic nature of the Rfp-Y region has recently been presented [Goto et al., 2022]. Future studies may reveal how all these regions are organised and maintained, and may give clues to how they are reorganised in the transition to the different arrangements in other species.

### Acknowledgments

The author thanks Dr. Jacqueline Smith, Dr. Magda Migalska, and Ms. Lan Huynh for critical reading.

### Conflict of Interest Statement

The author declares no conflicts of interest.

### Funding Sources

The author thanks the Wellcome Trust (Investigator Award 110106/A/15/Z) for support.

## 600 Alleles and 240 Haplotypes Identified for the Chicken BF-BL Region

(Prepared by C.A. Tregaskes, R.J. Martin, L. Huynh, N. Rocos, and J. Kaufman)

The major histocompatibility complex (MHC) of mammals is a large and complex region, with hundreds

of genes and much recombination, and encodes a few highly polymorphic classical class I and class II molecules that have central roles in immune responses [Kaufman, 2016]. The functional equivalent of the mammalian MHC in chickens is the BF-BL region, which is remarkably simple and compact with few genes, most of which are critical to the function of classical MHC molecules, so that this region was originally dubbed a “minimal essential MHC.” Moreover, recombination within the BF-BL region was considered to be rare, so that this region could exist as relatively stable haplotypes, with co-evolution between these closely linked genes leading to functional consequences [Kaufman et al., 1999b; Kaufman, 2018; Tregaskes and Kaufman, 2021]. However, most of these ideas arose from the analyses of a few “standard haplotypes” dating back to the original descriptions by Briles and coworkers [Miller et al., 2004; Miller and Taylor, 2016; Afrache et al., 2020].

We set out to understand more about the diversity of MHC alleles and haplotypes in different chicken populations, starting with reference strand-mediated conformational analyses (RSCA) followed by cloning and sequencing [Potts et al., 2019]. As the need for higher through-put became clear, we developed a polymerase chain reaction-next generation sequencing (PCR-NGS) system to type the classical class II B genes *BLB1* and *BLB2*, and the classical class I genes *BF1* and *BF2*. Taking advantage of the compact nature of chicken MHC genes, we amplified exon 2 through the intron to the end of exon 3 (roughly 750 nucleotides) from genomic DNA and used the Illumina MiSeq to paired-end sequence both exons from each gene (Fig. 36). Coupled with DNA isolation using relatively cheap reagents (which worked for most samples) and a double-barcoding system (12 pairs of barcoded primers each for BF and BL, and 96 barcoded Illumina adaptors), we were able to analyse up to 1,152 samples in a run. We developed a bespoke bioinformatics pipeline that automatically generated sequences for all the alleles present, compared them to known alleles and then assembled them into known haplotypes, leaving the unknown sequences to be analysed by inspection [Martin, 2021; C.A. Tregaskes, R.J. Martin, and J. Kaufman, unpublished].

The initial samples included DNA, blood cells or tissues primarily from experimental lines, red jungle fowl, commercial flocks, fancy birds, and African chickens provided by many collaborators. Altogether, 22 different MiSeq runs were performed covering roughly 20,000 samples. For some populations, we used the microsatellite LEI0258 [Fulton et al., 2006] and BF2-specific PCRs
<figure><img src="image_41.png" /><figcaption>**Fig. 36.** The basis for the PCR-NGS typing of the chicken MHC. Organisation of the BF-BL region and gene names from Kaufman et al. [1999b] (RING3 is now known as BRD2); primers are designated by lab names. Figure from Martin [2021].</figcaption></figure>

to confirm and extend the assignments [Bertzbach et al., 2022; L. Huynh, C.A. Tregaskes, and J. Kaufman, unpublished]. For some samples of blood cells, flow cytometry was performed to determine the expression level of the class I (BF) molecules on erythrocytes that is known to correlate inversely with peptide repertoire [Chappell et al., 2015], which in some cases was determined by immunopeptidomics. Almost all the experimental work is now complete, except for some PacBio sequencing that has become necessary to assign BLB sequences to the appropriate loci [N. Rocos, C.A. Tregaskes, and J. Kaufman, unpublished]. We identified roughly 600 alleles and found over 240 haplotypes, but there is much analysis to complete, so only an initial overview of some preliminary results will be summarised in this report, with some details expected to change as the analyses are refined.

For understanding alleles and haplotypes, we began by assembling the data known from the scientific literature as well as from nucleotide databases (such as NCBI/GenBank) [Afrache et al., 2020]. From the literature, 16 complete BF-BL haplotypes were known from sequencing cosmids, bacterial artificial chromosomes (BACs), and long-range PCR products. Other haplotypes could be assembled from complete or partial (exon 2-exon 3 for BF, exon 2 and exon 2-exon 3 for BLB) genomic sequences, as well as complete or partial cDNA sequences. However, many gene and cDNA sequences in the databases had to be ignored, even if published, since they were deposited from single studies and differed by only 1 or 2 nucleotides, with the associated papers revealing that only one amplification had been carried out, so most of those sequences were not separate alleles but the result of nucleotide misincorporation.

Altogether, 17 standard haplotypes seemed secure, and an additional 16 haplotypes were suggested [Afrache et al., 2020]. Even these data must be treated with some caution; for example, the B6 and B15 haplotypes reported in the largest haplotype-sequencing project [Hosomichi et al., 2008] have not yet been found in any sample examined by PCR-NGS.

It was very easy to assign the 339 class I sequences found by PCR-NGS (after 22 MiSeq runs) to the *BF1* and *BF2* loci [Martin, 2021] since they were found almost exclusively in different clades by neighbour-joining, maximum likelihood and minimum evolution tree building algorithms (Fig. 37). The exceptions include 9 alleles related to BF1\*0201 and BF1\*0901 which cluster together, as well as a couple of other sequences present in other clades, all in the BF2 part of the tree. The *BF2* locus was more polymorphic, with 247 *BF2* alleles compared to 92 *BF1* alleles. Despite largely being in one clade, *BF1* alleles had much sequence diversity, with deep branches in the BF1 clade tree. However, most of this variation was not obviously in the peptide-binding site, with 74% of the BF1 sequences having His9 and Asp24, which may mean a wide peptide repertoire (as recently found from the structure of BF1\*1901). Moreover, 89% of the BF1 sequences had an identical or near-identical sequence in the region of the C1/C2 epitope on the α1 helix, consistent with the suggested role of BF1 molecules as natural killer (NK) ligands [Ewald and Livant, 2004; Kim T et al., 2018].

In contrast to class I loci, it became very difficult to assign all 259 BLB sequences to the *BLB1* and *BLB2* loci.
<figure><img src="image_42.png" /><figcaption>**Fig. 37.** The chicken classical class I sequences mostly separate into 2 large clades (a), while classical class II B sequences are all mixed together in phylogenetic trees (b). BLB* indicates class II B sequences that could not easily be assigned to the *BLB1* or *BLB2* loci in the haplotypes examined. Figure from Martin [2021], derived from data available in 2020.</figcaption></figure>

[Martin, 2021], largely due to finding haplotypes with 2 new class II B sequences, both of which were most closely related to known BLB1 sequences. The known BLB1 and BLB2 sequences (as well as many identified by PCR-NGS) were mixed in phylogenetic trees (Fig. 37). Ongoing experiments using “between gene” primers and PacBio sequencing to assign sequences to the *BLB1* locus adjacent to *Blec* and to the *BLB2* locus adjacent to *BRD2* have resolved the ambiguities in 26 of 47 unclear BLB haplotypes [N. Ros, C.A. Tregaskes, and J. Kaufman, unpublished]. These experiments have revealed the same sequence in one locus in one haplotype and the other locus in a second haplotype, as well as one haplotype with the same sequence in both loci. It seems likely that most haplotypes will have to be checked to ensure that new sequences most closely related to known BLB2 sequences are actually located in the *BLB2* (that is *BRD2*-adjacent) locus. Given the facts that the *BLB1* and *BLB2* genes are in opposite transcriptional orientation and that most of the gene sequences are nearly identical, one possibility is that recombination between homologous sequences in the *BLB1* and *BLB2* loci leads to inversion; in the PacBio run done thus far, there has been no convincing evidence for such inversions.

The optimal choice of nomenclature for BF and BLB alleles continues to be unclear. The old accepted nomenclature was based on haplotypes, so the same sequence in two haplotypes would have different names [Miller et al., 2004]. Based on the nomenclature system originally described for human MHC alleles and widely used for other species [Ballingall et al., 2018; Afrache et al., 2020; Robinson et al., 2020], the gene designation would be separated from the allele designations by a star or asterisk, with distantly related alleles of a single locus differing in the first two numbers (e.g., BLB1*02 vs. BLB1*04), and with closely related alleles of a single locus having the same first two numbers and differing in the next two numbers after a colon (e.g., BLB1*02:01 and BLB2*02:02). Haplotypes would then be constructed by strings of alleles (e.g., BLB1*02:01-BLB2*02:02-BF1*02:04-BF2*02:05 or in short 2-2:02-2:04-2:05). This elegant solution ran into trouble from the criteria for close relationship, in that the number of sequence differences within clade of closely related sequences could exceed the number of sequence differences between 2 sequences from different clades. Moreover, the same BLB sequence has now been found experimentally in both the *BLB1* and *BLB2* loci, so how should it be named? At the moment, designations
for many sequences are simply ad hoc, as we struggle to develop a consistent approach.

Of the “standard haplotypes,” 7 were exhaustively analysed over some years in our lab, 5 of which were included among the 14 subsequently analysed by another lab in a single sequencing paper [Jacob et al., 2000; Wallny et al., 2006; Shaw et al., 2007; Hosomichi et al., 2008]. Of these 16 haplotypes, all had different *BF2* alleles except for two B15 haplotypes which differed in *BF1*. The original B15 haplotype described (and almost all subsequently) had no expressed *BF1* allele (like the B14 haplotype), but the B15 haplotype from a chicken line in Japan had a *BF1* allele present. Among the 242 haplotypes identified by PCR-NGS (although there are a few from published data that we have not found), 27 (11%) have no *BF1* allele amplified by the primers used. Originally Southern blots suggested an insertion in the BF1 loci that was not expressed [Wallny et al., 2006; Shaw et al., 2007], but the latest experiments with primers outside the gene have amplified this region in those haplotypes, and identified a deletion of the whole *BF1* gene which is the result of 2 short direct (but imperfect) repeats [N. Rocos, F.J. Coulter, and J. Kaufman, unpublished].

Of the “standard haplotypes,” B19 was identified as a recombinant of B12 and B15 haplotypes, and 3 haplotypes (B5, B8, and B11) were also found to be recombinants, although by “gene conversion” of long stretches of DNA [Wallny et al., 2006; Shaw et al., 2007; Hosomichi et al., 2008]. Among the 242 haplotypes, 128 (53%) could have arisen by recombination between *BLB2* and *BF1* (some with subsequent mutation to produce closely related alleles); 22 BLB haplotypes are in combination with 96 BF haplotypes (with closely related *BF* alleles combined, since they might have arisen from mutation subsequent to recombination). The most extreme is the BLB haplotype 5-5, which is associated with 25 different BF haplotypes (with closely related BF alleles combined). There is also apparent recombination between *BF1* and *BF2*, with 15 different *BLB1-BLB2-BF1* haplotypes in association with 37 *BF2* alleles. As an example, 4-8-4 is found with BF2\*24:01, 53:01, and 4 closely-related 43-type alleles.

The first analyses by RSCA were performed with high level (elite, great-grandfather) lines of commercial breeders, and we were shocked at the low diversity of these populations; some had only a single BF-BL haplotype. In order to better understand the commercial chickens that are actually in the field, we obtained farm-level samples from our collaborators, examining 6 broiler lines, 15 egg-layer lines, and 1 dual purpose line. The take-home message is that there are typically very few haplotypes, mostly 4 or 5

haplotypes above 1% genotype frequency, usually with 1 haplotype by far the majority. In particular, a haplotype not described before (made up of alleles previously described, provisionally called B31) is present in 33-64% genotypes of the 6 farm-level broiler flocks. Similarly, a previously undescribed haplotype provisionally called B9:02 dominates nearly all the brown egg layer flocks. If these numbers are representative, then there are billions of chickens in the field that are MHC homozygotes.

How do these commercial chickens survive with such low MHC diversity? Part of the answer may be that most of the high frequency haplotypes are those with BF2 alleles that have low cell surface expression and promiscuous peptide binding (for those with known peptide motifs). Such so-called “promiscuous haplotypes” are known to protect against a variety of economically important infectious diseases in chickens and have been suggested to act as generalists, in contrast to “fastidious haplotypes” which may act as specialists [Chappel et al., 2015; Kaufman, 2018; Tregaskes and Kaufman, 2021]. A few haplotypes with high-expressing *BF2* alleles are found in some populations; these may function as specialists or have some other useful attribute(s).

A wealth of information has already emerged from this PCR-NGS typing, but there is much more to be learned by finishing the detailed analysis of commercial, fancy, and African chickens. Moreover, there are many chickens worldwide that have not been examined by this kind of analysis, particularly in South, Southeast, and East Asia. As the typing methods benefit from longer reads that cover more genes, many interesting attributes of the chicken MHC are likely to be revealed.

### Acknowledgments

We thank Dr. Thomas Tan for setting up most of the first PacBio run, and many technicians, students, and collaborators that contributed work and samples to this effort over the last 7 years. We thank Dr. Jacqueline Smith and Dr. Magda Migalska for critical reading.

### Conflict of Interest Statement

The authors declare no conflicts of interest.

### Funding Sources

The authors thank the Wellcome Trust (Investigator Award 110106/A/15/Z to J.K.), the BBSRC (DTP PhD in Biological Sciences at the University of Cambridge to R.J.M.), and the University of Edinburgh for support.
# The Chicken Genomes and Their Diversities

(Prepared by J.-L. Han and O. Hanotte)

This review intends to provide a comprehensive summary of the status of chicken genome resources and what we have been learning from the analyses of genomic diversity of our commonest domestic poultry species.

## Chicken Genome Assemblies

The chicken was the first bird and agricultural animal species to have its genome sequenced. The first reference genome of 6.6× genome coverage from a female red jumboowl, known as "RJF #256" (inbred line, UDC 001), was published in December 2004 [International Chicken Genome Sequencing Consortium, 2004]. It was obtained through Sanger sequencing and included around one billion nucleotides and 20,000–23,000 annotated genes. This genome assembly was further improved by adding 14× genome coverage next-generation sequencing (NGS) data and referred to as Galgal3 or Gallus_gallus-2.1 (GenBank RefSeq assembly accession no. GCF_000002315.1 and GCF_000002315.2 released in November 2006) [Groenen et al., 2011]. It was followed by a de novo assembly of the "RJF #256," using all available Sanger and NGS reads available at the time and new Illumina short reads at 68.6× genome coverage (Galgal4, Gallus_gallus-4.0 with GCF_000002315.3 released in November 2011) [Ye et al., 2011; Schmid et al., 2015]. Then, another de novo assembly, based on third-generation sequencing data (PacBio RSII long reads at 50.6× genome coverage) of the "RJF #256" was merged with all Galgal4 sequences and referred to as Gallus_gallus-5.0 (GCF_000002315.4 released in December 2015) [Warren et al., 2017]. This genome assembly was finally upgraded by adding PacBio RSII long reads to a sequencing depth of around 80× and by generating a high chromatin proximity map to help in the order and orientation of the assembled contigs and scaffolds. It is referred to as GRCg6a (GCF_000002315.6 released in March 2018). This assembly has a 1.07 Gb total genome size. It includes 34 chromosomes with 1,402 contigs assembled into 524 scaffolds. The N50 length for the contigs is 17.66 Mb, while the scaffold N50 is 20.79 Mb. The NCBI *Gallus gallus* Annotation Release 104, including 17,477 protein-coding and 6,558 noncoding genes (released in May 2018), and the Ensembl release 94, including 16,878 coding and 7,166 noncoding genes (released in April 2018) (database version 106.6) complement the GRCg6a assembly.

## Development and Application of SNP Arrays for the Assessment of Genomic Variation

Considering the details on gene expression microarrays in an early review [Gheyas and Burt, 2013], we focus on the development of genotyping arrays in this summary. The first Illumina 3K SNP array was developed based on the selection and validation of 3,072 out of the 2.8 million SNPs at one SNP each in 3,072 bins distributed evenly throughout the chicken genome. It was based on the genome sequence of WASHUC1 assembly (Galgal2 released in February 2004 at https://genome.ucsc.edu/cgi-bin/hgGateway) with linkage information to account for the recombination rate of each chromosome [Aerts et al., 2007]. In addition, 34 SNPs in genes of interest were added [Muir et al., 2008a]. Then, a moderate density (60K) Illumina SNP BeadChip was developed using additional SNPs from the Illumina short reads of broiler and layer chicken populations aligned against Gallus_gallus-2.1.0 (released in May 2006), 454-read-based contigs of the "RJF #256," and the mitochondrial genome. It included 60,800 SNPs [Groenen et al., 2011]. A 600K Affymetrix Axiom high-density (HD) genotyping array was independently constructed based on 139 million SNPs identified by aligning the Illumina short reads of 243 chickens from 24 lines of commercial broilers, white-egg layers, and brown-egg layers as well as experimental inbred layers and unselected layer line against the Galgal4. They were scaled down to 1.8 million SNPs as a robust and tractable subset for HD array design, including those built in the Illumina 3K [Muir et al., 2008a] and 60K [Groenen et al., 2011] arrays, and SNPs among the 7 million SNPs identified from chicken populations of different lines [Rubin et al., 2010]. The 580,954 SNPs were validated by genotyping an additional 282 chickens, including trio samples from 3 types of commercial lines and traditional breeds [Kranis et al., 2013]. All segregating SNPs on this Affymetrix 600K array were evenly spaced across the genome, following the genetic map distances, instead of their physical distances, for both broiler and layer lines [Groenen et al., 2009], to account for the difference in recombination rates between macro- and microchromosomes [Rodionov, 1996; Groenen et al., 2000, 2009; International Chicken Genome Sequencing Consortium, 2004; Megens et al., 2009; Liu Z et al., 2021].

These SNP arrays have significantly facilitated genome-wide association studies (GWAS), paving the way for genomic selection, identification of selection signatures, fine mapping of QTLs, and detection of copy number variations (CNVs). Although the first genome-wide scan based on the WGS data at low genome coverage of 4
birds failed to identify selective sweeps for adaptive alleles
following chicken domestication [Wong et al., 2004], sev-
eral selective sweeps linked with the genes associated with
growth, appetite, and metabolic regulation were subse-
quently detected using enhanced genome-wide SNPs of
broilers, while one of the most striking selective sweeps at
*TSHR* locus has been considered as a “domestication” sig-
nature [Rubin et al., 2010; Elferink et al., 2012].

These SNP arrays were also widely used for the char-
acterization of population genomic diversity and genetic
structure of many wild, commercial, experimental, indig-
enous, and fancy chicken breeds/populations [Muir et al.,
2008b; Elferink et al., 2012; Malomane et al., 2019; Zhang
et al., 2020; Cendron et al., 2021], even though SNP ascer-
tainment bias might be of some concern [Malomane et
al., 2018; Geibel et al., 2021]. Also, population stratifica-
tion can lead to false associations in GWAS. Henceforth,
a careful examination of population genetic structure is
warranted [Kranis et al., 2013].

The Illumina 3K array was used for genotyping 2,551 informative SNPs in 2,580 chickens from commercial male and female broilers, white- and brown-egg layer pure lines, experimental, and traditional breeds. Analysis of commercial lines showed a loss of 50% or more of the genetic variability present in ancestral breeds, a consequence of founding effects in making these lines. It raises questions about the suitability of these lines to respond to future needs (consumers, societal, and producers' needs), as well as about their genetic repertoire for resistance to infectious disease challenges [Muir et al., 2008b]. The Illumina 60K array was used to genotype 51,076 autosomal SNPs in 67 populations, including Ceylon Junglefowl (Gallus lafayetti), red junglefowl (G. g. gallus and G. g. spadiceus), eight broiler sire lines, five broiler dam lines, 11 white-egg layer lines, 11 brown-egg layer lines, 19 traditional Dutch breeds, and 10 Chinese indigenous breeds [Elferink et al., 2012]. A phylogenetic tree rooted at the Ceylon Junglefowl separated the red junglefowls from all domestic chickens that were divided into 2 branches, one including brown-egg layers, broilers, and Chinese breeds, and the other white-egg layers and Dutch breeds. Among the broilers, the sire and dam lines were separated from each other, supporting their distinct origins.

The Affymetrix 600K array was evaluated for its utility
in inferring population stratification with samples of
known history through principal component analysis
(PCA). It was found that birds from the same line/breed
clustered together. The broiler lines were closer to the
brown-egg layers than to the white-egg layers. In con-

trast, the two white-egg layer lineages were separated
from each other, supporting different origins of the two
lines [Kranis et al., 2013].

Also, it was observed that the phylogeny reconstruc-
tions using genome-wide SNP arrays mirrored those
based on microsatellite data [Eding et al., 2002] and
known geographic origins and breeding history of the
studied populations [Elferink et al., 2012]. In particular,
the broilers and brown-egg layers shared their ancestry.
They were initially developed by crossing European and
Asian breeds, while the white-egg layers originated from
the single-combed White Leghorn of European origin
[Crawford, 1990; Muir et al., 2008b; Elferink et al., 2012].

The Affymetrix 600K array had been further applied
to several large-scale population genomic studies. For in-
stance, genotyping of 1,200 chickens, ranging from 41 to
469 samples of five Chinese breeds of Beijing-You, Hong-
shan, Shouguang, Taihe Silkie, and Tibetan, along with
White Leghorn originating from Italy, Houdan chicken
from France, and Rhode Island Red from USA, identified
a higher genomic diversity in most Chinese breeds com-
pared to exotic ones, while all breeds carried some unique
polymorphisms allowing successful assignment of all
samples back to the breeds of origins. Local Tibetan
chickens with high-altitude adaptability had a high level
of genetic admixture from White Leghorn [Nie et al.,
2019]. The addition of 69 samples (including five Chinese
game breeds, Cornish chicken, and red junglefowls) to
the 1,200 chicken dataset strengthened the distinct ge-
nomic variability present within each breed, with the
highest genomic uniqueness observed in White Leghorn,
Houdan, and Rhode Island Red. Cornish breed, known as
"Indian Game" carrying the genetic footprint of Malay
and other Oriental chicken blood, showed a close genom-
ic relationship with five Chinese game breeds, all sharing
a highly admixed genomic background of likely a single
origin. As expected, the red junglefowls showed the high-
est genomic diversity, followed by Cornish, four of the
five Chinese game breeds, other Chinese local breeds,
French Houdan, and commercial breeds, based on link-
age disequilibrium decay (LD), effective population size,
and runs of homozygosity (ROH) estimates [Zhang et al.,
2020]. A regional study on seven indigenous breeds in the
Jiangxi province of China indicated a similarly higher ge-
nomic diversity but smaller ROH in most Chinese indig-
enous breeds relative to European and commercial
breeds. Recent selection for meat and egg production
have resulted in reduced genomic diversity but increased
ROH in improved Chinese local breeds (Chen L et al.,
2019]. The genotyping of eight Italian local chicken
breeds showed unique genomic diversity in most breeds, calling for breed-specific conservation strategies [Cen-dron et al., 2021]. Also, genomic variation in three Chi-nese indigenous breeds (Baier Yellow, Beijing-You, and Langshan) maintained by ongoing ex-situ conservation programs were evaluated at three different generations (generations 7, 10, and 15 for Baier Yellow; 7, 10, and 13 for Beijing-You, and 10, 12, and 15 for Langshan). These conserved flocks were managed by keeping 30 males and 300 females per generation and implementing a random mating within families with one son selected from one sire family and one daughter from one dam family. There was no differentiation in population genetic structure within the breeds over the three generations [Zhang et al., 2020]. The genomic diversity of a live Norwegian poultry Genebank indicated that inbreeding level was high in all lines, while the relatively more inbred white-egg layers were differentiated from the brown-egg layers that con-tributed more to total genetic diversity. Though distinct from other commercial populations, the newly developed Norwegian commercial lines were closely related to them. These Norwegian Genebank lines were therefore believed to be of conservation value at national and international levels [Brekke et al., 2020].

Several customized SNP arrays have also been devel-
oped in recent years. For example, a 42K Illumina SNP
array was developed by private funds from the EW Group
(Visbeck, Germany) [Fulton, 2012]. Also, a proprietary
Affymetrix 50K SNP array was built based on a subset of
SNPs extracted from the Affymetrix 600K array to cap-
ture specific genetic diversity in highly selected and pedi-
greed populations, along with additional SNPs identified
in previous studies [Wolc et al., 2020]. It is expected that
the genotypes of this 50K SNPs may be imputed into the
600K SNPs using the parent, grandparent, and great-
grand-parent 600K SNP data [Herry et al., 2018; Psifidi et
al., 2021]. These two arrays were specifically designed for
QTL mapping and genomic selection in commercial lay-
er and broiler lines, and they were never made publicly
available.

Another Affymetrix 55K genotyping array (IASCHICK) incorporating specific SNPs identified by aligning the WGS reads from several Chinese indigenous chicken breeds, which were poorly represented in early arrays, against the Galgal4 [Liu et al., 2019], and an Illumina 50K BeadChip (PhenoixChip-I) containing unique SNPs from several commercial layer lines important to the Chinese market, based on the 1,846,003 SNPs screened by aligning their WGS reads against the GRCg6a [Liu et al., 2021], were recently developed. Both IASCHICK and

PhenoixChip-I arrays also include many candidate SNPs associated with important economic traits in chicken [Liu et al., 2019; Liu Z et al.,2021]. The IASCHICK array proved to be effective in detecting within-population ge- netic diversity in nine indigenous and improved Chinese breeds and three commercial layer and broiler lines, with calling rates ranging from 97.0% to 98.7% and polymor- phic SNPs ranging from 76.7% to 88.0% across the breeds/ lines. But the genotyping results from the PhenoixChip-I array showed some level of ascertainment bias. Still, the genetic variation identified by both IASCHICK and Phe- noixChip-I arrays had a sufficiently high resolution to support the assignment of all samples back to their ex- pected breeds/lines of origins, validating the power of 50K genome-wide SNP arrays for studying population genetic diversity and structure.

SNP genotyping arrays have also been used to analyze
copy number variations (CNVs) of genomic structural
variants (SVs) in the form of segmental insertion, dele-
tion, and duplication greater than 50 bp. They are a sig-
nificant source of genomic diversity underlying pheno-
typic variation. For instance, a duplicated sequence close
to the first intron of SOX5 has been linked to the pea-
comb phenotype [Wright et al., 2009], while an inverted
duplication including EDN3 is associated with dermal hy-
perpigmentation in chicken [Dorshorst et al., 2011]. In
addition, partial duplication of PRLR has been related to
late feathering [Elferink et al., 2008], whereas a CNV
linked with the HOXB7 and HOXB8 genomic region has
been associated with the beard phenotype [Yang KX et al.,
2020] in chicken.

Using the Illumina 60K array, up to 818 CNVs were identified in 184 White Leghorns and 233 brown-egg dwarf layers, of which 315 were unique. They aggregated into 209 copy number variation regions (CNVRs) on 27 autosomal chromosomes. These CNVRs were distributed proportionately to the chromosomal length, e.g., 14.7 CNVRs on macrochromosomes but 3.65 CNVRs on microchromosomes. The CNVRs shared by the two breeds suggest their relatively ancient origins, while some CNVRs were breed specific [Jia et al., 2013]. Also, 137 CNVRs were reported in 1,310 Beijing-You chickens [Zhou W et al., 2014]. Genotypes of 475 birds after the 11th generation of divergent selection for abdominal fat content in the same grandsire line of the Arbor Acres broilers revealed 438 and 291 CNVs, of which 271 (176 loss, 68 gain, and 27 loss + gain) and 188 (143 loss, 25 gain, and 20 loss + gain) CNVRs were low- and high-fat line specific, respectively. Differences in genetic drift and selection were thought to have contributed to the variation
in the numbers and distribution patterns of both CNVs and CNVRs between the two lines [Zhang H et al., 2014]. Genotypes of 554 chickens from an F₂ full-sib population, a cross between Xinghua and White Recessive Rock chickens, identified 1,875 CNVs distributed in 209 CNVRs, of which 109 were novel [Rao et al., 2016]. A follow-up analysis of the CNVs in this F₂ population revealed a polymorphic CNV overlapping with *SOX6*, with the number of CNVs positively associated with the expression of *SOX6*, which is associated with skeletal muscle development [Lin et al., 2018].

Only large CNVRs were identified in these studies using the Illumina 60K array due to its low marker density and non-uniform marker distribution. To improve the efficiency and reliability of SNV detection, the Affymetrix 600K array has been used in several studies. For example, Chinese indigenous chicken and exotic commercial breeds carried 5.1 and 3.3 CNVs per bird, respectively; both values were much higher than that reported earlier [Jia et al., 2013]. After the calibration of the CNVs between the Galg4 and Galgal3 for comparison with those detected in early studies, 153 CNVs were found to be novel [see Table 2 in Yi et al., 2015]. Genotyping of 48 deformed-beak and 48 normal Beijing-You chickens identified *LRIG2* as a candidate for beak deformity [Bai et al., 2018]. Four SNP chips (Illumina 42K, Affymetrix 600K, and two customized Affymetrix 50K chips) were successfully used to genotype and identify CNVs in 18,719 chickens from four pure lines and one commercial cross. Here, the genome landscape of CNVs was determined not only by the number of samples and genetic background of the lines, but also by the SNP density on the arrays: the higher the density of the arrays, the more the CNVs per sample [Drobik-Czwarno et al., 2018]. As many as 1,003 CNVs in 564 CNVRs were identified in 94 chickens of six local Italian breeds, contributing to their distinctiveness [Strillacci et al., 2017]. Also, 1,924 CNVs in 1,216 CNVRs were observed in 265 local Mexican chickens [Gorla et al., 2017]. These CNVRs were mapped on 28 autosomes in the Galgal4 [Gorla et al., 2017; Strillacci et al., 2017]. Using 23,214 CNVs and 5,042 CNVRs in 1,238 chickens of a Brazilian male broiler line, originating from the crossing of White Plymouth Rock and White Cornish breeds, CNV-based GWAS revealed potential candidate genes such as *KCNJ11*, *MyoD1*, and *SOX6* nearby several CNVs associated with growth traits [Fernandes et al., 2021].

In most of these studies, the same parameters implemented in the PENNCNV software, based on Hidden Markov Model (HMM), were applied to call CNVs from genotyping data, including signal intensity (log R ratio,

LRR), allelic intensity (B allele frequency, BAF), and marker distance and population frequency of allele B [Wang et al., 2007]. The HMM with default parameters but a cutoff for the standard deviation of LRR < 0.30 were considered [Jia et al., 2012; Zhang H et al., 2014; Zhou W et al., 2014; Rao et al., 2016]. The CNVs containing at least three consecutive SNPs were specifically chosen [Zhang H et al., 2014]. Aggregating overlapping CNVs determined CNVRs among individuals [Redon et al., 2006]. A common pattern was that most CNVs and CNVRs ranging from 68.8% to 82.9% were singletons and thus segregated among individuals within a breed/population [Yi et al., 2015; Gorla et al., 2017; Strillacci et al., 2017].

To reduce the genotyping cost, low-density SNP chips (e.g., 10K SNPs) have been used in populations previously studied using high-density SNP arrays followed by SNP imputation [Herry et al., 2018; Huang et al., 2018]. Other low-cost methods such as reduced representation sequencing based on restriction enzyme cleavage have also been attempted for SNP discovery, validation, and characterization in chicken populations. Among them, restriction-site-associated DNA sequencing (RAD-seq) [Zhai et al., 2015; Yang Z et al., 2020], specific locus amplified fragment sequencing (SLAF-seq) [Jin et al., 2015; Wang W et al., 2016, 2019; Li F et al., 2018, 2021], genotyping by genome reducing and sequencing (GGRS) [Liao et al., 2015, 2016; Zhao QB et al., 2018], and genotyping by sequencing (GBS) [Pértille et al., 2016; Zhang M et al., 2018; Zhang Y et al., 2021; Habimana et al., 2021] proved to be effective in identifying and genotyping some novel SNPs in chickens. However, these methods have not yet been widely applied.

## De Novo Assembly of the New Chicken Reference Genome

Two de novo haplotype assemblies using a trio of samples, including an F₁ hybrid female (bGalGal1) from a broiler hen (bGalGal2) mated with a White Leghorn cock (bGalGal3), were recently released: bGalGal1.mat.broiler.GRCg7b (GenBank and RefSeq assembly accession nos. GCA_016699485.1 and GCF_016699485.2, released in January 2021) and bGalGal1.pat.whiteleghornlayer.GRCg7w (GenBank and RefSeq assembly accession nos. GCA_016700215.2 and GCF_016700215.2, released in October 2021). They were assembled using PacBio Sequel I CLR, Illumina NovaSeq, Arima Genomics HiC, and Bi'onano Genomics DLS reads at 102.01× genome coverage. bGalGal1.mat.broiler.GRCg7b has a total genome length of 1.05 Gb in 677 contigs assembled into 214 scaffolds with N50 contigs of 18.83 Mb and N50 scaffolds of 90.86
Mb, while bGalGal1.pat.whiteleghornlayer.GRCg7w also had 1.05 Gb in total length in 685 contigs assembled into 276 scaffolds with N50 contigs of 17.74 Mb and N50 scaffolds of 90.56 Mb. There are 17,007 coding and 13,040 noncoding genes in GCA_016699485.1 and 16,884 coding and 13,294 noncoding genes in GCA_016700215.2 from the Ensembl release 104 published in January 2022 (Database version 108.7); while 18,023 coding and 7,330 noncoding genes in GCF_016699485.2, and 17,981 coding and 7,310 noncoding genes in GCF_016700215.2 are from the NCBI *Gallus gallus* Annotation Release 106 published in March 2022. It may be expected that these two new de novo assembled genomes will be of broad interest to the scientific and industrial communities, considering their highest sequencing depth and coverages, most complete annotations, and relevance to commercial chicken production [see article by Warren et al., this report].

## De Novo Assemblies of Indigenous and Commercial Chicken Breeds

Following the reduction in sequencing cost and the advent of new sequencing technologies, de novo genome sequences of chicken breeds of particular interest are being generated to identify the genetic control of their unique phenotypes. These are warranted for comparative genomic analyses of SNPs, insertions, and deletions (INDELs), structural variations (SVs), and coding and noncoding transcriptomes.

The first de novo draft genome of an indigenous chicken, the Korean Yeonsan Ogye, was released in December 2017 (Ogye1.0, GenBank Genome Accession no. GCA_002798355.1). This breed is characterized by having an entirely black external feature and internal organs. Following a hybrid de novo assembly pipeline, the genome was assembled into 8,241 pseudo-contigs and 1,906 scaffolds, which were further aligned and anchored to the GalGal4 chromosomes, based on high-depth Illumina HiSeq short reads (376.6×) and low-depth PacBio RS II long reads (9.7×). The Ogye1.0 genome size is 1.02 Gb with 7,721 contigs at N50 of 639.81 kb and 1,821 scaffolds at N50 of 90.11 Mb. Compared with the GalGal4, the Ogye1.0 genome has 551 SVs, including the duplication of the *FM* locus related to hyperpigmentation. Moreover, 15,766 coding and 6,900 long noncoding RNA genes were annotated based on transcriptomic data from 20 tissues, of which 946 were novel coding genes. However, 164 functional coding genes reported previously were not identified. The Ogye 1.1 genome was estimated to be 97.6% complete based on assessing single-copy orthologous genes using the Benchmarking Universal Single-Co-

py Orthologs (BUSCO), a value similar to the Galgal5 (97.4%) [Sohn et al., 2018].

Recently, more de novo genome sequences of indigenous chicken breeds have been assembled. They include the genome of a female Huxu chicken (GCA_024206055.1, submitted in July 2022). The genome is around 1.10 Gb with 54 contigs at N50 of 91.36 Mb and 40 scaffolds at N50 of 91.36 Mb. The PacBio RSII and ONT long reads and the Illumina NovaSeq short reads at 80.0× genome coverage were used to assemble the Huxu genome. Also, 20 genomes of 14 breeds were de novo assembled. They were all generated using long and short sequence reads. Specifically: PacBio Sequel II long reads (>87×), Illumina HiSeq short reads (>56×), and HiC data (>112×) for six genomes at chromosome level for Houdan chicken (GCA_024653045.1), Rhode Island Red (GCA_024652985.1), White Leghorn (GCA_024652995.1), Cornish (GCA_024653035.1), Silky (GCA_024653025.1), and Tibetan chicken (GCA_025370635.1); PacBio Sequel II long reads (>53×) and Illumina HiSeq short reads (>45×) for three genomes at scaffold level for Asil (GCA_024686355.1), Naked Neck (GCA_024686465.1), and Thailand Gamefowl (GCA_024686285.1); Illumina HiSeq short reads (around 134×) for 10 genomes at scaffold level (GCA_024653045.1 - Silky, GCA_024679355.1 - Daweishan, GCA_024679375.1 - Liyang, GCA_024679395.1 - Tibetan, GCA_024679415.1 - White Plymouth Rock, GCA_024679765.1 - Rhode Island Red, GCA_024679905.1 - White Leghorn, GCA_024686295.1 - Chahua, GCA_024686315.1 - Langshan chicken, and GCA_024687005.1-Cornish chicken); and one at contig level based on PacBio Sequel II long reads and Illumina HiSeq short reads (GCA_024686275.1 - Fayoumi). The completeness of these 20 genomes ranged from 92.4% to 95.3% (BUSCO analysis), comparable to the GRCg6a (95.4%). The pangenome analysis of these 20 genomes aligned to the GRCg6a identified 1,335 novel coding genes, among which many were housekeeping genes and genes involved in immune pathways. These immune-related genes had a 3-fold elevated substitution rate. There were also 3,011 new long noncoding RNAs absent in the GRCg6a [Li Y et al., 2022].

## WGS-Based Population Genome Analyses

As mentioned above, the SNP arrays were designed for genotyping polymorphisms at hundreds or thousands of specific locations across the chicken genome. They have been used to assess the ancestry of particular breeds populations/lines and identify signatures of selection and candidate genomic regions associated with complex and multifactorial phenotypes through GWAS, including
production and adaptive traits. However, they have some limitations. These SNP arrays largely include SNPs common either across different breeds/populations from a relatively large geographic coverage or within specific breeds/lines of limited ancestry and/or under intensive selection. These SNP arrays will therefore perform poorly for genotyping rare genetic variants. It is well known that genetic diversity is not equally distributed across populations of different ancestries, and some of these rare variants may become common in genetically isolated populations. Together with the challenge of uneven spacing of SNPs across the genome, they contribute to SNP ascertainment bias, which leads to biased inferences (e.g., proportion of admixture) [Lachance and Tishkoff, 2013; McTavish and Hillis, 2015; Dokan et al., 2021]. There are also limitations associated with the application of the SNP arrays in detecting CNVs (e.g., chromosomal translocations and inversions).

Considering the constraints of SNP arrays and the reduction of DNA sequencing costs, NGS-based WGS approach is now the most popular tool to infer the evolutionary history of chicken genomes, to assess the genetic diversity and population genetic structure at regional, continental, and global scales, to identify the genetic controls of specific traits of interest, and to screen for the signatures of selection for productive traits and environmental adaptation [Rubin et al., 2010; Mugal et al., 2013; Wang MS et al., 2015, 2016a, 2016b, 2017, 2020, 2021; Guo X, 2016, 2022; Guo Y et al., 2016, 2021; Zhang et al., 2016, 2022; Li D et al., 2017, 2019; Boschiero et al., 2018; Derks et al., 2018; Lawal et al., 2018, 2020; Sohrabi et al., 2018; Almeida et al., 2019; Talebi et al., 2020; Tiley et al., 2020; Wang Q et al., 2020; Gheyas et al., 2021, 2022; Li YD et al., 2021; Liu Z et al., 2021; Mariadassou et al., 2021; Rostamzadeh Mahdabi et al., 2021; Tan et al., 2021; Yang Z et al., 2021; Asadollahpour Nanaei et al., 2022; Chen X et al., 2022; Gao et al., 2022; Geibel et al., 2022; Li Y et al., 2022; Liu J et al., 2022; Sun et al., 2022; Wang S et al., 2022; Xu et al., 2022; Yuan J et al., 2022; Yuan X et al., 2022; Zeng et al., 2022].

For example, a novel panel of 64 exonic SNPs screened from WGS data were applied to the genetic characterization of Italian local breeds and proved to be cost-effective for genotyping many samples to aid genetic traceability and breeding programs [Viale et al., 2017]. Also, to further explore the deficit in homozygous carriers for 77 haplotypes in four purebred white- and brown-egg lines and two crossbred lines observed using the Illumina 60K array, the WGS data of 250 white-egg layers were annotated. This analysis identified 4,219 putative deleterious

variants, including 152 mutations relevant to embryonic lethality, at homozygous state. These deleterious variants present in genomic regions of low recombination rates have been subjected to purifying selection [Derks et al., 2018]. Many deleterious and stop-gain/loss SNPs were also observed in the WGS data of Brazilian meat and white-egg layer chickens [Boschiero et al., 2018].

Homozygous deleterious variants were also identified in the WGS data of red junglefowls and domestic chickens, with 2.95% more mutations in the chicken genome compared to its wild ancestor, which was interpreted as the result of the “cost of domestication.” Around 62.4% of these deleterious variants were in the heterozygote state and believed to be recessive [Wang MS et al., 2021]. Considering the possible occurrence of harmful mutations linked to favorable variants in positively selected sweeps and the positive relationship between recombination rate and purging efficiency, a genomic marker-assisted selection is highly recommended to minimize the frequency of undesirable functional mutations, while to sustainably improve both indigenous and commercial chicken genetic resources [Derks et al., 2018; Wang et al., 2021].

The WGS-based population landscape of SNPs in 10 Chinese indigenous breeds showed less diversity on the Z chromosome than autosomes that were likely under relatively strong selection pressures. Tibetan chickens were admixed with other breeds, while cockfighting Game chickens were closely related to red junglefowls. Strong signatures of selection were observed at genomic regions linked to the genes likely responsible for the rapid adaptation of Tibetan chickens to high altitudes [Li D et al., 2017, 2019]. The WGS data from 863 chickens and junglefowls from America, China, Indonesia, and Europe illustrated their general patterns of variability, ancestry, evolutionary relationship, and breeding history, and convincingly localized the main center of chicken domestication in Southeast Asia. The intensive genomic analyses of domestic chickens also detected several specific genetic admixture events with red junglefowls and other junglefowl species outside the proposed center of domestication [Wang MS et al., 2020]. Such admixture and/or introgression events have also been reported in another WGS study [Lawal et al., 2020]. Genomic footprints of admixture from indigenous large-sized Asian breeds and egg-laying Mediterranean breeds were identified in the gene pool of commercial meat and laying chickens, supporting their contributions to modern commercial meat and egg industries, respectively [Guo Y et al., 2021]. The first, and largest WGS dataset of African indigenous chickens, including 234 genomes from 24 Ethiopian chicken populations dis-
98,736 deletions in 11,123 CNVRs, accounting for 7% of the total autosomal size, and overlapping with 2,636 protein-coding genes were identified [Chen X et al., 2022]. Like in all previous SNP-array-based studies, the vast majority of the CNVRs were singletons, with only 152 CNVRs common to all 51 birds. Around 600 CNVRs, including 90 protein-coding genes, were breed specific, suggesting their functional importance in driving chicken phenotypic and adaptive evolution [Chen X et al., 2022]. These two analyses clearly demonstrated the power of the WGS data in CNV identification and functional annotation. Last but not least, the possibility of identifying SVs, though challenging for calling accuracy, was demonstrated from the NGS-WGS of commercial chicken genomes, among which deletions were believed to be more accurately detected compared with duplications, inversions, and translocation breakpoints [Geibel et al., 2022].

### Conflict of Interest Statement

The authors declare that they have no conflict of interest.

### Funding Sources

This study was funded by the Bill and Melinda Gates Foundation (BMGF) and with UK aid from the UK Government's Department for International Development (Grant Agreement OPP1127286) under the auspices of the Centre for Tropical Livestock Genetics and Health (CTLGH), established jointly by the University of Edinburgh, Scotland's Rural College (SRUC), and the International Livestock Research Institute (ILRI). The Chinese Government's contribution to CAAS-ILRI Joint Laboratory on Livestock and Forage Genetic Resources in Beijing (2018-GJHZ-01) is appreciated. The findings and conclusions contained within this article are those of the authors and do not necessarily reflect positions or policies of the BMGF nor the UK and Chinese Governments.

## Chicken Genomic Diversity Consortium: Large-Scale Genomics to Unravel the Origins and Adaptations of Chickens

(Prepared by S.R. Fiddaman, C. Kropp, M. Charles, P. Bardou, O. Lebrasseur, M. Derks, J. Schauer, C. Reimer, J. Geibel, A.A. Gheyas, A. Smith, R.D. Schnabel, M.L. Martin Cerezo, M. Nishibori, C.J.P. Godinez, J.K.N. Layos, J.M. Alfieri, H. Blackmon, G.N. Athrey, G. Larson, I. Ng'ang'a, W. Muir, M. Lange, D. Wright, H. Cheng, H. Simianer, S. Weigend, W. Warren, R. Crooijmans, O. Hanotte, J. Smith, M. Tixier-Boichard, and L.A.F. Frantz)

### Consortium Description

On October 25–26, 2019, a satellite meeting devoted to the preparation of a Chicken Genome Diversity Consortium.

attributed in different types of climates and productive systems, was generated to call up to 15 million SNPs mapped to the GRCg6a reference. High-quality variants have been used to assess population genomic diversity and screen for genomic regions under selection for environmental adaptations. The identified candidate regulatory genes could be epigenetic machineries driving rapid adaptation of African chickens of a relatively recent origin from Asian counterparts [Gheyas et al., 2021, 2022]. The WGS analysis of eight indigenous breeds from Guangxi, China, revealed genomic diversity similar to that of red junglefowl but limited genetic differentiation and little genetic admixture from commercial chickens [Sun et al., 2022].

The WGS-based phylogeny of all four junglefowl species was reconstructed to achieve a highly confident topology of the genus, which is crucial in inferring the effects of interspecific introgression and genetic admixture on chicken domestication processes. Following an intensive evaluation of major topological inconsistencies caused by different genetic/genomic datasets and bioinformatic methods, the genetic contamination of some captive junglefowl flocks is verified, while the importance of large number of genetic variants in different genomic components of many reliable samples (e.g., collected from their original ranges in the wild) to phylogenetic inference is substantiated [Tiley et al., 2020; Wang et al., 2020; Mariadassou et al., 2021].

WGS data analysis have also provided new insights on the ROH in chicken genomes. Compared to the red junglefowls with the least number of ROH, white layers carried the largest number of ROH per bird, mostly shorter than 1 Mb, while broilers tended to have relatively more ROH >2 Mb. Both SNP-based Wright's *F*<sub>IS</sub> and ROH-based *F*<sub>ROH</sub> metrics suggested higher inbreeding in the white layers compared to the broilers and red junglefowls [Talebi et al., 2020]. Candidate genes in the ROH islands may also be associated with QTLs responsible for productive traits [Talebi et al., 2020] and high-altitude adaptation [Yuan et al., 2022].

As expected, more CNVs and CNVRs were identified from the WGS data compared to the findings of SNP genotyping arrays. For instance, 12,955 CNVs in 5,467 CNVRs, accounting for 9.42% of the genome, were found in two Iranian indigenous and commercial chicken breeds, with 34% of these CNVRs overlapping with those identified in SNP-array-based analyses [Sohrabi et al., 2018]. Across 51 WGS datasets of Chinese indigenous breeds (Xinghua, Luxi Game, Beijing-You, and Silkie), commercial lines (Recessive White Rock and White Leghorn), and red junglefowls, 19,329 duplications and
tium was organised after the 11th European Symposium of Poultry Genetics in Prague. Researchers involved in chicken genomics from Europe, Africa, and China, discussed the objectives of such a consortium with some presenting their data. However, the technical aspects of how to share and jointly analyse the data were not finalized, nor was the funding model for the cost of data storage and computation. In 2021, an opportunity arose with the call for projects of the SuperMUC computing cluster of the Leibniz-Rechenzentrum in Germany. A new consortium of scientists re-launched the discussion to establish a project with the aim to explore how the high-throughput genomics age can be harnessed to answer evolutionary questions surrounding the chicken. The FARMGENOMIC project (23826) was accepted for funding in autumn 2021, gathering around 20 members from 10 institutions in Europe, North America, and Africa. This newly formed Chicken Genomic Diversity Consortium brings together members from a variety of disciplines, including genomics, palaeogenetics, animal breeding, immunology, organismal biology, evolutionary biology, and archaeology. Central to the consortium are the concepts of inclusivity and openness – all data are to be made available to all members of the consortium, and later distributed to the wider community, and collaborations between groups are fostered and actively encouraged. It is hoped this state-of-the-art resource, curated in-house by bioinformaticians, will enable the community to answer previously intractable questions in chicken evolution.

## Dataset Description

At the core of the consortium is a substantial genomic dataset of chickens and junglefowl. At the time of writing (September 2022), the dataset comprises 4,392 chicken and junglefowl genome sequences, of which 2,307 were derived from public databases, and the remainder provided by consortium members. In addition to domesticated chickens and red junglefowl (comprising all 5 sub-species: G. g. gallus, G. g. bankiva, G. g. jabouillei, G. g. murghi, G. g. spadiceus; total n = 291), we also included members of the congeneric Gallus species Gallus varius (n = 21), Gallus lafayettii (n = 12), and Gallus sonneratii (n = 15). Among the domesticated chickens, a wide array of geographical locations are represented (Africa, n = 1,047; East Asia, n = 856; South East Asia, n = 72; South Asia, n = 137; Middle East, n = 219; European fancy breeds, n = 462; North America, n = 835; South America, n = 15; Oceania, n = 24) as well as commercial birds (n = 329) and experimental lines (n = 42). The wide scope of the dataset aims to capture a significant proportion of the

extant genetic variation in the chicken genome. Furthermore, the addition of 15 ancient chicken genomes from Europe and the Middle East will provide supplemental time-depth, including a window into past genetic variation following the arrival of chickens into Europe from Southeast Asia (Fig. 38).

## Consortium Aims

The aims of the consortium are numerous and varied, reflecting the diverse interests of the contributing groups.

Specific scientific aims include:

1. *Deleterious alleles and possible inbreeding.* Breeds with high rates of inbreeding and potential health risks will be identified on the basis of genetic load and deleterious variants in sequence data. We will also investigate loss-of-function variation in relation to pseudogenes and adaptation.

2. *Structural variation.* The impact of structural variants (e.g., deletions and duplications) on trait variation will be assessed. This analysis aims to produce a catalog of structural variation and associated frequency estimates, as well as predicted functional consequences of the variants identified. We also aim to construct a graph genome from a diverse selection of breeds using a combination of long- and short-read sequencing technologies.

3. *Phenotype and trait adaptations.* We aim to identify causal gene variants that underlie adaptive traits. For instance, we are interested in covariation between genotypic variants and agro-pastoral markers to shed light on the genetic basis of adaptation to different environments. We will also investigate adaptation to phenotypic and production traits, such as feather colour and egg shell quality.

4. *Distribution of extant chicken genetic diversity.* Sequence data from such diverse geographical sources permits a detailed investigation of extant chicken diversity with respect to geographical spread. Within this investigation, finer scale analyses of diversity – particularly within the continents of Africa and Asia – are to be conducted.

5. *Evolutionary history of chickens.* The introduction of chickens into Europe (when and how many times) remains unclear. By comprehensively mapping the extant variation in chicken populations, we aim to build a high-quality reference panel for variants, which can be used to phase and impute genomes, including low-coverage ancient genomes from Europe dating to ~2000 years ago (a few centuries following the introduction of chickens in Europe). Using similar ap-
<figure><img src="image_43.png" /><figcaption>**Fig. 38.** Sampling of global *Gallus* spp. diversity. The map shows the sampling locations for the 4,392 genomes from domestic chickens and congeneric jungle fowl species. To illustrate group size, commercial birds and European fancy breeds are also included on the map, although physical sampling location is not presumed to be important for these birds.</figcaption></figure>

proaches, we also plan to decipher the evolutionary history of chickens in Neotropical America, in which chickens underwent a much more recent (~500 years ago) introduction.

6. *Evolutionary history of Gallus spp.* Combining data from domesticated chickens and congeneric jungle-fowl is expected to help answer questions regarding the ultimate origin of domestic chickens and the contribution of junglefowl to modern chicken ancestry.

7. *Evolution and adaptation in the immune system.* A simple prediction is that chickens have had to adapt to cope with (1) exposure to novel pathogens, and (2) increased intensity of pathogen pressure due to increased flock size and density of rearing. This is likely to have left signatures of adaptation at immune loci of the chicken genome. Genes such as the Toll-like receptors and other pattern-recognition receptors at the front line of defense against pathogens will be investigated for signals of selection. We aim to conduct in vitro testing to validate bioinformatic predictions of functional change in immune receptors using methods that are well-established within the consortium.

These lines of investigation will be synthesized into
several publications over the course of the consortium,
led by different principal investigators depending on ex-
pertise. At the outset, the consortium has aimed to be as

inclusive as possible, and as such, the studies listed above
are neither exhaustive nor limited to current members of
the consortium. The consortium welcomes input from
any groups wishing to make the best use of this genomic
resource.

Processing Pipeline

In order to provide complete consistency of analysis,
the entire dataset was re-processed from raw reads using
a state-of-the-art mapping and processing pipeline im-
plemented on the SuperMUC computing cluster of the
Leibniz-Rechenzentrum, Bavarian Academy of Science,
Germany (Fig. 39). All reads underwent pre-processing
(quality trimming, adaptor removing) with Fastp (v
0.21.0) then were mapped to the most recent version of
the chicken genome (GRCg7b) (GCA_016699485.1) with
BWA (v 0.7.17-r1188). The resulting BAM files from the
same samples were then merged with samtools (v 1.9).
For the variant calling, we generated gvcf using Elprep (v
5.1.1, compiled with go1.17; a reimplementation of GATK
in GO langage). These gvcfs were integrated into a GATK
genomicDB (gatk v4.2.3.0). To optimize performance, we
built 48 databases corresponding to partitioning the ge-
nome into 48 intervals of equal size (~20 Mb). Variant
calling was performed using GATK genotypeGVCFs to
obtain a VCF file by interval. We then obtained a global
<figure><img src="image_44.png" /><figcaption>**Fig. 39.** Processing pipeline. To ensure between-sample consistency, all samples have been re-processed from raw fastq reads. Reads underwent pre-processing and quality control before mapping to the latest version of the chicken genome (GRCg7b), variant calling, and generating a VCF.</figcaption></figure>

VCF file using GATK GatherVCFs. GATK VariantRecalibrator was then used to recalibrate variants using known SNPs.

## Project Timeline

The project began in 2021 and is expected to conclude (at least the first tranche of analyses) in 2023. The first phase of the project (Q3-Q4 2021) involved data gathering from both public and private sources and curation of associated metadata. In Q1 2022, the read files were quality checked to remove low quality samples and to check pre-processing from the variety of sequencing platforms included in the dataset. In Q2 2022, read mapping commenced, soon followed by variant calling. At the time of writing (September 2022), mapping and variant calling have been completed and the VCF will shortly be made available for further analyses.

## Data Hosting and Availability

The SuperMUC computing cluster will provide the processing power and storage capability to generate and store raw read files, alignment map files, and variant call files (VCF) for the duration of the project. The final VCF will be made available in the first instance to members of

the consortium, and will also be provided to the community for wider use. High quality SNPs will be made available to the community on GLOBUS, via sftp, and the European Variation Archive via the European Bioinformatics Institute.

## Conflict of Interest Statement

The authors have no conflicts of interest to declare.

## Funding Sources

The computational part of the project was funded by SuperMUC grant ID: 23826 awarded by The Leibniz Supercomputing Centre (LRZ), of the Bavarian Academy of Sciences and Humanities. The authors were supported by European Research Council grants (ERC-2013-StG-337574-UNDEAD and ERC-2019-StG-853272-PALAEOFARM) and by the Wellcome Trust (210119/Z/18/Z). The authors would like to acknowledge the Edinburgh Genomics Facility (Edinburgh, UK) for generation of the sequence data. This study was funded by the Bill and Melinda Gates Foundation (BMGF) and with UK aid from the UK Government's Department for International Development (Grant Agreement OPP1127286) under the auspices of the Centre for Tropical Livestock Genetics and Health (CTLGH), established jointly by the University of Edinburgh, SRUC (Scotland's Rural College), and the International Livestock Research Institute. The findings and conclusions contained within are those of the authors and do not necessarily reflect positions or policies of the BMGF nor the UK Government. We thank the CGIAR livestock program (CRP) for
supporting the sampling component of the research. Sequencing data provided by INRAE teams were produced with public sources of funding, coming from INRAE, the French National Research Agency (ANR), the European Commission (SABRE project) and its HORIZON 2020 program (FEED-A-GENE, IMAGE projects). Data funded by INRAE and ANR were previously described by Tixier-Boichard et al. [2020]. https://doi.org/10.20870/productions-animales.2020.33.3.4564. Sampling of data provided by FLI was funded by the German Federal Ministry of Education and Research (BMBF) via the SYNBREED project (FKZ 0315528E; www.synbreed.tum.de) and sequenced within the project IMAGE - Innovative Management of Animal Genetic Resources (www.imageh2020.eu, funded by the EU Horizon, 2020 research and innovation program No. 677353). Sequencing of the ancient genomes was funded by the Arts and Humanities Research Council (grant AH/L006979/1). O.L. is supported by the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement no. 895107.

## Biobanking and Candidate Genes for Genome Editing to Support the Tropical Poultry Improvement Programmes

(Prepared by C. Keambou Tiambo, C. Kamidi Muhonja, M. Ogugo, O. Ouko, E. Ilatsia, S. Kemp, A. Djikeng, and M. McGrew)

As the world increasingly relies on a handful of chicken breeds in intensified systems, a great diversity of tropical indigenous breeds is kept in backyard systems and on small farms, sometimes undergoing extinction threats through inbreeding and negative selection. If tropically adapted indigenous breeds disappear, the global poultry industry and the science community stand to lose access to adaptive genetic traits that have been nurtured and developed by local communities over millennia. The conservation of animal genetic resources through cryopreservation, referred to as biobanking, is an important component for the conservation and revival of rare or endangered species. While it was not previously possible to completely and efficiently biobank avian genetic material, a scientific innovation using primordial germ cells (PGCs) is changing the landscape and providing a way forward to preserve the biodiversity of tropical poultry breeds, hence also providing sustainable biological material from a more ethical 3Rs – Reduction, Refinement, Replacement – poultry biotechnology research and innovation. The tropical poultry PGC biobanking innovation includes the development and use of chickens that are devoid of their own sperm or ovum as recipients of the biobanked germ cells from a donor. With the introduction of the PGCs into the growing “sterile” embryo, the chick then develops into a surrogate fertile animal, but only produces

gametes (ovum or sperm) that are 100% genetically those of the donor chicken breed. A sire-dam crossing of the surrogates then allows restoration of indigenous chicken breeds from biobanked material in a manner that is animal welfare-friendly. Most importantly, the lab-based techniques developed as part of this innovation are teachable and transferable to partner institutions, thereby enabling countries across the tropics to adopt chicken genetic resource biobanking.

Africa's population is growing and transforming very fast, and will double by 2050, making food security the main challenge for the continent [UNDESA, 2017]. The rapid mutations in the demand for animal-source food, derived products and services, coupled with rising concerns of climate change, command a sustainable intensification of livestock production in Africa to accomplish food and nutritional security. Today, Africa must build the foundations to steer livestock on a sustainable development trajectory. This can be a short- to medium-term perspective if relying on short life cycle and locally adapted genetic resources.

Over 1,600 local chicken breeds have been identified globally [Eda, 2021], of which 126 are from Africa [Domestic Animal Genetic Resources Information System, 2022]. These breeds contain vast ranges of phenotypic and genetic diversity derived from the varied pathogenic, environmental, and selection conditions under which these ecotypes were developed.

Unfortunately, many of these local breeds are considered at risk due to the introduction and adoption of non-local breeds, the acceptance of intensive chicken production systems, changes in environment and disease conditions, and adverse development policies.

To minimize this genetic erosion, it is essential to increase knowledge of local breeds and production systems, improve planning, and raise awareness of the threat at the policy level. New innovations in genetic preservation technologies for chickens are also needed.

Conservation of poultry breeds and genetic lines pose particular challenges. One means of genetic diversity conservation in livestock species has been biobanking, whereby sperm, eggs or zygotes are preserved for future use. Cryopreservation of eggs and zygotes is not possible in avian species due to the large amount of lipid deposited in the female oocyte [Petitte, 2006; Whyte et al., 2016]. Other genetic preservation and propagation techniques such as cloning using somatic cell nuclear transfer are not possible, as embryo transfer cannot be done in avian species [Kjelland et al., 2014]. Recent developments by researchers from the International Livestock Research In-
stitute and the Centre for Tropical Livestock Genetics and Health (CTLGH), at the Roslin Institute, University of Edinburgh in the UK, have shown that the isolation and freezing of PGCs from chicken embryos can provide a new approach for biobanking poultry material. Biobanking of these PGCs will have a significant role in the conservation of African poultry genetic resources; further adding safeguards against the population and diversity losses that could threaten a breed's survival [Hall, 2013].

Furthermore, beyond its role as an important source of protein and a valuable model for the study of developmental biology, immunology, physiology, and neurology in vertebrates [Yasugi and Nakamura, 2000; Speedy, 2003; Mozdziak and Petitte, 2004; Stern, 2004, 2005], there is an increasing interest in generating genetically modified chickens resistant to specific pathogens, benefiting from the availability of gene manipulation techniques. Hence, the full potential of new chicken breeding tools such as genome editing needs to be exploited in addition to conventional technologies. Clustered regularly interspaced short palindromic repeats/CRISPR-associated protein (CRISPR/Cas)-based genome editing has rapidly become the most prevalent genetic engineering approach for developing improved chicken lines because of its simplicity, efficiency, specificity, and ease of use. Genome editing improves chicken breeds by conferring special attributes including specific chicken bioreactors, production of knock-in/out chickens for various production and adaptability traits, low-allergenicity eggs, or to serve as disease-resistance models [Chojnacka-Puchta and Sawicka, 2020]. In African countries with the most advanced regulations in animal biotechnologies like Kenya and Nigeria, such genome-edited animals with no foreign gene integration are not regulated as genetically modified organisms (GMOs). Researchers from the Centre for Tropical Livestock Genetics and Health (CTLGH) at the Roslin Institute and at the International Livestock Research Institute (ILRI) are using CRISPR/Cas-based genome editing for improving chicken productivity and adaptability.

## Biobanking African Chicken Genetic Resources for the Future

Primordial Germ Cell Isolation, Conservation, and Recovery for Production of Chimeric Chickens

PGCs are specialized stem cells that can be isolated from chick embryos, which – depending on the sex of the individual embryo – will eventually form a sperm or egg. Following isolation, these PGCs can be cultured and cryopreserved. Chickens are one of the few species from

which PGCs can easily be propagated in vitro to increase cell numbers up to 100,000 cells from a single embryo in 4 weeks [Whyte et al., 2015] and these cells can easily be cryopreserved [Glover and McGrew, 2012; Glover et al., 2013].

Biobanked chicken material is important in cases where a specific breed might be selected in the future to be scaled up to useful production levels. The genetic material can also be used to support research to isolate certain traits that can then be introduced into existing chicken populations.

When these biobanked chicken genes are needed, the preserved PGCs can be transferred into a 2-day-old “recipient” chick embryo. Part of this PGC biobanking innovation is the development and use of sterile recipient chicks. Because the recipient 2-day-old chick embryo is sterile, it lacks its own PGCs, and this eliminates the need to manage the PGCs of the recipient bird. Implanted with biobanked PGCs, the recipient chick grows up into a fertille bird. These adult birds have only had their reproductive cells changed to the genetics of the donor bird and will now act as surrogate parents. The recipient birds will still look like and have the genetic components of their breed, but they will produce sperm or eggs that are genetically identical to the original biobanked donor breed implanted.

The development of the PGC biobanking has revolutionized the ability to preserve tropical chicken genetic resources at ILRI-Nairobi. Tables 10 and 11 present the progress in African chicken ecotypes and lines of chicken already cryopreserved.

Along with the active biobanking of African chicken ecotypes and PGC lines, poultry biobanking activities are being scaled up through active knowledge transfer to African scientists in collaboration with the African Union-Inter African Bureau for Animal Resources (AU-IBAR) Training of Trainers (ToT) workshops in Kenya, Cameroon, and the Democratic Republic of Congo.

These workshops have led to increased knowledge of the role and importance of locally adapted chicken breeds and fostered greater understanding of the need for indigenous breed conservation. The Kenyan Agricultural and Livestock Research Organization (KALRO) has embraced the technology for the conservation of indigenous chicken ecotypes and plans to use it to sustain the development of the Kinyeji (local) chicken breeding programme. Further collaboration with KALRO is creating opportunities for greater upscaling and uptake by National Research Systems across Africa.
<table><caption>**Table 10.** African chicken ecotypes cryopreserved using the blood and blastoderm methods</caption><thead><tr><th>Country</th><th>Ecotype</th><th>Number of cell lines</th><th>Country</th><th>Ecotype</th><th>Number of cell lines</th></tr></thead><tbody><tr><td rowspan="10">KENYA</td><td>White leghorn</td><td>43</td><td rowspan="5">ETHIOPIA</td><td>Arobe</td><td>59</td></tr><tr><td>Karen (Nairobi)</td><td>7</td><td>Horro</td><td>29</td></tr><tr><td>Narok</td><td>14</td><td>Hawassa</td><td>32</td></tr><tr><td>Bomet</td><td>42</td><td rowspan="5">TANZANIA</td><td>Shinyanga</td><td>84</td></tr><tr><td>Siaya</td><td>23</td><td>Mwanza</td><td>11</td></tr><tr><td>Migori</td><td>20</td><td>Mbeya</td><td>43</td></tr><tr><td>Homabay</td><td>8</td><td>Morogoro</td><td>69</td></tr><tr><td>Kakamega</td><td>13</td><td></td><td></td><td></td></tr></tbody></table>

<table><caption>**Table 11.** Kenyan chicken ecotypes and lines cryopreserved using the embryonic gonad method</caption><thead><tr><th>Kenyan ecotypes</th><th>Male lines</th><th>Female lines</th></tr></thead><tbody><tr><td>Laikipia (LP)</td><td>44</td><td>43</td></tr><tr><td>Bungoma (BG)</td><td>9</td><td>18</td></tr><tr><td>Kilifi (KF)</td><td>4</td><td>1</td></tr><tr><td>Kakamega (KK)</td><td>14</td><td>13</td></tr><tr><td>Bomet (BM)</td><td>1</td><td>1</td></tr><tr><td>Homabay (HB)</td><td>21</td><td>19</td></tr><tr><td>Siaya (SY)</td><td>13</td><td>5</td></tr><tr><td>Kwale (KW)</td><td>0</td><td>1</td></tr><tr><td>KALRO chicken Improved lines 1 (KC1)</td><td>61</td><td>63</td></tr><tr><td>KALRO chicken Improved lines 2 (KC2)</td><td>75</td><td>41</td></tr><tr><td>Total</td><td>242</td><td>205</td></tr></tbody></table>

## Restoration of Poultry Biodiversity and Dissemination of Potential Elite Lines

### Using the Chimeras Conventional Crossbreeding

Germline chimeric chickens were produced at ILRI-Nairobi by transfer of primordial germ cells from indigenous chicken to White Leghorn. An average of 500 primordial germ cells from indigenous chicken were injected into the bloodstream through the dorsal aorta of stage 14-15 White Leghorn recipient embryos which were then incubated until hatching, and the chimera derived offspring were identified based on their feather colour (Fig. 40).

## Using the Gene Edited Surrogate Host Technologies
### The DDX4 Knockout (KO) Surrogate Host Technology

The creation of the DDX4 KO line was led by Mike McGrew at the Roslin Institute and originally described in Taylor et al. [2017]. Woodcock et al. [2019] demonstrated that the female chicken rendered sterile using

genome editing technology can be used as a surrogate host for transplanted cryopreserved germ cells, and only lay eggs of the transplanted rare chicken breed. The DDX4 KO surrogate hosts are genetically sterile female surrogate host chickens. This sterile female surrogate provides a major advance for the creation of genetically altered chicken lines and the preservation of rare breeds. The DDX4 KO line transmits 100% of its offspring from donor female primordial germ cells (PGCs). Therefore, the DDX4 KO line can produce offspring from genetically altered PGCs or PGCs from other breeds of chicken. Woodcock et al. [2019] have shown that by injecting PGCs from a heritage breed of Vantress chicken into DDX4 KO female embryos, all the offspring produced by these DDX4 females were derived from the donor heritage breed. Subsequent, artificial insemination of the DDX4 KO female surrogate host with frozen Vantress semen produced several pure heritage breed chicks.

## The iCaspase9 Surrogate Host Technology

The creation of the iCaspase9 line was led by Mike McGrew at the Roslin Institute and originally described by Ballantyne et al. [2021a]. The iCaspase9 surrogate hosts are conditionally sterile male and female surrogate host chickens. In the iCaspase9 surrogate host line the germ cell lineage of both males and females are chemically ablated. These conditionally sterile transgenic chickens provide a major advantage for creating genetically altered (GA) chicken lines. GA PGCs or PGCs from other chicken breeds can be introduced into sterile male and female iCaspase9 embryos. Once the iCaspase9 birds reach sexual maturity, mating between male and female iCaspase9 birds or Sire Dam Surrogate mating results in 100% transmission of the donors' PGCs in the first generation of offspring. The iCaspase9 surrogate hosts cut the time re-