# Novel and known signals of selection for fat deposition in domestic sheep breeds from Africa and Eurasia

Salvatore Mastrangelo, Hussain Bahbahani, Bianca Moioli, Abulgasim

Ahbara, Mohammed Al Abri, Faisal Almathen, Anne da Silva, Ibrahim

Belabdi, Baldassare Portolano, Joram M. Mwacharo, et al.

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► To cite this version:

Salvatore Mastrangelo, Hussain Bahbahani, Bianca Moioli, Abulgasim Ahbara, Mohammed Al Abri, et al.. Novel and known signals of selection for fat deposition in domestic sheep breeds from Africa and Eurasia. PLoS ONE, 2019, 14 (6), pp.e0209632. 10.1371/journal.pone.0209632. hal-02625639

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RESEARCH ARTICLE

# Novel and known signals of selection for fat deposition in domestic sheep breeds from Africa and Eurasia

Salvatore Mastrangelo<sup>1</sup>, Hussain Bahbahani<sup>2e</sup>, Bianca Moioli<sup>3c</sup>, Abulgasim Ahbara<sup>4,5</sup>, Mohammed Al Abri<sup>6</sup>, Faisal Almathen<sup>7</sup>, Anne da Silva<sup>8</sup>, Ibrahim Belabdi<sup>9,10</sup>, Baldassare Portolano<sup>1</sup>, Joram M. Mwacharo<sup>11</sup>, Olivier Hanotte<sup>4</sup>, Fabio Pilla<sup>12</sup>, Elena Ciani<sup>13*</sup>

1. Dipartimento di Scienze Agrarie, Alimentari e Forestali, University of Palermo, Palermo, Italy,

2. Department of Biological Sciences, Faculty of Science, Kuwait University, Safat, Kuwait, **3** Consiglio per la

ricerca in agricoltura e l'analisi dell'economia agraria (CREA), Monterotondo, Italy, **4** School of Life Sciences,

University of Nottingham, University Park, Nottingham, United Kingdom, **5** Department of Zoology, Faculty of

Sciences, Misurata University, Misurata, Libya, **6** Department of Animal and Veterinary Sciences, Sultan

Qaboos University, Oman, **7** Department of Public Health and Animal Welfare, College of Veterinary

Medicine, King Faisal University, Alhufuf, Al-Ahsa, Saudi Arabia, **8** Université de Limoges, INRA, PEREINE

EA7500, USC1061 GAMAA, Limoges, France, **9** Science Veterinary Institute, University of Blida, Blida,

Algeria, **10** Laboratory of Biotechnology related to Animal Reproduction (LBRA), University of Blida, Blida,

Algeria, **11** Small Ruminant Genomics, International Center for Agricultural Research in the Dry Areas

(ICARDA), Addis Ababa, Ethiopia, **12** Dipartimento Agricoltura, Ambiente e Alimenti, Università degli Studi

del Molise, Campobasso, Italy, **13** Dipartimento di Bioscienze, Biotecnologie e Biofarmaceutica, Università

degli Studi di Bari Aldo Moro, Bari, Italy

e These authors contributed equally to this work.

* elena.ciani@uniba.it

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OPEN ACCESS

**Citation:** Mastrangelo S, Bahbahani H, Moioli B, Ahbara A, Al Abri M, Almathen F, et al. (2019) Novel and known signals of selection for fat deposition in domestic sheep breeds from Africa and Eurasia. PLoS ONE 14(6): e0209632. https://doi.org/10.1371/journal.pone.0209632

**Editor:** Raluca Mateescu, University of Florida, UNITED STATES

**Received:** December 8, 2018

**Accepted:** May 28, 2019

**Published:** June 14, 2019

**Copyright:** © 2019 Mastrangelo et al. This is an open access article distributed under the terms of the [Creative Commons Attribution License](https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

**Data Availability Statement:** Genotype data used in this study are available at https://osf.io/qvswe/.

**Funding:** The authors received no specific funding for this work.

**Competing interests:** The authors have declared that no competing interests exist.

## Abstract

Genomic regions subjected to selection frequently show signatures such as within-population reduced nucleotide diversity and outlier values of differentiation among differentially selected populations. In this study, we analyzed 50K SNP genotype data of 373 animals belonging to 23 sheep breeds of different geographic origins using the *Rsb* (extended haplotype homozygosity) and *F<sub>ST</sub>* statistical approaches, to identify loci associated with the fat-tail phenotype. We also checked if these putative selection signatures overlapped with regions of high-homozygosity (ROH). The analyses identified novel signals and confirmed the presence of selection signature in genomic regions that harbor candidate genes known to affect fat deposition. Several genomic regions that frequently appeared in ROH were also identified within each breed, but only two ROH islands overlapped with the putative selection signatures. The results reported herein provide the most complete genome-wide study of selection signatures for fat-tail in African and Eurasian sheep breeds; they also contribute insights into the genetic basis for the fat tail phenotype in sheep, and confirm the great complexity of the mechanisms that underlie quantitative traits, such as the fat-tail.
## Introduction

Natural selection plays an important role in determining the individuals that are best adapted to novel and existing environmental conditions. Besides natural selection, artificial selection has been widely applied to livestock species to achieve more desirable/profitable phenotypes [1]. For instance, sheep (*Ovis aries*) have been selected since domestication, approximately 9,000 years ago [2]. This process of selection resulted in divergent sheep breeds, reared in different geographic regions due to their different adaptability. Among these, fat-tail are an important class of sheep breeds and represent about 25% of the world's sheep population [3] mainly distributed in the Middle East, North and East Africa and Central Asia. According to Xu et al. [4] fat tails represent the energy reserve necessary to survive critical conditions such as drought seasons and food shortage. This statement was emphasized by Mwacharo et al. [5] who confirmed that the fat-tails are the predominant sheep across the deserts of northern Africa, and in the highlands, semi-arid and arid environments of eastern and southern Africa while the thin-tails occur in Sudan and in the sub-humid and humid regions of West Africa.

The unique genetic patterns inscribed in the genome of individuals by natural and/or artificial selection are defined as signatures of selection, which are usually regions of the genome that harbor functionally important sequence variants [6]. Although human consumption of animal fat has dramatically reduced in preference of leaner meat, the investigation of the potential candidate genes involved in the fat-tail might contribute to exploring the genetics of fat deposition, energy storage and adaptation to climate changes [7–9]. With the aim to identify candidate genes with a potential role in these traits, several authors performed studies targeting the fat-tail phenotype contrasted with the thin-tail one. All authors used, for their comparisons, sheep of the same geographic regions to prevent referring to the fat-tail differentiation signals arising from different origins or isolation by distance. These studies included indigenous Chinese [4,8,10,11], Mediterranean-North African [9,12], Iranian [3] and Ethiopian breeds [13]. Recently, some studies have proposed the combined use of diverse datasets to gain greater power. These are based on the concept of meta-analysis, a tool for aggregating information from multiple independent studies to identify associations with genomic regions not revealed in the individual studies [14]. Therefore, in this study, the genomes of fat-tail sheep from different regions of Africa and Eurasia were contrasted with the genomes of thin-tail sheep of the same geographical area in a meta-analysis to identify new genomic regions underlying variation of this phenotype. In order to improve the specificity of signal detection, we combined two complimentary approaches (*Rsb* and *FST*); moreover, as distinguishing false positive genes from candidate genes is not straightforward, selection signatures of fat-tail were considered only when shared by two or more fat-tail breeds of different geographic origin, and substantiated by verifying whether the candidate genes in proximity of the anonymous markers of differentiation have a known, or assumed, role in fat deposition and adipogenesis in mammals.

## Materials and methods

### Samples, genotyping and quality control

A total of 373 animals belonging to 23 sheep breeds from different geographic regions were selected (Table 1). In particular, genotyped animals from Ethiopia (11 breeds) and Arabian peninsula (4 breeds) fat-tail breeds, together with two thin-tail breeds from Sudan were available from a recent study [13]; genotypic data of Barbaresca and Libyan Barbary were provided by a previous study [9], whereas genotypic data for Laticauda and the two Italian thin-tail breeds (Sardinian and Comisana) were available from a genome-wide analysis of genetic
<table><caption>Table 1. Breeds, number of animals (N), tail type, country and origin of genotyping data of the breeds used in the contrasting groups (fat- vs. thin-tail).</caption><thead><tr><th>Breed</th><th>N</th><th>Tail type</th><th>Country</th><th>Comparison</th><th>Data origin</th></tr></thead><tbody><tr><td>Bonga</td><td>9</td><td>long fat-tail</td><td>Ethiopia</td><td>1, 2</td><td>[13]</td></tr><tr><td>Doyogena</td><td>15</td><td>long fat-tail</td><td>Ethiopia</td><td>1, 2</td><td>[13]</td></tr><tr><td>Gesses</td><td>10</td><td>long fat-tail</td><td>Ethiopia</td><td>1, 2</td><td>[13]</td></tr><tr><td>Kido</td><td>10</td><td>long fat-tail</td><td>Ethiopia</td><td>1, 2</td><td>[13]</td></tr><tr><td>Loya</td><td>15</td><td>long fat-tail</td><td>Ethiopia</td><td>1, 2</td><td>[13]</td></tr><tr><td>Shubi Gemo</td><td>15</td><td>long fat-tail</td><td>Ethiopia</td><td>1, 2</td><td>[13]</td></tr><tr><td>Kefis</td><td>14</td><td>rumped</td><td>Ethiopia</td><td>1</td><td>[13]</td></tr><tr><td>Arabo</td><td>10</td><td>rumped</td><td>Ethiopia</td><td>1</td><td>[13]</td></tr><tr><td>Adane</td><td>12</td><td>rumped</td><td>Ethiopia</td><td>1</td><td>[13]</td></tr><tr><td>Molale (Menz)</td><td>15</td><td>short fat-tail</td><td>Ethiopia</td><td>1</td><td>[13]</td></tr><tr><td>Gafera (Washera)</td><td>15</td><td>short fat-tail</td><td>Ethiopia</td><td>1</td><td>[13]</td></tr><tr><td>Huri</td><td>7</td><td>fat-tail</td><td>Arabian peninsula</td><td>3</td><td>[13]</td></tr><tr><td>Naimi</td><td>7</td><td>fat-tail</td><td>Arabian peninsula</td><td>3</td><td>[13]</td></tr><tr><td>Najdi</td><td>6</td><td>fat-tail</td><td>Arabian peninsula</td><td>3</td><td>[13]</td></tr><tr><td>Omani</td><td>10</td><td>fat-tail</td><td>Arabian peninsula</td><td>3</td><td>[13]</td></tr><tr><td>Barbaresca</td><td>30</td><td>fat-tail</td><td>Italy</td><td>4</td><td>[9]</td></tr><tr><td>Laticauda</td><td>24</td><td>fat-tail</td><td>Italy</td><td>5</td><td>[15]</td></tr><tr><td>Libyan Barbary</td><td>23</td><td>fat-tail</td><td>Algeria</td><td>6</td><td>[9]</td></tr><tr><td>Hammari</td><td>7</td><td>thin-tail</td><td>Sudan</td><td>1,2,3</td><td>[13]</td></tr><tr><td>Kabashi</td><td>8</td><td>thin-tail</td><td>Sudan</td><td>1,2,3</td><td>[13]</td></tr><tr><td>Sardinian</td><td>24</td><td>thin-tail</td><td>Italy</td><td>4,5</td><td>[15]</td></tr><tr><td>Comisana</td><td>24</td><td>thin-tail</td><td>Italy</td><td>4,5</td><td>[15]</td></tr><tr><td>Sidaoun</td><td>39</td><td>thin-tail</td><td>Algeria</td><td>6</td><td>This study</td></tr></tbody></table>

https://doi.org/10.1371/journal.pone.0209632.t001

diversity for these breeds [15]. Moreover, a total of 39 blood samples of the Sidaoun breed were collected from different flocks in Algeria. These samples were genotyped using the Illumina OvineSNP50 BeadChip array.

Genotype data of all the considered animals, were combined in a single working dataset for the analyses. Chromosomal coordinates for each SNP were obtained from the latest release of the ovine genome sequence assembly Oar_v4.0. The combined dataset was filtered to remove animals with more than 10% missing genotypes, SNPs with a call rate lower than 95% and with a minor allele frequency (MAF) lower than 1%, and to exclude non-autosomal and unas-signed markers.

## Genetic relationships amongst breeds

Pair-wise genetic relationships were estimated using—mds-plot and—cluster options in PLINK 1.7 [16] and graphically represented by multidimensional scaling (MDS) analysis.

## Signatures of selection analysis

To analyze genome-wide selection signatures, the MDS results were used to categorize the 23 breeds into contrasting genetic groups for comparative analysis. Table 1 summarizes the description of the breeds used in the pair-wise comparisons. The contrasting groups were as follow:

1. Ethiopian fat-tail breeds (11 breeds) vs. two thin-tail breeds from Sudan (Hammari and Kabashi);
2. Ethiopian long fat-tail breeds (6 breeds) vs. the two thin-tail breeds from Sudan;

3. Arabian peninsula fat-tail (Naimi, Najdi, Omani and Huri) vs. the two thin-tail breeds from Sudan;

4. Barbaresca vs. two Italian thin-tail breeds (Sardinian and Comisana);

5. Laticauda vs. the two Italian thin-tail breeds;

6. Libyan Barbary vs. Algerian Sidaoun.

Inter-population analyses of the six fat- vs. thin-tail groups (Table 1) were performed using the Extended Haplotype Homozygosity (EHH)-derived statistic *Rsb* [17], as in Bahbahani et al. [18–19]. In particular, site-specific EHH (EHHs) for each SNP in each population was calculated by taking the average EHH for the two alleles of the SNP weighted by their squared allele frequencies. Integrated EHHs (iES) value for each SNP in each population was obtained upon integrating the area under curve of EHHs against distance describing the decay of EHHs for each SNP in each population. *Rsb* values are the natural log ratio of iES values between two populations. To identify statistically significant SNPs under selection in each of the six pair-wise comparisons (positive *Rsb* value), one-sided *P*-values (fat- vs. thin-tail group) were derived as –log10(1-Φ(*Rsb*)), where Φ(*Rsb*) represents the Gaussian cumulative distribution function. Inter-population genome-wide *F*<sub>ST</sub> and χ<sup>2</sup> analysis were also performed to corroborate the results obtained with the *Rsb* analysis. The following constraints were introduced to define the fat-tail selection signatures: 1) –log10 (*P*-value) ≥ 3.3, equivalent to a *P*-value of 0.0005, was used as a threshold to define significant *Rsb*; 2) candidate regions were retained if at least two SNPs, separated by ≤200 Kb, passed this threshold; 3) the candidate region was present in two or more pair-wise comparisons.

## Runs of homozygosity

Runs of homozygosity (ROH) were estimated using PLINK 1.7 [16]. The minimum length that constituted the ROH was set to one Mb. The following criteria were also used: (i) one missing SNP was allowed in the ROH, and up to one possible heterozygous genotype; (ii) the minimum number of SNPs that constituted the ROH was set to 30; (iii) the minimum SNP density per ROH was set to one SNP every 100 kb; (iv) maximum gap between consecutive homozygous SNPs of 1000 Kb [20]. This analysis was carried out within the same group or breed of the pair-wise comparisons above reported for the selection signature detection, with a total of six groups/breeds for fat tail and three groups/breeds for thin tail, respectively. The percentage of SNP residing within an ROH for a given breed was estimated by counting the number of times that each SNP appeared in a ROH and by dividing that number by the number of animals in each group/breed, allowing us to obtain a locus homozygosity range (from 0 to 1). To identify the genomic regions of “high homozygosity”, also called ROH islands, the top 0.9999 SNPs of the percentile distribution of the locus homozygosity range within each group/breed were selected.

## Gene annotation

Candidate regions identified by different approaches were used to annotate genes, that were either entirely or partially included within each selected region, using the NCBI Genome Data Viewer (https://www.ncbi.nlm.nih.gov/genome/gdv/browser/?context=gene&acc=101104604). Finally the biological functions of each annotated gene within the selection signatures was investigated via a comprehensive search of literature.
## Results

After quality control, 43,224 SNPs and 349 animals (6 to 39 per breed) were retained for the analyses (Table 1). To examine and visualize the genetic relationships among the 23 sheep breeds, we used a MDS plot of the pairwise identity-by-state distance. The results showed that most sheep breeds formed non-overlapping clusters and were clearly separate populations (Fig 1). The first dimension (C1) separated the Italian breeds from the Arabian Peninsula and African ones, likely reflecting different breeding histories. The second dimension (C2) distinguished Barbaresca from the other breeds. Therefore, with the exception of Barbaresca, the

<figure><img src="image_5.png" /><figcaption>**Fig 1.** Genetic relationships among the 23 sheep breeds defined through multidimensional scaling analysis. The first two components, C1 and C2, accounted for 14.03% and 3.66%, respectively of the total variation.</figcaption></figure>

https://doi.org/10.1371/journal.pone.0209632.g001
MDS grossly separated the breeds according to their genetic origin and/or to geographical proximity between their breeding areas. The Ethiopian breeds were separated into two groups: one including the three fat-rumped breeds and one short fat-tail (Molale), and one group including the long fat-tail breeds and the other short fat-tail breed (Gafera). Noteworthy, the MDS plot did not separate the breeds on the basis of the different tail phenotypes. Different geographic origin and genetic drift, most likely, with adaptation to different eco-climates, as well as ethnic, cultural and religious practices, that can impede gene flow, may have shaped this genetic sub-structuring [13].

In this study, selection signatures for fat-tail were identified using the *Rsb* approach, after corroboration with *F*<sub>ST</sub> approach. The markers with a significant inter-population *Rsb* are shown in S1 Table (from sheet “a” to sheet “f”), corresponding to the six comparisons (from 1 to 6) described in the material and methods. Likewise, in S2 Table (from sheet “a” to sheet “f”), genome-wide *F*<sub>ST</sub> and χ<sup>2</sup> values, with the corresponding Bonferroni corrected χ<sup>2</sup> *P*-values, are shown. A summary of the number of significant SNPs obtained with the different statistical methods in the six pair-wise comparisons is reported in Table 2.

Significant signals of differentiation between the fat-tail breed/group and the thin-tail of the corresponding region, that were shared by two or more fat-tail breeds/groups, are reported in S3 Table. In particular, the positions of the significant SNPs, with their corresponding probability values, are reported for each fat-tail breed/group. *F*<sub>ST</sub> values and Bonferroni corrected χ<sup>2</sup> *P*-values are reported only when achieved by significant SNPs located in the candidate region, or at distance ≤ 0.2 Mb up- and down-stream of the candidate region boundaries. A total of 14 candidate genomic regions on ten different chromosomes have been identified in two or more pair-wise comparisons (Table 3). The majority of shared fat-tail signals were observed for the two groups of Ethiopian breeds on chromosomes (OAR) 5, 6, 10, 18 and 19 and by the two breeds of Barbary sheep origin (Laticauda and Libyan Barbary) on OAR 3, 10, 12, 13. A total of 84 genes were found spanning the detected selection signature regions. Some of these identified candidate genes, that may be associated with fat deposition or related phenotypes, include: ANAPC1 and MSRB3 (OAR3), CXXC5 and PSD2 (OAR5), SLIT2 and EPHA5 (OAR6), CHP1 and OIP5 (OAR7), PCDH9 (OAR10), CDS2 and BMP2 (OAR13) and OAS2 (OAR17) (Table 1).

Remarkably, all the 14 candidate regions registered the presence of at least one marker attaining highly significant *Rsb* values, i.e. > 4, equivalent to a *P*-value of 0.0001. Moreover, in all the regions, except the one on OAR12 that is shared between Laticauda and Libyan Barbary, at least one significant χ<sup>2</sup> value was also registered in either one or both of the fat-tail breed/groups.

Several genomic regions that frequently appeared in a ROH were identified within each breed/group (S1 and S2 Figs, for fat- and thin-tail sheep breeds, respectively). Table 4 provides the chromosome position, and the start and end of the ROH islands. The top 0.9999 SNPs of

<table><caption>Table 2. Number of significant single nucleotide polymorphisms (SNPs) obtained with the two selection signature approaches in the six pair-wise comparisons.</caption><thead><tr><th colspan="2">Pair-wise comparison</th><th colspan="2">No. significant SNPs</th></tr><tr><th>Fat-tail group</th><th>Thin-tail group</th><th>Rsb P&lt;0.0005</th><th>Bonferroni corrected χ2 P-value ≤ 0.05</th></tr></thead><tbody><tr><td>Ethiopian fat-tail</td><td>Hammary and Kabashi</td><td>99</td><td>754</td></tr><tr><td>Ethiopian long fat-tail</td><td>Hammary and Kabashi</td><td>106</td><td>1,114</td></tr><tr><td>Arabian peninsula fat-tail</td><td>Hammary and Kabashi</td><td>64</td><td>108</td></tr><tr><td>Barbaresca</td><td>Sardinian and Comisana</td><td>638</td><td>2,402</td></tr><tr><td>Laticauda</td><td>Sardinian and Comisana</td><td>158</td><td>328</td></tr><tr><td>Libyan Barbary</td><td>Sidaoun</td><td>185</td><td>302</td></tr></tbody></table>

https://doi.org/10.1371/journal.pone.0209632.t002
<table><caption>Table 3. Candidate regions and genes identified in two or more pair-wise comparisons (see material and methods). Start/end positions are based on the ovine genome sequence assembly Oar_v4.0. Genes found in the literature to be associated with fat deposition or related phenotypes are shown in bold.</caption><thead><tr><th rowspan="2">OAR</th><th colspan="6">Pair-wise comparison group</th><th rowspan="2">Genes</th></tr><tr><th>1</th><th>2</th><th>3</th><th>4</th><th>5</th><th>6</th></tr><tr><th></th><th>start/end<br>(bp)</th><th>start/end<br>(bp)</th><th>start/end<br>(bp)</th><th>start/end<br>(bp)</th><th>start/end<br>(bp)</th><th>start/end<br>(bp)</th><th></th></tr></thead><tbody><tr><td>3</td><td></td><td></td><td>104,333,496<br>105,210,346</td><td></td><td></td><td>104,291,439<br>105,909,563</td><td>ZNF2, ZNF514, MRPS5, MALL, NPHP1, ACOXL, BUB1, BCL2L11, ANAPC1, MERTK</td></tr><tr><td>3</td><td></td><td></td><td></td><td></td><td>154,458,718<br>156,011,304</td><td>155,014,204<br>155,055,875</td><td>MSRB3, WIF1, LEMD3, TBC1D30, GNS, RASSF3, TBK1, SRGAP1, TMEM5</td></tr><tr><td>5</td><td>48,226,104<br>48,526,532</td><td>46,925,830<br>49,273,852</td><td></td><td></td><td></td><td></td><td>EGR1, ETF1, HSPA9, CTNNA1, SIL1, MATR3, PAIP2, SPATA24, SLC23A1, PROB1, MZB1, DNAJC18, TMEM173, ECSCR, UBE2D2, CXXC5, PSD2, NRG2, PURA, IGIP, PFDN1, HBEGF, CYSTM1, SLC4A9, TMCO6, WDR55, HARS2, NDUFA2</td></tr><tr><td>6</td><td></td><td></td><td></td><td>38,104,576<br>39,576,650</td><td>38,179,178<br>39,639,829</td><td></td><td>TRNAS TOP-UCA, LOC106991224, SLIT2</td></tr><tr><td>6</td><td>55,697,868<br>55,794,685</td><td>55,697,868<br>55,811,685</td><td></td><td></td><td></td><td></td><td>DTHD1</td></tr><tr><td>6</td><td>75,842,854<br>76,599,033</td><td></td><td></td><td></td><td>75,533,914<br>75,941,134</td><td></td><td>ADGRL3</td></tr><tr><td>6</td><td>80,325,742<br>80,531,786</td><td></td><td></td><td></td><td></td><td>80,878,591<br>80,912,095</td><td>EPHA5</td></tr><tr><td>7</td><td></td><td></td><td></td><td>33,736,820<br>33,937,483</td><td>33,565,208<br>33,841,590</td><td></td><td>INO80, EXD, CHP1, OIP5, NUSAP1, NDUF AF1, RTF1</td></tr><tr><td>10</td><td>40,258,505<br>45,063,369</td><td>40,594,254<br>45,416,672</td><td></td><td></td><td>40,957,582<br>45,416,672</td><td>42,194,236<br>45,155,143</td><td>PCDH9, LOC101121526, KLHL1</td></tr><tr><td>12</td><td></td><td></td><td></td><td></td><td>43,000,381<br>43,168,807</td><td>43,168,807<br>43,297,179</td><td>RERE, SLC45A1</td></tr><tr><td>13</td><td></td><td></td><td></td><td></td><td>46,565,715<br>49,137,513</td><td>47,583,113<br>49,208,171</td><td>CDS2, PROKR2, GPCPD1, CHGB, TRMT6, CRLS1, LRRN4, FERMT1, BMP2</td></tr><tr><td>17</td><td>61,094,671<br>61,631,272</td><td></td><td></td><td></td><td></td><td>61,496,170<br>61,658,381</td><td>OAS2, OAS1, RPH3, PTPN11, RPL6, HECTD4</td></tr><tr><td>18</td><td>1,895,285<br>2,129,462</td><td>1,980,832<br>2,243,903</td><td></td><td></td><td></td><td></td><td>ATP10A</td></tr><tr><td>19</td><td>51,617,073<br>51,789,681</td><td>51,649,648<br>51,819,178</td><td></td><td></td><td></td><td></td><td>MAP4, DHX30, SMARCC1</td></tr></tbody></table>

https://doi.org/10.1371/journal.pone.0209632.t003

the percentile distribution of locus homozygosity values led to the use of different thresholds for each breed/group (from 0.11 to 0.83), and a total of 15 genomic regions of high-homozygosity across breeds/groups were identified. Although the distribution of the ROH was relatively balanced and the signals were moderate in height, we found a few outstanding peaks with a high occurrence of ROH, especially in the Barbaresca breed (S1 Fig).

Discussion

In studies aiming to detect genomic signals for specific traits, for instance signals directly associated with fat deposition and adipogenesis, the major drawback is to detect strong differences (i.e. between fat-tail and thin-tail breeds) that are due either to different origins or to reproductive isolation, and not obviously involved in the trait (fat deposition). In this work, the fat-tail breeds from Ethiopia, Algeria, Arabian peninsula and southern Italy were pair-wise compared with their thin-tail counterparts from the closest geographical region. Therefore, the results reported herein provide the most complete genome-wide study of selection signatures for fat-tail in African and Eurasian sheep breeds. The signals that are detected between any two or
<table><caption>**Table 4. Run of homozygosity (ROH) islands identified within each breed/group.** The chromosome (OAR), the number of single nucleotide polymorphisms (SNPs) within each ROH island and the positions of the genomic regions (in base pairs, bp) are reported.</caption><thead><tr><th>Breeds/groups</th><th>OAR</th><th>N of SNPs</th><th>Start (bp)</th><th>End (bp)</th></tr></thead><tbody><tr><td rowspan="2">Ethiopian fat-tail (11 breeds)</td><td>5</td><td>17</td><td>48,278,057</td><td>49,199,542</td></tr><tr><td>10</td><td>34</td><td>36,757,445</td><td>39,446,610</td></tr><tr><td>Ethiopian long fat-tail (6 breeds)</td><td>5</td><td>29</td><td>47,692,576</td><td>49,199,542</td></tr><tr><td rowspan="3">Arabian peninsula fat-tail (4 breeds)</td><td>2</td><td>8</td><td>131,264,212</td><td>131,695,396</td></tr><tr><td>2</td><td>11</td><td>135,482,289</td><td>136,005,787</td></tr><tr><td>4</td><td>25</td><td>26,305,564</td><td>27,655,062</td></tr><tr><td>Barbaresca</td><td>6</td><td>48</td><td>34,851,127</td><td>38,495,020</td></tr><tr><td>Laticauda</td><td>3</td><td>7</td><td>13,571,685</td><td>13,899,340</td></tr><tr><td rowspan="2">Libyan Barbary</td><td>2</td><td>12</td><td>113,637,672</td><td>114,513,743</td></tr><tr><td>7</td><td>76</td><td>94,404,153</td><td>98,581,328</td></tr><tr><td>Sudan (Hammary and Kabashi)</td><td>1</td><td>9</td><td>198,471,933</td><td>198,933,003</td></tr><tr><td rowspan="3">Sidaoun</td><td>2</td><td>5</td><td>38,190,022</td><td>38,368,173</td></tr><tr><td>2</td><td>35</td><td>38,827,516</td><td>40,453,440</td></tr><tr><td>13</td><td>17</td><td>35,563,319</td><td>36,504,021</td></tr><tr><td>Italian thin-tail (Comisana and Sardinian)</td><td>2</td><td>64</td><td>71,595,057</td><td>75,092,467</td></tr></tbody></table>

https://doi.org/10.1371/journal.pone.0209632.t004

more simultaneous pair-wise comparisons might consequently be considered more reliable, also because they are shown by geographically distant sheep breeds.

While the fat-tail sheep of Algeria, Arabian peninsula and southern Italy were all long or semi-long fat-tail breeds, the Ethiopian fat-tail breeds included long-tail, short-tail and fat-rumped sheep breeds (Table 1). Therefore, for the Ethiopian sheep, two different pair-wise comparisons were performed, the first including all the 11 fat-tail breeds, and the second including only the 6 long fat-tail breeds. The assumption here was that the first comparison might elucidate the genes involved with adipogenesis, irrespective of the shape of the tail, while the second comparison would reveal findings that are more comparable to those observed in the breeds from the other regions, and are therefore possibly more likely to be linked to the fat-tail phenotype.

We used two different, but complementary, statistical approaches to identify putative selection signatures across the phenotypically different breeds. The $F_{ST}$ index of differentiation is among the most widely used statistic to detect signals of selection in differentially-selected populations, where usually a locus is putatively considered under differential selection if its pair-wise $F_{ST}$ has a rank percentile value of 0.01 or less. Because the level of genetic differentiation between each pair of breeds/groups of the six comparisons highly varied, the decision “a priori” of a rank percentile to accept significant $F_{ST}$ may disfavor the pairs presenting the highest genetic diversity. Therefore, following Moioli et al. [12] who showed that $F_{ST}$ and $\chi^2$ are highly correlated, in order to confirm with a statistical test which markers were significant, we calculated inter-population locus-specific $\chi^2$ values and considered significant the markers reaching a Bonferroni-adjusted $\chi^2$ P-values $\le 0.05$. Doing so, the different numbers of significant markers in each pair-wise comparison allows to appreciate their global genetic difference. However, since $F_{ST}$ and $\chi^2$ values are based on allele frequencies and might represent an isolated event occurring by chance, and not necessarily associated with fat-tail signals, in this case, the extended haplotype homozygosity derived statistic, Rsb, was preferentially used. The Rsb statistics has high power towards strong selection sweeps that have undergone fixation [17]. Indeed, the Rsb metrics is able to show the presence of selective sweeps resulting in complete fixation of some alleles in one population relative to the other. This statistic in fact
considers the whole haplotype region around one marker, or group of markers; the larger is the region in which the homozygous haplotype was maintained in the first breed in contrast with the second breed, the more reliable is the probability of carrying a fat-tail signal. The number of significant SNPs for inter-population *Rsb* is smaller than the number of significant SNPs for $\chi^2$ (Table 2) confirming that the first method is more stringent. In most cases, the number of SNPs that are significant with one method showed some correlation with the number of SNPs that were obtained with the second method: the higher was the number of the significant *Rsb* values, the higher was also the number of significant $F_{ST}$ values. The highest number of significant signals obtained with both methods was observed in the Barbaresca breed- 1.5% for inter-population *Rsb* and 5.6% for $F_{ST} / \chi^2$, while the lowest number was in the Arabian peninsula breeds: 0.15 and 0.25% with the two methods, respectively. In accordance with our results, Yuan et al. [11], in a selection signature analysis for tail type in sheep, identified seven and twenty-six regions using the extended haplotype homozygosity and $F_{ST}$ approaches, respectively, and only six small regions using both approaches. Bahbahani et al. [21] showed that the two common approaches (inter-population *Rsb* and $F_{ST}$), used to identify signatures of positive selection in East African Shorthorn Zebu, did not produce overlapping signals. Although the authors interpreted the observed absence of overlaps between *Rsb* and $F_{ST}$ analyses as a possible consequence of the selection time-scale, with *Rsb* being considered more suitable for detecting signatures of recent selection, it may also be hypothesized that false positives may occur when using both *Rsb* and $F_{ST}$. As the aim of this study was to identify loci most likely associated with fat-tail, we established that the candidate region(s) should be present in two or more pair-wise comparisons (Table 3). Therefore only the signatures satisfying these criteria and encompassing annotated genes will be subsequently discussed.

Another method for detecting signatures of positive selection based on intra-population analysis is the identification of high-homozygosity regions [6]. Since ROHs are normally abundant in regions under positive selection, their accumulation at specific loci, or islands, has been used to identify genomic regions that reflect directional selection in cattle [22], sheep [20,23], horse [24] and goat [25]. We therefore checked if such regions of high-homozygosity overlapped with putative selection signatures in the sheep breeds considered in this study.

The region in OAR3:104.2–105.9 Mb was identified in two comparisons: fat-tail sheep of the Arabian peninsula vs. Sudanese thin-tail sheep, and Libyan Barbary vs. Algerian Sidaoun. Out of the ten genes found in this region, only *ANAPC1* has been associated with obesity-related traits by Comuzzie et al. [26] in a Genome-Wide Association Study (GWAS) on Hispanic children. The signature on OAR3:154.0–155.6 Mb, detected in the Laticauda and the Libyan Barbary breeds, had already been reported for the Barbaresca, Laticauda and Chios breeds [9]. Yuan et al. [11], in a GWAS on seven indigenous Chinese sheep, by contrasting fat-tail versus thin-tail phenotypes, detected a signature in this region encompassing the *MSRB3*, that has been identified as a candidate gene associated with adaptation [27]. In a study on world sheep breeds, *MSRB3* was highlighted to have experienced high selection pressure [28]. Moreover, *MSRB3* is located within a genomic region identified as ovine quantitative trait locus (QTL) (QTL#127000) for tail fat deposition [11]. A large region on OAR5:47.0–49.0 Mb turned out here to encode putative fat deposition genes in the two groups of Ethiopian sheep: the one composed only by the long fat-tail, and the one including all the eleven Ethiopian fat-tail breeds. Although Fariello et al. [29] reported that this region encoded a signature differentiating prolific and non-prolific Asian sheep, the involvement of a signature in this region in the fat-tail phenotype had been previously reported [9,12]. This involvement is corroborated by genetic studies of body mass index in humans, describing a role played in obesity by the *CXXC5* gene in Americans [30] and the *PSD2* gene in the Japanese population [31]. Moreover, this selection signature overlapped with the ROH island identified in the Ethiopian fat-tail breeds, which include 17
and 29 homozygous markers respectively for the two groups of breeds (the eleven Ethiopian fat-tail, and the six Ethiopian long fat-tail). The signal on OAR6:38.1–39.6 Mb was detected in Barbaresca and Laticauda and was previously reported as selection signature for fat-tail in these two breeds, also when compared with 13 Italian thin-tail breeds [9]. It includes the *SLIT2* gene, a potential candidate for internal organ weights in Simmental beef cattle [32] and therefore possibly connected with fat deposition. This signal is worth investigating further, because it encompasses a large region (1.5 Mb) where the Barbaresca showed 27 SNP markers with allele frequency patterns that are highly differentiated from the Italian thin-tail breeds, 15 of which exceeded the significant threshold of *Rsb P*-value < 0.0001. Moreover, this genomic region partially overlapped with the ROH island on OAR6 detected in Barbaresca and shared by more than 80% of the individuals of this breed. The Laticauda showed less significant signals in the same region, with the exception of a highly significant one (-log10(*P*-value) = 7.68) at position 38,345,613 bp. Moreover, several markers attained high *F*<sub>ST</sub> values and significant *χ*<sub>2</sub> values in the pair-wise comparisons of both breeds. However, because the two breeds have Barbary origin, it is also possible that this signature encodes loci inherited from North-African breeds. The selection signature on OAR6:55.6–55.7 Mb was shown in this study to be present in the two groups of Ethiopian breeds and it has never been reported before as a fat-tail signal. On the same chromosome, at position 76.5–77.5 Mb, we identified another signal in the Ethiopian group (eleven breeds) and the Laticauda, which has been previously described in the Barbaresca but not in the Laticauda [9]. Another candidate region identified on OAR6:80.6–81.0 Mb, shown by the Ethiopian group (eleven breeds) and the Libyan Barbary and including the *EPHA5* gene was detected. This gene is involved in feed conversion ratio in Nellore cattle [33]. The signal on OAR7:33.5–33.9 Mb was detected in the Barbaresca and Laticauda, as well as in a previous study [9]. While these authors could not assign an obvious role of any of the genes located in this region, Lirangi et al. [34] suggested that the *CHP1* gene is involved in cellular fat storage. Inoue et al. [35] revealed that *OIP5* promotes proliferation of pre- and mature-adipocytes and contributes adipose hyperplasia; moreover, an increase of *OIP5* may associate with development of obesity. A common candidate region located on OAR10:40.2–45.0 Mb observed in the two groups of Ethiopian sheep, Libyan Barbary and Laticauda, includes the *PCDH9* gene. The same signature was detected by Kim et al. [36] who contrasted the Egyptian Barki sheep with British breeds, and referred to it as signal of adaptation to arid environments. However, the Barki is a fat-tail sheep while the British are all thin-tail breeds. We would then propose the idea that this is a signal of fat-deposition, this being corroborated by the presence of *PCDH9*, reported by Wang et al. [37] as a candidate gene for obesity in humans. The signal on OAR12:43.0–43.3 Mb, shared by the Laticauda and the Libyan Barbary has not been reported previously as a fat-tail signal and the two genes (*RERE* and *SLC45A1*) included in the region do not appear to have any evident connection with fat deposition. On OAR13:45.5–48.4 Mb, a selection signature shared by the Laticauda and the Libyan Barbary was identified. The region was also reported as a fat-tail signature in several studies [9–11]. The strong linkage disequilibrium between the SNPs in this OAR13 region with a missense mutation in exon 1 of the *BMP2* gene (OAR13:48,552,093–48,897,111 bp) was demonstrated by Moioli et al. [12] in the Laticauda fat-tail and Altamurana thin-tail sheep. Yuan et al. [11] emphasized that *BMP2* may play important roles in fat tail formation. However, here *BMP2* does not appear to be the only candidate gene. This signature spans a size > 3Mb, and Laticauda showed a very high *Rsb* value (-log10-*P*-value = 7.96) at position 46,582,744 bp. A transcriptome profile analysis of adipose tissues from fat- and short-tailed Chinese sheep identified *CDS2* among the differentially expressed genes [38]. This gene which spans 46,560,029 to 46,605,239 bp of OAR13, encompasses the significant marker mentioned above. Animal QTLdb identified a QTL (QTL#127011) for tail fat deposition [11] that overlapped this selection signature. Another candidate region on
OAR17:61.0–61.6 Mb, shared by the Ethiopian group (eleven breeds) and the Libyan Barbary has not been detected previously as a fat-tail signal in sheep. However, Fox et al. [39] associated the OAS2 gene found in this region to body fat distribution. Finally, the candidate regions on OAR18:1.8–2.2 Mb, and OAR19:51.6–51.8 Mb, shared by the two groups of Ethiopian breeds have not been reported previously as fat-tail signals and the five genes included in the two regions (*ATP10A* on OAR18, and *CDC25A*, *MAP4*, *DHX30*, *SMARCC1* on OAR19) do not appear to have any obvious connection with fat deposition. It must be emphasized that the six Ethiopian long fat-tail breeds used in the comparison 2 were a subset of the eleven breeds analyzed in the comparison 1. Therefore, signals identified in comparison 2 may reflect those found in comparison 1. Anyhow, other studies also reported putatively selected genes mapped in the same two regions, with no obvious roles in sheep tail type or fat deposition [3,11]. Moreover, the complexity of the fat-tail phenotype [10] may partly justify the high number of signals detected in the pair-wise comparisons.

One limitation of this study may be the different sample sizes used in the contrasting groups, and in particular in the comparison 1 (140 vs 15 animals). However, several studies have been carried out to identify selection signatures contrasting groups based on distinct phe-notypes with considerably different numbers of individuals [1,25,40,41]. In our study, several genomic regions potentially under selection in at least two comparisons corroborate with pre-vious studies, highlighting the importance of these regions and also supporting these results, as well as the design of the comparisons used to identify the genomic regions. As reported above, only two ROH islands identified in fat-tail breeds overlapped with the selection signatures. One reason for the little overlap is that ROH might detect selection related to any trait, while contrasting thin and fat tail breeds is more likely to detect signal related to this trait. However, Purfield et al. [23] reported a significant moderate correlation between the occurrence of SNPs in a ROH and two different statistical approaches (*F*<sub>ST</sub> and HapFLK) for identifying putative selection signatures, and showed two ROH islands that overlapped with selection signatures. Moreover, the ROH island on OAR5 identified in the Ethiopian breeds was also identified in the Lori Bakhtiari fat-tail breed [3]. The authors reported that this increase in homozygosity would be consistent with selection for mutations affecting fat-tail size several thousand years following domestication. Therefore, although the existence of ROH islands has been attributed to several factors (recombination events, demography) [20,42], our results corroborate the hypothesis that regions of high-homozygosity may indeed harbor targets of positive selection, as also observed in previous studies [22,23,25,43].

In this study, we report so far the most complete genome-wide study of selection signatures for fat-tail in sheep. We identified novel signals and confirmed the presence of selection signatures in the genomic regions that harbor candidate genes that are known to affect fat deposition. These findings also confirm the great complexity of the mechanisms underlying quantitative traits, such as the fat-tail, and further confirm the hypothesis that many different genes are involved in the phenotype. However, it is important to highlight that among the candidate genomic regions, false positives may still be a possibility. Therefore, further studies using different populations and the new ovine high-density SNP chip will be required to confirm and refine our results and investigate the role of specific genes. Notwithstanding, the selection signatures reported here provide comprehensive insights into the genetic basis underlining the fat tail phenotype in sheep.

**Supporting information**

**S1 Table. Significant markers for *Rsb*.** a) Ethiopian fat-tail breeds (eleven breeds) contrasted with two thin-tail breeds from Sudan (Hammari and Kabashi); b) Ethiopian long fat-tail
breeds (six breeds) contrasted with two thin-tail breeds from Sudan; c) Arabian Peninsula fat-tail breeds (Naimi, Najdi, Omani and Huri) contrasted with two thin-tail breeds from Sudan; d) Barbaresca fat-tail breed contrasted with two Italian thin-tail breeds (Sardinian and Comisana); e) Laticauda fat-tail breed contrasted with two Italian thin-tail breeds; f) Libyan Barbary fat-tail breed contrasted with the Sidaoun thin-tail breed.

(XLS)

**S2 Table. Significant markers for $F_{ST} / \chi^2$.** a) Ethiopian fat-tail breeds (eleven breeds) contrasted with two thin-tail breeds from Sudan (Hammari and Kabashi); b) Ethiopian long fat-tail breeds (six breeds) contrasted with two thin-tail breeds from Sudan; c) Arabian Peninsula fat-tail breeds (Naimi, Najdi, Omani and Huri) contrasted with two thin-tail breeds from Sudan; d) Barbaresca fat-tail breed contrasted with two Italian thin-tail breeds (Sardinian and Comisana); e) Laticauda fat-tail breed contrasted with two Italian thin-tail breeds; f) Libyan Barbary fat-tail breed contrasted with the Sidaoun thin-tail breed.

(XLS)

**S3 Table. Significant signals of differentiation between the fat-tail breed/group and the thin-tail of the corresponding region, that were shared by two or more fat-tail breedsgroups.**

(XLS)

**S1 Fig. Regions of homozygosity in the fat-tail groups/breeds.** From top to bottom, and from right to left: Ethiopian fat-tail (11 breeds); Ethiopian long fat-tail (6 breeds); Arabian peninsula (Naimi, Najdi, Omani and Huri); Laticauda; Barbaresca; Libyan Barbary. The threshold values (black lines) were based on the criterion of representing the top 0.9999 SNP of the percentile distribution of locus homozygosity values within each group or breed.

(TIF)

**S2 Fig. Regions of homozygosity in the thin-tail groups/breeds.** From top to bottom: Italian breeds (Sardinian and Comisana); Sudanese breeds (Hammari and Kabashi); Sidaoun. The threshold values (black lines) were based on the criterion of representing the top 0.9999 SNP of the percentile distribution of locus homozygosity values within each group or breed.

(TIF)

**Author Contributions**

**Conceptualization:** Bianca Moioli, Joram M. Mwacharo, Olivier Hanotte, Fabio Pilla, Elena Ciani.

**Data curation:** Salvatore Mastrangelo, Hussain Bahbahani, Bianca Moioli, Elena Ciani.

**Formal analysis:** Salvatore Mastrangelo, Hussain Bahbahani, Bianca Moioli, Elena Ciani.

**Investigation:** Salvatore Mastrangelo, Hussain Bahbahani, Bianca Moioli, Elena Ciani.

**Methodology:** Salvatore Mastrangelo, Hussain Bahbahani, Bianca Moioli, Elena Ciani.

**Resources:** Abulgasim Ahbara, Mohammed Al Abri, Faisal Almathen, Anne da Silva, Ibrahim Belabdi, Baldassare Portolano, Olivier Hanotte, Fabio Pilla, Elena Ciani.

**Supervision:** Bianca Moioli, Joram M. Mwacharo, Olivier Hanotte, Fabio Pilla, Elena Ciani.

**Writing – original draft:** Salvatore Mastrangelo, Bianca Moioli, Elena Ciani.
**Writing - review & editing:** Hussain Bahbahani, Abulgasim Ahbara, Mohammed Al Abri, Faisal Almathen, Anne da Silva, Baldassare Portolano, Joram M. Mwacharo, Olivier Hanotte, Fabio Pilla.

References

1. Brito LF, Kijas JW, Ventura RV, Sargolzaei M, Porto-Neto LR, Cánovas A, et al. Genetic diversity and signatures of selection in various goat breeds revealed by genome-wide SNP markers. BMC Genomics. 2017; 18: 229. https://doi.org/10.1186/s12864-017-3610-0 PMID: 28288562

2. Handley LL, Byrne K, Santucci F, Townsend S, Taylor M, Bruford MW. Genetic structure of European sheep breeds. Heredity. 2007; 99:620. https://doi.org/10.1038/sj.hdy.6801039 PMID: 17700634

3. Moradi MH, Nejati Navaremi A, Moradi-Shahrbabak M, Dodds K, McEwan JC. Genomic scan of selective sweeps in thin and fat tail sheep breeds for identifying of candidate regions association with fat deposition. BMC Genet. 2012; 13:10. https://doi.org/10.1186/1471-2156-13-10 PMID: 22364287

4. Xu SS, Ren X, Yang GL, Xie XL, Zhao YX, Zhang M, et al. Genome-wide association analysis identifies the genetic basis of fat deposition in the tails of sheep (Ovis aries) Anim Genet. 2017; 48:560–569. https://doi.org/10.1111/age.12572 PMID: 28677334

5. Mwacharo JM, Kim ES, Elbeltagy AR, Aboul-Naga AM, Rischkowsky BA, Rothschild MF. Genomic foot-prints of dryland stress adaptation in Egyptian fat-tail sheep and their divergence from East African and western Asia cohorts. Sci Rep. 2017; 7:7647. https://doi.org/10.1038/s41598-017-06670-6

6. Qanbari S, Simianer H. Mapping signatures of positive selection in the genome of livestock. Liv Sci. 2014; 166:133–143.

7. Lv FH, Agha S, Kantanen J, Colli L, Stucki S, Kijas JW. et al. Adaptations to Climate-Mediated Selective Pressures in Sheep. Mol Biol Evol. 2014; 31:3324–3343. https://doi.org/10.1093/molbev/msu264 PMID: 25249477

8. Yang J, Li WR, Lv FH, He SG, Tian SL, Peng WF, et al. Whole-genome sequencing of native sheep provides insights into rapid adaptations to extreme environments. Mol Biol Evol. 2016; 33:2576–2592. https://doi.org/10.1093/molbev/msw129 PMID: 27401233

9. Mastrangelo S, Moioli B, Ahbara A, Latairish S, Portolano B, Pilla F, et al. Genome-wide scan of fat-tail sheep identifies signals of selection for fat deposition and adaptation. Anim Prod Sci. 2018a;doi.org/10.1071/AN17753.

10. Wei C, Wang H, Liu G, Wu M, Cao J, Liu Z, et al. Genome-wide analysis reveals population structure and selection in Chinese indigenous sheep breeds. BMC Genomics. 2015; 16:194. https://doi.org/10.1186/s12864-015-1384-9 PMID: 25888314

11. Yuan Z, Liu E, Kijas JW, Zhu C, Hu S, Ma X, et al. Selection signature analysis reveals genes associated with tail type in Chinese indigenous sheep. Anim Genet. 2017; 48:55–66. https://doi.org/10.1111/age.12477 PMID: 27807880

12. Moioli B, Pilla F, Ciani E. Signatures of selection identify loci associated with fat tail in sheep. J Anim Sci. 2015; 93:4660–4669. https://doi.org/10.2527/jas.2015-9389 PMID: 26523558

13. Ahbara A, Bahbahani H, Almathen F, Al Abri M, Agoub MO, Abeba A, et al. Genome-wide variation and putative candidate regions and genes associated with fat deposition and tail morphology in Ethiopian indigenous sheep. Front Genet. 2019; 9:699. https://doi.org/10.3389/fgene.2018.00699 PMID: 30687385

14. Riggio V, Pong-Wong R, Sallé G, Usai MG, Casu S, Moreno CR, et al. A joint analysis to identify loci underlying variation in nematode resistance in three European sheep populations. J Anim Breed Genet. 2014; 31(6), 426–436.

15. Ciani E, Crepaldi P, Nicoloso L, Lasagna E, Sarti FM, Moioli B, et al. Genome-wide analysis of Italian sheep diversity reveals a strong geographic pattern and cryptic relationships between breeds. Anim Genet. 2014; 45:256–266. https://doi.org/10.1111/age.12106 PMID: 24303943

16. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007; 81:559–575. https://doi.org/10.1086/519795 PMID: 17701901

17. Tang K, Thornton KR, Stoneking M. A new approach for using genome scans to detect recent positive selection in the human genome. PLoS Biol. 2007; 5(7): e171. https://doi.org/10.1371/journal.pbio.0050171 PMID: 17579516

18. Bahbahani H, Tiijani A, Mukasa C, Wragg D, Almathen F, Nash O, et al. Signature of selection for environmental adaptation and zebu x taurine hybrid fitness in East African Shorthorn Zebu. Front Genet. 2017; 8: 1. https://doi.org/10.3389/fgene.2017.00001
19. Bahbahani H, Salim B, Almathen F, Al Enezi F, Mwacharo JM, Hanotte O. Signatures of positive selection in African Butana and Kenana dairy zebu cattle. PLoS one. 2018; 13(1): e0190446. https://doi.org/10.1371/journal.pone.0190446 PMID: 29300786

20. Mastrangelo S, Tolone M, Sardina MT, Sottile G, Sutera AM, Di Gerlando R, et al. Genome-wide scan for runs of homozygosity identifies potential candidate genes associated with local adaptation in Valle del Belice sheep. Genet Sel Evol. 2017; 49(1):84. https://doi.org/10.1186/s12711-017-0360-z PMID: 29137622

21. Bahbahani H, Clifford H, Wragg D, Mbole-Kariuki MN, Van Tassell C, Sonstegard T, et al. Signatures of positive selection in East African Shorthorn Zebu: A genome-wide single nucleotide polymorphism analysis. Sci Rep. 2015; 5:11729. https://doi.org/10.1038/srep11729 PMID: 26130263

22. Mastrangelo S, Sardina MT, Tolone M, Di Gerlando R, Sutera AM, Fontanesi L, et al. Genome-wide identification of runs of homozygosity islands and associated genes in local dairy cattle breeds. Animal. 2018c; 12:(12) 2480-2488. https://doi.org/10.1017/S1751731118000629 PMID: 29576040

23. Purfield DC, McParland S, Wall E, Berry D.P The distribution of runs of homozygosity and selection signatures in six commercial meat sheep breeds. PLoS ONE. 2017; 12:e0176780. https://doi.org/10.1371/journal.pone.0176780 PMID: 28463982

24. Metzger J, Karwath M, Tonda R, Beltran S, Águeda L, Gut M, et al. Runs of homozygosity reveal signatures of positive selection for reproduction traits in breed and non-breed horses. BMC Genomics. 2015; 16:764. https://doi.org/10.1186/s12864-015-1977-3 PMID: 26452642

25. Talenti A, Bertolini F, Pagnacco G, Pilla F, Ajmone-Marsan P, Rothschild MF et al. The Valdostana goat: a genome-wide investigation of the distinctiveness of its selective sweep regions. Mamm Genome. 2017; 28: 114-128. https://doi.org/10.1007/s00335-017-9678-7 PMID: 28255622

26. Comuzzie AG, Cole SA, Laston SL, Voruganti VS, Haack K, Gibbs RA, et al. Novel genetic loci identified for the pathophysiology of childhood obesity in the Hispanic population. PLoS One. 2012; 7: e51954. https://doi.org/10.1371/journal.pone.0051954 PMID: 23251661

27. Wei C, Wang H, Liu G, Zhao F, Kijas JW, Ma Y, et al. Genome-wide analysis reveals adaptation to high altitudes in Tibetan sheep. Sci Rep. 2016; 6:26770. https://doi.org/10.1038/srep26770 PMID: 27230812

28. Kijas JW, Lenstra JA, Hayes B, Boitard S, Porto Neto LR, San Cristobal M, et al. Genome-wide analysis of the world's sheep breeds reveals high levels of historic mixture and strong recent selection. PLoS Biol. 2012; 10:e1001258. https://doi.org/10.1371/journal.pbio.1001258 PMID: 22346734

29. Fariello MI, Servin B, Tosser-Klopp G, Rupp R, Moreno C, San Cristobal M, et al. Selection signatures in worldwide sheep populations. PLoS ONE. 2014; 9:e103813. https://doi.org/10.1371/journal.pone.0103813 PMID: 25126940

30. Locke AE, Kahali B, Berndt SI, Justice AE, Pers TH, Day FR, et al. Genetic studies of body mass index yield new insights for obesity biology. Nature. 2016; 518:197-206.

31. Akiyama M, Okada Y, Kanai M, Takahashi A, Momozawa Y, Ikeda M, et al. Genome-wide association study identifies 112 new loci for body mass index in the Japanese population. Nature Genet. 2017; 49:1458. https://doi.org/10.1038/ng.3951 PMID: 28892062

32. An B, Xia J, Chang T, Wang X, Miao J, Xu L, et al. Genome-wide association study identifies loci and candidate genes for internal organ weights in Simmental beef cattle. Physiol Genomics. 2018; https://doi.org/10.1152/physiolgenomics.00022.2018 PMID: 29676954

33. de Almeida Santana MH, Oliveira Junior GA, Cesar AS, Freua MC, da Costa Gomes R, da Luz ES, et al. Copy number variations and genome-wide associations reveal putative genes and metabolic pathways involved with the feed conversion ratio in beef cattle. J Appl Genet. 2016; 7:495-504.

34. Lirangi M, Meydani M, Zingg JM, Azzi A. α-Tocopheryl-phosphate regulation of gene expression in preadipocytes and adipocytes. Biofactors. 2012; 38:450-7. https://doi.org/10.1002/biof.1051 PMID: 23047815

35. Inoue K, Maeda N, Mori T, Sekimoto R, Tsushima Y, Matsuda K, et al. Possible Involvement of Opa-Interacting Protein 5 in Adipose Proliferation and Obesity. PLoS ONE. 2014; 9:e87661. https://doi.org/10.1371/journal.pone.0087661 PMID: 24516558

36. Kim ES, Elbeltagy AR, Aboul-Naga AM, Rischkowsky B, Sayre B, Mwacharo JM, et al. Multiple genomic signatures of selection in goats and sheep indigenous to a hot arid environment. Heredity. 2016; 116:255-64. https://doi.org/10.1038/hdy.2015.94 PMID: 26555032

37. Wang K, Li WD, Zhang CK, Wang Z, Glessner JT, Grant SF, et al. A genome-wide association study on obesity and obesity-related traits. PLoS One. 2011; 28; 6(4):e18939. https://doi.org/10.1371/journal.pone.0018939 PMID: 21552555

38. Wang X, Zhou G, Xu X, Geng R, Zhou J, Yang Y, et al. Transcriptome profile analysis of adipose tissues from fat and short-tailed sheep. Gene. 2014; 549:252-7. https://doi.org/10.1016/j.gene.2014.07.072 PMID: 25088569
39. Fox CS, Liu Y, White CC, Feitosa M, Smith AV, Heard-Costa N, et al. Genome-wide association for abdominal subcutaneous and visceral adipose reveals a novel locus for visceral fat in women. PLoS Genet. 2012; 8:e1002695. https://doi.org/10.1371/journal.pgen.1002695 PMID: 22589738

40. Bertolini F, Servin B, Talenti A, Rochat E, Kim ES, Oget C, et al. Signatures of selection and environmental adaptation across the goat genome post-domestication. Genet Sel Evol. 2018; 50(1):57. https://doi.org/10.1186/s12711-018-0421-y PMID: 30449276

41. Porto-Neto LR, Lee SH, Sonstegard TS, Van Tassell CP, Lee HK, Gibson JP, et al. Genome-wide detection of signatures of selection in K orean H anwoo cattle. Anim. Genet. 2014; 45(2):180-190. https://doi.org/10.1111/age.12119 PMID: 24494817

42. Bosse M, Megens HJ, Madsen O, Paudel Y, Frantz LA, Schook LB, et al. Regions of homozygosity in the porcine genome: consequence of demography and the recombination landscape. PLoS Genet. 2012; 8:e1003100. https://doi.org/10.1371/journal.pgen.1003100 PMID: 23209444

43. Bertolini F, Figueiredo Cardoso T, Marras G, Nicolazzi EL, Rothschild MF, Amills M, et al. Genome-wide patterns of homozygosity provide clues about the population history and adaptation of goats. Genet Select Evol. 2018; 50:59.