were significantly associated with WS at the chromosome level and located on GGA1, GGA17, and GGA18 (Fig. 1, Table 2). This finding was not in favor of a monogenic inheritance of WS since several QTLs seemed to be involved in addition to the polygenic effect included in the model (see Methods section). Moreover, no co-localization with SNPs previously identified for pHu in the same genetic population was observed [23], which is not in favor of common genetic variants between the two traits. In order to explore whether pHu could indirectly influence WS, intra-line QTL detection of WS was performed (Additional file 1: Figure S1). No significant SNPs were detected in the pHu- line, while eight SNPs were significant at the chromosome threshold in the pHu + line (Table 3). They confirmed the two regions previously detected on GGA1 and GGA17 in the entire population and identified a new one on GGA20. The SNP identified with the lowest *p*-value was also the most significant SNP associated with WS in the entire population (Gga_rs13899127 on GGA1). Five SNPs significant at the GGA1 threshold co-localized with this SNP. Furthermore, we recovered the same SNP on GGA17 as the one associated with WS in the entire population (Gga_rs15804842).

For this SNP, the minor allele frequency (MAF) was similar in both lines (MAF = 0.446 and MAF = 0.478 in the pHu + and pHu- lines, respectively), but the SNP effect was higher in the pHu + line than in the pHu- line ($\beta$ = -3.2 x 10⁻¹ and $\beta$ = -6.5 x 10⁻², respectively). This illustrated that unless no co-localization was observed between WS and pHu QTLs, there might be an interaction between the genetic control of WS and the metabolic and physiological status of the two lines.

## Body composition and meat quality traits

Three SNPs were significant at the genome threshold, while 39 SNPs were significant at the chromosome level for body composition and meat quality traits (Table 2). These SNPs were mostly associated with CL and drip loss (DL), color parameters, and *Pectoralis major* and *minor* yields (PMY and PmY, respectively). They defined 15 QTL regions distributed over 11 chromosomes. As for WS, these results strongly suggested a polygenic inheritance of the studied carcass and meat quality parameters.

More than one third of the detected SNPs were located on GGA4 and defined two QTL regions (QTL3, QTL4). The first one (QTL3) was defined by a unique SNP (GGaluGA263381 at 65.97 Mb) which controlled both PMY and BMY. Interestingly, this SNP was also close to significance for WS ($p$ = 3.5 x 10⁻⁵, not shown in the table). The second one (QTL4) (90.82 to 91.20 Mb) contained three genome-wide significant SNPs for CL as well as several chromosome-wide

<table><caption>**Table 2** Significant SNPs for white striping, body composition, and meat quality traits</caption><thead><tr><th>Traits<sup>a</sup></th><th>QTL<sup>b</sup></th><th>GGA</th><th>SNP ID</th><th>Position<sup>c</sup></th><th>P-value<sup>d</sup></th></tr></thead><tbody><tr><td>L*</td><td>QTL1</td><td>1</td><td>Gga_rs13841646</td><td>26,417,615</td><td>7.27 x 10<sup>-6</sup></td></tr><tr><td>L*</td><td>QTL1</td><td>1</td><td>GGaluGA009377</td><td>26,433,696</td><td>7.27 x 10<sup>-6</sup></td></tr><tr><td>L*</td><td>QTL1</td><td>1</td><td>Gga_rs15215403</td><td>26,454,554</td><td>7.27 x 10<sup>-6</sup></td></tr><tr><td>WS</td><td>QTL2</td><td>1</td><td>Gga_rs13899127</td><td>87,535,862</td><td>4.62 x 10<sup>-6</sup></td></tr><tr><td>PMY</td><td>QTL3</td><td>4</td><td>GGaluGA263381</td><td>65,974,471</td><td>6.96 x 10<sup>-6</sup></td></tr><tr><td>BMY</td><td>QTL3</td><td>4</td><td>GGaluGA263381</td><td>65,974,471</td><td>9.13 x 10<sup>-6</sup></td></tr><tr><td>CL</td><td>QTL4</td><td>4</td><td>Gga_rs16454334</td><td>90,828,977</td><td>8.38 x 10<sup>-6</sup></td></tr><tr><td>CL</td><td>QTL4</td><td>4</td><td>GGaluGA271193</td><td>90,877,143</td><td>1.39 x 10<sup>-6</sup></td></tr><tr><td>CL</td><td>QTL4</td><td>4</td><td>GGaluGA271212</td><td>90,940,405</td><td>7.30 x 10<sup>-6</sup></td></tr><tr><td>CL</td><td>QTL4</td><td>4</td><td>GGaluGA271252</td><td>91,016,447</td><td>1.12 x 10<sup>-5</sup></td></tr><tr><td>CL</td><td>QTL4</td><td>4</td><td>Gga_rs13776465</td><td>91,056,759</td><td>6.05 x 10<sup>-6</sup></td></tr><tr><td>CL</td><td>QTL4</td><td>4</td><td>Gga_rs16454745</td><td>91,098,157</td><td><strong>1.02 x 10<sup>-8</sup></strong></td></tr><tr><td>DL</td><td>QTL4</td><td>4</td><td>Gga_rs16454745</td><td>91,098,157</td><td>5.15 x 10<sup>-6</sup></td></tr><tr><td>L*</td><td>QTL4</td><td>4</td><td>Gga_rs16454745</td><td>91,098,157</td><td>9.01 x 10<sup>-6</sup></td></tr><tr><td>b*</td><td>QTL4</td><td>4</td><td>Gga_rs16454745</td><td>91,098,157</td><td>1.59 x 10<sup>-5</sup></td></tr><tr><td>CL</td><td>QTL4</td><td>4</td><td>Gga_rs14506759</td><td>91,113,465</td><td><strong>2.27 x 10<sup>-7</sup></strong></td></tr><tr><td>DL</td><td>QTL4</td><td>4</td><td>Gga_rs14506759</td><td>91,113,465</td><td>2.32 x 10<sup>-6</sup></td></tr><tr><td>L*</td><td>QTL4</td><td>4</td><td>Gga_rs14506759</td><td>91,113,465</td><td>5.51 x 10<sup>-6</sup></td></tr><tr><td>b*</td><td>QTL4</td><td>4</td><td>Gga_rs14506759</td><td>91,113,465</td><td>1.17 x 10<sup>-5</sup></td></tr><tr><td>CL</td><td>QTL4</td><td>4</td><td>Gga_rs15648179</td><td>91,195,923</td><td><strong>1.76 x 10<sup>-9</sup></strong></td></tr><tr><td>DL</td><td>QTL4</td><td>4</td><td>Gga_rs15648179</td><td>91,195,923</td><td>1.70 x 10<sup>-6</sup></td></tr><tr><td>PMY</td><td>QTL5</td><td>5</td><td>Gga_rs14515278</td><td>11,832,057</td><td>1.99 x 10<sup>-5</sup></td></tr><tr><td>DL</td><td>QTL6</td><td>5</td><td>Gga_rs13794130</td><td>20,549,458</td><td>1.64 x 10<sup>-6</sup></td></tr><tr><td>DL</td><td>QTL6</td><td>5</td><td>GGaluGA278338</td><td>20,902,598</td><td>1.81 x 10<sup>-5</sup></td></tr><tr><td>CL</td><td>QTL6</td><td>5</td><td>Gga_rs16475257</td><td>20,970,166</td><td>7.14 x 10<sup>-6</sup></td></tr><tr><td>PmY</td><td>QTL7</td><td>5</td><td>GGaluGA289933</td><td>53,765,237</td><td>1.23 x 10<sup>-5</sup></td></tr><tr><td>SF</td><td>QTL8</td><td>5</td><td>Gga_rs14554568</td><td>56,426,990</td><td>2.26 x 10<sup>-5</sup></td></tr><tr><td>PMY</td><td>QTL9</td><td>8</td><td>GGaluGA333125</td><td>28,864,644</td><td>1.79 x 10<sup>-5</sup></td></tr><tr><td>a*</td><td>QTL10</td><td>12</td><td>Gga_rs14032735</td><td>3,153,630</td><td>3.59 x 10<sup>-5</sup></td></tr><tr><td>a*</td><td>QTL10</td><td>12</td><td>Gga_rs14033649</td><td>3,956,551</td><td>3.67 x 10<sup>-5</sup></td></tr><tr><td>b*</td><td>QTL11</td><td>14</td><td>GGaluGA101062</td><td>5,003,931</td><td>5.36 x 10<sup>-5</sup></td></tr><tr><td>WS</td><td>QTL12</td><td>17</td><td>Gga_rs15804842</td><td>2,376,482</td><td>5.07 x 10<sup>-5</sup></td></tr><tr><td>WS</td><td>QTL13</td><td>18</td><td>Gga_rs14108368</td><td>2,766,387</td><td>5.02 x 10<sup>-5</sup></td></tr><tr><td>PmY</td><td>QTL14</td><td>19</td><td>Gga_rs14115815</td><td>1,020,966</td><td>5.39 x 10<sup>-5</sup></td></tr><tr><td>PmY</td><td>QTL14</td><td>19</td><td>Gga_rs15044017</td><td>1,037,551</td><td>5.64 x 10<sup>-5</sup></td></tr><tr><td>DL</td><td>QTL15</td><td>24</td><td>Gga_rs16197733</td><td>4,335,811</td><td>1.58 x 10<sup>-5</sup></td></tr><tr><td>b*</td><td>QTL15</td><td>24</td><td>Gga_rs16197733</td><td>4,335,811</td><td>6.12 x 10<sup>-5</sup></td></tr><tr><td>a*</td><td>QTL16</td><td>25</td><td>Gga_rs15561568</td><td>1,745,235</td><td>1.14 x 10<sup>-4</sup></td></tr><tr><td>a*</td><td>QTL16</td><td>25</td><td>GGaluGA194453</td><td>1,793,966</td><td>6.68 x 10<sup>-5</sup></td></tr><tr><td>a*</td><td>QTL16</td><td>25</td><td>Gga_rs16741571</td><td>2,129,327</td><td>4.12 x 10<sup>-5</sup></td></tr><tr><td>CCY</td><td>QTL17</td><td>26</td><td>GGaluGA197797</td><td>4,554,077</td><td>5.24 x 10<sup>-5</sup></td></tr><tr><td>b*</td><td>QTL18</td><td>27</td><td>Gga_rs16205408</td><td>2,040,050</td><td>1.08 x 10<sup>-4</sup></td></tr></tbody></table>

<footnote><sup>a</sup>L* lightness, WS white striping, PMY *Pectoralis major* yield, BMY breast meat yield, CL cooking losses, DL drip loss, b* yellowness, PmY *Pectoralis minor* yield, SF shear force, a* redness, CCY curing-cooking yield</footnote>

<footnote><sup>b</sup> Name of the QTL region</footnote>

<footnote><sup>c</sup> Positions are indicated on galgal5 assembly</footnote>

<footnote><sup>d</sup> Genome-wide significant P-values are indicated in bold</footnote>
<table><caption>**Table 3** Significant SNPs for white striping in the pHu + line</caption><thead><tr><th>GGA</th><th>SNP ID</th><th>Position<sup>a</sup></th><th>P-value</th></tr></thead><tbody><tr><td>1</td><td>Gga_rs13899127</td><td>87,535,862</td><td>1.33 × 10<sup>-6</sup></td></tr><tr><td>1</td><td>Gga_rs13899410</td><td>87,752,766</td><td>2.20 × 10<sup>-6</sup></td></tr><tr><td>1</td><td>GGaluGA030724</td><td>87,760,003</td><td>2.20 × 10<sup>-6</sup></td></tr><tr><td>1</td><td>Gga_rs14856166</td><td>88,384,404</td><td>2.98 × 10<sup>-6</sup></td></tr><tr><td>1</td><td>Gga_rs13900230</td><td>88,416,292</td><td>2.98 × 10<sup>-6</sup></td></tr><tr><td>1</td><td>Gga_rs13900307</td><td>88,438,949</td><td>2.98 × 10<sup>-6</sup></td></tr><tr><td>17</td><td>Gga_rs15804842</td><td>2,376,482</td><td>1.11 × 10<sup>-5</sup></td></tr><tr><td>20</td><td>Gga_rs15177428</td><td>10,347,742</td><td>2.78 × 10<sup>-5</sup></td></tr></tbody></table>

<sup>a</sup> Positions are indicated on galgal5 assembly

significant SNPs for DL, lightness (L\*) and yellowness (b\*) of the meat. There were two common SNPs between DL, CL, L\* and b\* (Gga_rs1654745 at 91.09 Mb and Gga_rs14506759 at 91.11 Mb) and one more common SNP between CL and DL at 91.20 Mb (Gga_rs15648179). As illustrated in Fig. 2, the QTL4 region appeared to be a pleiotropic region controlling both the WHC (through DL and CL), and the meat color (through L\* and b\*). Although strong genetic correlations have already been reported between these parameters and the meat pHu in chickens [24, 25], no QTL for pHu was found nearby [23]. Similarly, we did not observe any co-localization with QTL of WS, which can also impair WHC and meat color [11].

Two additional pleiotropic regions were evidenced (QTL6, QTL15), one for DL and CL on GGA5 (20.55 to 20.97 Mb), and the other, for DL and b\* on GGA24 (4.33 Mb).

A significant positive genetic relationship between WS and intramuscular fat content (IMF) had previously been reported in this population [19], suggesting that IMF could be a useful quantitative predictor of WS. However, no significant SNPs were detected for IMF in the current study, maybe because this trait was controlled by many genes with too small effects. In the same way, no QTLs were found for the measurements of TBA-RS index, a marker of lipid peroxidation.

## Identification of candidate genes
### WS phenotype

Identification of genomic regions involved in the control of WS is an opportunity to improve our understanding of the molecular mechanisms underlying the appearance of this defect. For that, we looked for candidate genes within several regions of interest. They comprised the three significant WS-QTL regions (on GGA1, GGA17, and GGA18), the GGA4 region for which a pleiotropic effect with BMY was suggested, and two additional suggestive regions for which SNPs (Gga_rs16576992 at 4.51 Mb on GGA7 and GGaluGA071697 at 16.43 Mb

on GGA10) close to significance were detected ($p$ = 7.52 × 10⁻⁵ and 6.60 × 10⁻⁵ respectively).
Using an R software package (Gviz) [26], we visualized the gene environment around these different regions of interest (Additional file 2: Figure S2, Additional file 3: Figure S3, Additional file 4: Figure S4, Additional file 5: Figure S5 and Additional file 6: Figure S6). Potential candidate genes were selected based on their proximity with a significant SNP and/or their biological function (Table 4). Most of the selected genes are involved in muscular structure and processes related to muscle fiber regeneration and repair (i.e., MYH15, MYH1E, MYH1B, MYH1F, MYH13, MYOCD), adiposis and fibrosis (PDGFRα), extracellular matrix or sarcolemma composition (i.e., COL6A3, FN1, SGCB), muscle metabolism (PNPLA7), and human neuromuscular disorders (i.e., FN1, COL6A3, SGCB, LRSAM1).
The Myosin Heavy chain 15 gene (MYH15) located in the region of interest on GGA1 (Additional file 2: Figure S2) is involved in the contraction, development, and regeneration of avian skeletal muscles [27]. The most significant SNP associated with WS is located in the gene itself.
The Platelet Derived Growth Factor Receptor Alpha gene (PDGFRα), which is located upstream of the SNP of interest for WS and BMY on GGA4 (Additional file 3: Figure S3), is known to be a marker of fibro-adipogenic precursors (FAPs) recently characterized as playing a role in muscle regeneration and repair. Under pathological conditions like muscular dystrophy, FAPs differentiate into fibroblasts and adipocytes, leading to fat and connective tissue infiltration. In addition, FAPs promote myoblast differentiation in co-cultivation experiments [28]. The Sarcoglycan Beta gene (SGCB) is another gene of interest for the GGA4 region (Additional file 3: Figure S3) as it codes for the beta sarcoglycan protein, a member of the sarcoglycan complex located to the sarcolemma. This subcomplex of the dystrophin-associated glycoprotein complex (DAG complex) contributes to muscle tissue structure maintenance and stability and transfer of mechanical strength all along the sarcolemma during muscle contraction. Mutations in this gene were identified in human limb-girdle (LGMD1E) muscular dystrophy [29]. It has been shown that transgenic beta-sarcoglycan-deficient mice exhibited progressive muscular dystrophy with extensive degeneration and regeneration of muscle fibers [30]. These mice also exhibited muscular hypertrophy and whitish stripes within the muscles [30]. This makes SGCB an interesting positional and functional candidate gene for the QTL3 region, for which a pleiotropic effect on WS and BMY was suggested.
The Fibronectin 1 gene (FN1) is the closest gene to the WS QTL region on GGA7 (Additional file 4: Figure S4)
<figure><img src="image_24.png" /></figure>

and encodes a glycoprotein, which contributes to the
creation of fibers and extracellular matrix during tissue
repair after degeneration. In addition, an increase in fi-
bronectin levels is a biomarker of Duchenne muscular
dystrophy [31]. The *Collagen type VI Alpha 3-chain* gene
(COL6A3) is another interesting gene for this region
(Additional file 4: Figure S4). It produces collagen
molecules found in the extracellular matrix and surround-
ing cells that make up the muscles used for movement.
Mutations in this gene are associated with Bethlem myop-
athy and Ullrich congenital myopathy [32].

One candidate gene in the region of interest on
GGA17 (Additional file 5: Figure S5) is the *Leucine-rich
Repeat and Sterile Alpha Motif containing 1* gene
(LRSAM1). It encodes an ubiquitin-protein ligase with a
role in sorting internalized cell-surface receptor proteins.
Mutations in the *LRSAM1* gene have been shown to
cause an axonal form of Charcot-Marie-Tooth hereditary

neuropathy (CMT2P), characterized by progressive
muscle weakness and atrophy [33]. A second candidate
gene, *Patatin-like Phospholipase domain containing 7*
gene (*PNPLA7*), is an insulin-regulated lysophospholi-
pase expressed in muscle and fat. The regulation of its
expression by nutritional status and insulin suggests a
role in the catabolism of lipid precursors and/or
mediators that affect energy metabolism in mammals
[34]. Furthermore, another member of the patatin
family, *PNPLA2*, is involved in neutral lipid storage dis-
ease associated with myopathy that is characterized by
the accumulation of triglycerides in multiple tissues [35].
The last candidate gene of this region, *Tubulin Beta-4B*
chain (*TUBB4B*), codes for a protein (tubulin) which is
the major constituent of microtubules. Microtubules are
major constituents of the cytoskeleton and have major
roles in maintenance of the cell structure and intracellu-
lar transport.
<table><caption>**Table 4** Set of genes selected for eQTL detection</caption><thead><tr><th>Trait</th><th>Candidate gene</th><th>Name</th><th>Gene ID</th><th>GGA</th><th>Start<sup>a</sup></th><th>End<sup>a</sup></th></tr></thead><tbody><tr><td>WS</td><td>MYH15</td><td>myosin heavy chain 15</td><td>395,534</td><td>1</td><td>87,501,148</td><td>87,547,719</td></tr><tr><td>WS</td><td>PDGFRα</td><td>platelet derived growth factor receptor alpha</td><td>395,509</td><td>4</td><td>65,808,218</td><td>65,842,682</td></tr><tr><td>WS</td><td>SGCB</td><td>sarcoglycan β</td><td>422,760</td><td>4</td><td>66,587,612</td><td>66,593,633</td></tr><tr><td>WS</td><td>FN1</td><td>fibronectin 1</td><td>396,133</td><td>7</td><td>4,389,536</td><td>4,439,464</td></tr><tr><td>WS</td><td>COL6A3</td><td>collagen type VI alpha 3 chain</td><td>396,548</td><td>7</td><td>4,808,221</td><td>4,861,524</td></tr><tr><td>WS</td><td>LRSAM1</td><td>leucine-rich repeat and sterile alpha motif containing 1</td><td>417,265</td><td>17</td><td>2,026,150</td><td>2,051,581</td></tr><tr><td>WS</td><td>PNPLA7</td><td>patatin-like phospholipase domain containing 7</td><td>427,774</td><td>17</td><td>2,080,356</td><td>2,196,812</td></tr><tr><td>WS</td><td>TUBB4B</td><td>tubulin beta 4B class Mb</td><td>417,255</td><td>17</td><td>2,368,202</td><td>2,370,299</td></tr><tr><td>WS</td><td>MYH1E</td><td>myosin heavy chain 1E</td><td>427,788</td><td>18</td><td>588,354</td><td>611,030</td></tr><tr><td>WS</td><td>MYH1B</td><td>myosin heavy chain 1B</td><td>374,069</td><td>18</td><td>633,783</td><td>651,055</td></tr><tr><td>WS</td><td>MYH1F</td><td>myosin heavy chain 1F</td><td>768,566</td><td>18</td><td>431,376</td><td>449,075</td></tr><tr><td>WS</td><td>MYH13</td><td>myosin heavy chain 13</td><td>8735</td><td>18</td><td>346,675</td><td>384,341</td></tr><tr><td>WS</td><td>MYOCD</td><td>myocardin</td><td>427,790</td><td>18</td><td>830,396</td><td>930,411</td></tr><tr><td>CL, DL, L*,b*</td><td>DYSF</td><td>dysferlin</td><td>425,353</td><td>4</td><td>91,211,949</td><td>91,279,004</td></tr><tr><td>CL, DL, L*,b*</td><td>CAV3</td><td>caveolin 3</td><td>378,796</td><td>12</td><td>19,314,666</td><td>19,318,095</td></tr><tr><td>CL, DL, L*,b*</td><td>CAPN3</td><td>calpain 3</td><td>423,233</td><td>5</td><td>25,645,486</td><td>25,673,883</td></tr></tbody></table>

<sup>a</sup>Positions are indicated on galgal5 assembly

Finally, in addition to *MYH15* on GGA1, four fast myosin heavy chain isoforms (i.e. *MYH1E*, *MYH1B*, *MYH1F*, and *MYH13*) and myocardin (*MYOCD*) were selected. As shown in Additional file 6: Figure S6, these genes are grouped within a cluster located at the beginning of GGA18 (0.35 to 0.93 Mb), near the QTL region associated with WS (2.77 Mb). Out of all the groups of myosins, we chose to focus on them because they are fairly well annotated. Furthermore, a previous histological study on the same genetic lines highlighted a greater expression of embryonic (*MYH1B*) and neonatal (*MYH1F*) myosin heavy chain isoforms and a lower expression of adult myosin heavy chain (*MYH1E*) in the pHu + than in the pHu- muscle, which is consistent with a stronger muscle fiber regeneration process [36]. In addition, a transcriptomic study on the same model showed differential expression of *MYH13* and *MYOCD* genes between normal and WS fillets [37].

## Other meat quality phenotypes

Because of its high level of significance and pleiotropic effect on CL, DL, L* and b*, we looked for candidate genes within the QTL4 region (Table 2). Visualization of the gene environment showed that the *dysferlin* (*DYSF*) gene (91.21–91.27 Mb) is close to the QTL region (90.82 to 91.20 Mb) (Additional file 7: Figure S7). The *dysferlin* gene encodes a protein that is found in the sarcolemma. It is involved in sarcolemma repair and some results suggest that *dysferlin* may also be involved in the formation of new muscle fibers (regeneration) and inflammation [38]. Furthermore, *dysferlin* was identified as

a gene that is mutated in limb-girdle muscular dystrophy and some congenital myopathies. Some studies demonstrated that *dysferlin*-null mice developed progressive muscular dystrophy caused by failure of plasma membrane repair in muscle [38, 39]. Two genes encoding for skeletal muscle proteins (i.e. *caveolin 3* and *calpain 3*) were added to the set of genes of interest both for their interaction with the *dysferlin* gene [40, 41], and their involvement in human limb-girdle muscular dystrophies (LGMD1C and LGMD2A, respectively) [42, 43]. *Caveolin 3* (*CAV3*) may play a key role in the fusion of myoblasts into myotubes during the maturation process of muscle fibers. *Calpain 3* (*CAPN3*) is an intracellular protease specific to the muscle, involved in the calcium-dependent proteolytic system. They are known to catalyze the limited proteolysis of proteins (i.e., desmin and vimentin) involved in cytoskeletal modelling or signal transduction and to play a role in regeneration processes [44, 45].

Of the 16 candidate genes considered in the study, whether for WS or meat quality phenotypes, more than half are components of the extracellular matrix, sarcolemma, or muscles. Interestingly, several of them (i.e., *COL6A3*, *CAV3*, *CAPN3*, *DYSF*, *SGCB*) are associated with, or are members of the dystrophin complex. This complex contains dystrophin, which is at the origin of the genetic defect causing Duchenne muscular dystrophy (DMD) in humans, and establishes a direct link between the actin cytoskeleton and the extracellular matrix [46]. Thus, it participates in maintaining the extracellular matrix and sarcolemma. A perturbation within this
complex or these associated proteins, such as a loss of
the mechanical link between the cytoskeleton, extracel-
lular matrix, and sarcolemma, may result in muscle
fragility, contraction-induced damage, and necrosis [47].

eQTL detection

GWAS can be applied in order to map eQTLs that
control the transcript level of genes. Co-localization
between phenotypic QTLs and eQTLs is particularly
useful in order to identify candidate genes, which are
potentially causative [48]. In the current study, the
expression of all identified candidate genes was quan-
tified using RT-qPCR. After GWAS analysis, 132
SNPs were found to be significantly associated with
molecular phenotypes including 24 SNPs significant at
the genome threshold (Additional file 8: Table S1).
They defined 21 eQTL regions located on 16
chromosomes.

eQTLs are categorized as *cis* or *trans*, where *cis* eQTLs represent a polymorphism physically located near the gene itself, for example, a promoter polymorphism that gives rise to differential expression of the gene [48]. In the current study, we defined as *cis* eQTLs, SNPs that were within 1 Mb of the annotated start or stop site of the corresponding structural gene. eQTLs that did not fulfill this condition were considered to be *trans* eQTLs. These regions could, for example, contain a polymorphism in a transcription factor that correspondingly modulates the level of transcripts for target genes [48]. Of the 21 eQTL regions, three were *cis* eQTLs and 18 were *trans* eQTLs.

Cis eQTLs

The first *cis* eQTL relates to *LRSAM1* gene. Out of 31
significant SNPs associated with its expression, 28 were
located in a region close to the gene (eQTL15) and one
was in the gene itself (Gga_rs15034052 at 2.04 Mb).
Eleven out of these 29 SNPs were significant at the
genome threshold (Fig. 3, Additional file 8: Table S1).

The second *cis* eQTL relates to *MYH1F* gene. Of the 16 SNPs significantly associated with its expression, two were significant at the genome threshold on GGA18 (Additional file 9: Figure S8, Additional file 8: Table S1). The expression of *MYH1F* is associated with 14 SNPs, which are close to the gene (eQTL16) and located within a region containing the cluster of myosin heavy chain isoforms.

The last *cis* eQTL relates to *CAV3* gene. GWAS analysis identified 29 SNPs significantly associated with gene expression and which defined two eQTL regions, one *cis* eQTL on GGA12 (eQTL13) (Additional file 10: Figure S9) and one *trans* eQTL on GGA18 (eQTL17) (Additional file 8: Table S1). In contrast to the two other *cis* eQTLs, the *CAV3* cis eQTL was significant only at the chromosome level.

Trans eQTLs

Among all *trans* eQTLs (Additional file 8: Table S1), four regions seem to control several molecular phenotypes. The first one is on GGA2 (eQTL3), where a single SNP is associated with the expression of *PNPLA7* and *DYSF* genes (Gga_rs14139566 at 12.55 Mb). The second, on GGA6 (eQTL9), includes several SNPs for the *MYH1B*, *DYSF*

<figure><img src="image_25.png" /><figcaption>**Fig. 3** Manhattan plot showing the association of SNPs with *LRSAM1* expression. Black line represents the 5% genome-wide threshold and blue line the 5% GGA17-wide threshold</figcaption></figure>
and *PDGFRα* (Gga_rs14581613 at 2.03 Mb). The third region is located on GGA22 (eQTL18) where two single SNPs are identified (GGaluGA186934 at 2.90 Mb and GGaluGA186952 at 2.94 Mb), for *LRSAM1*, *PNPLA7* and *TUBB4B* gene expression. Finally, one last region appears to control several molecular phenotypes on GGA4 (eQTL6). This region is associated with the expression of *FN1*, *MYH13*, *COL6A3*, and *CAPN3*. Of the six SNPs significantly associated with these molecular phenotypes, half are associated with at least two gene expressions: one single SNP between *FN1* and *MYH13* (Gga_rs16454745 at 91.10 Mb), another between *FN1* and *COL6A3* (Gga_rs14506759 at 91.11 Mb), and finally a last between the four genes (Gga_rs15648179 and 91.20 Mb) (Fig. 4). This last region seems particularly interesting since it regulates the expression of three genes involved in human muscle diseases, as a biological marker (*FN1*) or genetic markers (*COL6A3* and *CAPN3*).

## Co-localization between QTLs and eQTLs

Interestingly, several co-localizations between QTL and eQTL regions were observed on GGA4, GGA5, and GGGA17, which could suggest causative genes and gene networks involved in the variability of meat quality traits or breast meat yield.

### White striping

The first co-localization concerns the region of GGA17 associated with both the expression of *LRSAM1* gene and WS defect (eQTL15 and QTL12) (Additional file 8: Table S1, Additional file 11: Table S2). The level of significance was much higher for the molecular phenotype than for the WS phenotype, which suggested a tighter regulation of the gene expression by this region. Although three genes of interest (*LRSAM1*, *PNPLA7*, *TUBB4B*) were tested for this region, only *LRSAM1* was significantly regulated and appeared to be a putative

<figure><img src="image_26.png" /></figure>
causative gene. In addition to being a positional and ex-
pressional candidate gene, *LRSAM1* is also a functional
candidate gene, since a mutation in this gene results in a
form of hereditary motor and sensory neuropathy where
slowly progressive distal muscle weakness and atrophy
were observed [33].

A *cis* eQTL for the neonatal myosin heavy chain form *MYH1F* (0.30 to 0.83 Mb) was also observed near the QTL associated with WS on the region of interest on GGA18 (2.77 Mb). Embryonic (*MYH1B*) and neonatal (*MYH1F*) forms of myosin heavy chain are found in breast muscle during its early development or in muscle fibers regeneration. A characterization of the muscle fibers was previously performed in the pHu + and pHu- lines. The muscle fibers of the pHu + birds were characterized by a higher number of cells expressing the developmental embryonic and neonatal isoforms of the fast myosin heavy chain [36], as well as a higher incidence of WS than was found in pHu- muscle fibers [19]. *MYH1F* could be an interesting molecular marker of WS in relation to the regeneration process. However, we cannot conclude that there is a single mutation affecting this molecular phenotype and WS, since the positions of the QTL and eQTL were rather distant.

Body composition and meat quality traits

A first co-localization was found on GGA5 between PMY QTL (QTL5) and SGCB-eQTL (eQTL7) (Additional file 8: Table S1, Additional file 11: Table S2). As previously reported, mutations within this gene result in a loss or a large decrease in the sarcoglycan complex [30]. Mice carrying the mutation are hypertrophic and larger than wild-type phenotypes. No significant SNP for WS was found in this region in the current study, which did not support any role of the region in the genetic relationship observed between PMY and WS in this population [19]. Nevertheless, our results indicated that SGCB could be an interesting molecular actor to consider in future studies on the control of muscle development and integrity.

Another co-localization was found on GGA4, between
the previously identified pleiotropic region (QTL4)
containing the SNPs strongly associated with CL, DL,
and color parameters (L* and b*), and the eQTL region
controlling FN1, COL6A3, MYH13, and CAPN3
expression (eQTL6). This region seems to regulate meat
quality phenotypes possibly related to muscle cell integ-
rity and expression of genes involved in human neuro-
muscular disorders, as well as in the structure and
composition of muscle fibers.

The QTL mapping revealed several regions of im-
portance for the control of meat quality phenotypes.
The eQTL analysis suggested some candidate genes
and molecular networks associated with WS and meat

quality traits. On the other hand, the eQTL analysis
for *MYH15*, *SGCB*, and *DYSF* did not evidence any
co-localizations with WS nor with meat quality traits.
However, we cannot exclude an effect of these genes.
They remain interesting candidate genes whose action
could be modulated by mutations in the coding re-
gion, leading to variations in protein stability or en-
zymatic activity, or by post-translation modification
[48]. This was the case for the *RN* gene (Rendement
Napole) in pigs where a loss of function mutation in
the *PRKAG3* gene is associated with an excess of
glycogen content in the skeletal muscles [49]; or for
the *MSTN* gene (myostatin), for which a loss of
function mutation results in muscular hypertrophy in cat-
tle and sheep [50]. Further analyses are needed to eluci-
date the role of these genes in the initiation of biological
processes involved in the appearance of WS defect and
more generally in poultry meat quality variations.

Conclusions

This study reported the first QTLs for WS in chicken. It
did not support the existence of a major gene, but was in
favor of a polygenic inheritance of the defect and of the
other studied meat quality traits. We identified several
candidate genes involved in muscle metabolism and struc-
ture, and also in neuromuscular disorders for some of
them. The eQTL analyses confirmed that they were part
of molecular networks associated with WS and meat qual-
ity phenotypes and suggested a few putative causative
genes. This study provides a first set of molecular and
genetic markers for WS that will have to be validated and
enriched by the study of additional genetic populations
and the integration of molecular and genetic information.

Methods

Birds and housing

Meat pHu reflects the level of muscle glycogen content and is a determining criterion of meat quality. In this study we used two broiler lines divergently selected for *Pectoralis major* pHu according to a breeding scheme described in Alnahhas et al. [25] and which exhibit WS defect.

WS, body composition, and meat quality traits were
evaluated in 558 birds (253 males and 305 females) from
the 6th generation of divergent selection (G6). Of this
total, 278 broilers (135 males and 143 females) were
from the pHu + line (selected for high breast pHu value)
and 280 broilers (118 males and 162 females) from the
pHu- line (selected for low breast pHu value). Birds were
reared as a single population (males and females from
both lines), following standard rearing practices and had
ad libitum access to feed and water. They were produced
in two successive batches and slaughtered at 6 weeks of
age at the PEAT experimental unit (INRA, Centre Val de
Loire, Nouzilly, France). After hanging the birds on the
processing chain, they were automatically stunned in a water bath using a constant electric current and slaughtered manually by cutting the carotid artery and the jugular vein at the ventral surface of the neck from inside the oral cavity. Samples of *P. major* were collected fifteen minutes after slaughter, snap-frozen in liquid nitrogen and stored at -80 °C until further analysis. Breasts were visually graded a day after slaughter by one trained person. The categories of WS were defined according to a modified version of the scale of Kuttappan et al. [14] to account for the lower degree of severity in our experimental population (0 = normal, absence of WS, 1 = moderate white striping, corresponding to striation thickness ≤ 1 mm, and 2 = severe occurrence of white striping, corresponding to striation thickness ≥ 1 mm). Body composition was characterized for all birds through the measurement of BMY, abdominal fat percentage (AFP), PMY, PmY and thigh yield (TY) expressed in relation to body weight (BW). Breast meat quality was evaluated on *Pectoralis major* muscle through the measurement of color parameters (L*, a*, b*), CL, DL, Warner-Bratzler shear force (SF) of cooked meat, curing-cooking yield (CCY), IMF, and TBA-RS index. All these measurements were realized as described in Alnahhas et al. [19].

## Genotyping

The 558 birds were genotyped at Labogena Laboratory (Jouy en Josas, France) using the Illumina chicken SNP 57 K Beadchip containing 57,636 SNPs. Quality control of the genotype data was performed using PLINK 1.9 and included animal call rate (> 95%), SNP call rate (> 95%), and minor allele frequency (> 5%). Finally, after filters, all animals were kept and 40,195 out of 57,636 SNPs distributed along 28 autosomal chromosomes were validated for further analyses.

## Gene expression

Gene expression was determined by RT-qPCR performed on one batch of animals from the 6th generation of divergent selection (145 pHu+, 136 pHu-), as described by Beaucercq et al. [36]. Total RNA was extracted from *Pectoralis major* muscle sampled for each individual, using RNA NOW (Ozyme, St Quentin en Yvelines, France). Ten µg of RNA from each sample were reverse-transcribed using RNase H⁻MMLV reverse transcriptase (Superscript II, Invitrogen, Illkirch, France) and random primers (Promega, Charbonnières les Bains, France). Primers targeting the studied genes were designed with Primer3 version 4.0.0 [51, 52]. The list of primer sequences is available in Additional file 11: Table S2. Their products of amplification were analyzed by electrophoresis and further sequenced.

The level of mRNA expression of candidate genes was quantified using a Fluidigm Biomark microfluidic device (Fluidigm, South San Francisco, CA, USA) according to the manufacturer's protocol (ADP37). Because *MYH15* and *PDGFRα* genes were added to the list of candidate genes later, their expression was quantified by RT-qPCR using a Roche LightCycler® 480 II (Roche Applied Science, Penzberg, Upper Bavaria, Germany) and the Takyon® (Eurogentec, Liege, Belgium), according to the manufacturer's recommendations. Quantitative PCR conditions were set at 95 °C for 5 min, followed by forty-five cycles of 10 s at 95 °C, 20 s at 60 °C and 10 s at 72 °C. Two references were used: *PDE3B* (phosphodiesterase 3B), which is invariant whatever the sex and degree of severity of WS within and between the lines, was used as the housekeeping gene to normalize the Ct values; and a mix of chicken *Pectoralis major* muscle cDNA. The calculation of absolute mRNA levels was based on the PCR efficiency and the threshold cycle (CT) deviation of an unknown cDNA versus the control cDNA according to the equation proposed by Pfaffl [53]. The normalized Ct values (ΔΔCt) were used as the molecular phenotype for the GWAS analyses.

## QTL and eQTL detections

Genome-wide association studies were performed on classical and molecular phenotypes using an univariate linear mixed model (LMM) implemented in GEMMA (Genome-wide Efficient Mixed Model Association) software [54]. The model used for analyses was:

$$y = W\alpha + x\beta + u + \epsilon,$$

where y is a n-vector of phenotypes for n individuals, W the n x c matrix of covariates which contains fixed effects of sex and batch (only sex effect for eQTL detection) and including a column of 1 s for the general mean, α the c-vector of the corresponding coefficients including the intercept, x the n-vector of marker genotypes, β a p-vector of marker effects, u the n vector of polygenic effects, and ϵ the n-vector of the residuals. u follows a multivariate normal distribution: u ~ MVN_n(0, λτ⁻¹K) where τ⁻¹ is the variance of the residual errors, λ is the ratio between the genetic variance (variance explained by the SNPs) and the residual variance, and K is a known n x n relationship matrix estimated from genotypes, using an n x p matrix of genotypes (X):

$$K = \frac{1}{p} \sum_{i=1}^{p} (x_i - 1_n \bar{x}_i)(x_i - 1_n \bar{x}_i)^T.$$

This expression was used to calculate K where xᵢ is the ith column representing genotypes of ith SNP, x̄ᵢ is the sample mean and 1_n is a n × 1 vector of 1's. ϵ follows
a multivariate normal distribution: $\epsilon \sim MVN_n(0, \tau^{-1}I_n)$ where $I_n$ is an n x n identity matrix.

To control the family-wise error rate (FWER) in the context of multiple hypothesis testing, a Bonferroni correction at 0.05 significant level was applied for both genome-wide and chromosome-wide thresholds (significant level = $\alpha$/number of SNPs). Any SNP with a $P$-value < 1.21 × 10⁻⁶ was considered to be significantly associated at the genome-wide threshold. The chromosome-wide significance thresholds ranged from 7.56 × 10⁻⁶ to 3.36 × 10⁻⁴, depending on the chromosome size.

## Additional files

**Additional file 1: Figure S1.** Manhattan plot showing the association of SNPs with WS in the pHU +(a) and the pHU- (b) line. Black line represents the 5% genome-wide threshold, red line the 5% GGA1-wide threshold, blue line the 5% GGA17-wide threshold and purple line the 5% GGA20-wide threshold. (DOCX 1204 kb)

**Additional file 2: Figure S2.** Gene environment of the region of interest for WS on GGA1. The first track corresponds to the chromosome 1 where the region of interest is indicated by a red line. The second track corresponds to the genomic axis for the region of interest. The third track corresponds to the gene model based on GalGal5 assembly. The last track corresponds to the Manhattan plot of the region of interest where the orange line represents the chromosome threshold and the most significant SNP for WS is highlighted in red. (PDF 8 kb)

**Additional file 3: Figure S3.** Gene environment of the region of interest for WS and BMY on GGA4. The first track corresponds to the chromosome 4 where the region of interest is indicated by a red box. The second track corresponds to the genomic axis for the region of interest. The third track corresponds to the gene model based on GalGal5 assembly. The two last tracks correspond to the Manhattan plot of BMY and WS respectively, on the region of interest, where the orange line represents the chromosome threshold and the most significant SNP for WS and BMY is highlighted in red. (PDF 10 kb)

**Additional file 4: Figure S4.** Gene environment of the region of interest for WS on GGA7. The first track corresponds to the chromosome 7 where the region of interest is indicated by a red box. The second track corresponds to the genomic axis for the region of interest. The third track corresponds to the gene model based on GalGal5 assembly. The last track corresponds to the Manhattan plot of the region of interest where the orange line represents the chromosome threshold and the most significant SNP is highlighted in red. (PDF 8 kb)

**Additional file 5: Figure S5.** Gene environment of the region of interest for WS on GGA17. The first track corresponds to the chromosome 17 where the region of interest is indicated by a red box. The second track corresponds to the genomic axis for the region of interest. The third track corresponds to the gene model based on GalGal5 assembly. The last track corresponds to the Manhattan plot of the region of interest where the orange line represents the chromosome threshold and the most significant SNP is highlighted in red. (PDF 9 kb)

**Additional file 6: Figure S6.** Gene environment of the region of interest for WS on GGA18. The first track corresponds to the chromosome 18 where the region of interest is indicated by a red box. The second track corresponds to the genomic axis for the region of interest. The third track corresponds to the gene model based on GalGal5 assembly. The last track corresponds to the Manhattan plot of the region of interest where the orange line represents the chromosome threshold and the most significant SNP is highlighted in red. (PDF 10 kb)

**Additional file 7: Figure S7.** Gene environment of the pleiotropic region on GGA4. The first track corresponds to the chromosome 4 where the region of interest is indicated by a red box. The second track

corresponds to the genomic axis for the region of interest. The third track corresponds to the gene model based on GalGal5 assembly. The last track corresponds to the Manhattan plot of CL, which has the most significant SNP in the pleiotropic region. Orange line represents the chromosome threshold and the most significant SNP is highlighted in red. (PDF 9 kb)

**Additional file 8: Table S1.** Significant SNPs for the level of expression of the 16 candidate genes. (DOCX 36 kb)

**Additional file 9: Figure S8.** Manhattan plot showing the association of SNPs with MYH1F expression. Black line represents the 5% genome-wide threshold and blue line the 5% GGA18-wide threshold. (DOCX 560 kb)

**Additional file 10: Figure S9.** Manhattan plot showing the association of SNPs with CAV3 expression. Black line represents the 5% genome-wide threshold and green line the 5% GGA12-wide threshold. (DOCX 585 kb)

**Additional file 11: Table S2.** Primer sequences of the 16 candidate genes and the housekeeping gene (PDE3B). (DOCX 14 kb)

## Acknowledgements

We thank LABOGENA laboratory for the genotyping analyses. We thank the staff of the poultry breeding facilities (PEAT, INRA, 37380, Nouzilly, France) and of the Avian Research Unit (URA, INRA, 37380, Nouzilly, France) for their technical assistance.

## Funding

The selection program was funded by INRA. Phenotyping and genotyping of the birds used for QTL detection was funded by the RFI CASDAR program no. 1309 OPTMANDE supported by the French Ministry of Agriculture. This funding body had no role in the design of the study and collection, analysis, and interpretation of data, or in writing the manuscript.

## Availability of data and materials

All the data that support the conclusions of the study are included in the paper and additional files.

## Authors' contributions

ELBD, CB, and YJ are at the origin of the conception of the program and defined the experimental design. ELBD and CB supervised the phenotyping. EP was in charge of QTL and eQTL detection analyses, under the supervision of ELBD, CHA, and SB. CHA and SB contributed to the choice of tools and methodologies used for GWAS analysis. SAB, EG, and EP designed and tested the oligonucleotide primers used for quantitative RT-qPCR analysis. EG and SAB performed RNA extraction. CP and CB supervised the identification of functional candidate genes within QTL regions. EP drafted the first version of the manuscript under the supervision of ELBD. All the authors read and approved the final manuscript.

## Ethics approval

All the procedures needed for the reproduction and phenotyping of the two lines (described under program 00880.02) were approved by the Ethics Committee for Animal Experimentation in Val de Loire (registered under N° 19 by the National Committee). No permissions were required for the collection of the two studied chicken lines owned by INRA.

## Consent for publication

Not applicable

## Competing interests

The authors declare that they have no competing interests.

## Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

## Author details

¹BOA, INRA, Université de Tours, 37380 Nouzilly, France. ²Hubbard SAS, 22800 Mauguérand, Le Foeil-Quintin, France. ³GenPhySE, Université de Toulouse, INRA, INPT, INP-ENVT, 31320 Castanet Tolosan, France.
Received: 1 December 2017 Accepted: 13 March 2018

Published online: 20 March 2018

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<figure><img src="image_27.png" /><figcaption>Additional file 1: Figure S1.</figcaption></figure>
<figure><img src="image_28.png" /><figcaption>Additional file 2: Figure S2.</figcaption></figure>
<figure><img src="image_29.png" /><figcaption>Additional file 3: Figure S3.</figcaption></figure>
<figure><img src="image_30.png" /><figcaption>Additional file 4: Figure S4.</figcaption></figure>
<figure><img src="image_31.png" /><figcaption>Additional file 5: Figure S5.</figcaption></figure>
<figure><img src="image_32.png" /><figcaption>Additional file 6: Figure S6.</figcaption></figure>
<figure><img src="image_33.png" /><figcaption>Additional file 7: Figure S7.</figcaption></figure>
<table><caption>Additional file 8: Table S1.</caption><thead><tr><th>Phenotype*</th><th>eQTL</th><th>GGA</th><th>SNP ID</th><th>Position** (pb)</th><th>P-value***</th><th>Corresponding QTL</th></tr></thead><tbody><tr><td>MYH15</td><td>eQTL1</td><td>1</td><td>Gga_rs15350490</td><td>93828600</td><td>2.95 x 10<sup>-6</sup></td><td></td></tr><tr><td>MYH15</td><td>eQTL1</td><td>1</td><td>GGaluGA033104</td><td>93885278</td><td>2.95 x 10<sup>-6</sup></td><td></td></tr><tr><td>DYSF</td><td>eQTL2</td><td>2</td><td>Gga_rs14138708</td><td>11017062</td><td>9.66 x 10<sup>-6</sup></td><td></td></tr><tr><td>PNPLA7</td><td>eQTL3</td><td>2</td><td>Gga_rs14139566</td><td>12547472</td><td>1.29 x 10<sup>-6</sup></td><td></td></tr><tr><td>DYSF</td><td>eQTL3</td><td>2</td><td>Gga_rs14139566</td><td>12547472</td><td>5.21 x 10<sup>-6</sup></td><td></td></tr><tr><td>SGCB</td><td>eQTL4</td><td>2</td><td>Gga_rs14142253</td><td>14889365</td><td><strong>5.52 x 10<sup>-7</sup></strong></td><td></td></tr><tr><td>SGCB</td><td>eQTL5</td><td>4</td><td>Gga_rs15633060</td><td>82783597</td><td>2.02 x 10<sup>-6</sup></td><td></td></tr><tr><td>SGCB</td><td>eQTL5</td><td>4</td><td>Gga_rs14498069</td><td>82805850</td><td>1.33 x 10<sup>-6</sup></td><td></td></tr><tr><td>FN1</td><td>eQTL6</td><td>4</td><td>GGaluGA271193</td><td>90877143</td><td>1.13 x 10<sup>-5</sup></td><td>QTL4</td></tr><tr><td>FN1</td><td>eQTL6</td><td>4</td><td>GGaluGA271252</td><td>91016447</td><td>1.38 x 10<sup>-5</sup></td><td>QTL4</td></tr><tr><td>FN1</td><td>eQTL6</td><td>4</td><td>Gga_rs13776465</td><td>91056759</td><td>6.82 x 10<sup>-6</sup></td><td>QTL4</td></tr><tr><td>FN1</td><td>eQTL6</td><td>4</td><td>Gga_rs16454745</td><td>91098157</td><td><strong>1.27 x 10<sup>-7</sup></strong></td><td>QTL4</td></tr><tr><td>MYH13</td><td>eQTL6</td><td>4</td><td>Gga_rs16454745</td><td>91098157</td><td>1.21 x 10<sup>-5</sup></td><td>QTL4</td></tr><tr><td>FN1</td><td>eQTL6</td><td>4</td><td>Gga_rs14506759</td><td>91113465</td><td><strong>4.33 x 10<sup>-7</sup></strong></td><td>QTL4</td></tr><tr><td>COL6A3</td><td>eQTL6</td><td>4</td><td>Gga_rs14506759</td><td>91113465</td><td>1.29 x 10<sup>-5</sup></td><td>QTL4</td></tr><tr><td>FN1</td><td>eQTL6</td><td>4</td><td>Gga_rs15648179</td><td>91195923</td><td><strong>1.21 x 10<sup>-6</sup></strong></td><td>QTL4</td></tr><tr><td>COL6A3</td><td>eQTL6</td><td>4</td><td>Gga_rs15648179</td><td>91195923</td><td>1.62 x 10<sup>-5</sup></td><td>QTL4</td></tr><tr><td>MYH13</td><td>eQTL6</td><td>4</td><td>Gga_rs15648179</td><td>91195923</td><td>1.04 x 10<sup>-5</sup></td><td>QTL4</td></tr><tr><td>CAPN3</td><td>eQTL6</td><td>4</td><td>Gga_rs15648179</td><td>91195923</td><td>2.85 x 10<sup>-6</sup></td><td>QTL4</td></tr><tr><td>SGCB</td><td>eQTL7</td><td>5</td><td>Gga_rs14514700</td><td>10813149</td><td>1.45 x 10<sup>-5</sup></td><td>QTL5</td></tr><tr><td>MYH15</td><td>eQTL8</td><td>6</td><td>Gga_rs14569880</td><td>7846186</td><td><strong>3.59 x 10<sup>-7</sup></strong></td><td></td></tr><tr><td>MYH1B</td><td>eQTL9</td><td>6</td><td>GGaluGA300550</td><td>18876300</td><td>2.24 x 10<sup>-5</sup></td><td></td></tr><tr><td>MYH1B</td><td>eQTL9</td><td>6</td><td>GGaluGA300856</td><td>19644770</td><td>2.16 x 10<sup>-5</sup></td><td></td></tr><tr><td>MYH1B</td><td>eQTL9</td><td>6</td><td>Gga_rs16004588</td><td>19885672</td><td><strong>1.99 x 10<sup>-7</sup></strong></td><td></td></tr><tr><td>MYH1B</td><td>eQTL9</td><td>6</td><td>GGaluGA300927</td><td>19916374</td><td>2.53 x 10<sup>-5</sup></td><td></td></tr><tr><td>MYH1B</td><td>eQTL9</td><td>6</td><td>Gga_rs14747990</td><td>19919971</td><td>1.20 x 10<sup>-5</sup></td><td></td></tr><tr><td>MYH1B</td><td>eQTL9</td><td>6</td><td>Gga_rs16551619</td><td>20173119</td><td>4.72 x 10<sup>-6</sup></td><td></td></tr><tr><td>MYH1B</td><td>eQTL9</td><td>6</td><td>Gga_rs14581509</td><td>20191350</td><td><strong>1.12 x 10<sup>-6</sup></strong></td><td></td></tr><tr><td>MYH1B</td><td>eQTL9</td><td>6</td><td>GGaluGA301022</td><td>20209447</td><td>3.47 x 10<sup>-6</sup></td><td></td></tr><tr><td>MYH1B</td><td>eQTL9</td><td>6</td><td>Gga_rs16551732</td><td>20300076</td><td>7.30 x 10<sup>-6</sup></td><td></td></tr><tr><td>MYH1B</td><td>eQTL9</td><td>6</td><td>Gga_rs14581613</td><td>20340618</td><td>5.41 x 10<sup>-6</sup></td><td></td></tr><tr><td>DYSF</td><td>eQTL9</td><td>6</td><td>Gga_rs14581613</td><td>20340618</td><td>2.26 x 10<sup>-5</sup></td><td></td></tr><tr><td>PDGFRα</td><td>eQTL9</td><td>6</td><td>Gga_rs14581613</td><td>20340618</td><td>2.58 x 10<sup>-5</sup></td><td></td></tr><tr><td>MYH1B</td><td>eQTL9</td><td>6</td><td>GGaluGA301058</td><td>20348510</td><td><strong>6.38 x 10<sup>-7</sup></strong></td><td></td></tr><tr><td>MYH1B</td><td>eQTL9</td><td>6</td><td>Gga_rs14581782</td><td>20466842</td><td>1.24 x 10<sup>-6</sup></td><td></td></tr><tr><td>MYH1B</td><td>eQTL9</td><td>6</td><td>Gga_rs15795080</td><td>20497904</td><td>1.24 x 10<sup>-6</sup></td><td></td></tr><tr><td>MYH1B</td><td>eQTL9</td><td>6</td><td>Gga_rs16552220</td><td>20899730</td><td>7.18 x 10<sup>-6</sup></td><td></td></tr><tr><td>MYH1B</td><td>eQTL9</td><td>6</td><td>Gga_rs14582418</td><td>21428502</td><td><strong>3.45 x 10<sup>-7</sup></strong></td><td></td></tr><tr><td>MYH1B</td><td>eQTL9</td><td>6</td><td>Gga_rs16552487</td><td>21451023</td><td><strong>1.78 x 10<sup>-7</sup></strong></td><td></td></tr><tr><td>MYH1B</td><td>eQTL9</td><td>6</td><td>Gga_rs14582456</td><td>21523608</td><td><strong>2.90 x 10<sup>-7</sup></strong></td><td></td></tr><tr><td>MYH1B</td><td>eQTL9</td><td>6</td><td>GGaluGA301386</td><td>21789185</td><td>5.41 x 10<sup>-6</sup></td><td></td></tr><tr><td>MYH1B</td><td>eQTL9</td><td>6</td><td>Gga_rs15796546</td><td>22181753</td><td>3.21 x 10<sup>-5</sup></td><td></td></tr><tr><td>MYH1F</td><td>eQTL10</td><td>8</td><td>GGaluGA327044</td><td>14981718</td><td>1.24 x 10<sup>-5</sup></td><td></td></tr><tr><td>MYH1F</td><td>eQTL10</td><td>8</td><td>GGaluGA327060</td><td>15017199</td><td>1.46 x 10<sup>-5</sup></td><td></td></tr><tr><td>FN1</td><td>eQTL11</td><td>9</td><td>Gga_rs14670735</td><td>3247110</td><td>2.86 x 10<sup>-5</sup></td><td></td></tr></tbody></table>
<table>
  <tr>
    <td>MYHCD</td>
    <td>eQTL12</td>
    <td>11</td>
    <td>Gga_rs15624232</td>
    <td>17522949</td>
    <td>1.72 x 10<sup>-5</sup></td>
    <td></td>
  </tr>
  <tr>
    <td>CAV3</td>
    <td>eQTL13</td>
    <td>12</td>
    <td>GGaluGA089543</td>
    <td>18654409</td>
    <td>2.41 x 10<sup>-5</sup></td>
    <td></td>
  </tr>
  <tr>
    <td>CAV3</td>
    <td>eQTL13</td>
    <td>12</td>
    <td>Gga_rs14986262</td>
    <td>18689560</td>
    <td>1.67 x 10<sup>-5</sup></td>
    <td></td>
  </tr>
  <tr>
    <td>CAV3</td>
    <td>eQTL13</td>
    <td>12</td>
    <td>Gga_rs14048799</td>
    <td>18767047</td>
    <td>6.13 x 10<sup>-6</sup></td>
    <td></td>
  </tr>
  <tr>
    <td>CAV3</td>
    <td>eQTL13</td>
    <td>12</td>
    <td>GGaluGA089616</td>
    <td>18780013</td>
    <td>6.13 x 10<sup>-6</sup></td>
    <td></td>
  </tr>
  <tr>
    <td>CAV3</td>
    <td>eQTL13</td>
    <td>12</td>
    <td>Gga_rs14048831</td>
    <td>18798000</td>
    <td>6.13 x 10<sup>-6</sup></td>
    <td></td>
  </tr>
  <tr>
    <td>CAV3</td>
    <td>eQTL13</td>
    <td>12</td>
    <td>Gga_rs14048897</td>
    <td>18828242</td>
    <td>6.13 x 10<sup>-6</sup></td>
    <td></td>
  </tr>
  <tr>
    <td>CAV3</td>
    <td>eQTL13</td>
    <td>12</td>
    <td>Gga_rs14048907</td>
    <td>18840435</td>
    <td>6.13 x 10<sup>-6</sup></td>
    <td></td>
  </tr>
  <tr>
    <td>CAV3</td>
    <td>eQTL13</td>
    <td>12</td>
    <td>Gga_rs15673122</td>
    <td>18850967</td>
    <td>6.13 x 10<sup>-6</sup></td>
    <td></td>
  </tr>
  <tr>
    <td>CAV3</td>
    <td>eQTL13</td>
    <td>12</td>
    <td>Gga_rs15673151</td>
    <td>18869930</td>
    <td>4.42 x 10<sup>-6</sup></td>
    <td></td>
  </tr>
  <tr>
    <td>CAV3</td>
    <td>eQTL13</td>
    <td>12</td>
    <td>Gga_rs14048959</td>
    <td>18883392</td>
    <td>4.42 x 10<sup>-6</sup></td>
    <td></td>
  </tr>
  <tr>
    <td>CAV3</td>
    <td>eQTL13</td>
    <td>12</td>
    <td>GGaluGA089676</td>
    <td>18904308</td>
    <td>4.42 x 10<sup>-6</sup></td>
    <td></td>
  </tr>
  <tr>
    <td>CAV3</td>
    <td>eQTL13</td>
    <td>12</td>
    <td>Gga_rs14048983</td>
    <td>18910817</td>
    <td>4.42 x 10<sup>-6</sup></td>
    <td></td>
  </tr>
  <tr>
    <td>CAV3</td>
    <td>eQTL13</td>
    <td>12</td>
    <td>Gga_rs13716246</td>
    <td>18942750</td>
    <td>4.42 x 10<sup>-6</sup></td>
    <td></td>
  </tr>
  <tr>
    <td>CAV3</td>
    <td>eQTL13</td>
    <td>12</td>
    <td>Gga_rs15673825</td>
    <td>19090345</td>
    <td>9.50 x 10<sup>-6</sup></td>
    <td></td>
  </tr>
  <tr>
    <td>CAV3</td>
    <td>eQTL13</td>
    <td>12</td>
    <td>Gga_rs13716306</td>
    <td>19099718</td>
    <td>9.50 x 10<sup>-6</sup></td>
    <td></td>
  </tr>
  <tr>
    <td>CAV3</td>
    <td>eQTL13</td>
    <td>12</td>
    <td>Gga_rs14049158</td>
    <td>19109704</td>
    <td>9.50 x 10<sup>-6</sup></td>
    <td></td>
  </tr>
  <tr>
    <td>CAV3</td>
    <td>eQTL13</td>
    <td>12</td>
    <td>Gga_rs13716307</td>
    <td>19119257</td>
    <td>9.50 x 10<sup>-6</sup></td>
    <td></td>
  </tr>
  <tr>
    <td>CAV3</td>
    <td>eQTL13</td>
    <td>12</td>
    <td>Gga_rs14049196</td>
    <td>19157040</td>
    <td>1.09 x 10<sup>-5</sup></td>
    <td></td>
  </tr>
  <tr>
    <td>CAV3</td>
    <td>eQTL13</td>
    <td>12</td>
    <td>Gga_rs14049226</td>
    <td>19188389</td>
    <td>1.09 x 10<sup>-5</sup></td>
    <td></td>
  </tr>
  <tr>
    <td>CAV3</td>
    <td>eQTL13</td>
    <td>12</td>
    <td>Gga_rs14049233</td>
    <td>19202371</td>
    <td>2.34 x 10<sup>-5</sup></td>
    <td></td>
  </tr>
  <tr>
    <td>CAV3</td>
    <td>eQTL13</td>
    <td>12</td>
    <td>GGaluGA089825</td>
    <td>19211057</td>
    <td>2.34 x 10<sup>-5</sup></td>
    <td></td>
  </tr>
  <tr>
    <td>CAV3</td>
    <td>eQTL13</td>
    <td>12</td>
    <td>Gga_rs15675016</td>
    <td>19468890</td>
    <td>1.19 x 10<sup>-5</sup></td>
    <td></td>
  </tr>
  <tr>
    <td>CAV3</td>
    <td>eQTL13</td>
    <td>12</td>
    <td>GGaluGA090066</td>
    <td>19512769</td>
    <td>1.19 x 10<sup>-5</sup></td>
    <td></td>
  </tr>
  <tr>
    <td>CAV3</td>
    <td>eQTL13</td>
    <td>12</td>
    <td>Gga_rs15675344</td>
    <td>19678293</td>
    <td>2.96 x 10<sup>-5</sup></td>
    <td></td>
  </tr>
  <tr>
    <td>CAV3</td>
    <td>eQTL13</td>
    <td>12</td>
    <td>Gga_rs15675477</td>
    <td>19824102</td>
    <td>2.96 x 10<sup>-5</sup></td>
    <td></td>
  </tr>
  <tr>
    <td>CAV3</td>
    <td>eQTL13</td>
    <td>12</td>
    <td>GGaluGA090146</td>
    <td>19873243</td>
    <td>2.96 x 10<sup>-5</sup></td>
    <td></td>
  </tr>
  <tr>
    <td>CAV3</td>
    <td>eQTL13</td>
    <td>12</td>
    <td>GGaluGA090149</td>
    <td>19885805</td>
    <td>2.96 x 10<sup>-5</sup></td>
    <td></td>
  </tr>
  <tr>
    <td>TUBB4B</td>
    <td>eQTL14</td>
    <td>13</td>
    <td>Gga_rs14055240</td>
    <td>3931630</td>
    <td>2.22 x 10<sup>-5</sup></td>
    <td></td>
  </tr>
  <tr>
    <td>TUBB4B</td>
    <td>eQTL14</td>
    <td>13</td>
    <td>Gga_rs14055227</td>
    <td>3946268</td>
    <td>4.02 x 10<sup>-5</sup></td>
    <td></td>
  </tr>
  <tr>
    <td>TUBB4B</td>
    <td>eQTL14</td>
    <td>13</td>
    <td>GGaluGA091901</td>
    <td>3952155</td>
    <td>4.02 x 10<sup>-5</sup></td>
    <td></td>
  </tr>
  <tr>
    <td>TUBB4B</td>
    <td>eQTL14</td>
    <td>13</td>
    <td>Gga_rs14055198</td>
    <td>3978869</td>
    <td>2.22 x 10<sup>-5</sup></td>
    <td></td>
  </tr>
  <tr>
    <td>LRSAM1</td>
    <td>eQTL15</td>
    <td>17</td>
    <td>Gga_rs15807988</td>
    <td>1193728</td>
    <td>1.08 x 10<sup>-5</sup></td>
    <td>QTL12</td>
  </tr>
  <tr>
    <td>LRSAM1</td>
    <td>eQTL15</td>
    <td>17</td>
    <td>GGaluGA112420</td>
    <td>1341951</td>
    <td>2.34 x 10<sup>-5</sup></td>
    <td>QTL12</td>
  </tr>
  <tr>
    <td>LRSAM1</td>
    <td>eQTL15</td>
    <td>17</td>
    <td>Gga_rs14104648</td>
    <td>1384574</td>
    <td><b>1.65 x 10<sup>-7</sup></b></td>
    <td>QTL12</td>
  </tr>
  <tr>
    <td>LRSAM1</td>
    <td>eQTL15</td>
    <td>17</td>
    <td>GGaluGA112483</td>
    <td>1437473</td>
    <td>6.41 x 10<sup>-6</sup></td>
    <td>QTL12</td>
  </tr>
  <tr>
    <td>LRSAM1</td>
    <td>eQTL15</td>
    <td>17</td>
    <td>GGaluGA112512</td>
    <td>1496390</td>
    <td>1.31 x 10<sup>-5</sup></td>
    <td>QTL12</td>
  </tr>
  <tr>
    <td>LRSAM1</td>
    <td>eQTL15</td>
    <td>17</td>
    <td>Gga_rs15034779</td>
    <td>1497553</td>
    <td>1.31 x 10<sup>-5</sup></td>
    <td>QTL12</td>
  </tr>
  <tr>
    <td>LRSAM1</td>
    <td>eQTL15</td>
    <td>17</td>
    <td>Gga_rs14104352</td>
    <td>1724666</td>
    <td><b>1.52 x 10<sup>-7</sup></b></td>
    <td>QTL12</td>
  </tr>
  <tr>
    <td>LRSAM1</td>
    <td>eQTL15</td>
    <td>17</td>
    <td>Gga_rs15806580</td>
    <td>1740184</td>
    <td><b>5.32 x 10<sup>-7</sup></b></td>
    <td>QTL12</td>
  </tr>
  <tr>
    <td>LRSAM1</td>
    <td>eQTL15</td>
    <td>17</td>
    <td>Gga_rs13734851</td>
    <td>1756449</td>
    <td><b>1.09 x 10<sup>-6</sup></b></td>
    <td>QTL12</td>
  </tr>
  <tr>
    <td>LRSAM1</td>
    <td>eQTL15</td>
    <td>17</td>
    <td>GGaluGA112716</td>
    <td>1901705</td>
    <td><b>9.65 x 10<sup>-10</sup></b></td>
    <td>QTL12</td>
  </tr>
  <tr>
    <td>LRSAM1</td>
    <td>eQTL15</td>
    <td>17</td>
    <td>GGaluGA112724</td>
    <td>1916155</td>
    <td><b>9.91 x 10<sup>-10</sup></b></td>
    <td>QTL12</td>
  </tr>
  <tr>
    <td>LRSAM1</td>
    <td>eQTL15</td>
    <td>17</td>
    <td>Gga_rs15034195</td>
    <td>1932600</td>
    <td><b>9.55 x 10<sup>-10</sup></b></td>
    <td>QTL12</td>
  </tr>
  <tr>
    <td>LRSAM1</td>
    <td>eQTL15</td>
    <td>17</td>
    <td>Gga_rs15034122</td>
    <td>1967526</td>
    <td><b>1.25 x 10<sup>-7</sup></b></td>
    <td>QTL12</td>
  </tr>
  <tr>
    <td>LRSAM1</td>
    <td>eQTL15</td>
    <td>17</td>
    <td>Gga_rs14104015</td>
    <td>1985536</td>
    <td>2.36 x 10<sup>-6</sup></td>
    <td>QTL12</td>
  </tr>
  <tr>
    <td>LRSAM1</td>
    <td>eQTL15</td>
    <td>17</td>
    <td>Gga_rs15034085</td>
    <td>2008784</td>
    <td><b>9.47 x 10<sup>-8</sup></b></td>
    <td>QTL12</td>
  </tr>
  <tr>
    <td>LRSAM1</td>
    <td>eQTL15</td>
    <td>17</td>
    <td>Gga_rs15034052</td>
    <td>2036405</td>
    <td><b>2.00 x 10<sup>-7</sup></b></td>
    <td>QTL12</td>
  </tr>
  <tr>
    <td>LRSAM1</td>
    <td>eQTL15</td>
    <td>17</td>
    <td>Gga_rs14103950</td>
    <td>2071102</td>
    <td>2.17 x 10<sup>-6</sup></td>
    <td>QTL12</td>
  </tr>
</table>
<table><tr><td>LRSAM1</td><td>eQTL15</td><td>17</td><td>GGaluGA112834</td><td>2105706</td><td>2.68 x 10<sup>-6</sup></td><td>QTL12</td></tr><tr><td>LRSAM1</td><td>eQTL15</td><td>17</td><td>Gga_rs10728503</td><td>2196528</td><td>4.98 x 10<sup>-5</sup></td><td>QTL12</td></tr><tr><td>LRSAM1</td><td>eQTL15</td><td>17</td><td>Gga_rs14103847</td><td>2241378</td><td>2.58 x 10<sup>-5</sup></td><td>QTL12</td></tr><tr><td>LRSAM1</td><td>eQTL15</td><td>17</td><td>Gga_rs14103837</td><td>2245302</td><td>1.42 x 10<sup>-5</sup></td><td>QTL12</td></tr><tr><td>LRSAM1</td><td>eQTL15</td><td>17</td><td>GGaluGA112954</td><td>2283348</td><td><b>2.15 x 10<sup>-7</sup></b></td><td>QTL12</td></tr><tr><td>LRSAM1</td><td>eQTL15</td><td>17</td><td>GGaluGA112991</td><td>2317679</td><td>2.88 x 10<sup>-5</sup></td><td>QTL12</td></tr><tr><td>LRSAM1</td><td>eQTL15</td><td>17</td><td>Gga_rs15804842</td><td>2376482</td><td>1.33 x 10<sup>-5</sup></td><td>QTL12</td></tr><tr><td>LRSAM1</td><td>eQTL15</td><td>17</td><td>GGaluGA113079</td><td>2433579</td><td>1.99 x 10<sup>-5</sup></td><td>QTL12</td></tr><tr><td>LRSAM1</td><td>eQTL15</td><td>17</td><td>GGaluGA113081</td><td>2442434</td><td>1.30 x 10<sup>-5</sup></td><td>QTL12</td></tr><tr><td>LRSAM1</td><td>eQTL15</td><td>17</td><td>Gga_rs16753087</td><td>2656173</td><td>8.78 x 10<sup>-6</sup></td><td>QTL12</td></tr><tr><td>LRSAM1</td><td>eQTL15</td><td>17</td><td>GGaluGA113309</td><td>2766427</td><td>4.14 x 10<sup>-5</sup></td><td>QTL12</td></tr><tr><td>LRSAM1</td><td>eQTL15</td><td>17</td><td>GGaluGA113338</td><td>2833719</td><td>5.41 x 10<sup>-5</sup></td><td>QTL12</td></tr><tr><td>MYH1F</td><td>eQTL16</td><td>18</td><td>Gga_rs15809155</td><td>297096</td><td>5.03 x 10<sup>-6</sup></td><td></td></tr><tr><td>MYH1F</td><td>eQTL16</td><td>18</td><td>Gga_rs14105451</td><td>306139</td><td>5.03 x 10<sup>-6</sup></td><td></td></tr><tr><td>MYH1F</td><td>eQTL16</td><td>18</td><td>Gga_rs14105472</td><td>343079</td><td>4.40 x 10<sup>-6</sup></td><td></td></tr><tr><td>MYH1F</td><td>eQTL16</td><td>18</td><td>GGaluGA117789</td><td>343205</td><td>5.03 x 10<sup>-6</sup></td><td></td></tr><tr><td>MYH1F</td><td>eQTL16</td><td>18</td><td>Gga_rs15809447</td><td>469140</td><td>5.03 x 10<sup>-6</sup></td><td></td></tr><tr><td>MYH1F</td><td>eQTL16</td><td>18</td><td>Gga_rs15809547</td><td>517541</td><td>5.03 x 10<sup>-6</sup></td><td></td></tr><tr><td>MYH1F</td><td>eQTL16</td><td>18</td><td>Gga_rs14105637</td><td>523233</td><td>5.03 x 10<sup>-6</sup></td><td></td></tr><tr><td>MYH1F</td><td>eQTL16</td><td>18</td><td>Gga_rs15035593</td><td>544688</td><td>5.03 x 10<sup>-6</sup></td><td></td></tr><tr><td>MYH1F</td><td>eQTL16</td><td>18</td><td>Gga_rs14105707</td><td>583202</td><td><b>7.08 x 10<sup>-7</sup></b></td><td></td></tr><tr><td>MYH1F</td><td>eQTL16</td><td>18</td><td>Gga_rs14105726</td><td>601151</td><td>4.40 x 10<sup>-6</sup></td><td></td></tr><tr><td>MYH1F</td><td>eQTL16</td><td>18</td><td>Gga_rs14105739</td><td>610899</td><td><b>1.17 x 10<sup>-6</sup></b></td><td></td></tr><tr><td>MYH1F</td><td>eQTL16</td><td>18</td><td>Gga_rs15809816</td><td>619881</td><td>4.53 x 10<sup>-6</sup></td><td></td></tr><tr><td>MYH1F</td><td>eQTL16</td><td>18</td><td>Gga_rs15810060</td><td>699832</td><td>5.03 x 10<sup>-6</sup></td><td></td></tr><tr><td>MYH1F</td><td>eQTL16</td><td>18</td><td>Gga_rs13506093</td><td>828426</td><td>7.91 x 10<sup>-6</sup></td><td></td></tr><tr><td>CAV3</td><td>eQTL17</td><td>18</td><td>Gga_rs15826142</td><td>6504359</td><td>2.12 x 10<sup>-5</sup></td><td></td></tr><tr><td>CAV3</td><td>eQTL17</td><td>18</td><td>Gga_rs14112328</td><td>6532115</td><td>6.24 x 10<sup>-5</sup></td><td></td></tr><tr><td>LRSAM1</td><td>eQTL18</td><td>22</td><td>GGaluGA186934</td><td>2899623</td><td>1.67 x 10<sup>-5</sup></td><td></td></tr><tr><td>PNPLA7</td><td>eQTL18</td><td>22</td><td>GGaluGA186934</td><td>2899623</td><td>2.78 x 10<sup>-5</sup></td><td></td></tr><tr><td>TUBB4B</td><td>eQTL18</td><td>22</td><td>GGaluGA186934</td><td>2899623</td><td>1.12 x 10<sup>-5</sup></td><td></td></tr><tr><td>LRSAM1</td><td>eQTL18</td><td>22</td><td>GGaluGA186952</td><td>2939179</td><td>2.36 x 10<sup>-5</sup></td><td></td></tr><tr><td>PNPLA7</td><td>eQTL18</td><td>22</td><td>GGaluGA186952</td><td>2939179</td><td>1.06 x 10<sup>-4</sup></td><td></td></tr><tr><td>TUBB4B</td><td>eQTL18</td><td>22</td><td>GGaluGA186952</td><td>2939179</td><td>1.48 x 10<sup>-6</sup></td><td></td></tr><tr><td>TUBB4B</td><td>eQTL18</td><td>22</td><td>GGaluGA186964</td><td>2968318</td><td>1.94 x 10<sup>-5</sup></td><td></td></tr><tr><td>MYH1E</td><td>eQTL19</td><td>23</td><td>Gga_rs14289870</td><td>3040351</td><td>1.24 x 10<sup>-5</sup></td><td></td></tr><tr><td>MYHCD</td><td>eQTL20</td><td>24</td><td>Gga_rs14293376</td><td>1575682</td><td>1.51 x 10<sup>-5</sup></td><td></td></tr><tr><td>MYH1B</td><td>eQTL21</td><td>27</td><td>Gga_rs16207951</td><td>3938987</td><td>8.56 x 10<sup>-5</sup></td><td></td></tr></table>

Co-localization with QTL associated with WS and meat quality traits is indicated in the last column.

* MYH15 myosin heavy chain 15, DYSF dysferlin, PNPLA7 patatin-like phospholipase domain containing 7, SGCB sarcoglycan beta, FN1 fibronectin 1, MYH13 myosin heavy chain 13, COL6A3 collagen type 6 alpha 3, CAPN3 calpain 3, MYH1B embryonic myosin heavy chain 3, PDGFRα platelet derived growth factor receptor alpha, MYH1F neonatal myosin
heavy chain, *MYHCD* myosin heavy chain CD, *CAV3* caveolin 3, *TUBB4B* tubulin beta 4B class IVb, *LRSAM1* leucine rich repeat and sterile alpha motif containing 1, *MYH1E* adult myosin heavy chain

** Positions are indicated on galgal5 assembly

*** Genome-wide significant *P*-values are indicated in bold
<figure><img src="image_34.png" /><figcaption>Additional file 9: Figure S8.</figcaption></figure>
<figure><img src="image_35.png" /><figcaption>Additional file 10: Figure S9.</figcaption></figure>
<table><caption>Additional file 11: Table S2.</caption><thead><tr><td>Gene</td><td>Gene ID</td><td>Forward primer</td><td>Reverse primer</td></tr></thead><tbody><tr><td>MYH15</td><td>395534</td><td>TTCAAGCAAACCCAGCCCTA</td><td>GATTTTTCAGCAACCGGGTG</td></tr><tr><td>PDGFRα</td><td>395509</td><td>GAGTCACAAAAGCCGTGGACAT</td><td>TGTTTTCTCACAGGACCGCTC</td></tr><tr><td>SGCB</td><td>422760</td><td>TGTAGAGCGCAGGAACGTCAAT</td><td>AATCACCGCCCAAGATAACGAG</td></tr><tr><td>COL6A3</td><td>396548</td><td>AGCCCAAAGTGACCTACACG</td><td>GCAGATGTCCATGACATTTTCAG</td></tr><tr><td>FN1</td><td>396133</td><td>CACAAACACCAACGTCAACT</td><td>GTTTGGATGGTAATCGTGGCAG</td></tr><tr><td>LRSAM1</td><td>417265</td><td>AGGTCCTCAATGTGAAAGGCA</td><td>TTGCAGAGGAACTGCTGAATG</td></tr><tr><td>PNPLA7</td><td>427774</td><td>TTTCACCATCAAGGCCAATCG</td><td>GTCCAGCCTCAACTTCCATCCA</td></tr><tr><td>TUBB4B</td><td>417255</td><td>CAGACCGGATTATGAACACCTT</td><td>CACCGTATGTTGGCGTAGTT</td></tr><tr><td>MYH13</td><td>768487</td><td>GGAATGACAACTCCTCACGCT</td><td>GCTAGCTGGAAAGTCACTCTGG</td></tr><tr><td>MYH1B</td><td>374069</td><td>AGGAGCTGTCCAATGTCAACCTC</td><td>GCAGAAGAAAGCAACAGAGGGTTC</td></tr><tr><td>MYH1E</td><td>427788</td><td>CCAAATTCCGCAAGATCCAAC</td><td>CTTATGCCACTTTGTTGTCACGAC</td></tr><tr><td>MYH1F</td><td>768566</td><td>AGCTGTCCAATGTCAACCTTTCC</td><td>TGCCTCAGGTCACACTTTAGC</td></tr><tr><td>MYOCD</td><td>427790</td><td>GTCTGAGCACTCCTTGCTGATT</td><td>CGTTGTTCATGGAATGCGG</td></tr><tr><td>DYSF</td><td>425353</td><td>GGTCGGGATGAGCCAAACAT</td><td>GTTCGGGAAGGCGTAGATGA</td></tr><tr><td>CAPN3</td><td>423233</td><td>CAAACCAGTGCTCATTCCCT</td><td>GCCTGACCCACACTGATTTT</td></tr><tr><td>CAV3</td><td>378796</td><td>GAAAGGCAGCTACACCACCT</td><td>GTAGATGCGGCTGACACACT</td></tr><tr><td>PDE3B</td><td>423064</td><td>CACCATCTCAGCGAAAATCACA</td><td>TCTCCTAGCTGCCGCTCATCTT</td></tr></tbody></table>
# Chapitre 2 : Nouveaux indicateurs et marqueurs moléculaires des défauts WS et WB

Afin de comprendre la mise en place des dysfonctionnements cellulaires qui conduisent aux défauts WS et WB et d'identifier des biomarqueurs de ces myopathies, des analyses histopathologiques, protéomiques et transcriptomiques ont été réalisées à partir d'échantillons de muscles P. major issus de la lignée à forte croissance et à fort développement musculaire décrite précédemment (Cf. Deuxième partie, II.2). Les analyses histologiques et protéomiques ont été réalisées en amont de l'analyse transcriptomique, ce qui a permis de raffiner le choix des échantillons considérés dans cette étape.

## I. Caractérisation histologique des défauts WS et WB

Des sections transversales de muscle P. major de dix micromètres ont été réalisées pour l'ensemble des 4 groupes (8 échantillons par groupe) : T indemne de défaut (WS=0 et WB=0), WS uniquement atteint de WS sévère (WS=2 et WB=0), WB uniquement atteint de WB modéré ou sévère (WS=0 et WB= 1 ou 2) et WSWB atteint de WS et WB sévères (WS=2 et WB=2). Une coloration au Trichrome de Gomori a été utilisée pour évaluer les modifications structurales au sein des différents muscles (Figure 18) et a également permis de valider la qualité des tissus après décongélation et coupe.

Les observations histologiques des muscles atteints de WS et/ou de WB sont en accord avec ce qui a déjà été décrit dans la littérature (Kuttappan et al., 2013c ; Sihvo et al., 2013 ; Ferreira et al., 2014 ; Mazzoni et al., 2015 ; Petracci et al., 2015). Elles montrent des fibres en nécrose et de tailles variables, parfois hypercontractées, ainsi que des signes de régénération (splitting et régénération segmentaire) et d'inflammation (infiltration de cellules inflammatoires, foyer inflammatoire comme sur la **Figure 18 B**). Les tissus conjonctifs endomysial et périmysial sont très étendus dans les muscles sévèrement atteints par les deux défauts à la fois ou uniquement par le défaut WB (**Figure 18 C et D**). Ils sont légèrement plus développés que la normale dans les muscles WS et parfois dans les muscles macroscopiquement indemnes de défaut. Il y a également beaucoup de tissu adipeux dans le périmysium des muscles WSWB et les muscles WS (**Figure 18 B**), peu dans le périmysium des muscles WB et peu voire pas du tout dans les muscles T.
<figure><img src="image_36.png" /><figcaption>**Figure 18** : Coloration Trichrome de Gomori de muscles P. major visuellement indemnes de défaut (A) et atteints de WS sévère (B). WB sévère (C) ou des deux défauts à la fois (D). Les flèches indiquent des fibres hypercontractées, les astérisques noirs des fibres en nécrose, l'astérisque blanc un foyer inflammatoire et les têtes de flèches des vacuoles présentes au sein des fibres. Échelle : 100 µm</figcaption></figure>

De manière intéressante, on observe une présence importante de vacuoles au sein des fibres des muscles WB et WSWB (**Figure 18 C et D**) ainsi que des fibres « fantôme », phénomènes qui restent plutôt rares voire absents dans les autres groupes de muscles. Ces phénomènes n'ont jamais été décrits dans les muscles présentant les défauts WS et/ou WB. Ces vacuoles pourraient être le signe de processus d'autophagie pathologiques qui conduiraient, à terme, à vider entièrement le contenu de la cellule et donc à l'apparition de fibres « fantôme ». De plus, comme l'ont également montré certaines études (Kuttappan et al., 2013c ; Russo et al., 2015 ; Trocino et al., 2015 ; Sihvo et al., 2016 ; Papah et al., 2017), les muscles macroscopiquement indemnes de défaut présentent, dans notre étude, des lésions de type nécrose, une extension des tissus endomysium et périmysium, quelques infiltrations de tissu adipeux et de cellules inflammatoires, quelques fibres hypercontractées et des phénomènes de régénération. Ceci indique qu'une partie des muscles macroscopiquement indemnes de défauts ne peuvent pas être considérés comme des muscles normaux, et souligne l'intérêt d'une caractérisation histologique fine des défauts en complément de la notation macroscopique des filets.
## II. Analyse du profil protéomique en lien avec l'apparition des défauts WS et WB

Le profil protéomique des 39 muscles P. major issus des 4 groupes du second dispositif (i.e., groupes T, WS, WB et WSWB) a été analysé en considérant la fraction soluble des protéines musculaires (i.e., protéines sarcoplasmiques), c'est-à-dire principalement les enzymes impliquées dans le métabolisme énergétique. L'extraction et le dosage des protéines sarcoplasmiques à partir des échantillons de muscles broyés, ainsi que les gels 2D, l'analyse d'images des gels pour détecter les spots différentiels (un spot = une protéine) et l'annotation des spots (MALDI TOV, spectrométrie de masse) ont été réalisés par l'équipe QUAPA de l'INRA de Theix. Ces travaux étaient menés dans le cadre du projet TECNOVIA (2015-2018), financé par les régions Bretagne et Pays de la Loire dont l'objectif était de caractériser les défauts technologiques des viandes blanches (porc et poulet) et de rechercher des voies d'amélioration par la génétique et l'alimentation. Ils faisaient partie intégrante d'un des premiers volets qui consistait à acquérir des connaissances sur les bases moléculaires et physiologiques impliquées dans l'apparition des défauts d'intégrité musculaires chez le poulet.

Un total de 81 protéines a été annoté. Une analyse de variance a été réalisée pour comparer les groupes T, WS, WB et WSWB pour chaque protéine identifiée. Les protéines différentiellement exprimées (DE) entre les groupes ont été déterminées par comparaison deux à deux. Les protéines ont été considérées DE si la p-value était ≤ 0,05. Seulement 6 protéines différentielles ont été identifiées entre les muscles normaux (T) et les muscles atteints de WS sévère (WS). On observe plus de protéines différentielles quand on compare les muscles atteints de WB modéré ou sévère (WB) et des muscles normaux (i.e., 14), même si le nombre de protéines concernées reste relativement peu élevé. Ce nombre augmente significativement lorsque l'on compare les muscles sains et ceux atteints des deux défauts (WSWB). Ainsi, 39 protéines différentielles ont été identifiées entre ces deux types de muscles (Tableau 10), avec 19 et 20 protéines qui étaient respectivement surabondantes et sous-abondantes dans les muscles WSWB. Les protéines surabondantes dans ces muscles étaient majoritairement impliquées dans des processus métaboliques liés à la réponse au stimulus (réponse aux espèces réactives de l'oxygène ROS, réponse inflammatoire et réponse au stress oxydatif) alors que celles sous abondantes étaient principalement impliquées dans des processus glycolytiques et la gluconéogenèse. Ces résultats, en accord avec l'analyse protéomique de Kuttappan et al. (2017), pourraient indiquer une diminution de l'activité glycolytique dans les muscles sévèrement atteints par les deux défauts, ainsi que la présence d'un stress oxydatif important.
Le faible nombre de protéines DE entre les muscles normaux et les muscles atteints de WS, pourrait en partie être dû au fait que l'analyse protéomique n'a porté que sur la fraction soluble et donc les protéines sarcoplasmiques du muscle. Or les gènes candidats identifiés lors de l'analyse génétique du défaut WS (Cf. Chapitre 1) suggèrent un impact plus important du développement de ce type de défaut sur la structure du muscle, et donc les protéines myofibrillaires et de la matrice extracellulaire (MEC), que sur le métabolisme musculaire. Il aurait certainement fallu exploiter également la fraction des protéines myofibrillaires et insolubles de la MEC, pour pouvoir comparer les profils protéiques complets des quatre groupes de muscles.

<table><caption>Tableau 10 : Protéines différentielles entre les muscles visuellement indemnes de défauts et les muscles sévèrement atteints par du WS et du WB</caption><thead><tr><td>UniProt ID</td><td>Protéine</td><td>Gène (GeneID)</td><td>Sur régulation</td><td>p-value</td><td>Fonction</td></tr></thead><tbody><tr><td>Q5ZLN1</td><td>Phosphoglycerate mutase 1</td><td>PGAM1 (428969)</td><td>T</td><td>0,0002</td><td>Métabolisme des glucides (glycolyse, gluconéogenèse)</td></tr><tr><td>E1BXC2</td><td>S-formylglutathione hydrolase</td><td>ESD (418856)</td><td>T</td><td>0,0006</td><td>Processus catabolique du formaldéhyde</td></tr><tr><td>G1K303</td><td>Hydroxyacylglutathione hydrolase</td><td>HAGH (416537)</td><td>T</td><td>0,0006</td><td>Processus catabolique du méthylglyoxal en D-lactate par le S-lactoyl-glutathion</td></tr><tr><td>Q5ZHN1</td><td>Charged multivesicular body protein 2a</td><td>CHMP2A (776036)</td><td>T</td><td>0,0019</td><td></td></tr><tr><td>Q5ZHV4</td><td>Phosphoglycerate mutase</td><td>BPGM (418172)</td><td>T</td><td>0,002</td><td>Métabolisme des glucides (glycolyse, gluconéogenèse)</td></tr><tr><td>E1BT93</td><td>Uncharacterized protein</td><td></td><td>T</td><td>0,0026</td><td></td></tr><tr><td>P02604</td><td>Myosin light chain 1, skeletal muscle isoform</td><td>MYL1 (396470)</td><td>T</td><td>0,0027</td><td>Contraction muscle strié</td></tr><tr><td>P07322</td><td>Bete-enolase</td><td>ENO3 (396016)</td><td>T</td><td>0,0028</td><td>Métabolisme des glucides (glycolyse, gluconéogenèse)</td></tr><tr><td>P00548</td><td>Pyruvate kinase</td><td>PKM (396456)</td><td>T</td><td>0,003</td><td>Métabolisme des glucides (glycolyse, gluconéogenèse)</td></tr><tr><td>A0A1D5PA73</td><td>Glycerol-3-phosphate dehydrogenase [NAD(+)]</td><td>ASIC1 (426883)</td><td>T</td><td>0,0031</td><td>Processus métabolique des glucides; processus catabolique du glycérol-3-phosphate</td></tr><tr><td>F1NN63</td><td>Uncharacterized protein</td><td>PGM1 (424691)</td><td>T</td><td>0,0047</td><td>Processus métabolique du glucose, processus de biosynthèse du glycogène</td></tr><tr><td>P84172</td><td>Elongation factor Tu, mitochondrial</td><td>TUFM (430889)</td><td>T</td><td>0,0056</td><td>Elongation</td></tr><tr><td>Q5ZKV4</td><td>Cytosolic Fe-S cluster assembly factor NUBP2</td><td>NUBP2 (416402)</td><td>T</td><td>0,0096</td><td></td></tr><tr><td>E1C155</td><td>Aldehyde dehydrogenase 6 family member A1</td><td>ALDH6A1 (423345)</td><td>T</td><td>0,0101</td><td>Catabolisme des acides aminés à chaîne ramifiée</td></tr><tr><td>P00940</td><td>Triosephosphate isomerase</td><td>TPI1 (396435)</td><td>T</td><td>0,0156</td><td>Métabolisme des glucides (glycolyse, gluconéogenèse)</td></tr><tr><td>A0A1D5PPU3</td><td>Uncharacterized protein</td><td>LACTB2 (420178)</td><td>T</td><td>0,0168</td><td></td></tr><tr><td>F1NAX3</td><td>Glycerol-3-phosphate phosphatase</td><td>PGP (416559)</td><td>T</td><td>0,017</td><td>Processus de biosynthèse du glycérol; processus métabolique des glycérophospholipides;</td></tr></tbody></table>
<table><tr><td>P00565</td><td>Creatine kinase M-type</td><td>CKM (396507)</td><td>T</td><td>0,0172</td><td>Réponse au stress</td></tr><tr><td>Q7SX63</td><td>Heat shock protein 70</td><td>HSP70 (423504)</td><td>T</td><td>0,0257</td><td>Réponse cellulaire au stress</td></tr><tr><td>P08250</td><td>Apolipoprotein A-I</td><td>APOA1 (396536)</td><td>WSWB</td><td>0,0001</td><td>Processus métabolique du cholestérol; Processus métabolique des triglycérides</td></tr><tr><td>F1P593</td><td>Heat shock protein beta-1</td><td>HSPB1 (396227)</td><td>WSWB</td><td>0,0002</td><td>Réponse au stress; transduction de signal intracellulaire (voies de signalisation VEGF, NF-kappaB et MAPK)</td></tr><tr><td>A0A1D5NW68</td><td>Serum albumin</td><td>ALB (396197)</td><td>WSWB</td><td>0,0004</td><td>Transport</td></tr><tr><td>A0A1D5PPL5</td><td>Prefoldin subunit 3</td><td>VBP1 (422355)</td><td>WSWB</td><td>0,0006</td><td>Repliement des protéines</td></tr><tr><td>P41263</td><td>Retinol-binding protein 4</td><td>RBP4 (396166)</td><td>WSWB</td><td>0,0022</td><td>Gluconéogenèse, homéostasie du glucose, processus métabolique du rétinol</td></tr><tr><td>D5M8S2</td><td>DJ-1</td><td>DJ-1 (395277)</td><td>WSWB</td><td>0,0028</td><td>Réponse cellulaire au peroxyde d'hydrogène; détoxification de l'ion mercure; processus métabolique du peroxyde d'hydrogène</td></tr><tr><td>A0A1D5P0D2</td><td>Uncharacterized protein</td><td></td><td>WSWB</td><td>0,003</td><td></td></tr><tr><td>A0A1I7Q422</td><td>Transthyretin</td><td>TTR (396277)</td><td>WSWB</td><td>0,0032</td><td>Processus métabolique du rétinol; transport des hormones thyroïdiennes</td></tr><tr><td>R4GM10</td><td>Fructose-bisphosphate aldolase</td><td>ALDOC (395492)</td><td>WSWB</td><td>0,0044</td><td>Glycolyse; processus métabolique fructose1-6 biphosphate aldolase; gluconéogenèse</td></tr><tr><td>F1P1L8</td><td>Uncharacterized protein</td><td>SERPINB6 (420895)</td><td>WSWB</td><td>0,0054</td><td></td></tr><tr><td>A0A1D5NYF3</td><td>Uncharacterized protein</td><td>FABP4 (374165)</td><td>WSWB</td><td>0,006</td><td>Régulation de la réponse inflammatoire, catabolisme des triglycérides</td></tr><tr><td>Q8JG64</td><td>Protein disulfide-isomerase A3</td><td>PDIA3 (373899)</td><td>WSWB</td><td>0,0091</td><td>Homéostasie redox cellulaire, régulation positive de la voie de signalisation apoptotique extrinsèque, repliement des protéines, réponse au stress du réticulum endoplasmique</td></tr><tr><td>P31395</td><td>Stathmin</td><td>STMN1 (396057)</td><td>WSWB</td><td>0,0099</td><td>Régulation de l'organisation du cytosquelette; régulation de la polymérisation ou de la dépolymérisation des microtubules</td></tr><tr><td>A0A1D5PDK4</td><td>Adenosine deaminase</td><td></td><td>WSWB</td><td>0,01</td><td></td></tr><tr><td>Q90593</td><td>78 kDa glucose-regulated protein</td><td>HSPA5 (396487)</td><td>WSWB</td><td>0,0173</td><td>Réponse cellulaire à la privation de glucose; réponse cellulaire à l'interleukine-4; régulation négative du processus apoptotique; protéolyse impliquée dans le processus catabolique des protéines cellulaires</td></tr><tr><td>A0A1D5P8K2</td><td>Uncharacterized protein</td><td>HSPB2 (414136)</td><td>WSWB</td><td>0,0187</td><td></td></tr><tr><td>F1NVF3</td><td>Uncharacterized protein</td><td></td><td>WSWB</td><td>0,0224</td><td></td></tr><tr><td>E1BZ05</td><td>Desmin</td><td>DES (395906)</td><td>WSWB</td><td>0,0247</td><td>Organisation du filament intermédiaire</td></tr><tr><td>P02789</td><td>Ovotransferrin</td><td>TF (396241)</td><td>WSWB</td><td>0,0377</td><td>Réponse en phase aiguë; séquestration extracellulaire d'ion fer</td></tr></table>
### III. Choix du dispositif expérimental pour l'analyse transcriptomique

L'analyse des profils en protéines sarcoplasmiques des muscles a révélé que les groupes T et WSWB présentaient des différences substantielles par comparaison aux autres groupes atteints seulement par un seul des défauts (WS ou WB). Ceci nous a conduit à ne conserver que ces deux groupes de muscles lors de l'analyse transcriptomique. Par ailleurs, l'analyse histologique de ces muscles a montré qu'une proportion significative des muscles macroscopiquement indemnes de défauts (issus du groupe T) présentait plusieurs types de lésions musculaires. Pour cette raison, il nous a semblé important de considérer dans l'analyse un « vrai » contrôle négatif, c'est-à-dire un groupe de muscles à la fois macroscopiquement et histologiquement indemnes des lésions associées aux défauts WS ou WB afin de mieux caractériser les processus biologiques impliqués dans la mise en place de ces physiopathologies. Nous avons donc inclus dans le dispositif expérimental dédié à l'analyse transcriptomique des animaux issus d'une souche à croissante lente maintenue à l'INRA (la lignée de chair X33). Ses performances de croissance sont proches de celles des animaux utilisés en production Label Rouge ou Biologique qui sont abattus à un âge deux fois plus élevé que le poulet standard. A 42 jours, le poids vif de ces animaux était trois fois moins élevé que celui des animaux issus de la souche commerciale (1,0 vs 3,3 kg) et le rendement en muscle P. major était 25% moins élevé (13,5 vs 17,9 %). D'un point de vue histologique, les muscles P. major de ces animaux ne présentaient aucune lésion musculaire (Figure 19).

<figure><img src="image_37.png" /><figcaption>**Figure 19** : Coloration Trichrome de Gomori de muscle P. major issu de la souche à croissance plus lente X33.</figcaption></figure>

Échelle : 100 µm
Inclure une lignée à croissance lente dans laquelle aucun défaut n’a été observé permettait également de pouvoir dissocier les changements biologiques induits par la sélection sur le long terme sur la croissance et le développement musculaire des dérégulations biologiques associées à l’établissement des défauts WS et WB.
IV. Les analyses de l'expression différentielle et de réseaux de co-expression de gènes révèlent des mécanismes moléculaires et des biomarqueurs candidats impliqués dans les myopathies du filet chez le poulet - Article 2

Afin de progresser dans la compréhension des modifications structurales et des acteurs
moléculaires impliqués dans les pathologies musculaires WS et WB et d'identifier des
biomarqueurs pouvant servir d'outils de diagnostic des myopathies, nous avons réalisé une
analyse transcriptomique sur puce poulet Agilent 8 × 60K. Cette étude a été associée à une
caractérisation histologique fine des 24 muscles P. major inclus dans le dispositif, à savoir : 8
muscles issus de poulets à croissance lente X33 et 16 issus de poulets commerciaux à croissance
rapide dont les filets sont visuellement indemnes de défauts (T, n=8) ou sévèrement atteints par
les deux types de défauts (WSWB, n=8).

L'analyse histologique de ces muscles a montré une modification de l'architecture musculaire
induite par la sélection sur la croissance et le développement musculaire, impliquant la mise en
place de phénomènes de fibrose et d'adipose, même chez les muscles visuellement indemnes
de défauts mais qui étaient exacerbés dans les muscles sévèrement atteints, ainsi que la
diminution de la vascularisation et une plus forte variation de la taille des fibres. Elle a
également permis d'identifier des phénotypes histologiques fins utiles pour quantifier avec
précision le degré des lésions musculaires et d'améliorer de manière significative le diagnostic
qui est actuellement basé sur la notation visuelle ou la palpation du muscle après l'abattage.

L'analyse différentielle a mis en évidence un total de 10 482 gènes DE entre les trois groupes
(p-value ajustée <0,05) avec un fold-change ≥ 1,2. L'analyse fonctionnelle par annotation «
Gene Ontology » a montré que la sélection en faveur de la croissance rapide et du rendement
en filet a progressivement conduit à la mise en place d'un environnement métabolique et
structurel du muscle favorable au développement des myopathies. En particulier, cet
environnement a pu conduire à des modifications des voies métaboliques, passant d'un
métabolisme glycolytique vers des voies cataboliques alternatives pour produire de l'énergie,
qui ont entraîné une réponse adaptative au stress oxydatif ainsi que des premiers signes de
processus inflammatoires, de régénération et de fibrose. Tous ces processus sont intensifiés
dans les muscles atteints de myopathies sévères, dans lesquels de nouveaux mécanismes liés
aux défenses cellulaires et au remodelage sont également activés.
L'étude des corrélations entre les niveaux d'expression des gènes et les phénotypes
histologiques quantitatifs à l'aide de la construction de réseaux de gènes (WGCNA) a constitué
une approche complémentaire de l'analyse différentielle. Ce portrait moléculaire et
histologique permet d'émettre des hypothèses quant aux mécanismes sous-jacents aux défauts
WS et WB et apporte des résultats originaux avec l'identification de biomarqueurs dont
l'expression est fortement corrélée avec les principales lésions histologiques associées aux
défauts. En effet, l'analyse intégrée de ces données (génétiques, moléculaires et histologiques)
a permis l'identification d'un set de biomarqueurs des myopathies dont l'intérêt devra être
validé dans une plus grande population et sur d'autres fonds génétiques.
Differential expression and co-expression gene network analyses reveal
molecular mechanisms and candidate biomarkers involved in breast muscle
myopathies in chicken

Eva Pampouille<fnref n="1" />,<fnref n="2" />, Christelle Hennequet-Antier<fnref n="1" />, Christophe Praud<fnref n="1" />, Amélie Juanchich<fnref n="1" />,
Aurélien Brionne<fnref n="1" />, Estelle Godet<fnref n="1" />, Thierry Bordeau<fnref n="1" />, Fréderic Fagnoul<fnref n="2" />, Elisabeth Le Bihan-
Duval<fnref n="1" />, Cécile Berri<fnref n="1" />*

<footnote n="1">¹BOA, INRA, Université de Tours, 37380 Nouzilly, France</footnote>

<footnote n="2">²Hubbard SAS, Mauguérand, 22800 Le Foeil - Quintin, France</footnote>

<footnote>*Corresponding author: cecile.berri@inra.fr</footnote>
The broiler industry is facing an increasing prevalence of breast myopathies, such as white striping (WS) and wooden breast (WB), and the precise aetiology of these occurrences remains poorly understood. To progress our understanding of the structural changes and molecular pathways involved in these myopathies, a transcriptomic analysis was performed using an 8x60K Agilent chicken microarray and histological study. The study used *pectoralis major* muscles from three groups: slow-growing animals (n=8), fast-growing animals visually free from defects (n=8), or severely affected by both WS and WB (n=8). In addition, a weighted correlation network analysis was performed to investigate the relationship between modules of co-expressed genes and histological traits. Functional analysis showed that selection for fast growing and breast meat yield has progressively led to conditions favouring metabolic shifts towards alternative catabolic pathways to produce energy, leading to an adaptive response to oxidative stress and the first signs of inflammatory, regeneration and fibrosis processes. All these processes are intensified in muscles affected by severe myopathies, in which new mechanisms related to cellular defences and remodelling are also activated. However, our study opens new perspectives for myopathy diagnosis by highlighting fine histological phenotypes and genes whose expression was strongly correlated with defects.

Introduction

The poultry industry relies on the production of fast-growing chickens, which are slaughtered at high weights and intended for cutting and processing. It is currently facing severe myopathic defects, such as white striping (WS), which is characterized by the appearance of lipid and connective inclusions that develop parallel to the muscle fibres¹⁻³, or wooden breast (WB), which is characterized by a heterogeneity of colour, excessive surface exudate and a loss of muscle elasticity⁴,⁵. These two defects significantly impair breast fillet appearance and therefore consumer purchases⁶. Compared to normal fillets, these defects also affect meat
nutritional value by increasing fat and connective tissue content and lowering muscle protein
content<sup>7-11</sup> and water holding capacity (WHC), resulting in higher drip (DL) and cooking (CL)
loss, lower curing cooking yield (CCY) and marinade uptake<sup>3,5,11,12</sup>. Breast fillets that are
severely affected by WS or WB are difficult to market fresh and are more often valued as raw
materials for processing or downgraded for pet food in the most severe cases<sup>13</sup>. Therefore,
these defects lead to significant economic losses for the poultry industry. Moreover, an
increasing incidence of muscular defects is likely to degrade the image of chicken meat by
consumers, as attested by the increasing number of articles dedicated to the subject in the
specialized or general press.

Although myodegenerative defects are now widely described in the literature<sup>1-4,14</sup>, their precise aetiology remains unclear. What is certain is that the frequency and severity of these defects increase with the growth rate of the animals, their slaughter weight and their breast meat yield (BMY)<sup>3,7,15</sup>. In addition, their increasing occurrence corresponds to changes in current production practices, particularly the use of increasingly efficient genotypes and the slaughter of heavy broilers, whose meat is intended for processing. The genetic basis of WS and WB has been recently demonstrated<sup>16,17</sup>, but the genes involved in the control of these defects are still unknown despite the recent identification of QTL, which controls WS<sup>18</sup>. In one study several chromosomal regions of interest in WS were suggested, and the results indicated a polygenic determinism for the trait and include a list of putative candidate genes that primarily affect muscle metabolism, fibre structure and human neuromuscular disorders<sup>18</sup>. Therefore, improving understanding of the molecular mechanisms that underlie muscle susceptibility to WS and WB remains an important objective from the perspective of more efficient selection and improved breeding conditions.

Recent transcriptomic studies have described changes in muscle gene expression related to the
occurrence and severity of myodegenerative defects measured in fast-growing broilers<sup>19-23</sup>. In
the current study, we compared the gene expression profile of the *pectoralis major* muscle between birds obtained from a slow-growing chicken line (SG) and birds obtained from a modern fast-growing line; all birds were visually scored as either severely affected by both WS and WB defects (FG-WSWB) or free from defects (FG-C). We further included a slow-growing line in which no defect was observed with the aim of identifying the biological changes induced by long-term selection on body weight and muscle development that has resulted in the establishment of the myopathies. This study is also focused on the search for fine and histological traits that can be used to quantify muscular defects with the aim of correlating them with gene expression and identifying biomarkers of myopathic muscles.

## Results

### Animals and descriptive statistics of meat quality phenotypes

The transcriptomic analysis was performed on 3 × 8 individuals obtained from the SG, FG-C and FG-WSWB groups. FG individuals were obtained from a grand-parental population of 176 42-day-old broilers visually scored for WS and WB. Individuals were chosen to be representative of either the control (i.e., without any apparent defects) or the severely affected (i.e., affected with both the WS and WB defects) categories. SG birds were obtained from a slow-growing INRA experimental line that was also slaughtered at 42 days of age. Only body weight (BW) and *pectoralis major* yield (PMY) were measured in this line. Student's t-test revealed that BW at 42 days of age was more than three times higher (3324 vs. 999 g; p-value ≤ 0.0001) and PMY was 25% higher (17.9 vs. 13.5%; p-value ≤ 0.0001) in fast-growing broilers than in slow-growing birds.

The average phenotype values of the FG-C and FG-WSWB groups are described in Additional file 1. FG-C and FG-WSWB chickens showed similar BW and abdominal fatness (AFP). BMY did not differ between FG-WSWB and FG-C chickens (p-value = 0.07), while
the difference between the groups was significant when considering only PMY (p-value ≤ 0.05). FG-WSWB breast muscles exhibited much greater DL and CL (p-value ≤ 0.001 and p-value ≤ 0.01, respectively) and tended to have lower CCY and higher lightness (L*) (p-value = 0.07) than were observed in FG-C chickens. They did not differ in other meat quality traits, including lipid peroxidation index (evaluated through TBA-RS) after storage and shear force (SF) value after cooking.

## Fine and quantitative histological traits

To quantify muscular defects, quantitative histological traits were measured in *pectoralis major* muscle cross sections obtained from the three groups (SG, FG-C and FG-WSWB) (Table 1). A quantification of fibrosis and adiposis was performed using a collagen VI-bodipy co-labelling technique (Figure 1A-C). The percentage of the area labelled with collagen VI (representative of fibrosis) on the microscopic field was 4.2 and 6.9 times higher in FG-C and FG-WSWB muscles, respectively, than in SG *pectoralis major* muscles (p-values ≤ 0.0001).

Thus, compared to SG muscles, both FG-C and FG-WSWB muscles showed extended endomysial and perimysial connective tissues, but the rate was 1.6 lower in the FG muscles macroscopically unaffected by WS and WB than in affected muscles. It is interesting to note that the percentage of the area labelled with collagen VI in one sample of the FG-WSWB group and one sample of the FG-C group was very different than the average value obtained in their respective groups, i.e., it was lower for the FG-WSWB sample (8.1%) and higher for the FG-C sample (13.6%). This suggests that these samples were either misclassified macroscopically or phenotypically intermediate between the FG-C and the FG-WSWB classes. However, the percentage of the area occupied by bodipy 493 staining (representative of adiposis) was approximately three times larger in FG-WSWB than in FG-C *pectoralis major* muscles (p-value ≤ 0.01) and was almost non-existent in SG *pectoralis major* muscles.
These results indicate that muscle histological architecture is modified according to the selection on growth rate and that fibrosis and adiposis establishment are exacerbated in cases of severe defects.

Capillary density and fibre size were measured using laminin-fibronectin co-immunolabelling (Figure 1D-F). The number of capillaries in the microscopic field was 1.5 and 2 times lower in severely affected muscles (FG-WSWB) than in macroscopically unaffected muscles (FG-C) (p-value ≤ 0.05) and normal muscles (SG) (p-value ≤ 0.0001), respectively. In addition, the structure and location of the capillaries appeared to be modified in FG-WSWB muscles as we noticed the presence of lumens in their capillaries, likely because of vasodilatation, and of connective tissue extensions that altered the connection between capillaries and muscle fibres (Figure 1D-F).

As expected, the number of fibres in a microscopic field was significantly lower in FG than in SG muscles (p-values ≤ 0.0001) due to the difference in fibre size (measured as the minimal Feret's diameter), which was significantly lower in SG than in FG muscles (p-values ≤ 0.0001) (Table 1). The number of fibres per capillary was approximately 2 to 3 for FG muscles and approximately 6 for SG muscles. However, if we consider the fibre surface supplied by one capillary, it was almost two times larger in FG (20169 µm² for FG-C and 22150 µm² for FG-WSWB) than in SG muscles (12961 µm²) (p-values ≤ 0.05) due to the difference in fibre size and conjunctive tissue extension. Interestingly, neither the number of fibres per capillary nor the averaged fibre size was different between FG-C and FG-WSWB muscles. However, there was significantly more fibre size variation in FG-WSWB muscles than in SG and FG-C muscles (p-values ≤ 0.01), probably due to the strong regeneration process that was observed in parallel with an increase in regenerative fibre foci frequency.

## Differential gene expression analysis
To gain a better understanding of the molecular pathways underlying *pectoralis major* muscle susceptibility to WS and WB defects, a custom chicken 8 × 60K Gene Expression Agilent Microarray was used to perform the transcriptomic analysis of muscles obtained from the SG, FG-C and FG-WSWB groups. Among the 61,657 probes spotted on the array, 43,688 (71%) were expressed, and 36,473 (59%) were annotated (corresponding to 12,272 genes) based on the NCBI EntrezGene database and Galgal5 chicken genome assembly. Based on quantitative histological characterization and a multidimensional scaling plot of gene expression, three samples (i.e., the two samples previously identified in the histological analysis as misclassified into groups according to the macroscopic examination of defects and another sample from the FG-C group) were considered outliers and were not kept in the differential analysis. The analysis of 21 samples was performed using a linear model and pairwise comparisons and revealed that 10,482 of the annotated genes were differentially expressed (DE) genes with a fold-change |FC| ≥ 1.2 in at least one of the three comparisons (adjusted p-value < 0.05). Among these, 6,725 genes were DE between SG and FG-C, 7,887 were DE between FG-C and FG-WSWB, and 9,608 were DE between SG and FG-WSWB. The log2-transformed FC of the DE genes ranged from -3.48 to 6.64 for FG-C vs. SG, from -6.81 to 9.19 for FG-WSWB vs. SG and from -3.87 to 4.22 for FG-WSWB vs. FG-C, indicating the presence of substantial gene expression differences between these groups.

The top 10 DE genes in the three comparisons based on adjusted p-value and log2 FC are presented in Table 2 and Additional file 2, respectively. These genes were mainly involved in the extracellular matrix, collagen fibrils and cytoskeleton organization, muscle contraction, the inflammation response, calcium homeostasis, oxidoreductase activity and angiogenesis.

A hierarchical clustering was applied to the list of 10,482 DE genes with an |FC| ≥ 1.2 to group them into clusters according to their expression profile. A modular height cut-off value in the hierarchical tree allowed the identification of six clusters of DE genes that showed
similar profiles of expression within clusters and a clear discrimination among the three
groups (Figure 2A). The number of genes and the expression profile of each cluster is
presented in Figure 2B. Clusters c1 and c4, which represented 12% of the DE genes,
discriminated SG from FG muscles (regardless of the presence of defects). In all, 79% of DE
genes were found in clusters c2 and c5, which were associated with both growth rate
variations and the severity of defects. The expression of the 3,117 DE genes in cluster c5
gradually increase, while that of the 5,168 DE genes belonging to cluster c2 decreased from
SG to FG-C and FG-C to FG-WSWB. Among all DE genes, only 9% appeared to be linked to
the presence of severe WS and WB defects and were either expressed at higher levels (cluster
c6) or lower levels (cluster c3) in the FG-WSWB group than in the FG-C and SG groups.

Functional analysis of DE genes

All expressed genes on the array were annotated by Gene Ontology for Biological Process
(BP) based on NCBI EntrezGene chicken IDs (taxonomic id: 9031) and orthologues. Among
the 12,272 expressed genes on the array, 6,232 were annotated with at least one BP GO term
(51%). The enrichment analysis was performed independently for each DE genes cluster with
all expressed genes used as the background (Additional file 3). Two hundred and sixty-seven
GO terms were enriched in at least one cluster (p-value < 0.01), and 264 were unique,
indicating that clusters of genes were very specific and different from each other (Table 3).
The number of enriched GO terms in each cluster varied between 8 (cluster c3) and 128
 cluster c5).

Co-expression network analysis and module-trait relationships

The histological study identified fine phenotypes to accurately quantify the degree of muscle
damage and significantly improve diagnosis, which is currently based on visual notation or
muscle palpation after slaughter. Thus, studying the correlations between gene expression
levels and histological quantitative phenotypes using gene network construction served as a
complementary approach to the differential analysis. Moreover, this approach made it
possible to make no a priori conclusions about the groups since it was based on continuous
phenotypic variables and thus kept all the samples (n=24) and took into account the variability
in cell disorders highlighted at the histological level in the correlation analysis. From the
normalized gene expression matrix, a weighted correlation network analysis (WGCNA) was
performed to investigate the relationship between modules of co-expressed genes and
histological traits and identify candidate biomarkers of the defects. Only the histological
phenotypes allowed us to discriminate muscles severely affected by WS and WB from the
two other groups based on, i.e., collagen VI and adipose tissue contents, the number of
capillaries and fibre size variation (Table 1) were kept for this analysis. Fifteen co-expression
modules containing 50 to 6516 genes were constructed. In Additional file 4, correlations
between eigengenes and quantitative phenotypes show that several modules are related to one
or more histological traits. We chose to focus on modules that had a mean gene significance
(i.e., mean of correlations between gene expression and phenotype, GS) greater than 0.5
(Additional file 5) and absolute value of the module-histological trait correlation higher than
0.7 (Additional file 4). Hence, the light green (55 genes), royal blue (140 genes), brown (6516
genes), black (420 genes), and blue (3393 genes) modules were selected for further analysis.

The light green module was strongly positively related to collagen VI content (0.89) and to
variation in fibre size (0.85) but negatively related to the number of capillaries (-0.71). Brown
and royal blue modules were negatively related to collagen VI content (-0.87 and -0.76,
respectively) but strongly positively related to the number of capillaries (0.73 and 0.79,
respectively). The blue module was negatively related to collagen VI content (-0.79) and
variation in fibre size (-0.74), while the black module was positively correlated with variation
in fibre size (0.84). Interestingly, no module was highly correlated with adipose tissue
content.

Genes for which expression was both highly correlated with a histological phenotype (GS ≥ 0.7) and significant modules eigengene (Module Membership, MM ≥ 0.7) were considered hub genes. Hence, 4,894 hub genes were associated with collagen VI labelling, 1,456 with the number of capillaries and 915 with variation in fibre size.

Identification of candidate biomarkers for severe myopathies

Genes that were DE between FG-C and FG-WSWB muscles (adjusted p-value < 0.05) and hub genes identified in the weighted correlation network analysis were combined in a Venn diagram to identify the most confident biomarkers related to muscle disorders involved in severe WS and WB pathologies (Figure 3).

Among the 6,418 DE genes between FG-C and FG-WSWB muscles, more than one-third (36%) were not found to be strongly correlated with any of the three histological phenotypes studied. The vast majority of hub genes were DE between FG-WSWB and FG-C muscles. Only 17, 4, and 8% of hub genes that were related to collagen, number of capillaries, and variation of the fibre size, respectively, were not DE between FG-WSWB and FG-C muscles. Among the 7,679 genes included in the Venn diagram, 17 were common among all the lists, 1,611 (21%) between 3 lists, and 2,731 (36%) between two lists. Except for the 17 genes that were common to all the lists, no gene was common between the lists of genes correlated with the number of capillaries and variation in fibre size or between the lists of genes correlated with each of the three histological phenotypes. A total of 548 genes were common between the list of genes related to collagen VI and variation in fibre size, of which 527 were DE between FG-WSWB and FG-C muscles. Finally, 1,336 genes were highly correlated with
both collagen VI and the number of capillaries, of which 1,084 were DE between FG-WSWB and FG-C muscles.

## Validation using RT-qPCR for a subset of genes

Among the genes found to be DE among the groups, a subset of 12 genes was chosen for validation by RT-qPCR. Genes were selected according to several criteria, including their functional role, their belonging to clusters, and, for some of them, their location within QTL regions recently identified for WS defect in broiler chickens¹⁸. The expression level of DE genes obtained by RT-qPCR was compared with expression obtained by microarrays. For each tested gene, the DE between groups observed on the microarray was confirmed with the exception of one gene, *LRSAM1*, the expression of which was only slightly correlated between RT-qPCR and microarray results (0.34). Except for this gene, expression measured by RT-qPCR and microarray was highly correlated, as shown by Pearson's correlation coefficients between 0.71 and 0.99 (Figure 4). Moreover, the gene expression values between groups obtained by qPCR were consistent with the categorization of these genes to different clusters.

Among the identified genes, the expression of the myosin heavy chain 15 (*MYH15*), which is involved in the contraction, development, and regeneration of avian skeletal muscles²⁴, was dramatically higher (x771) in FG-WSWB than in SG muscles, while the difference between FG-C and SG muscles was much less dramatic (x60). As for *MYH15*, fibronectin 1 (*FN1*) expression was the highest in FG-WSWB and the lowest in SG, but the difference between the FG groups was much lower than that for *MYH15*. Platelet-derived growth factor receptor alpha (*PDGFRA*), collagen type VI alpha 3-chain (*COL6A3*) and leucine-rich repeat and sterile alpha motif-containing 1 (*LRSAM1*) were expressed at significantly highest levels in FG-WSWB than in FG-C and SG muscles. Conversely, the expression levels of adult
(MYH1E) and neonatal (MYH1F) myosin heavy chain isoforms were lower in the FG-WSWB group than in the SG and FG-C groups. Other genes varied between the slow-growing and fast-growing groups, regardless of the presence of WS and WB defects. This was the case for caveolin 3 (CAV3) and embryonic myosin heavy chain isoform (MYH1B), whose expression was higher in FG muscles, and for myocardin (MYOCD), calpain 3 (CAPN3) and protein phosphatase-1 regulatory subunit 3A (PPP1R3A), whose expression was lower in FG muscles than in SG muscles.

## Discussion

In recent years, the broiler industry has faced the emergence of serious meat quality defects, such as WS or WB, which affect both the competitiveness and the image of poultry production. Although research has intensified on this subject in recent years, the aetiology of WS and WB defects is still not understood except that it is now well-established that WS and WB conditions relate to an abnormal extent of connective and adipose tissue, which is associated with muscle fibre necrosis and regeneration processes²,⁴. One hypothesis is that these processes are the result of a long-term effect of genetic selection and intensification of production methods that have led to huge progress in terms of broiler growth and breast muscle development, which have also likely reached physiological limits. A comparison of muscles obtained from slow-growing chickens with muscles obtained from fast-growing broilers either affected or not affected by WS and WB defects allowed us to specify structural and metabolic changes that result from the long-term selection of growth in among those involved in the onset of severe myopathies (Additional file 3).

## Long-term effects of selection on muscle structure and metabolism
Clusters c1 and c4 include genes whose expression in muscle has been altered either positively or negatively in response to long-term selection for growth (SG ≠ FG-C) but were not associated with the occurrence of WB and WS syndromes (FG-C = FG-WSWB). Their functions relate to non-specific biological processes and general functions, such as metabolic processes, signal transduction and the immune system (Table 3). Among these genes, those participating in the "positive regulation of glycogen metabolic process" (GO:0070875, Additional file 3) function were expressed at lower levels in FG than in SG muscles, suggesting a decrease in the use of glycogen in fast-growing muscles independent of the presence of WS and WB defects. This finding is consistent with previous studies that clearly demonstrated the negative impact of selection for growth and/or breast meat yield on muscle glycogen content²⁵,²⁶. Among the genes likely to be involved in such changes, we identified PPP1R3A, whose expression was much lower in FG than in SG muscles (Figure 4), as an important actor. PPP1R3A encode a muscle-specific regulatory subunit of protein phosphatase 1, which binds glycogen with high affinity, activates glycogen synthase, and inhibits glycogen phosphorylase. It has been reported that the overexpression of PPP1R3A leads to increased glycogen content²⁷, while its KO results in a drastic reduction (90%) in the glycogen content in muscle²⁸. Moreover, PPP1R3A was recently identified as the most interesting candidate gene for a major QTL region for ultimate pH (pHu)²⁹ that reflects muscle glycogen reserves³⁰. The depletion of glycogen reserves in the muscle of fast-growing lines could lead to a predisposing metabolic environment favourable to the development of myopathic syndromes. Indeed, many studies have reported that the occurrence of WS and WB defects is often associated with high meat ultimate pH³,⁵,¹⁷,³¹⁻³³ or low muscle glycogen content¹⁹,³⁴. Consistent with these findings, the occurrence and severity of WS were higher in a broiler line selected for high ultimate pH (pHu+) than in its counterpart selected for low ultimate pH (pHu-)¹⁷. Moreover, in a recent genetic study, several QTL-controlling WS were
identified in the pHu+ line but not in the pHu- one¹⁸, suggesting a possible interaction
between the genetic control of WS and the metabolic status of muscle.

In addition to a decrease in glycogen stores, it has been shown that the haem pigment content
of muscles also decreases with selection for growth and breast meat yields²⁵, probably
indicating that there is less muscle vasculature in fast than in slow-growing lines. In the
present study, we observed that the expression of *MYOCD* was 3-fold lower in FG than in SG
muscles. *MYOCD* is involved in extracellular matrix modelling and expressed in smooth
muscle cell-containing tissues, in which it may play a crucial role in the differentiation of the
smooth muscle cell lineage and vascularization. Interestingly, *MYOCD* was also recently
identified in two different studies of QTL regions that control breast muscle yield³⁵ and WS
occurrence¹⁸ in slow- and fast-growing chicken lines, respectively. In parallel with the
decrease in the expression of *MYOCD* observed in FG muscles (affected or not affected by
defects), we report that compared to SG muscles, in FG muscles, there was a strong increase
in the fibre surface supplied by one capillary (Table 1). Although this parameter was similar
between FG-C and FG-WSWB chickens, we found that the extension of endomysial and
perimysial connective tissues observed in the latter moved the capillaries away from the fibre
membrane (Figure 1D-F). This could be an aggravating factor that could further lower oxygen
and nutrient supply to fibres in muscles affected by WS and WB defects.

Altogether, our observations formally demonstrate a recurrent assumption presented in the
literature<sup>2,19,26,36</sup>, namely the suggestion that increasing breast muscle weight and yield *via*
selection through hypertrophy of existing fibres has led to a lower capillary density. This may
have led to an inadequate supply of oxygen and nutrients to the muscle fibres, which likely
promotes the excessive use of glycogen and probably increases oxidative stress in fast-
growing muscles.
In the present study, we also observed that *CAPN3* is expressed at 3-fold lower levels in FG than in SG muscles (Figure 4). *CAPN3* is an intracellular protease specific to muscle that is involved in the calcium-dependent proteolytic system. A deficiency in this enzyme results in sarcomere disorganization and muscular limb-girdle muscular dystrophy type R1 (LGMDR1 calpain3-related) in humans³⁷. In mice, its expression decreases following muscle denervation or denervation-devascularization injury, which induces muscle degeneration and regeneration³⁸. Its lower expression in FG muscles is more consistent with the lower amount of vasculature observed in this line, as atrophy induced by denervation was not observed in such muscles. Although the embryonic myosin heavy chain isoform *MYH1B* is normally no longer expressed in adult muscle, its expression was increased in FG muscles (Figure 4) and was one of the top 10 DE genes between the FG-C and SG groups (Additional file 2). The expression of *CAV3*, which plays a key role in the fusion of myoblasts into myotubes during the maturation process of muscle fibres, was also higher in FG muscles than in SG muscles.

These observations support the notion that selection for growth and breast muscle yield contributes to the occurrence of degeneration-regeneration processes in FG muscles, independent of the development of WS and WB myopathies.

**How has progressive metabolic modification due to selection resulted in myopathic disorders in broiler breast muscle tissues?**

If not directly related to myopathies, the metabolic and structural changes induced by long-term selection on growth performance and breast muscle yield and evidenced by functional analysis of clusters c1 and c4 have likely created a favourable physiological context for their onset. Clusters c2 and c5 indicate what types of biological processes are involved in the progression of myopathic defects in relation to growth variations. These two clusters accounted for almost 80% of the DE genes (Figure 2). Several genes belonging to cluster c5,
in which gene expression progressively increased from SG to FG-C and from FG-C to FG-WSWB, are involved in the generation of superoxide anion (GO:0042554, Additional file 3), a reactive oxygen species (ROS) issued from the first electronic reduction of oxygen during oxidative phosphorylation³⁹. Among these genes, some contribute to the oxidative stress response (ARNT, HIF1-α, GPX8, ALDOC, and CRYAB), and these include heat-shock proteins (HSBP1, HSPA5, HSF2, HSPB7, and HSPH1); these genes are known to be expressed at substantially higher levels during oxidative stress⁴⁰. These results strongly support the notion that selection for growth progressively resulted in increased oxidative stress and that this process was exacerbated in fast-growing muscle affected by WS and WB. It is worth noting that elevated ROS levels contribute to human muscular dystrophies, such as Duchenne muscular dystrophy⁴¹, and that excessive ROS production alters calcium homeostasis via the release of calcium from the sarcoplasmic reticulum and leads to skeletal muscle necrosis and atrophy⁴². Indeed, several genes belonging to cluster c5 (such as P2RX4, ATP2B1, ANXA6, ATP2A3, SLC8A1, ANXA2, ATP2A2, and SLC24A2) participate in calcium ion transport, indicating the possible release of calcium at the origin of muscle calcium-dependent proteolysis and tissue degeneration⁴¹, which has been reported in WS and WB muscles.

Cluster c5 includes a great number of genes involved in the production of inflammatory molecules, such as cytokines, chemokines, cell adhesion molecules and tissue-degrading enzymes. They are grouped into several GO terms, including “inflammatory response” (GO:0006954), “positive regulation of cytokine secretion” (GO:0050715), “regulation of cytokine-mediated signalling pathway” (GO:0001959), “regulation of interleukin-1 secretion” (GO:0050704), “positive regulation of interleukin-12 production” (GO:0032735), and “positive regulation of tumour necrosis factor (TNF) production” (GO:0032760) (Additional file 3). TNF is a multifunctional proinflammatory cytokine, and one of its isoforms (i.e.,
TNFAIP6) was in the top 10 DE genes between the FG-WSWB and FG-C groups (Table 2, Additional file 2). It induces the expression of metalloproteinases, which are known to degrade several components of the extracellular matrix-cytoskeleton linkage in skeletal muscle and to thereby lead to extracellular alterations and myopathy⁴². Among the 14 matrix metalloproteinases included on the microarray, seven belong to cluster c5 (MMP2, MMP10, MMP14, MMP16, MMP17, MMP24, MMP27, and MMP23B). Consistent with the increase in the inflammatory response found in cluster c5, we observed the activation of the immune system cascade. This included 20% of the genes in cluster c5, with one of the three most significant functions the “innate immune response” (GO:0045087, Additional file 3). We identified several chemokines, cell adhesion molecules and extracellular proteasomes that can favour the infiltration of macrophages, lymphocytes and neutrophils. Such infiltrations, observed in cases of muscle injury, are also frequently observed in muscles affected by WS and WB¹,²,⁴,¹⁴,⁴³. There are also several cytokines that are produced during the inflammatory response, such as TNF-α or IL-6, that play an important role in muscle regeneration. TNF-α attracts muscle satellite cells to the damaged site, promotes satellite cell proliferation by activating NF-κB signalling, and stimulates differentiation by activating the p38 signalling pathway. IL-6 stimulates the migration, proliferation and differentiation of myoblasts⁴⁴.

After injury, muscle regeneration mediated by muscle stem cells is switched on. Muscle stem cells (or satellite cells) are the major contributors to muscle regeneration and remain quiescent under normal conditions⁴⁴. After muscle trauma, they are activated, giving rise to a proliferative myoblast population that differentiates to restore damaged myofibres. The “regulation of skeletal muscle satellite cell proliferation” (GO:0014842, Additional file 3) was significantly enriched in cluster c5. It includes genes involved in the activation of muscle satellite cell proliferation (FGF2, SDC4, GPC1, PAX7, and STAT3), indicating the activation of the muscle repair process. This is consistent with the decrease observed in the expression of
the adult and neonatal myosin heavy chain isoforms, *MYH1E* and *MYH1F*, which belong to cluster c2, and the increase observed in the expression of *MYH15*, which is involved in the regeneration of avian skeletal muscles²⁴ and was one of the top 10 DE genes in the three comparisons (Additional file 2). Moreover, cluster c5 also included several genes that contribute to collagen formation (GO:0032964, GO:0032967, GO:0030199), the GO term “collagen fibril organization” being one of the three most enriched terms of this cluster (Additional file 3). All these observations were consistent with our histological study that showed that there was an increase in both the relative surface occupied by collagen VI across SG, FG-C and FG-WSWB muscles and the coefficient of variation of fibre size for FG-WSWB muscles compared to the other groups (Table 1). Collectively, our molecular and histological observations support the hypothesis that regeneration processes are more activated in fast than in slow-growing muscles, with this phenomenon exacerbated in muscles affected by WS and WB (Figure 4).

Functional analysis of clusters c2 and c5 also revealed several enriched GO terms related to energetic pathways, especially glycogen metabolism, glucose use, cellular respiration and ATP production (Additional file 3). This finding reveals that there was a progressive decrease (SG > FG-C > FG-WSWB) in the expression of genes encoding glycolytic enzymes (*PFKM*, *ALDOB*, *TPI1*, *GAPDH*, *PGK1*, *PGAM1*, *ENO3* and *PKM*), suggesting a lower level of anaerobic glycolytic activity, which is already observed in muscles affected by WS and WB¹¹,¹⁹,³⁴,⁴⁵. This decrease in anaerobic glycolytic activity is concomitant with an alteration in mitochondrial cellular respiration. Indeed, several genes belonging to cluster c2 (SG > FG-C > FG-WSWB) were related to “cellular respiration” (GO:0045333), “aerobic respiration” (GO:0009060), “ATP synthesis coupled electron transport” (GO:0042773), and “mitochondrial respiratory chain complex I and IV assembly” (GO:0032981-GO:0033617) metabolic processes (Additional file 3). These genes are primarily involved in the production
of electron transport chain components in mitochondria (CYCS, COX16, SCO1, SURF1, TIMM21, BCS1L, FOXRED2...) and the Krebs cycle (ACO2, PDHB, MDH1, MDH2, PDHA1). Moreover, several genes belonging to cluster c5 (SG < FG-C < FG-WSWB) are involved in carbohydrate metabolic processes, such as gluconeogenesis (FBP1, PCK2, GALE, and ALDOC), carbohydrate derivative transport, nucleotide-sugar biosynthesis, the pentose phosphate pathway (RBKS, RPE, TKT, ALDOC, DERA, FBP1, PGD, and PGM2) and glycogenolysis (GCG, INPP5K). All these results clearly indicate that selection by itself has markedly modified the metabolic pathways used to produce energy in muscles and that these metabolic modifications are even more pronounced in muscles affected by WS and WB. This metabolic reorientation may be related to the progressive decrease in carbohydrate (especially glycogen) availability that has accompanied selection for growth and breast meat yield<sup>25,26</sup> and that is also observed in muscles affected by WS or WB<sup>5,21,34</sup>. It is likely that these metabolic modifications have contributed to the appearance of muscle disorders in fast-growing birds. Indeed, it was recently shown that muscles exhibiting a heightened ability to store glycogen used carbohydrates as the main source of energy, whereas those lacking glycogen used energy produced from amino acid catabolism and lipid oxidation, leading to an adaptive response to oxidative stress and muscle regeneration processes<sup>21,46</sup>.

**Mechanisms of cellular defence and remodelling are specifically involved in case of severe myopathies**

Functional analysis of clusters c3 and c6 highlighted the biological processes specifically activated in fast-growing muscles that are severely affected by WS and WB. Genes belonging to these clusters account for 9% of the total DE genes. Cluster c6 contained genes that are overexpressed in cases of severe WS and WB. Its most enriched GO term is related to oxygen transport (GO:0015671, Additional file 3) and encompasses genes encoding for oxygen
carriers (*HBE*, *HBBA*, *HBM*, *HBA1*, and *HBBR*), likely indicating insufficient oxygen supply in muscles. Another group of functions relates to glycosaminoglycan and proteoglycan synthesis ("protein O-linked glycosylation *via* threonine" (GO:0018243) and "proteoglycan metabolic process" (GO:0006029)), which are both components of fibrous tissue (Additional file 3). This is fully in agreement with histological observations that showed significant infiltration and extension of connective tissues within muscles (Figure 1). At the molecular level, we noticed a strong increase in FG-WSWB muscles of that expressed *PDGFRA* (Figure 4), a marker of fibro-adipogenic precursors (FAPs), which were recently characterized as playing a role in muscle regeneration and repair processes. Indeed, FAPs differentiate into fibroblasts and adipocytes under pathological conditions, such as muscular dystrophy, leading to connective and fat tissue infiltration<sup>47</sup>. Similarly, the expression of *COL6A3* and *FN1*, which respectively contribute to the formation of collagen VI and are found in most connective tissues, fibres and extracellular matrix (ECM) during tissue repair after degeneration, were expressed at substantially higher levels in FG-WSWB muscles (Figure 4), in accordance with the high proportion of ECM that characterizes these muscles. *FN1* was also one of the top 10 DE genes between FG-WSWB and FG-C (Table 2).

Interestingly, cluster c6 was associated with several GO terms related to neurogenesis, such as "positive regulation of neurogenesis" (GO:0050769), "axonogenesis" (GO:0007409), "neuron differentiation" (GO:0030182), "positive regulation of synapse assembly" (GO:0051965), "regulation of neuron differentiation" (GO:0045664), and "neuron projection extension" (GO:1990138) (Additional file 3). Genes belonging to these functions are mostly involved in axon guidance and the formation or maintenance of neuromuscular junctions (such as *AGRN*, *SLIT2*, *SEMA3A*, *EPHB3* and *NTRK2*). This is likely more of a sign of the involvement of neuromuscular junction maintenance and repair than of motor neuron injury, which would result in foci containing atrophic fibres<sup>48</sup>, which were not observed in FG-WSWB muscles.
Indeed, because neuromuscular junction structure alterations occur in affected muscles, axons would need to be attracted and redirected to neuromuscular junctions. Many factors that promote myofibre regeneration and repair, such as semaphorin 3A (SEMA3A), which is expressed by satellite cells in injured muscle, also serve as promoters of axonal sprouting and guidance and neuromuscular junction repair as nerves grow towards newly regenerated myofibres⁴⁹. If cluster c6 is associated with the activation of several mechanisms, such as oxygen transport, axon guidance, and neuromuscular junction repair, then in the case of severe myopathy, cluster c3 could be associated with limitations in the development of cellular defence systems. Indeed, it contains genes whose expression is downregulated in muscles affected by WS and WB, with some of these genes participating in apoptosis and cytokinesis, two processes involved in cell defence or survival. This could be a sign that these cells fail to develop effective mechanisms of regeneration, and this defect is probably responsible for the substantial alteration of the structure of muscle and muscle functions observed in muscles severely affected by WS and WB.

**Identification of quantitative histological phenotypes and molecular markers for the
diagnosis of muscle disorders in chicken**

Our study, like other recent studies¹⁹,²⁰,²²,³⁵, helps to advance our understanding of the biological processes that are involved in the development of myopathies in the breast muscle tissues of modern broiler lines. It also provides several finely defined histological phenotypes, which were used to precisely quantify the degree of muscle damage and significantly improve diagnosis, which is currently based on visual or palpation scoring of muscle after slaughter.

The study of these fine histological phenotypes helps researchers to better describe the major structural changes that occur in muscles affected by WS and WB, and we also used them to determine, through a combination of DE analysis and weighted correlation network analysis,
the most confident molecular markers related to muscle disorders involved in severe WS and
WB pathologies (Figure 3).

This led to a set of 17 relevant biomarkers that were DE between the FG-C and FG-WSWB
groups and highly correlated with histological phenotypes related to myopathy (Table 4).

Three of these genes have not yet been characterized. Among the others, most are involved in
muscle regeneration phenomena (*PDLIM1*), fibrosis (*COL6A2*), hypoxia (*ARNT2*, *HIF1AN*,
and *PTGS1*) and axon guidance (*CADM1*, *SLIT3* and *NAV3*). These functions are the main
biological processes that are specifically activated in fast-growing muscles severely affected
by WS and WB defects (see the functional annotation of clusters c3 and c6). We also propose
to enrich this set of biomarkers by 5 additional genes (*MYH15*, *FN1*, *COL6A3*, *MYH1E*, and
*MYH1F*) that were strongly DE between the FG-C and FG-WSWB groups, were highly
correlated to two of the three histological phenotypes studied, and were located in the QTL
region controlling the WS defect in the breast muscle tissues of chicken<sup>18</sup> (Table 4). Among
these five genes, *MYH15* was the only one that was highly correlated with the number of
capillaries. This slow-type myosin, also called ventricular myosin in chicken because it is
mainly expressed in heart, is transiently expressed in embryonic chick skeletal muscles and
then re-expressed during muscle regeneration<sup>24</sup>. Interestingly, it is associated with bovine
pulmonary hypertension related to high altitude, suggesting that *MYH15* could be involved in
the response to hypoxic conditions<sup>50</sup>. The ability of this set of 22 genes to discriminate
muscles with severe myopathies from non-affected muscles should now be tested in larger
populations obtained from different genetic backgrounds.

In conclusion, the present study provides new information on the biological processes that
lead to the occurrence of severe myopathy in the breast muscles of modern broiler lines. By
comparing slow-growing to fast-growing muscles that were affected or not affected by
myopathies, we were able to specify molecular pathways that were altered by the long-term
selection of growth performance to those directly involved in the development of muscle
disorders. Our data show that selection for fast growing and breast meat yield has
progressively led to inadequate supplies of oxygen and the nutrients that promote
carbohydrate depletion in muscle. Such changes provided favourable conditions for metabolic
modifications that shift cells toward use of alternative catabolic pathways to produce energy,
leading to an adaptive response to oxidative stress and the first signs of inflammatory,
regenerative and fibrotic processes. All these processes are dramatically intensified in muscles
affected by myopathies, as are new mechanisms related to cellular defence and remodelling to
allow the muscles to function (oxygen transport, neurogenesis). Finally, our study highlights
the fine histological phenotypes and genes that have expression profile that are highly
correlated with muscle disorders involved in the development of myopathies, thus opening
new perspectives for a more fine diagnosis and a better screening tool for broilers affected
these such defects.

**Methods**

**Animals, sample collection, muscle and meat quality measurements**

The present study was carried out on *pectoralis major* muscles obtained from two chicken genotypes: an experimental slow-growing (SG) chicken line selected by INRA and a grand-parental commercial modern fast-growing (FG) line highly selected for growth and breast muscle yield. Birds of the two genotypes were reared following standard rearing practices, had *ad libitum* access to feed and water and were slaughtered at 6 weeks of age at the PEAT experimental unit (INRA, Centre Val de Loire, Nouzilly, France). The slaughter and carcass processing conditions of the FG broilers are fully described in Alnahhas et al.⁵¹. Samples of the pectoralis major muscle were collected fifteen minutes after slaughter and either directly
snap-frozen in liquid nitrogen for molecular analysis or stored in isopentane cooled with liquid nitrogen for histological purposes. All muscle samples were then stored at -80 °C until further analysis. One day after slaughter, three trained people visually graded *pectoralis major* muscles for WS and WB. Both WS and WB were categorized separately on a three-point scale: 0 = absence of defects, 1 = moderate severity of defects, and 2 = high severity of defects. Defects were scored according to the widths of white stripes based on the scale of Kuttappan et al.⁶ for WS in addition to visual characteristics and hardness on palpation for WB.

Body composition was characterized for all SG and FG birds through the measurement of *pectoralis major* yield (PMY) expressed in relation to body weight (BW). Breast meat yield (BMY), abdominal fat yield (AFP), and *pectoralis minor* yield (PmY) were also measured in FG birds as were several breast meat quality traits that were evaluated in *pectoralis major* muscle: ultimate pH (pHu) and the colour parameters L*, a*, b*, which were measured 24 hours post-mortem, drip loss (DL) during a 4-day storage period, cooking loss (CL), Warner Bratzler shear force (SF) of cooked meat, curing-cooking yield (CCY) and the thiobarbituric acid-reactive substance index (TBA-RS), used as markers of lipid peroxidation and measured after 8 days of storage. All these measurements were obtained as described in Alnahhas et al.¹⁷.

Three groups of eight animals were selected for further molecular and histological analyses: eight animals obtained from the SG line and two groups of eight animals selected from among the 200 FG birds according to their extreme visual score for WS and WB defects and DL, CL and CCY phenotypes. The 8 FG-C birds were visually free of any defects and exhibited the highest water-holding capacity, and the 8 FG-WSWB birds were affected by both the WS and WB defects (average score of 2 for both WS and WB) and exhibited poor water-holding capacity.
## Histochemical traits

Immunohistochemistry was performed on 10 µm-thick *pectoralis major* muscle cross-sections obtained from the FG-C, FG-WSWB and SG groups. All primary antibodies were obtained from the Developmental Studies Hybridoma Bank, created by the NICHD of the NIH, maintained at The University of Iowa, Department of Biology, Iowa City, IA, and developed by Douglas M. Fambrough, as follows: 39 (anti-collagen VI), B3/D6 (anti-fibronectin), and 31/31-2 (anti-laminin). Secondary antibodies and conjugated streptavidin were obtained from Southern Biotech, Birmingham, AL, USA, as follows: goat anti-mouse IgG1 conjugated to a TR antibody, goat anti-mouse IgG H+L conjugated to a biotin antibody, and streptavidin conjugated to Cy2. The washing procedure consisted of incubating cross-sections for 3 × 5 minutes in phosphate-buffered saline solution. Slides were mounted in Mowiol medium (Sigma, Lyon, France) and stored at 4°C in a dark chamber before they were observed.

Whatever the labelling procedure, six images per sample were selected randomly (captured at 10× and/or 20× magnification using a Leica MC170 camera; Leica Microsystems, Nanterre, France) on a Leica DMRB microscope.

## Fibrosis and adiposis quantification

The Collagen VI-Bodipy co-labelling procedure was used to quantify the extension of connective and adipose tissues within muscle. Muscle cross-sections were fixed with 4% paraformaldehyde solution, incubated in 10% goat serum (Sigma, Lyon, France) for 30 minutes, and then incubated with primary Collagen VI antibody (1/200) for 2 hours. After the tissues were washed, muscle cross-sections were incubated with a secondary goat anti-mouse IgG1 conjugated to a TR antibody (1/500) for 1 hour, washed again, and incubated with Bodipy 493 (4,4-Difluoro-1,3,5,7,8-Pentamethyl-4-Bora-3a,4a-Diaza-s-Indacene, 1/3000, Sigma, Lyon, France) for 15 minutes. The relative surface areas labelled with collagen VI and
Bodipy were quantified using the Area Fraction Parameter function in ImageJ software after transformation into black and white images (8-bit) and threshold adjustment.

### Capillary density and fibre size measurement

A fibronectin-laminin co-labelling procedure was used to label capillary membranes (fibronectin) and basal lamina surrounding muscle fibres (laminin). Muscle cross-sections were incubated in 10% goat serum for 30 minutes and with primary fibronectin antibody (1/100) for 1 hour. After they were washed, cross-sections were incubated with secondary goat anti-mouse IgG H+L conjugated to biotin antibody (1/1000) for 45 minutes, washed, and incubated with streptavidin conjugated to Cy2 (1/500) for 45 minutes. After they were washed, the cross sections were incubated with primary laminin antibody (1/50) for 1 hour, washed, and finally incubated with secondary goat anti-mouse IgG1 conjugated to a TR antibody (1/1000) for 45 minutes. Capillaries were counted manually, and fibre size was determined using the minimum Feret's diameter parameter function of the ImageJ software after transformation into black and white images (8-bit) and threshold adjustment.

### Microarray expression profiling

#### RNA extraction

Total RNA was extracted from *pectoralis major* muscle samples using the RNeasy® Mini kit (Qiagen, Valencia, CA, USA). Residual genomic DNA was removed by DNase I treatment (Qiagen, Valencia, CA, USA). RNA concentration was measured using a NanoDrop ND-1000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA), and RNA integrity was checked according to an RNA Integrity Number (RIN > 8) using RNA 6000 Nano chips run on a Bioanalyzer 2100 (Agilent Technologies, Santa Clara, CA, USA).

#### Labelling and hybridization
Muscle transcriptional profiling was performed using an 8 × 60K Agilent Gallus gallus custom-commercial array (Agilent Technologies, Santa Clara, CA, USA) containing 61,657 probes (Platform GPL20588 in the U.S. National Center for Biotechnology Information Gene Expression Omnibus (GEO) microarray database). RNA labelling and microarray processing were performed by the @BRIDGe platform (INRA, UMR GABI, Jouy-en-Josas) using the procedure described in Jacquier et al.⁵². The microarray datasets were submitted to the GEO microarray database with the accession number GSE127806.

## Re-annotation of the microarray chip on the current Galgal5 chicken genome

To compare the DE genes identified in this study and the genetic markers of WS provided by our previous study¹⁸, we re-annotated the chip.

The transcriptome alignment on the Galgal5 chicken assembly (EntrezGene database) of all the probes deposited on the chip was carried out using the *blastn* algorithm (hybridization tolerance of two mismatches and a single probe-associated gene as parameters), which is available in the BLAST + suite (ncbi-blast-2.6.0 +)⁵³, on the cluster obtained from the Genotoul bioinfo platform (http://bioinfo.genotoul.fr/). Probes were annotated on a Galgal5 assembly as reported in Additional file 6.

Probes with a hybridization tolerance of two mismatches and a single probe-associated gene were considered correctly annotated and were kept for further analysis.

## Differential expression analysis

First-step filtering was applied to keep only those probes that were expressed in this study (43,688 probes). The fluorescence signal for each probe was log2-transformed and normalized by median centring for each array. The differences in expression among the three groups (SG, FG-C, and FG-WSWB) were computed using the log2 transformation of the fold-change (log2FC). The differential analysis was performed using a linear model to identify DE probes among all pairwise comparisons of the three groups implemented in R package limma
version3.32.7 (Linear Model for Microarray Data)⁵⁴. The p-values were adjusted for multiple
testing by the Benjamini-Hochberg method⁵⁵ to control the False Discovery Rate (FDR).
Probes were considered DE when the adjusted p-value was ≤ 0.05.

## Hierarchical clustering

To identify clusters of DE genes, hierarchical clustering was performed on the gene
expression matrix. The correspondence between expressed probes and annotated genes was
determined using the re-annotation of the chip. Genes with at least one Galgal5 annotated DE
probe, |FC| ≥ 1.2 and the highest mean expression probe level in one of groups were kept.
Normalized expression (after log2-transformation) was scaled for each gene. Hierarchical
clustering on the scaled gene expression matrix was based on the Pearson correlation distance
and an average link aggregation distance. A modular height cut-off value in the hierarchical
tree obtained using the cutreeDynamic function of dynamicTreeCut package⁵⁶ (with the
following parameters: deepSplit=1, minClusterSize=300) was applied to identify clusters of
DE genes that shared similar expression profiles across the three groups (FG-C, FG-WSWB
and SG).

## Functional enrichment analysis

All the genes expressed on the chip were annotated by Gene Ontology⁵⁷,⁵⁸ for Biological
Process categories according to the NCBI EntrezGene database using orthologues. Functional
enrichment tests were performed using the ViSEAGO R package, which is available at
https://forgemia.inra.fr/umr-boa/viseago. Enrichment for functions within each cluster of
genes was tested using a Fisher's exact test and the "elim" algorithm (p<0.01) with all
expressed genes used as the background.

## Co-expression network analysis
To study the correlations between gene expression levels and histological quantitative phenotypes as a complementary approach to the differential analysis, we applied a co-expression analysis using the R package Weighted Correlation Network Analysis (WGCNA)⁵⁹.

## Network construction

The co-expression analysis was performed using an expression matrix based on the 24 samples and 11,999 expressed and annotated genes. The unsigned connected network was built based on the adjacency matrix between genes. From the gene expression matrix, Pearson's correlations between every pair of genes were computed and raised to a selected power of $\beta=7$ using the pickSoftThreshold function to reach a scale-free topology index ($R^2$) of at least 0.60. The adjacency matrix was turned into a Topological Overlap Measure (TOM) matrix, which can be used to assess the degree of shared neighbours between pairs of genes.

## Module detection

A hierarchical clustering of the genes based on the TOM dissimilarity measure followed by a modular height cut-off value of branches in the hierarchical tree using the cutreeDynamic function (deepSplit=4, minClusterSize=30) was performed to detect modules of co-expressed genes. The module eigengene, which was the first principal component of each module and represented the expression value of each module, was calculated. Modules with expression profiles that were very similar (correlation = 0.90) were merged since there was a high probability that genes belonging to these modules are highly co-expressed.

## Module-trait relationships

Only the histological phenotypes that allowed discrimination of muscles severely affected by WS and WB from other muscles (i.e., collagen VI labelling, adipose tissue, number of capillaries and coefficient of variation of fibre size) were kept for the analysis. The module eigengene was used to detect biologically relevant modules. Indeed, module-trait relationships
were estimated using Pearson's correlation between the module eigengene and the trait of interest.

Genes for each module with high Gene Significance (GS ≥ 0.7) corresponding to the absolute value of the correlation between gene expression and the histological trait and high Module Membership (MM ≥ 0.7), which was defined as the absolute value of the correlation of the module eigengene and the gene expression profile, were defined as hub genes.

## Validation by RT-qPCR

To validate the results obtained on the microarray, RT-qPCR was performed to examine the relative expression of 12 genes selected according to several criteria, including their functional role, their belonging to each of the identified clusters, and, for some of them, their location within QTL regions recently identified as controlling the WS defect in broiler chickens¹⁸. Total RNA was extracted from *pectoralis major* muscle samples using RNA NOW (Ozyme, St Quentin en Yvelines, France). Ten micrograms of RNA from each sample were reverse-transcribed using RNase H-MMLV reverse transcriptase (Superscript II, Invitrogen, Illkirch, France) and random primers (Promega, Charbonnières les Bains, France). Primers targeting the studied genes were designed with Primer3 version 4.0.0⁶⁰,⁶¹. The list of primer sequences is available in Additional file 7. The products of amplification were analysed by electrophoresis and further sequenced.

Gene expression was quantified by qPCR using a Roche LightCycler® 480 II (Roche Applied Science) and Takyon® mix (Eurogentec, Seraing, Belgium) according to the manufacturer's recommendations. Quantitative PCR conditions were set at 95 °C for 5 min, followed by forty-five cycles of 10 s at 95 °C, 20 s at 60 °C and 10 s at 72 °C. *18S ribosomal RNA* was used as a housekeeping gene to normalize the CT values as its expression was invariant between groups. A mix of chicken *pectoralis major* muscle cDNA served as a reference
sample (control cDNA). The calculation of absolute mRNA levels was based on the PCR
efficiency and the threshold cycle (CT) deviation of an unknown cDNA versus the control
cDNA according to the equation proposed by Pfaffl⁶².
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## Acknowledgements

We thank the @BRIDGe platform (INRA, UMR GABI, Jouy-en-Josas) for performing the RNA labelling and microarray processing. We also thank the PEGASE Genetics and Genomics team (INRA, UMR1348, Rennes, France) for developing the 8 x 60K Agilent custom platform.

Finally, we thank the staff of the poultry breeding facilities (PEAT, INRA, 37380, Nouzilly, France) and of the Avian Research Unit (UMR BOA, INRA, Université de Tours, 37380, Nouzilly, France) for their technical assistance.

## Authors' contributions

CB and ELBD supervised the study, defined the experimental design and supervised the phenotyping with FF. AB and EP performed the reannotation of the chip based on the current genome Galgal5. EP was in charge of differential analysis with CHA, functional annotation with AB and weighted correlation network analysis. CHA and AJ actively participated in the discussion of the functional analysis and weighted correlation network analysis and supervised EP. EP and EG designed and tested the oligonucleotide primers, extracted RNA and performed quantitative RT-PCR analyses. EP and TB carried out the histological characterization of muscles under the supervision of CP. EP drafted the first version of the manuscript under the supervision of CB and ELBD. All the authors read and approved the final manuscript.

## Additional information

### Ethics approval

Experimentation was conducted in the INRA PEAT experimental facilities (Nouzilly, France) and authorized for animal experimentation by the French veterinary service, which is the
competent authority (D-37-175-1). The scientist in charge of the experimentation received
training and personal authorization (37-112). In agreement with the Ethical Committee for
Animal Experimentation in Val de Loire (C2EA-19), the experiments reported here did not
require approval from a specific ethical committee since only classical rearing practices for
broilers were used. No permissions were required to collect the chicken lines owned by
INRA.

**Consent for publication**

Not applicable

**Availability of data and materials**

The microarray data were submitted to the Gene Expression Omnibus (GEO) microarray
database (accession number GSE127806).

**Competing interests**

The authors declare that they have no competing interests.

**Funding**

The phenotyping of the birds used for transcriptomic analysis and microarray processing was
performed by the @BRIDGe platform (INRA, UMR GABI, Jouy-en-Josas) and funded by the
TECNOVIA program, which is supported by the Bretagne and Pays de la Loire regions
(France). This funding body had no role in the design of the study, the collection, analysis, and
interpretation of the data or the writing of the manuscript.
**Figure legends**

**Figure 1** Collagen VI-Bodipy co-labelling (A-C) and fibronectin labelling (D-F) fluorescence micrographs of SG (A and D), FG-C (B and E) and FG-WSWB (C and F) *pectoralis major* muscles. Capillaries in the affected muscles (F) are indicated by arrows. Scale bar: 100 µm.

A, B, and C: 10× objective, D, E and F: 20× objective.

<figure><img src="image_38.png" /></figure>
**Figure 2** Heat map gene cluster classification of the SG, FG-C and FG-WSWB samples (A). Samples are shown in columns and DE genes in rows, and the scaled expression levels are depicted by a colour gradient: upregulated and downregulated genes are shown in red and blue, respectively. DE genes were grouped by a hierarchical clustering analysis in both rows and columns. Hierarchical clustering for the scaled gene expression matrix was based on the Pearson correlation distance and the average link aggregation distance. A modular height cut-off value in the hierarchical tree allowed the identification of six clusters (c1-c6) of DE genes with similar expression profiles (B). The dashed line indicates the average expression profile. The number of DE genes belonging to each cluster is indicated in parenthesis.

<figure><img src="image_39.png" /></figure>
B

c1 (n=957)

<figure><img src="image_40.png" /></figure>

c2 (n=5168)

<figure><img src="image_41.png" /></figure>

c3 (n=473)

<figure><img src="image_42.png" /></figure>

c4 (n=333)

<figure><img src="image_43.png" /></figure>

c5 (n=3117)

<figure><img src="image_44.png" /></figure>

c6 (n=434)

<figure><img src="image_45.png" /></figure>

SG

FG-C

FG-WSWB

SG

FG-C

FG-WSWB
**Figure 3** Venn diagram showing the number of genes that were common between two or more analyses (DE: gene differentially expressed (adjusted p-value < 0.05) between FG-C and FG-WSWB muscles, collagen VI: hub genes for collagen VI labelling, capillary: hub genes for number of capillaries, CV fib. size: hub genes for variation in fibre size).

<figure><img src="image_46.png" /></figure>
**Figure 4** Relative expression by RT-qPCR of the 12 genes used for validation. The relative level of gene expression in each group was normalized according to the lowest value, which was arbitrarily defined as 1. Error bars indicate ± SE. <sup>a-b-c</sup> Analysis of variance performed with the group factor for each gene. Different superscripts above bars indicate significant differences between groups, as determined by pairwise comparison (p-values ≤ 0.05). The r coefficient indicates the Pearson's correlation between gene expression measured by RT-qPCR and microarray.

<figure><img src="image_47.png" /></figure>
Table legends

<table><caption>**Table 1** Means and standard deviations for histological traits measured in *pectoralis major* muscle cross-sections.</caption><thead><tr><th>Traits</th><th>SG (N=8)</th><th>FG-C (N=8)</th><th>FG-WSWB (N=8)</th><th>p-value</th></tr></thead><tbody><tr><td>Collagen VI, %*</td><td>2.13 ± 0.73<sup>a</sup></td><td>9.06 ± 2.96<sup>b</sup></td><td>14.73 ± 4.14<sup>c</sup></td><td>1.58 × 10<sup>-7</sup></td></tr><tr><td>Adipose tissue, %*</td><td>0.05 ± 0.12<sup>a</sup></td><td>0.36 ± 0.27<sup>a</sup></td><td>1.10 ± 0.82<sup>b</sup></td><td>1.29 × 10<sup>-3</sup></td></tr><tr><td>Number of fibres per field</td><td>203.7 ± 21.2<sup>a</sup></td><td>51.1 ± 11.4<sup>b</sup></td><td>45.1 ± 9.5<sup>b</sup></td><td>2.50 × 10<sup>-15</sup></td></tr><tr><td>Number of capillaries per field</td><td>36.7 ± 8.2<sup>a</sup></td><td>24.0 ± 7.2<sup>b</sup></td><td>17.0 ± 5.3<sup>c</sup></td><td>3.44 × 10<sup>-4</sup></td></tr><tr><td>Number of fibres per capillary</td><td>6.3 ± 2.3<sup>a</sup></td><td>2.3 ± 0.9<sup>b</sup></td><td>2.9 ± 1.2<sup>b</sup></td><td>1.20 × 10<sup>-4</sup></td></tr><tr><td>Fibre surface per capillary, µm<sup>2</sup></td><td>12961 ± 3985<sup>a</sup></td><td>20169 ± 4248<sup>b</sup></td><td>22150 ± 5681<sup>b</sup></td><td>3.89 × 10<sup>-3</sup></td></tr><tr><td>Fibre size, µm<sup>**</sup></td><td>25.0 ± 1.8<sup>a</sup></td><td>55.2 ± 8.5<sup>b</sup></td><td>48.2 ± 11.4<sup>b</sup></td><td>5.22 × 10<sup>-6</sup></td></tr><tr><td>Coefficient of variation of fibre size, %</td><td>25.5 ± 2.0<sup>a</sup></td><td>37.8 ± 7.9<sup>a</sup></td><td>50.5 ± 10.7<sup>b</sup></td><td>3.14 × 10<sup>-3</sup></td></tr></tbody></table>

<footnote>*Relative area (%) occupied by collagen VI or adipose tissue.</footnote>

<footnote>**Calculated from the Feret's minimum diameter.</footnote>

<footnote>a, b, c Analysis of variance performed with the group factor for each trait. Different superscripts in each line indicate significant differences between groups, as determined by pairwise comparison (p-values ≤ 0.05).</footnote>
<table><caption>**Table 2** Top 10 DE genes for each comparison based on adjusted p-value.</caption><thead><tr><th>Gene symbol*</th><th>Gene ID</th><th>Description</th><th>Adjusted p-value</th><th>log FC</th></tr></thead><tbody><tr><td colspan="5"><strong>FG.C vs. SG</strong></td></tr><tr><td><strong>MUSTN1</strong></td><td>404773</td><td>musculoskeletal, embryonic nuclear protein 1</td><td>9.39× 10<sup>-14</sup></td><td>5.02</td></tr><tr><td>GOLGB1</td><td>426868</td><td>golgin B1</td><td>1.36× 10<sup>-12</sup></td><td>-2.80</td></tr><tr><td><strong>TNNT2</strong></td><td>396433</td><td>troponin T2, cardiac type</td><td>1.63× 10<sup>-12</sup></td><td>4.87</td></tr><tr><td>RGCC</td><td>418833</td><td>regulator of cell cycle</td><td>1.63× 10<sup>-12</sup></td><td>4.09</td></tr><tr><td>PIH1D3</td><td>422184</td><td>PIH1 domain containing 3</td><td>2.27× 10<sup>-12</sup></td><td>-2.25</td></tr><tr><td>MTPN</td><td>395702</td><td>myotrophin</td><td>3.82× 10<sup>-12</sup></td><td>1.83</td></tr><tr><td>DUSP5</td><td>423890</td><td>dual specificity phosphatase 5</td><td>4.12× 10<sup>-12</sup></td><td>3.84</td></tr><tr><td>SPP1</td><td>395210</td><td>secreted phosphoprotein 1</td><td>4.20× 10<sup>-12</sup></td><td>6.45</td></tr><tr><td><strong>RPS6KA1L</strong></td><td>396579</td><td>ribosomal protein S6 kinase A1-like</td><td>4.20× 10<sup>-12</sup></td><td>2.37</td></tr><tr><td>RUNX1</td><td>396152</td><td>runt-related transcription factor 1</td><td>7.43× 10<sup>-12</sup></td><td>3.22</td></tr><tr><td colspan="5"><strong>FG-WSWB vs. SG</strong></td></tr><tr><td><strong>TNNT2</strong></td><td>396433</td><td>troponin T2, cardiac type</td><td>6.44× 10<sup>-16</sup></td><td>7.08</td></tr><tr><td>TNNT1</td><td>396009</td><td>troponin T1, slow skeletal type</td><td>2.09× 10<sup>-15</sup></td><td>5.85</td></tr><tr><td>TUBAL3</td><td>416694</td><td>tubulin alpha like 3</td><td>2.09× 10<sup>-15</sup></td><td>4.38</td></tr><tr><td>KCNK2</td><td>770954</td><td>potassium two pore domain channel subfamily K member 2</td><td>2.09× 10<sup>-15</sup></td><td>4.79</td></tr><tr><td><strong>RPS6KA1L</strong></td><td>396579</td><td>ribosomal protein S6 kinase A1-like</td><td>2.09× 10<sup>-15</sup></td><td>3.35</td></tr><tr><td><strong>MUSTN1</strong></td><td>404773</td><td>musculoskeletal, embryonic nuclear protein 1</td><td>2.09× 10<sup>-15</sup></td><td>5.18</td></tr><tr><td>APTX</td><td>395173</td><td>aprataxin</td><td>2.09× 10<sup>-15</sup></td><td>-2.86</td></tr><tr><td>MYH15</td><td>395534</td><td>myosin heavy chain 15</td><td>3.12× 10<sup>-15</sup></td><td>7.37</td></tr><tr><td><strong>MDK</strong></td><td>423196</td><td>midkine</td><td>4.40× 10<sup>-15</sup></td><td>5.21</td></tr><tr><td>VDAC1</td><td>416320</td><td>voltage dependent anion channel 1</td><td>4.40× 10<sup>-15</sup></td><td>-2.76</td></tr><tr><td colspan="5"><strong>FG-WSWB vs. FG-C</strong></td></tr><tr><td>AKR1D1</td><td>418107</td><td>aldo-keto reductase family 1 member D1</td><td>1.91× 10<sup>-11</sup></td><td>4.08</td></tr><tr><td>FBLN1</td><td>373979</td><td>fibulin 1</td><td>5.16× 10<sup>-11</sup></td><td>3.85</td></tr><tr><td>FN1</td><td>396133</td><td>fibronectin 1</td><td>6.17× 10<sup>-11</sup></td><td>2.42</td></tr><tr><td>TNFAIP6</td><td>424315</td><td>TNF alpha induced protein 6</td><td>6.86× 10<sup>-11</sup></td><td>3.38</td></tr><tr><td><strong>MDK</strong></td><td>423196</td><td>midkine</td><td>7.21× 10<sup>-11</sup></td><td>3.62</td></tr><tr><td>ENPP2</td><td>420361</td><td>ectonucleotide pyrophosphatase/phosphodiesterase 2</td><td>1.72× 10<sup>-10</sup></td><td>2.93</td></tr><tr><td>OLFML3</td><td>419882</td><td>olfactomedin like 3</td><td>1.72× 10<sup>-10</sup></td><td>1.98</td></tr><tr><td>FKBP10</td><td>427013</td><td>FK506 binding protein 10</td><td>1.72× 10<sup>-10</sup></td><td>1.94</td></tr><tr><td>CCDC180</td><td>772014</td><td>coiled-coil domain containing 180</td><td>3.59× 10<sup>-10</sup></td><td>-1.76</td></tr><tr><td>CTHRC1</td><td>420262</td><td>collagen triple helix repeat containing 1</td><td>5.66× 10<sup>-10</sup></td><td>4.22</td></tr></tbody></table>

<footnote>*Common genes between two or three comparisons are indicated in bold.</footnote>
<table><caption>**Table 3** Functional enrichment of each cluster of DE genes.</caption><thead><tr><th>Clusters</th><th>Number of DE genes</th><th>Number of enriched terms</th><th>Main enriched function</th></tr></thead><tbody><tr><td>Cluster c1</td><td>957</td><td>24</td><td>metabolic process (protein, glycogen, nucleic acid);<br>immune system (lymphocyte, immune effector process);<br>signal transduction (intracellular steroid hormone<br>receptor)</td></tr><tr><td>Cluster c2</td><td>5168</td><td>51</td><td>metabolic process (cellular respiration, aerobic<br>respiration, protein, ATP, nucleic acid); gene<br>expression; cellular component organization (vacuole,<br>Golgi, autophagosome); macromolecular complex<br>assembly (mitochondrial respiratory chain complex);<br>actin cytoskeleton organization</td></tr><tr><td>Cluster c3</td><td>473</td><td>8</td><td>cellular process (apoptosis, cytokinesis); response to<br>stimulus (oxidative stress, epidermal growth factor);<br>regulation of transcription</td></tr><tr><td>Cluster c4</td><td>333</td><td>18</td><td>metabolic process (protein); response to stimulus<br>(adaptive immune response, cellular response to tumour<br>necrosis); signal transduction</td></tr><tr><td>Cluster c5</td><td>3117</td><td>128</td><td>metabolic process (protein, lipid, carbohydrate);<br>biosynthetic process (collagen, lipid); organization<br>(actin cytoskeleton, collagen fibril, extracellular<br>matrix); body defence, immune system; cell migration,<br>adhesion, proliferation (skeletal muscle satellite cell,<br>fibroblast), development, morphogenesis (angiogenesis)</td></tr><tr><td>Cluster c6</td><td>434</td><td>38</td><td>cell migration, adhesion, proliferation; signal<br>transduction (Wnt, Rho protein signalling pathway);<br>transport (oxygen); actin cytoskeleton organization;<br>metabolic process (protein, proteoglycan); neurogenesis</td></tr></tbody></table>
<table><caption>**Table 4** Set of candidate biomarkers of severe breast muscle myopathies identified based on combinations of genes that were differentially expressed between FG-WSWB and FG-C muscles and the WGCNA analyses as well as QTL mapping for WS.</caption><thead><tr><th>Gene Symbol</th><th>Gene ID</th><th>Description</th></tr></thead><tbody><tr><td>ARNT2</td><td>415481</td><td>aryl hydrocarbon receptor nuclear translocator 2</td></tr><tr><td>EYA2</td><td>395745</td><td>EYA transcriptional coactivator and phosphatase 2</td></tr><tr><td>PTGS1</td><td>427752</td><td>prostaglandin-endoperoxide synthase 1 (prostaglandin G/H synthase and cyclooxygenase)</td></tr><tr><td>CADM1</td><td>419762</td><td>cell adhesion molecule 1</td></tr><tr><td>SLIT3</td><td>374173</td><td>slit guidance ligand 3</td></tr><tr><td>PDLIM1</td><td>428948</td><td>PDZ and LIM domain 1</td></tr><tr><td>GPR162</td><td>418288</td><td>G protein-coupled receptor 162</td></tr><tr><td>TBC1D19</td><td>422800</td><td>TBC1 domain family member 19</td></tr><tr><td>COL6A2</td><td>396292</td><td>collagen type VI alpha 2 chain</td></tr><tr><td>HIF1AN</td><td>428952</td><td>hypoxia inducible factor 1 subunit alpha inhibitor</td></tr><tr><td>LRWD1</td><td>417511</td><td>leucine rich repeats and WD repeat domain containing 1</td></tr><tr><td>NAV3</td><td>417869</td><td>neuron navigator 3</td></tr><tr><td>LOC421935</td><td>421935</td><td>uncharacterized</td></tr><tr><td>ADORA1</td><td>374212</td><td>adenosine A1 receptor</td></tr><tr><td>LOC107051073</td><td>107051073</td><td>uncharacterized</td></tr><tr><td>ATP8B1</td><td>426857</td><td>ATPase phospholipid transporting 8B1</td></tr><tr><td>C9H21ORF2</td><td>424866</td><td>chromosome 9 C21orf2 homolog</td></tr><tr><td colspan="3"><em>Located in a QTL region for WS</em></td></tr><tr><td>MYH15</td><td>395534</td><td>myosin heavy chain 15</td></tr><tr><td>COL6A3</td><td>396548</td><td>collagen type VI alpha 3 chain</td></tr><tr><td>FN1</td><td>396133</td><td>fibronectin 1</td></tr><tr><td>MYH1E</td><td>427788</td><td>myosin heavy chain 1E, skeletal muscle</td></tr><tr><td>MYH1F</td><td>768566</td><td>myosin, heavy chain 1F, skeletal muscle</td></tr></tbody></table>
## Additional files

**Additional file 1** Means and standard deviations for body weight, body composition and meat quality traits measured at 6 weeks of age.

<table><thead><tr><th>Traits*</th><th>FG-C (N=8)</th><th>FG-WSWB (N=8)</th><th>p-value**</th></tr></thead><tbody><tr><td><strong>Growth and body composition</strong></td><td></td><td></td><td></td></tr><tr><td>BW (g)</td><td>3213 ± 211</td><td>3435 ± 284</td><td>0.10</td></tr><tr><td>PMY (%)</td><td>17.06 ± 1.38</td><td>18.72 ± 1.14</td><td><strong>0.02</strong></td></tr><tr><td>PmY (%)</td><td>3.96 ± 0.30</td><td>3.74 ± 0.36</td><td>0.20</td></tr><tr><td>BMY (%)</td><td>21.0 ± 1.55</td><td>22.5 ± 1.41</td><td>0.07</td></tr><tr><td>AFP (%)</td><td>1.61 ± 0.27</td><td>1.79 ± 0.34</td><td>0.28</td></tr><tr><td><strong>Pectoralis major quality traits</strong></td><td></td><td></td><td></td></tr><tr><td>pHu</td><td>5.92 ± 0.11</td><td>5.91 ± 0.20</td><td>0.83</td></tr><tr><td>L*</td><td>50.1 ± 2.66</td><td>53.0 ± 2.95</td><td>0.07</td></tr><tr><td>a*</td><td>-0.93 ± 0.43</td><td>-0.92 ± 0.39</td><td>0.95</td></tr><tr><td>b*</td><td>10.2 ± 0.65</td><td>11.1 ± 1.35</td><td>0.14</td></tr><tr><td>DL (%)</td><td>2.09 ± 0.84</td><td>4.00 ± 0.95</td><td><strong>8.07 × 10<sup>-4</sup></strong></td></tr><tr><td>CL (%)</td><td>13.3 ± 2.95</td><td>19.2 ± 2.91</td><td><strong>1.26 × 10<sup>-3</sup></strong></td></tr><tr><td>CCY (%)</td><td>82.6 ± 2.45</td><td>78.2 ± 5.59</td><td>0.07</td></tr><tr><td>SF (N/cm<sup>2</sup>)</td><td>12.8 ± 0.94</td><td>13.2 ± 1.63</td><td>0.56</td></tr><tr><td>TBA-RS</td><td>0.50 ± 0.16</td><td>0.59 ± 0.22</td><td>0.37</td></tr></tbody></table>

<footnote>* BW body weight, PMY pectoralis major yield, PmY pectoralis minor yield, BMY breast meat yield, AFP abdominal fat percentage, pHu pectoralis major ultimate pH, L* lightness, a* redness, b* yellowness, DL drip loss, CL cooking losses, CCY curing-cooking yield, SF shear force value of the cooked meat, TBA-RS thiobarbituric acid-reactive substance of the raw meat.</footnote>

<footnote>**Two-tailed Welch's mean values equality t-test at 95% confidence level</footnote>
<table><caption>Additional file 2 Top 10 DE genes for each comparison based on log FC.</caption><thead><tr><th>Gene symbol*</th><th>Gene ID</th><th>Description</th><th>log FC</th><th>Adjusted p-value</th></tr></thead><tbody><tr><td colspan="5"><strong>FG.C vs. SG</strong></td></tr><tr><td>SPP1</td><td>395210</td><td>secreted phosphoprotein 1</td><td>6.64</td><td>5.53 × 10<sup>-12</sup></td></tr><tr><td>MYH1B</td><td>374069</td><td>myosin heavy chain 1B, skeletal muscle (embryonic isoform)</td><td>5.97</td><td>2.19 × 10<sup>-11</sup></td></tr><tr><td>CSRP3</td><td>422979</td><td>cysteine and glycine rich protein 3</td><td>5.46</td><td>1.20 × 10<sup>-9</sup></td></tr><tr><td>MYH1A</td><td>417309</td><td>myosin heavy chain 1A, skeletal muscle</td><td>5.20</td><td>5.02 × 10<sup>-9</sup></td></tr><tr><td>TPM3</td><td>770103</td><td>tropomyosin 3</td><td>5.19</td><td>1.66 × 10<sup>-10</sup></td></tr><tr><td>MUSTN1</td><td>404773</td><td>musculoskeletal, embryonic nuclear protein 1</td><td>5.07</td><td>6.83 × 10<sup>-13</sup></td></tr><tr><td>TNNT2</td><td>396433</td><td>troponin T2, cardiac type</td><td>4.93</td><td>3.17 × 10<sup>-12</sup></td></tr><tr><td>MYH15</td><td>395534</td><td>myosin heavy chain 15</td><td>4.91</td><td>4.19 × 10<sup>-10</sup></td></tr><tr><td>LOC769899</td><td>769899</td><td>avidin-related protein 4/5-like</td><td>4.81</td><td>6.04 × 10<sup>-7</sup></td></tr><tr><td>PLN</td><td>396378</td><td>phospholamban</td><td>4.78</td><td>1.61 × 10<sup>-10</sup></td></tr><tr><td colspan="5"><strong>FG-WSWB vs. SG</strong></td></tr><tr><td>MYH15</td><td>395534</td><td>myosin heavy chain 15</td><td>7.88</td><td>1.33 × 10<sup>-14</sup></td></tr><tr><td>SPP1</td><td>395210</td><td>secreted phosphoprotein 1</td><td>7.86</td><td>1.84 × 10<sup>-14</sup></td></tr><tr><td>TPM3</td><td>770103</td><td>tropomyosin 3</td><td>7.55</td><td>1.88 × 10<sup>-14</sup></td></tr><tr><td>CSRP3</td><td>422979</td><td>cysteine and glycine rich protein 3</td><td>7.36</td><td>6.54 × 10<sup>-13</sup></td></tr><tr><td>PTX3</td><td>548626</td><td>pentraxin 3</td><td>7.24</td><td>2.24 × 10<sup>-14</sup></td></tr><tr><td>TNNI1</td><td>421161</td><td>troponin I type 1 (skeletal, slow)</td><td>7.18</td><td>4.49 × 10<sup>-15</sup></td></tr><tr><td>TNNT2</td><td>396433</td><td>troponin T2, cardiac type</td><td>7.17</td><td>7.64 × 10<sup>-16</sup></td></tr><tr><td>OCM2</td><td>396531</td><td>oncomodulin 2</td><td>7.13</td><td>1.55 × 10<sup>-12</sup></td></tr><tr><td>MYH1B</td><td>374069</td><td>myosin heavy chain 1B, skeletal muscle (embryonic isoform)</td><td>7.12</td><td>1.71 × 10<sup>-13</sup></td></tr><tr><td>MYOZ2</td><td>422682</td><td>myozenin 2</td><td>6.75</td><td>3.21 × 10<sup>-14</sup></td></tr><tr><td colspan="5"><strong>FG-WSWB vs. FG-C</strong></td></tr><tr><td>CTHRC1</td><td>420262</td><td>collagen triple helix repeat containing 1</td><td>4.22</td><td>5.66 × 10<sup>-10</sup></td></tr><tr><td>AKR1D1</td><td>418107</td><td>aldo-keto reductase family 1 member D1</td><td>4.08</td><td>1.91 × 10<sup>-11</sup></td></tr><tr><td>TNFAIP6</td><td>424315</td><td>TNF alpha induced protein 6</td><td>3.92</td><td>1.72 × 10<sup>-10</sup></td></tr><tr><td>FBLN1</td><td>373979</td><td>fibulin 1</td><td>3.85</td><td>5.16 × 10<sup>-11</sup></td></tr><tr><td>MDK</td><td>423196</td><td>midkine</td><td>3.84</td><td>6.18 × 10<sup>-10</sup></td></tr><tr><td>THBS2</td><td>414837</td><td>thrombospondin 2</td><td>3.77</td><td>6.30 × 10<sup>-10</sup></td></tr><tr><td>PTN</td><td>418125</td><td>pleiotrophin</td><td>3.53</td><td>1.18 × 10<sup>-6</sup></td></tr><tr><td>PTX3</td><td>548626</td><td>pentraxin 3</td><td>3.53</td><td>1.90 × 10<sup>-8</sup></td></tr><tr><td>FRMD5</td><td>396204</td><td>nephroblastoma overexpressed</td><td>-3.46</td><td>2.75 × 10<sup>-9</sup></td></tr><tr><td>MYH15</td><td>395534</td><td>myosin heavy chain 15</td><td>3.28</td><td>1.60 × 10<sup>-8</sup></td></tr></tbody></table>

<footnote>*Common genes between two or three comparisons are indicated in bold.</footnote>
**Additional file 3** Functional enrichment analysis of each cluster of DE genes that shared a similar expression profile (see Excel file Additional_file_3.xlsx).

**Additional file 4** Relationships between gene expression modules and histological traits.

Each row corresponds to a module eigengene, and each column corresponds to one histological trait. Each cell contains the corresponding correlation and its associated p-value (shown in brackets). The table is colour-coded by correlation according to the colour legend.

<figure><img src="image_48.png" /></figure>
**Additional file 5** Mean gene significance across modules for collagen (A), the number of capillaries (B) and the coefficient of variation for fibre size (C).

A

Gene significance across modules

<figure><img src="image_49.png" /></figure>

B

Gene significance across modules

<figure><img src="image_50.png" /></figure>

C

Gene significance across modules

<figure><img src="image_51.png" /></figure>
**Additional file 6** Probe annotation based on the Galgal5 assembly (see Excel file Additional_file_6.xlsx).

**Additional file 7** Primer sequences and gene ID of the subset of genes used for RT-qPCR validation.

<table><thead><tr><td>Gene</td><td>Gene ID</td><td>Forward primer</td><td>Reverse primer</td></tr></thead><tbody><tr><td>CAPN3</td><td>423233</td><td>CAAACCAGTGCTCATTCCT</td><td>GCCTGACCCACACTGATTTT</td></tr><tr><td>CAV3</td><td>378796</td><td>GAAAGGCAGCTACACCACCT</td><td>GTAGATGCGGCTGACACACT</td></tr><tr><td>COL6A3</td><td>396548</td><td>AGCCCAAAGTGACCTACACG</td><td>GCAGATGTCCATGACATTTTCAG</td></tr><tr><td>FN1</td><td>396133</td><td>CACAAACACCAACGTCAACT</td><td>GTTTGGATGGTAATCGTGGCAG</td></tr><tr><td>LRSAM1</td><td>417265</td><td>AGGTCCTCAATGTGAAAGGCA</td><td>TTGCAGAGGAACTGCTGAATG</td></tr><tr><td>MYH15</td><td>395534</td><td>TTCAAGCAAACCCAGCCCTA</td><td>GATTTTTCAGCAACCGGGTG</td></tr><tr><td>MYH1B</td><td>374069</td><td>AGGAGCTGTCCAATGTCAACCTC</td><td>GCAGAAGAAAGCAACAGAGGGTTC</td></tr><tr><td>MYH1E</td><td>427788</td><td>CCAAATTCCGCAAGATCCAAC</td><td>CTTATGCCACTTTGTTGTCACGAC</td></tr><tr><td>MYH1F</td><td>768566</td><td>AGCTGTCCAATGTCAACCTTTCC</td><td>TGCCTCAGGTCACACTTTAGC</td></tr><tr><td>MYOCD</td><td>427790</td><td>GTCTGAGCACTCCTTGCTGATT</td><td>CGTTGTTCATGGAATGCGG</td></tr><tr><td>PDGFRα</td><td>395509</td><td>GAGTCACAAAAGCCGTGGACAT</td><td>TGTTTTCTCACAGGACCGCTC</td></tr><tr><td>PPP1R3A</td><td>101747406</td><td>GGCAAACAACGACGACAAAAAC</td><td>TTCTTCCTTGCTGCTTGGTGC</td></tr></tbody></table>
DISCUSSION GENERALE
La filière avicole repose en grande partie sur la production de poulets à croissance rapide, abattus à des poids élevés et destinés aux filières de découpe et de transformation. Depuis près de 10 ans, celle-ci est confrontée à des défauts d'intégrité musculaires s'apparentant à des myopathies, tels que le white striping et le wooden breast, observés essentiellement sur le filet des poulets de chair. Les conséquences de ces défauts sur la qualité de la viande sont multiples : dégradation de l'aspect du produit, modification de la valeur nutritionnelle de la viande, et mauvaise capacité de rétention d'eau qui s'exprime par des pertes à la cuisson plus élevées et des rendements technologiques plus faibles que ceux des filets normaux. Les viandes présentant ces défauts ne peuvent pas être vendues en frais et sont transformées en produits élaborés, voire déclassées dans les cas les plus sévères. Ces défauts sont donc à l'origine de pertes économiques importantes et d'une dégradation de l'image de la filière viande de volailles. Même si ces défauts sont aujourd'hui largement présents en abattoirs et que des études antérieures aient démontré un lien défavorable avec la croissance et le rendement en filet des poulets, leur étiologie reste mal comprise.

Sur le plan scientifique, il est important de bien comprendre les causes et les mécanismes sous-
jacents pour rechercher des solutions génétiques, nutritionnelles et d'élevage permettant
d'atténuer voire d'éliminer ces myopathies sur le terrain. Les recherches menées à ce jour ont
amélioré notre connaissance des processus biologiques impliqués dans l'apparition des défauts
musculaires de la viande, mais pour l'instant, aucune solution n'a été trouvée pour réduire de
manière significative leur incidence sans affecter les performances des animaux. Nous pouvons
néanmoins supposer qu'une partie de la solution passera par l'amélioration génétique des
souches en complément de solutions nutritionnelles et de l'amélioration des pratiques
d'élevage. La mise en œuvre de ces solutions sera facilitée par la mise au point d'outils de
diagnostic basés sur des indicateurs et des biomarqueurs permettant un phénotypage fin des
défauts. L'étude de l'architecture génétique des défauts contribuera également à l'identification
de marqueurs génétiques et de gènes candidats utiles au développement de la sélection
génomique.

Dans ce contexte, les profils protéomiques et transcriptomiques de muscles atteints ou non par
les défauts WS et WB ont été étudiés. L'analyse transcriptomique a fait l'objet d'une analyse
et d'une discussion dans un article présenté précédemment. Grâce à la comparaison d'animaux
à croissance lente ou à croissance rapide, atteints ou non de myopathies, nous avons cherché à
dissocier les voies moléculaires altérées par la sélection à long terme sur les performances de
croissance des animaux de celles spécifiquement activées en cas de myopathies sévères. En