Mapping QTL for growth and shank traits in chickens divergently selected for high or low body weight

Ankra-Badu, Georgina A.; Bihan-Duval, Élisabeth Le; Mignon-Grasteau, Sandrine; Pitel, Frédérique; Beaumont, Catherine; Duclos, M. J.; Simon, Jean; Carré, Wilfrid; Porter, Tom E.; Vignal, Alain; Cogburn, Larry A.; Aggrey, S. E.

doi:10.1111/j.1365-2052.2009.02017.x

Cited by 34 publications

(24 citation statements)

References 41 publications

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“…For CW and EW, one consistent region was identified (about 78.47 Mb to 79.53 Mb) on GGA4, which corresponded to QTL regions previously reported [22], [23]. There were seven significant SNPs located near four functional genes ( LCORL , LAP3 , LDB2 , and TAPT1 ).…”

Section: Discussionsupporting

confidence: 64%

Genome-Wide Association Study Identifies Loci and Candidate Genes for Body Composition and Meat Quality Traits in Beijing-You Chickens

et al. 2013

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Body composition and meat quality traits are important economic traits of chickens. The development of high-throughput genotyping platforms and relevant statistical methods have enabled genome-wide association studies in chickens. In order to identify molecular markers and candidate genes associated with body composition and meat quality traits, genome-wide association studies were conducted using the Illumina 60 K SNP Beadchip to genotype 724 Beijing-You chickens. For each bird, a total of 16 traits were measured, including carcass weight (CW), eviscerated weight (EW), dressing percentage, breast muscle weight (BrW) and percentage (BrP), thigh muscle weight and percentage, abdominal fat weight and percentage, dry matter and intramuscular fat contents of breast and thigh muscle, ultimate pH, and shear force of the pectoralis major muscle at 100 d of age. The SNPs that were significantly associated with the phenotypic traits were identified using both simple (GLM) and compressed mixed linear (MLM) models. For nine of ten body composition traits studied, SNPs showing genome wide significance (P<2.59E−6) have been identified. A consistent region on chicken (Gallus gallus) chromosome 4 (GGA4), including seven significant SNPs and four candidate genes (LCORL, LAP3, LDB2, TAPT1), were found to be associated with CW and EW. Another 0.65 Mb region on GGA3 for BrW and BrP was identified. After measuring the mRNA content in beast muscle for five genes located in this region, the changes in GJA1 expression were found to be consistent with that of breast muscle weight across development. It is highly possible that GJA1 is a functional gene for breast muscle development in chickens. For meat quality traits, several SNPs reaching suggestive association were identified and possible candidate genes with their functions were discussed.

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Section: Discussionsupporting

confidence: 64%

Genome-Wide Association Study Identifies Loci and Candidate Genes for Body Composition and Meat Quality Traits in Beijing-You Chickens

et al. 2013

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“…HNF4G (region R6) knockout mice have a higher bodyweight at 7 weeks and a reduced feed and water intake compared to wild type mice [40]. Additionally, a quantitative trait locus (QTL) associated with bodyweight from 3 to 7 weeks overlaps region R6 [41]. Given the biological function of HNF4G , the putative selective sweep detected at R6 might have been a direct consequence of selection for bodyweight traits.…”

Section: Discussionmentioning

confidence: 99%

Signatures of Selection in the Genomes of Commercial and Non-Commercial Chicken Breeds

et al. 2012

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Identifying genomics regions that are affected by selection is important to understand the domestication and selection history of the domesticated chicken, as well as understanding molecular pathways underlying phenotypic traits and breeding goals. While whole-genome approaches, either high-density SNP chips or massively parallel sequencing, have been successfully applied to identify evidence for selective sweeps in chicken, it has been difficult to distinguish patterns of selection and stochastic and breed specific effects. Here we present a study to identify selective sweeps in a large number of chicken breeds (67 in total) using a high-density (58 K) SNP chip. We analyzed commercial chickens representing all major breeding goals. In addition, we analyzed non-commercial chicken diversity for almost all recognized traditional Dutch breeds and a selection of representative breeds from China. Based on their shared history or breeding goal we in silico grouped the breeds into 14 breed groups. We identified 396 chromosomal regions that show suggestive evidence of selection in at least one breed group with 26 of these regions showing strong evidence of selection. Of these 26 regions, 13 were previously described and 13 yield new candidate genes for performance traits in chicken. Our approach demonstrates the strength of including many different populations with similar, and breed groups with different selection histories to reduce stochastic effects based on single populations.

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“…And many candidate genes were reported to have important effects on growth [4]–[6]. Moreover, a large number of quantitative trait loci (QTLs) for chicken growth have been identified [7]–[11]. However, the genetic mechanisms in chicken growth system are still unknown and, polymorphism or QTL alone can not provide adequate explanations for them.…”

Section: Introductionmentioning

confidence: 99%

Comparison of the Genome-Wide DNA Methylation Profiles between Fast-Growing and Slow-Growing Broilers

et al. 2013

PLoS ONE

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IntroductionGrowth traits are important in poultry production, however, little is known for its regulatory mechanism at epigenetic level. Therefore, in this study, we aim to compare DNA methylation profiles between fast- and slow-growing broilers in order to identify candidate genes for chicken growth. Methylated DNA immunoprecipitation-sequencing (MeDIP-seq) was used to investigate the genome-wide DNA methylation pattern in high and low tails of Recessive White Rock (WRRh; WRRl) and that of Xinhua Chickens (XHh; XHl) at 7 weeks of age. The results showed that the average methylation density was the lowest in CGIs followed by promoters. Within the gene body, the methylation density of introns was higher than that of UTRs and exons. Moreover, different methylation levels were observed in different repeat types with the highest in LINE/CR1. Methylated CGIs were prominently distributed in the intergenic regions and were enriched in the size ranging 200–300 bp. In total 13,294 methylated genes were found in four samples, including 4,085 differentially methylated genes of WRRh Vs. WRRl, 5,599 of XHh Vs. XHl, 4,204 of WRRh Vs. XHh, as well as 7,301 of WRRl Vs. XHl. Moreover, 132 differentially methylated genes related to growth and metabolism were observed in both inner contrasts (WRRh Vs. WRRl and XHh Vs. XHl), whereas 129 differentially methylated genes related to growth and metabolism were found in both across-breed contrasts (WRRh Vs. XHh and WRRl Vs. XHl). Further analysis showed that overall 75 genes exhibited altered DNA methylation in all four contrasts, which included some well-known growth factors of IGF1R, FGF12, FGF14, FGF18, FGFR2, and FGFR3. In addition, we validate the MeDIP-seq results by bisulfite sequencing in some regions.ConclusionsThis study revealed the global DNA methylation pattern of chicken muscle, and identified candidate genes that potentially regulate muscle development at 7 weeks of age at methylation level.

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Mapping QTL for growth and shank traits in chickens divergently selected for high or low body weight

Cited by 34 publications

References 41 publications

Genome-Wide Association Study Identifies Loci and Candidate Genes for Body Composition and Meat Quality Traits in Beijing-You Chickens

Genome-Wide Association Study Identifies Loci and Candidate Genes for Body Composition and Meat Quality Traits in Beijing-You Chickens

Signatures of Selection in the Genomes of Commercial and Non-Commercial Chicken Breeds

Comparison of the Genome-Wide DNA Methylation Profiles between Fast-Growing and Slow-Growing Broilers

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