Integration of GWAS, CNV and selection signature reveals candidate genes for abdominal fat regulation in chickens Carcass fat content is an economically important trait in commercial chickens. The use of genome-wide high density SNPs may improve the power and resolution to identify QTLs, putative candidate genes and copy number variations (CNVs), for selection programs. The main goal of this study was to identify genomic windows and putative candidate genes for carcass fat content. We checked the overlap of QTL with regions demonstrating signatures of selection and inherited CNVs identified in the same population. A total of 497 42 day-old chickens from the EMBRAPA F2 Chicken Resource Population developed for QTL studies were genotyped with the 600K SNP genotyping array (Affymetrix®), and phenotyped for carcass fat content weight (CFCW) and carcass fat content on a dry matter basis (CFCDM). After quality control, a total of 480 samples and 371,557 SNPs annotated in autosomal chromosomes (GGA1-28) based on Gallus_gallus-5.0 (NCBI) were kept for further analysis. GWAS analyses were performed with GenSel software using BayesB method (π=0.9988) to identify genomic windows associated with CFCW or CFC%. We identified 15 genomic windows associated with CFC% on GGA1, 7, 15, 20 and 28, and from those, we identified two adjacent windows on GGA7 considered as the same QTL explaining 1.31 and 2.18% of the genetic variance for CFCW and CFC%, respectively. This QTL overlapped with one regions previsiouly know to regulate abdominal fat in chickens and the QTL region encompassed two putative candidate genes overlapping with signatures of selection and inherited CNVs. Our findings are helpful to better understand the genetic regulation of fatness in chickens. Keywords: cnv, gwas, selection signatures, 600k snp genotyping array

Luiz Coutinho, Gabriel Moreira, Thais Godoy, Clarissa Boschiero, Aline Cesar, James Reecy, Mônica Ledur, Dorian Garrick, Vinicius Silva, Richard Crooijmans, Martien Groenen

Proceedings of the World Congress on Genetics Applied to Livestock Production, Volume Biology - Growth and Development, , 356, 2018
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