SNP and INDEL detection in a QTL region on chicken chromosome 2 associated with muscle deposition

Abstract

Genetic improvement is important for the poultry industry, contributing to increased efficiency of meat production and quality. Because breast muscle is the most valuable part of the chicken carcass, knowledge of polymorphisms influencing this trait can help breeding programs. Therefore, the complete genome of 18 chickens from two different experimental lines (broiler and layer) from EMBRAPA was sequenced, and SNPs and INDELs were detected in a QTL region for breast muscle deposition on chicken chromosome 2 between microsatellite markers MCW0185 and MCW0264 (105,849-112,649 kb). Initially, 94,674 unique SNPs and 10,448 unique INDELs were identified in the target region. After quality filtration, 77% of the SNPs (85,765) and 60% of the INDELs (7828) were retained. The studied region contains 66 genes, and functional annotation of the filtered variants identified 517 SNPs and three INDELs in exonic regions. Of these, 357 SNPs were classified as synonymous, 153 as non-synonymous, three as stopgain, four INDELs as frameshift and three INDELs as non-frameshift. These exonic mutations were identified in 37 of the 66 genes from the target region, three of which are related to muscle development (DTNA, RB1CC1 and MOS). Fifteen non-tolerated SNPs were detected in several genes (MEP1B, PRKDC, NSMAF, TRAPPC8, SDR16C5, CHD7, ST18 and RB1CC1). These loss-of-function and exonic variants present in genes related to muscle development can be considered candidate variants for further studies in chickens. Further association studies should be performed with these candidate mutations as should validation in commercial populations to allow a better explanation of QTL effects.

High-throughput and Cost-effective Chicken Genotyping Using Next-Generation Sequencing

“…These newly discovered SNPs in exonic regions include a QTL region on chromosome 3 associated with fatness in chickens (0.98%)31 and another on chromosome 2 associated with muscle deposition (0.59%)32. These exonic variants (2,590) were classified into functional categories due to their potential to alter the tri-dimensional structure and function of the translated protein33.…”

Unraveling important genetic associations and differential methylation profiles using reduced genome sequencing in chickens

“…These error rates are in agreement with the low heterozygous call rate (0.90>call rate>0.95) and high heterozygous CV We also found a small increase in the proportion of SNPs (3.3%) in exonic regions compared to a recent functional classification of 15 million SNPs detected from diverse chicken populations (2.2%) , or when compared to the WCGR. These newly discovered SNPs in exonic regions include a QTL region on chromosome 3 associated with fatness in chickens (0.98%) (MOREIRA et al, 2015) and another on chromosome 2 associated with muscle deposition (0.59%) (GODOY et al, 2015). These exonic variants (2,590) were classified into functional categories due to their potential to alter the tri-dimensional structure and function of the translated protein (NG;HENIKOFF, 2003).…”

“…For better understanding of complex traits control in a segregating F2 population, our research group have focused the attention on genetic association and linkage analyses using different approaches, as: candidate genes (BOSCHIERO et al, 2013;FELÍCIO et al, 2013aFELÍCIO et al, , 2013bPÉRTILLE et al, 2015) and QTL mapping (NONES et al, 2006(NONES et al, , 2012AMBO et al, 2008AMBO et al, , 2009CAMPOS et al, 2009), respectively, and more recently, NGS approaches (GODOY et al, 2015;MOREIRA et al, 2015). We have presented here the first study using a higher density of SNPs in this F2 population with GWAS purpose.…”

“…generated from the same 10 chickens used in this study (TT and CC parental lines). The comparison between these two datasets was performed using CRAN (data .table and reshape2) repository packages for R. More details of the sequencing process of WCGR SNP data can be found in recent publications (GODOY et al, 2015;MOREIRA et al, 2015).…”

“…Following these, some of the studies have focused their attention on the identification of SNPs in functional and positional candidate genes and to test their association on target QTL regions (BOSCHIERO et al, 2013;FELÍCIO et al, 2013aFELÍCIO et al, , 2013bPÉRTILLE et al, 2015) . With the advent of next-generation sequencing (NGS), it was possible to identify a global SNP profile and to perform genome-wide association studies (GWAS) to find novel QTL regions and also to refine the previously published regions (GODOY et al, 2015;MOREIRA et al, 2015). Parallel to our group researches, several manuscripts have been published in this field related with: QTLs mapping (TATSUDA; FUJINAKA, 2001;KONING et al, 2003;IKEOBI et al, 2004;ZHOU et al, 2006;NASSAR;GORAGA;BROCKMANN, 2012BROCKMANN, , 2013, SNP identification in candidate genes (NIE; ZHANG; LEI, 2003;SHEN et al, 2012), SNP identification in target QTLs (AHSAN et al, 2013;ROUX et al, 2014;LI et al, 2015) and GWAS (XIE et al, 2012;LUO et al, 2013aLUO et al, , 2013bMORRIS et al, 2013;PARK et al, 2013;SUN et al, 2013SUN et al, , 2014 using the chicken as a model.…”