Association of IGF1 and KDM5A polymorphisms with performance, fatness and carcass traits in chickens

Abstract

Two functional and positional candidate genes were selected in a region of chicken chromosome 1 (GGA1), based on their biological roles, and also where several quantitative trait loci (QTL) have been mapped and associated with performance, fatness and carcass traits in chickens. The insulin-like growth factor 1 (IGF1) gene has been associated with several physiological functions related to growth. The lysine (K)-specific demethylase 5A (KDM5A) gene participates in the epigenetic regulation of genes involved with the cell cycle. Our objective was to find associations of selected single-nucleotide polymorphisms (SNPs) in these genes with performance, fatness and carcass traits in 165 F2 chickens from a resource population. In the IGF1 gene, 17SNPs were detected, and in the KDM5A gene, nine SNPs were detected. IGF1 SNP c.47673G>A was associated with body weight and haematocrit percentage, and also with feed intake and percentages of abdominal fat and gizzard genotype × sex interactions. KDM5A SNP c.34208C>T genotype × sex interaction affected body weight, feed intake, percentages of abdominal fat (p=0.0001), carcass, gizzard and haematocrit. A strong association of the diplotype × sex interaction (p<0.0001) with abdominal fat was observed, and also associations with body weight, feed intake, percentages of carcass, drums and thighs, gizzard and haematocrit. Our findings suggest that the KDM5A gene might play an important role in the abdominal fat deposition in chickens. The IGF1 and KDM5A genes are strong candidates to explain the QTL mapped in this region of GGA1.

Genetic parameters and mapping quantitative trait loci associated with tibia traits in broilers

“…These markers have been previously associated with QTL for tibia plateau angle (Sharman et al, 2007) and femur bone mineral density (Rubin et al, 2007). In this same region, two candidate genes associated with performance traits, fat deposition and carcass percentage are mapped: insulin-like growth factor 1 (IGF1) and lysine (K)-specific demethylase 5A (KDM5A) (Boschiero et al, 2013). Sharman et al (2007) also mapped QTL for tibia weight on GGA1, in the region flanked by markers MCW0297 and LEI0146.…”

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

“…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.…”

“…This small effect also was previously Also is important to check if these SNPs are within QTL regions previously published. In the past years, many studies identified QTLs associated with performance traits in different chickens populations (TATSUDA;FUJINAKA, 2001;KONING et al, 2003;IKEOBI et al, 2004;ZHOU et al, 2006;NONES et al, 2006NONES et al, , 2012AMBO et al, 2008AMBO et al, , 2009BOSCHIERO et al, 2013;NASSAR;GORAGA;BROCKMANN, 2012BROCKMANN, , 2013 (1,458 QTLs described in the Animal QTLdb) aiming to map loci that control these traits. Recently, to better understand these loci, studies have also applied GWAS with performance traits in chickens (ZHOU et al, 2006;GU et al, 2011;XIE et al, 2012).…”

“…Therefore, we also checked the overlap of these SNPs only with QTLs mapped using specifically the same traits and the same F2 population (NONES et al, 2006;AMBO et al, 2009;BOSCHIERO et al, 2013;FELÍCIO et al, 2013b) used in this study. From 23 different QTL intervals, we identified 12 SNPs overlapping with seven of them (see the QTLs bolded in APPENDIX G and I to check the QTL list).…”

“…Several studies have been conducted in this population using markers randomly distributed in the genome (microsatellites), which have allowed the identification of several QTLs for production traits (NONES et al, 2006(NONES et al, , 2012AMBO et al, 2008AMBO et al, , 2009CAMPOS et al, 2009). 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).…”