Abstract

We performed a pairwise epistatic interaction test using the chicken 60 K single nucleotide polymorphism (SNP) chip for the 11th generation of the Northeast Agricultural University broiler lines divergently selected for abdominal fat content. A linear mixed model was used to test two dimensions of SNP interactions affecting abdominal fat weight. With a threshold of P<1.2×10−11 by a Bonferroni 5% correction, 52 pairs of SNPs were detected, comprising 45 pairs showing an Additive×Additive and seven pairs showing an Additive×Dominance epistatic effect. The contribution rates of significant epistatic interactive SNPs ranged from 0.62% to 1.54%, with 47 pairs contributing more than 1%. The SNP-SNP network affecting abdominal fat weight constructed using the significant SNP pairs was analyzed, estimated and annotated. On the basis of the network’s features, SNPs Gga_rs14303341 and Gga_rs14988623 at the center of the subnet should be important nodes, and an interaction between GGAZ and GGA8 was suggested. Twenty-two quantitative trait loci, 97 genes (including nine non-coding genes), and 50 pathways were annotated on the epistatic interactive SNP-SNP network. The results of the present study provide insights into the genetic architecture underlying broiler chicken abdominal fat weight.

Highlights

  • Epistasis – the interactions between polymorphic loci, such as single nucleotide polymorphism (SNP), genes or quantitative trait loci (QTLs) – is a hot topic in quantitative genetics [1]

  • Performing genome-wide SNPs interaction analysis represents the step for detecting the variations of quantitative traits, because single-locus tests cannot identify the interactions among SNPs, genes or other genetic or environmental factors [10]

  • The contribution rate of the significant epistatic interactive SNP pairs ranged from 0.62% to 1.54%, with 47 pairs having a contribution rate of more than 1%

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Summary

Introduction

Epistasis – the interactions between polymorphic loci, such as SNPs, genes or quantitative trait loci (QTLs) – is a hot topic in quantitative genetics [1]. Epistasis is a major factor that determines variation in quantitative phenotypes [2]. Performing genome-wide SNPs interaction analysis represents the step for detecting the variations of quantitative traits, because single-locus tests cannot identify the interactions among SNPs, genes or other genetic or environmental factors [10]. The markers detected by GWAS only explained a fraction of the heritable variance [11,12,13]. Identifying markers that show interactions would help to explain a higher proportion of the heritable variance. The SNP-SNP and gene-gene interactions can provide new insights into the genetic basis of complex traits

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