Abstract
Using GWAS to identify candidate genes associated with cattle morphology traits at a functional level is challenging. The main difficulty of identifying candidate genes and gene interactions associated with such complex traits is the long-range linkage disequilibrium (LD) phenomenon reported widely in dairy cattle. Systems biology approaches, such as combining the Association Weight Matrix (AWM) with a Partial Correlation in an Information Theory (PCIT) algorithm, can assist in overcoming this LD. Used in a multi-breed and multi-phenotype context, the AWM-PCIT could aid in identifying udder traits candidate genes and gene networks with regulatory and functional significance. This study aims to use the AWM-PCIT algorithm as a post-GWAS analysis tool with the goal of identifying candidate genes underlying udder morphology. We used data from 78,440 dairy cows from three breeds and with own phenotypes for five udder morphology traits, five production traits, somatic cell score and clinical mastitis. Cows were genotyped with medium (50k) or low-density (7 to 10k) chips and imputed to 50k. We performed a within breed and trait GWAS. The GWAS showed 9,830 significant SNP across the genome (p < 0.05). Five thousand and ten SNP did not map a gene, and 4,820 SNP were within 10-kb of a gene. After accounting for 1SNP:1gene, 3,651 SNP were within 10-kb of a gene (set1), and 2,673 significant SNP were further than 10-kb of a gene (set2). The two SNP sets formed 6,324 SNP matrix, which was fitted in an AWM-PCIT considering udder depth/ development as the key trait resulting in 1,013 genes associated with udder morphology, mastitis and production phenotypes. The AWM-PCIT detected ten potential candidate genes for udder related traits: ESR1, FGF2, FGFR2, GLI2, IQGAP3, PGR, PRLR, RREB1, BTRC, and TGFBR2.
Highlights
Genetic architecture of complex phenotypes in cattle includes many loci affecting a given trait [1]
Our study identified three lead SNP associated with front teat placement (FTP) and fore udder attachment (FUA)
Our study suggests the usefulness of system-based approaches to identify candidate genes from interacting gene networks in a multi-breed context
Summary
Genetic architecture of complex phenotypes in cattle includes many loci affecting a given trait [1]. The incomplete variance capture may be due to causal mutations with low allele frequencies and in incomplete linkage disequilibrium (LD) with markers [2] To reduce this LD, we can either do a between breed analysis with a large sample of genotyped cows [3] or combine the results of a within breed GWAS in a multi-breed context. Previous studies have demonstrated that polymorphic sites that segregate within and across bovine populations can be studied using imputed low-to-dense genotypes [4,5] Such genotypes have been used in model organisms and dairy cattle leading to the identification of candidate causal variants or closely neighboring variants that control complex phenotypes [6,7]. If the AWM SNP matrix is used in combination with a Partial Correlation (PC) in an Information Theory (IT) framework, and for correlated phenotypes, it is possible to generate gene networks with regulatory and functional significance for udder related phenotypes
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