Abstract
While single nucleotide polymorphisms (SNPs) associated with multiple phenotype have been reported, the knowledge of pleiotropy of uncorrelated phenotype is minimal. Principal components (PCs) and uncorrelated Cholesky transformed traits (CT) were constructed using 25 raw traits (RTs) of 2841 dairy bulls. Multi-trait meta-analyses of single-trait genome-wide association studies for RT, PC and CT in bulls were validated in 6821 cows. Most PCs and CTs had substantial estimates of heritability, suggesting that genes affect phenotype via diverse pathways. Phenotypic orthogonalizations did not eliminate pleiotropy: the meta-analysis achieved an agreement of significant pleiotropic SNPs (p < 1 × 10−5, n = 368) between RTs (416), PCs (466) and CTs (425). From this overlap we identified 21 lead SNPs with 100% validation rate containing two clusters: one consisted of DGAT1 (chr14:1.8 M+), MGST1 (chr5:93 M+), PAEP (chr11:103 M+) and GPAT4 (chr27:36 M+) affecting protein, milk and fat yield and the other included CSN2 (chr6:87 M+), MUC1 (chr3:15.6 M), GHR (chr20:31.2 M+) and SDC2 (chr14:70 M+) affecting protein and milk yield. Combining beef cattle data identified correlated SNPs representing CAPN1 (chr29:44 M+) and CAST (chr 7:96 M+) loci affecting beef tenderness, showing pleiotropic effects in dairy cattle. Our findings show that SNPs with a large effect on one trait are likely to have small effects on other uncorrelated traits.
Highlights
Understanding genetic control of mammalian phenotype, including body growth, health outcomes and metabolic pathways can improve patient treatment[1], knowledge of evolution[2] and agricultural efficiency[3]
Among the raw traits (RTs) and Cholesky transformed traits (CT) there was a tendency for the traits with the highest heritability to have the highest number of significant single nucleotide polymorphisms (SNPs) (Table 1)
Prot), fat (02.Fat) and milk (03.Milk) yield RTs and CTs had the largest numbers of significant SNPs (>100) and had the highest estimated heritability (>0.8)
Summary
Understanding genetic control of mammalian phenotype, including body growth, health outcomes and metabolic pathways can improve patient treatment[1], knowledge of evolution[2] and agricultural efficiency[3]. Most mammalian phenotypes are quantitative or complex traits, whose variation is controlled by many genomic mutations with small effects and by environmental effects. It is expected that correlated traits share some causal variants and this has been observed in humans[5] and livestock[6, 7]. It is possible that uncorrelated traits share some causal variants This possibility can be tested by transforming a set of correlated traits into uncorrelated traits, for instance, by principal components (PCs) analysis[8, 9]. If genes influence a set of traits through a limited number of physiological pathways, it may result in that only the first few PCs showed strong genetic effects leading to a simple picture of pleiotropy. We examined the effects in dairy cattle of SNPs significantly associated with quantitative traits in beef cattle[7]
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