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Abstract
BackgroundGenome-wide association studies (GWAS) have been successfully implemented in cattle research and breeding. However, moving from the associations to identify the causal variants and reveal underlying mechanisms have proven complicated. In dairy cattle populations, we face a challenge due to long-range linkage disequilibrium (LD) arising from close familial relationships in the studied individuals. Long range LD makes it difficult to distinguish if one or multiple quantitative trait loci (QTL) are segregating in a genomic region showing association with a phenotype. We had two objectives in this study: 1) to distinguish between multiple QTL segregating in a genomic region, and 2) use of external information to prioritize candidate genes for a QTL along with the candidate variants.ResultsWe observed fixing the lead SNP as a covariate can help to distinguish additional close association signal(s). Thereafter, using the mammalian phenotype database, we successfully found candidate genes, in concordance with previous studies, demonstrating the power of this strategy. Secondly, we used variant annotation information to search for causative variants in our candidate genes. The variant information successfully identified known causal mutations and showed the potential to pinpoint the causative mutation(s) which are located in coding regions.ConclusionsOur approach can distinguish multiple QTL segregating on the same chromosome in a single analysis without manual input. Moreover, utilizing information from the mammalian phenotype database and variant effect predictor as post-GWAS analysis could benefit in candidate genes and causative mutations finding in cattle. Our study not only identified additional candidate genes for milk traits, but also can serve as a routine method for GWAS in dairy cattle.
Highlights
Genome-wide association studies (GWAS) have been successfully implemented in cattle research and breeding
We applied a genome-wide associate studies (GWAS) analysis approach that automatically and iteratively accounts for the effects of quantitative trait loci (QTL) identified in previous iteration(s), a similar approach to conditional analysis implemented in GCTA [11]
The impact of pre-correction on type I error rate was assessed by analyzing simulated data with the effect of a quantitative trait nucleotide (QTN) added to the real phenotypic data
Summary
Genome-wide association studies (GWAS) have been successfully implemented in cattle research and breeding. The development of high density single nucleotide polymorphism (SNP) arrays and next-generation sequencing (NGS) technologies have made genome-wide marker sets available in many organisms [1, 2] Combining these with phenotypic records on many individuals, genome-wide associate studies (GWAS) present a powerful tool to undercover genetic variants associated with variation in the phenotype [3]. To resolve these issues, we need additional information over and above association statistics. Other sources of additional information like variants’ annotation [8] and evolutionary conservation scores [9] have been used These analyses need to be designed on a case-by-case basis [10]. Their implementation is challenging in livestock due to the sparsity of annotation data
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