Dissecting closely linked association signals in combination with the mammalian phenotype database can identify candidate genes in dairy cattle

BMC Genetics
Zexi CaiGoutam Sahana

Abstract

Genome-wide association studies (GWAS) have been successfully implemented in cattle research and breeding. However, moving from the associations to identifying the causal variants and revealing 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 variant. We 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 mut...Continue Reading

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Software Mentioned

GCTA
COJO
Ensembl
Minimac2
VEP
VEP ( Variant Effect Predictor )
PLINK
IMPUTE2
BEAGLE
UMD

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