QTL of small effect account for a large part of the genetic variance in economic traits in dairy cattle but are difficult to be detected. Gene expression data may help to identify such QTL. The objective of this study was to combine results from a Bayesian prediction model, GWAS, expression QTL (eQTL) and expression data to detect variants that are associated both with QTL and gene expression. We identified a number of regions that show a strong correlation between gene expression and local GEBVs for milk traits and fertility, and are therefore likely to harbour regulatory mutations affecting both gene expression and the milk traits. While these regions often explained a large proportion of the variance in expression, the amount of variation in the milk traits explained was small to modest. The results suggest that gene expression levels can be useful in identifying QTL for complex traits, provided the mutation affecting the complex trait is regulatory. Keywords: gene expression, GWAS, dairy cattle, QTL
Proceedings of the World Congress on Genetics Applied to Livestock Production, Volume Biology & Species - Bovine (dairy) 1, , 251, 2018
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