Abstract
BackgroundIdentifying causative mutations or genes through which quantitative trait loci (QTL) act has proven very difficult. Using information such as gene expression may help to identify genes and mutations underlying QTL. Our objective was to identify regions associated both with production traits or fertility and with gene expression, in dairy cattle. We used three different approaches to discover QTL that are also expression QTL (eQTL): 1) estimate the correlation between local genomic estimated breeding values (GEBV) and gene expression, 2) investigate whether the 300 intervals explaining most genetic variance for a trait contain more eQTL than 300 randomly selected intervals, and 3) a colocalisation analysis. Phenotypes and genotypes up to sequence level of 35,775 dairy bulls and cows were used for QTL mapping, and gene expression and genotypes of 131 cows were used to identify eQTL.ResultsWith all three approaches, we identified some overlap between eQTL and QTL, though the majority of QTL in our dataset did not seem to be eQTL. The most significant associations between QTL and eQTL were found for intervals on chromosome 18, where local GEBV for all traits showed a strong association with the expression of the FUK and DDX19B. Intervals whose local GEBV for a trait correlated highly significantly with the expression of a nearby gene explained only a very small part of the genetic variance for that trait. It is likely that part of these correlations were due to linkage disequilibrium (LD) in the interval. While the 300 intervals explaining most genetic variance explained most of the GEBV variance, they contained only slightly more eQTL than 300 randomly selected intervals that explained a minimal portion of the GEBV variance. Furthermore, some variants showed a high colocalisation probability, but this was only the case for few variants.ConclusionsSeveral reasons may have contributed to the low level of overlap between QTL and eQTL detected in our study, including a lack of power in the eQTL study and long-range LD making it difficult to separate QTL and eQTL. Furthermore, it may be that eQTL explain only a small fraction of QTL.
Highlights
Identifying causative mutations or genes through which quantitative trait loci (QTL) act has proven very difficult
Colocalisation of QTL and expression QTL (eQTL) Using the programme eCAVIAR, we examined the intervals with significant QTL to see if there was a SNP with a high posterior probability that was both the QTL and an eQTL by estimating the probability that a variant is causal in both the genome wide association studies (GWAS) and eQTL analysis, the colocalization posterior probability (CLPP)
False discovery rate For the GWAS, eQTL study and the correlation between local Genomic estimated breeding value (GEBV) and gene expression, the false discovery rate (FDR) was calculated as FDR = nSign/(t × nTests), where nSign is the number of sequence variants or correlations with a p-value ≤ t and nTests the total number of tests, and t the significance threshold used. Both QTL and eQTL are common and this fact, combined with the long-range linkage disequilibrium (LD) in cattle, means that a QTL will often be in LD with an eQTL
Summary
Identifying causative mutations or genes through which quantitative trait loci (QTL) act has proven very difficult. Using information such as gene expression may help to identify genes and mutations underlying QTL. Identifying the causative mutation or gene through which a QTL acts has proven very difficult. This difficulty is largely because of the small effect size of most QTL, extensive long-range linkage disequilibrium (LD). Littlejohn et al [10] have recently shown that a major QTL influencing production traits in dairy cattle is an eQTL related to the expression of MGST1 and Kemper et al [11] found a QTL for milk yield that is likely an eQTL for SLC37A1. In general, few QTL have been convincingly shown to be eQTL for the same reasons that make QTL identification difficult. eQTL and QTL are both common and so, given the long-range LD in cattle, it is likely that an eQTL will be in LD with a QTL and appear associated with the phenotype
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