Abstract

Genetic variants which affect complex traits (causal variants) are thought to be found in functional regions of the genome. Identifying causal variants would be useful for predicting complex trait phenotypes in dairy cows, however, functional regions are poorly annotated in the bovine genome. Functional regions can be identified on a genome-wide scale by assaying for post-translational modifications to histone proteins (histone modifications) and proteins interacting with the genome (e.g., transcription factors) using a method called Chromatin immunoprecipitation followed by sequencing (ChIP-seq). In this study ChIP-seq was performed to find functional regions in the bovine genome by assaying for four histone modifications (H3K4Me1, H3K4Me3, H3K27ac, and H3K27Me3) and one transcription factor (CTCF) in 6 tissues (heart, kidney, liver, lung, mammary and spleen) from 2 to 3 lactating dairy cows. Eighty-six ChIP-seq samples were generated in this study, identifying millions of functional regions in the bovine genome. Combinations of histone modifications and CTCF were found using ChromHMM and annotated by comparing with active and inactive genes across the genome. Functional marks differed between tissues highlighting areas which might be particularly important to tissue-specific regulation. Supporting the cis-regulatory role of functional regions, the read counts in some ChIP peaks correlated with nearby gene expression. The functional regions identified in this study were enriched for putative causal variants as seen in other species. Interestingly, regions which correlated with gene expression were particularly enriched for potential causal variants. This supports the hypothesis that complex traits are regulated by variants that alter gene expression. This study provides one of the largest ChIP-seq annotation resources in cattle including, for the first time, in the mammary gland of lactating cows. By linking regulatory regions to expression QTL and trait QTL we demonstrate a new strategy for identifying causal variants in cattle.

Highlights

  • Finding the genetic variants which lead to different phenotypes has been the goal of geneticists for many years

  • 86 ChIP-seq datasets were generated as shown in Supplementary Table 1

  • Quality of the ChIP-seq assay was assessed by calculating the Jensen-Shannon Distance (JSD) between the sample and input and cross strand correlation metrics

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Summary

Introduction

Finding the genetic variants which lead to different phenotypes has been the goal of geneticists for many years. In the dairy industry, finding genetic variants which affect phenotypes would improve selective breeding using genomic selection (MacLeod et al, 2016). Genomic selection relies on associations between genotypes and phenotypes to predict the phenotypes of animals. This association could be based on linkage disequilibrium (LD) between a SNP and the causal variant rather than a direct effect of the SNP itself. Identifying the causal variant would prevent breakdown of LD over time and extend genomic predictions to populations with different LD (Hayes et al, 2016)

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