Quantifying the contribution of sequence variants with regulatory and evolutionary significance to 34 bovine complex traits

Abstract

Many genome variants shaping mammalian phenotype are hypothesized to regulate gene transcription and/or to be under selection. However, most of the evidence to support this hypothesis comes from human studies. Systematic evidence for regulatory and evolutionary signals contributing to complex traits in a different mammalian model is needed. Sequence variants associated with gene expression (eQTLs) and concentration of metabolites (mQTLs), and under histone modification marks in several tissues were discovered from multi-omics data of over 400 cattle. Variants under selection and evolutionary constraint were identified using genome databases of multiple species. These analyses defined 30 sets of variants and for each set we estimated the genetic variance the set explained across 34 complex traits in 11,923 bulls and 32,347 cows with 17,669,372 imputed variants. The per-variant trait heritability of these sets across traits was highly consistent (r>0.94) between bulls and cows.Based on the per-variant heritability, conserved sites across 100 vertebrate species and mQTLs ranked the highest, followed by eQTLs, young variants, those under histone modification marks and selection signatures. From these results, we defined a Functional-And-Evolutionary Trait Heritability (FAETH) score indicating the functionality and predicted heritability of each variant. In 7,551 Danish cattle, the high FAETH-ranking variants had significantly increased genetic variances and genomic prediction accuracies in 3 production traits compared to the low FAETH-ranking variants. The FAETH framework combines the information of gene regulation, evolution and trait heritability to rank variants and the publicly available FAETH data provides a set of biological priors for cattle genomic selection worldwide.

Genome-wide fine-mapping identifies pleiotropic and functional variants that predict many traits across global cattle populations

“…Analysis overview. Our genome-wide fine-mapping analysis utilised two major sources of information: first, the GWAS effects of 17.7 million sequence variants on 34 Cholesky-decorrelated traits 23,24 in bulls and cows (Supplementary Table 1) where a small multi-trait p-value 11 indicates a variant to be pleiotropic and second, the Functional-And-Evolutionary Trait Heritability (FAETH) score 7 where a high score indicates the high functional and evolutionary significance of these 17.7 M variants. Our genome-wide fine-mapping in Holstein (9739 ♂/22,899 ♀), Jersey (2059 ♂/6174 ♀), mixed breed (0 ♂/2850 ♀) and Australian Red breeds (125 ♂/424 ♀) had five major steps as described in Fig.…”

“…Sequence variant prioritisation using pleiotropy and functionality. The 17.7 million sequence variants were first ranked by their multi-trait p-values divided by their FAETH score 7 . Variants that had low multi-trait p-value and high FAETH score, i.e., pleiotropic and functional, would be top-ranked.…”

“…Three variants that are prioritized by the Australian 80k selection and have p < 0.05 in the US GWAS are also splicing sQTLs in an Australian RNA sequencing dataset 28 ; the target genes (shown in red colour parenthesis) of these sQTLs have altered intron excision ratio (FDR < 0.05). Variants in the region that are conserved across 100 vertebrate species (conserved100w) and are historically young 7 are also labelled.…”

“…Even with wholegenome sequence data, it is difficult to identify the causal variants because their effects are typically small, and there are too many variants in linkage disequilibrium (LD) 5 . These difficulties in identifying causal variants may be reduced by statistical analysis that fits all variants simultaneously 6 and by the use of information on the likely function of genomic sites [7][8][9] . When these methods are applied to a small genomic region, it is known as fine-scale mapping 10 .…”

“…In this paper, we describe a method that approximates this ideal and applies it to data on 44,000 dairy cattle from three breeds (Holstein, Jersey and Australian Red) with records on 34 traits including milk production, fertility, management and body conformation (average h 2 of 0.42 ± 0.04 in bulls and 0.16 ± 0.03 in cows). First, we reduce the number of variants from 17 to 1.7 M by carrying out a multi-trait GWAS using single variant regression incorporating the Functional-And-Evolutionary Trait Heritability (FAETH) score, a publicly available ranking of cattle sequence variants based on their functionality and predicted heritability 7 . The set of 1.7 M variants is further reduced to 165k by variant clustering and pruning for LD.…”