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 (expression quantitative trait loci [eQTLs]) and concentration of metabolites (metabolic quantitative trait loci [mQTLs]) and under histone-modification marks in several tissues were discovered from multiomics 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 additional 7,551 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 provide a set of biological priors for cattle genomic selection worldwide.

Effect direction meta-analysis of GWAS identifies extreme, prevalent and shared pleiotropy in a large mammal

“…That is, the average number of traits affected by a single-nucleotide polymorphisms (SNP) in this group is 9.63. Additional analysis with functional data 12,13 indicated that both single-trait and multi-trait FDRed can help find informative variants at lenient p value thresholds where conventional FDR was large (Supplementary Note 1). When imposing a significance threshold in only one sex, at the multitrait p value threshold of 1e−6, the multi-trait FDRed was 0.14 in bulls and 0.2 in cows (Supplementary Data 5).…”

“…All calculations regarding this clustering were done separately in each sex. Firstly, variants were ranked by their multi-trait p values adjusted (divided) by their functional-and-evolutionary trait heritability (FAETH) score 13 . Then, variants ranked within the top 10% within each sex were selected for LD pruning, using Plink 1.9 38 measured as the correlation between the genotypes of variant i and variant j .…”

Identification of eQTLs and sQTLs associated with meat quality in beef

“…A 1 Mbp region nearby DGAT1 harbors three polymorphisms that are able to explain a high amount of the SNP variance in the set. Xiang et al [40] classified 17,669,372 imputed variants into 30 sets of markers. This classification included categories such as inter-species conserved markers, polymorphisms associated with metabolic traits (several milk metabolites), expression regulation related polymorphisms (gene and exon expression QTLs, sQTLs, and allele-specific expression QTLs), and markers with evolutionary roles.…”

Constrained non-coding sequence provides insights into regulatory elements and loss of gene expression in maize

“…Yet understanding how those genomes work without ENCODE scale projects for each species will require that we use evolutionary approaches to identify key functional regions. DNA sequences subject to purifying selection across different species reflect functional constraints (Xiang et al 2019;Finucane et al 2015). The detection of conserved non-coding sequence (CNS) in plants is an ongoing challenge(Van de Velde et al 2016) and recent studies (Tu et al 2020;Ricci et al 2019;Lu et al 2019) could provide new insight into their function.…”