Genetic score omics regression and multi-trait meta-analysis detect widespread cis-regulatory effects shaping bovine complex traits

Abstract

To complete the genome-to-phenome map, transcriptome-wide association studies (TWAS) are performed to correlate genetically predicted gene expression with observed phenotypic measurements. However, the relatively small training population assayed with gene expression could limit the accuracy of TWAS. We propose Genetic Score Omics Regression (GSOR) correlating observed gene expression with genetically predicted phenotype, i.e., genetic score. The score, calculated using variants near genes with assayed expression, provides a powerful association test between cis-effects on gene expression and the trait. In simulated and real data, GSOR outperforms TWAS in detecting causal/informative genes. Applying GSOR to transcriptomes of 16 tissue (N~5000) and 37 traits in ~120,000 cattle, multi-trait meta-analyses of omics-associations (MTAO) found that, on average, each significant gene expression and splicing mediates cis-genetic effects on 8~10 traits. Supported by Mendelian Randomisation, MTAO prioritised genes/splicing show increased evolutionary constraints. Many newly discovered genes/splicing regions underlie previously thought single-gene loci to influence multiple traits.

Genomic Regions Associated with Milk Composition and Fertility Traits in Spring-Calved Dairy Cows in New Zealand

“…Other candidate genes identified for FP and PP in this population were IQANK1, ZC3H3, and RHPN1 . The gene IQANK1 is related to the regulation of barbed-end actin filament capping (GO:2000812); this gene is a neighbour gene of DGAT1 and has been shown to have causal effects on milk production traits independent of linkage disequilibrium [ 40 ]. The gene ZC3H3 is involved in mRNA 3’-end processing (GO:0031124), positive regulation of the activin receptor signaling pathway (GO:0032927), and the regulation of mRNA polyadenylation (GO:1900363), whilst RHPN1 codes negative regulation of stress fiber assembly (GO:0051497).…”

The big challenge for livestock genomics is to make sequence data pay

“…Second, we have to assume that functional genomics data contain specific enough information that we can distinguish the causative variants that are relevant to our trait of interest, when there are multiple genuine causative variants for different traits. There are likely to be multiple linked causative variants (Abell et al, 2022;Xiang et al, 2022a) for many traits, and consequently a very large number of variants that are genuinely causal for different traits will occur close to each other. Here, methods that identify tissues and conditions that are enriched for variance in particular traits (Liu et al, 2022) may be helpful to find relevant tissue-specific variant annotations.…”