Gene function & annotation

Identification of causal variants and genes for quantitative traits

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Abstract

Most genetic variants associated with quantitative traits (QTL) reside in non-coding regions and are presumed to influence phenotypes through regulatory mechanisms such as histone modification and gene expression. Identifying causal variants underlying quantitative traits, and the genes through which they act, remains challenging due largely to linkage disequilibrium (LD) and complex genomic architectures.This paper synthesizes findings from Ghoreishifar et al. (2024, 2025, and 2026), which collectively introduce and apply complementary sequence-based and multi-omics approaches to address these challenges.First, we summarize a sequence-based method for identifying causal regulatory variants underlying histone QTLs using allele-specific binding (ASB) information from ChIP-seq data. The approach leverages population-level sequence variation and assumes that true causal ASB variants (asbQTLs) share common regulatory sequence features and consistent allelic effects. A linear model developed in their study successfully predicted the direction of allelic effects for approximately 75% of asbQTLs, outperforming machine learning models such as gapped-kmer support vector machine (gkmSVM). These results demonstrate that incorporating population-level genetic diversity may improve pinpointing causal variant.Second, we review integrative multi-omics analyses designed to identify target genes (eGenes) for QTLs. By combining RNA sequencing, genomic prediction, genome-wide association study (GWAS), and genetic score omics regression (GSOR), Ghoreishifar et al. (2025, 2026) identified eGenes whose expression is associated with milk composition traits. Rather than relying on variant-level colocalization, the authors assessed genome-wide enrichment of co-occurring GWAS signals and eGenes across fixed genomic windows. This window-based enrichment strategy avoids restrictive assumptions about the number of causal variants per locus.Across studies, significant enrichment was observed between eGenes and QTLs, and gene-set enrichment analyses highlighted biologically coherent pathways related to lipid metabolism and transmembrane transport. Known causal genes for milk composition, including DGAT1 and SLC50A1, were among the prioritized candidates, supporting the biological relevance of the framework. Overall, this synthesis illustrates how integrating multi-omics and gene-set enrichment analyses can yield robust, biologically meaningful insights into the regulatory basis of complex traits, helping bridge the gap between genotype, gene regulation, and phenotype.

Keywords: 2026

How to Cite:

Ghoreishifar, M., Chamberlain, A., Pryce, J. & Goddard, M., (2026) “Identification of causal variants and genes for quantitative traits”, World Congress on Genetics Applied to Livestock Production Digital Archive 2026(1): 2286180. doi: https://doi.org/10.31274/wcgalp.24574

Rights: 1