Genome-wide association study (GWAS) analyses have identified numerous loci associated with economic traits in cattle. Many of these loci reside in noncoding regions, and the regulatory mechanisms through which they influence complex traits remain poorly understood. Here, we integrated 657 RNA-seq libraries from 275 Huaxi cattle across three tissues (longissimus dorsi muscle, liver, and subcutaneous backfat) with ∼ 10 million imputed SNP genotypes to systematically map cis-molecular quantitative trait loci (cis-molQTLs) across four transcriptomic regulatory layers: gene expression (eQTLs), splicing (sQTLs), alternative polyadenylation (aQTLs), and RNA editing (edQTLs). These cis-molQTL classes display distinct genomic distributions and functional enrichments, yet operate in a coordinated manner within complex trait regulatory networks and are significantly enriched near GWAS- and QTLdb-reported loci for growth, carcass, and meat quality traits. Using 1788 genotyped and phenotyped Huaxi cattle, a GREML framework showed that these multi-layer cis-molQTL SNPs collectively explain 61.9% of total SNP-based heritability across 19 complex traits. Incorporating cis-molQTL annotations into genomic prediction models, including MultiBLUP, BayesRC, and molGBLUP, improved prediction accuracy for most traits relative to the baseline GBLUP model (mean increase of 0.05), highlighting the value of multi-layer regulatory variation for functionally informed genomic prediction and precision breeding.
Shiyuan Qiu, Lili Du, Bo-Yu Zhang et al.· Genomics, Proteomics & Bioin...· 0 citations
Structural variations (SVs) represent a significant source of genomic diversity, with demonstrated roles in livestock gene expression and traits. However, a comprehensive understanding of the SV landscape across large sample sets and its impact on gene regulation in cattle remains incomplete. This study aimed to construct high-fidelity pangenome graphs by integrating both assembly-based and whole-genome sequencing (WGS) derived SV catalogs. We evaluated the efficacy of pangenome graphs for SV genotyping and identified 80,328 high-quality SVs from a cohort of 2929 samples. We systematically characterized these SVs, including their linkage disequilibrium with single nucleotide polymorphisms (SNPs), functional annotations, formation mechanisms, and genomic distributions. Furthermore, we generated paired WGS (24.4 ×) and blood RNA-seq data in 170 Simmental cattle. Utilizing our pangenome graphs, we identified 637 SV-expression quantitative trait loci (SV-eQTL), which accounted for 10.81% of expression heritability of target genes, with 38.09% of the effects linked to promoter/enhancer regions. Forty-six of these SV-eQTL were replicated using CattleGTEx results through SV imputation using a joint SNP-SV reference panel. Notably, insertions in the GHSR gene were significantly associated with its expression levels, likely linked to Bos indicus cattle adaptation to heat tolerance. Our findings provide novel insights into the SV landscape and its contribution to gene regulation, underscoring its importance in cattle genetics and genomics.
