Pangenome Graph Reveals the Structural Variation Landscape in 2929 Cattle Samples and Its Impact on Gene Regulation.

Abstract

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.