Pangenome-based structural variant imputation enables large-scale genotype-phenotype studies in dairy cattle
Abstract
Abstract Pangenomes of several species have been assembled recently, facilitating the detection and genotyping of structural variants. As part of the FarmGTEx Project, we previously constructed a Holstein pangenome (H20D) based on 40 phased haploid assemblies. Here, we use this breed specific pangenome to genotype 93,059 structural variants from whole-genome sequences of 1,571 cattle. We then develop a Holstein pangenome variation imputation reference panel we name HolPIP. Leveraging HolPIP, we impute 86.65% (68,354/78,886) of structural variants for 50,299 bulls with Beagle R² ≥ 0.8. Using these imputed structural variants and phenotypes for 43 complex traits, we conduct GWAS, identifying 1,225 structural variant-trait associations. We next use fine-mapping to prioritize 32 high-confidence candidate structural variants, including a 75-bp deletion in ANKRD11 linked to dairy form, rump width, and stature, as well as an insertion in DHX32 associated with RNA metabolism. Compared to SNPs across various functional annotations, structural variants show a stronger genome-wide enrichment across most complex traits in cattle, suggesting that structural variants may have an important contribution to the genetic basis of dairy traits.
Article Details
Authors (18)
Liu Yang
Junjian Wang
Guangdong Provincial Key Laboratory of New Drug Design and Evaluation, School of Pharmaceutical Sciences, Sun Yat-sen University
Kristen Kuhn
Wenli Li
State Key Laboratory for Crop Stress Resistance and High-Efficiency Production, Shaanxi Key Laboratory of Natural Products & Chemical Biology, College of Chemistry & Pharmacy, Northwest A&F University, 3 Taicheng Road, Yangling, Shaanxi 712100, China
Geoffrey Zanton
Mahesh Neupane
Clarissa Boschiero
John B. Cole
Bingjie Li
Department of Statistics and Data Science
Congjun Li
Ransom L. Baldwin, VI
Curtis P. Van Tassell
Benjamin D. Rosen
Timothy P. L. Smith
Jicai Jiang
Lingzhao Fang
Li Ma
George E. Liu