Fast Bayesian fine-mapping of 35 production, reproduction and body conformation traits with imputed sequences of 27K Holstein bulls

Abstract

Imputation has been routinely used to infer sequence variants in large genotyped populations based on reference populations of sequenced individuals. With increasing numbers of animals sequenced and the implementation of the 1000 Bull Genomes Project, fine-mapping of causal variants for complex traits is becoming possible in cattle. Using 404 ancestor bull sequences as reference, we imputed over 3 million selected sequence variants to 27,214 Holstein bulls with highly reliable phenotypes (breeding values) for 35 production, reproduction, and body conformation traits. We first performed whole-genome single-marker scans for each of the 35 traits using a mixed-model association test. The single-trait association statistics were then merged into multi-trait tests of 3 trait groups: production, reproduction, and body conformation. Both single- and multi-trait GWAS results were used to identify 282 candidate QTL regions for fine-mapping in the cattle genome. To facilitate fast and powerful fine-mapping analyses, we developed a Bayesian Fine-MAPping approach (BFMAP) to integrate fine-mapping with functional enrichment analysis. Our fine-mapping results identified 69 promising candidate genes for dairy traits, including ABCC9, VPS13B, MGST1, SCD, MKL1, and CSN1S1 for production traits; CHEK2, GC, and KALRN for reproduction traits; and TMTC2, ARRDC3, ZNF613, CCND2, and FGF6 for body conformation traits. Based on existing functional annotation data for cattle, we revealed biologically meaningful enrichment in our fine-mapped variants that can be readily tested in functional validation studies. In summary, these results demonstrated the utility of a fast Bayesian approach for fine-mapping and functional enrichment analysis, identified candidate causative genes and variants, and enhanced our understanding of the genetic basis of complex traits in dairy cattle.