Functional annotation and Bayesian fine-mapping reveals candidate genes for important agronomic traits in Holstein bulls

Jicai Jiang, John B. Cole, Ellen Freebern, Yang Da, Paul M. VanRaden, Li Ma

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

A hundred years of data collection in dairy cattle can facilitate powerful studies of complex traits. Cattle GWAS have identified many associated genomic regions. With increasing numbers of cattle sequenced, fine-mapping of causal variants is becoming possible. Here we imputed selected sequence variants to 27,214 Holstein bulls that have highly reliable phenotypes for 35 production, reproduction, and body conformation traits. We performed single-marker scans for the 35 traits and multi-trait tests of the three trait groups, revealing 282 candidate QTL for fine-mapping. We developed a Bayesian Fine-MAPping approach (BFMAP) to integrate fine-mapping with functional enrichment analysis. Our fine-mapping identified 69 promising candidate genes, including ABCC9, VPS13B, MGST1, SCD, MKL1, CSN1S1 for production, CHEK2, GC, KALRN for reproduction, and TMTC2, ARRDC3, ZNF613, CCND2, FGF6 for conformation traits. Collectively, these results demonstrated the utility of BFMAP, identified candidate genes, and enhanced our understanding of the genetic basis of cattle complex traits.

Original languageEnglish (US)
Article number212
JournalCommunications biology
Volume2
Issue number1
DOIs
StatePublished - Dec 1 2019

Bibliographical note

Funding Information:
We thank the Council on Dairy Cattle Breeding (CDCB) for the access to the genotype data. We thank the 1000 Bull Genomes Project for providing genome references for sequence imputation. This work was supported in part by AFRI grant number 2016-67015-24886 and 2018-67015-28128 from the USDA National Institute of Food and Agriculture (NIFA) and BARD grant number US-4997-17 from the US-Israel Binational Agricultural Research and Development (BARD) Fund. JBC and PMV were also supported by appropriated projects 1265-31000-096-00, Improving Genetic Predictions in Dairy Animals Using Phenotypic and Genomic Information, and 8042-31000-104-00, Enhancing Genetic Merit of Ruminants Through Genome Selection and Analysis, of the Agricultural Research Service of the United States Department of Agriculture. Mention of trade names or commercial products in this article is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the US Department of Agriculture. The USDA is an equal opportunity provider and employer. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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