Genome-wide fine-mapping identifies pleiotropic and functional variants that predict many traits across global cattle populations

Ruidong Xiang (), Iona M. MacLeod, Hans D. Daetwyler, Gerben Jong, Erin O’Connor, Chris Schrooten, Amanda J. Chamberlain and Michael E. Goddard
Additional contact information
Ruidong Xiang: The University of Melbourne
Iona M. MacLeod: Agriculture Victoria, AgriBio, Centre for AgriBiosciences
Hans D. Daetwyler: Agriculture Victoria, AgriBio, Centre for AgriBiosciences
Gerben Jong: Cooperation CRV
Erin O’Connor: CRV Ambreed
Chris Schrooten: CRV BV
Amanda J. Chamberlain: Agriculture Victoria, AgriBio, Centre for AgriBiosciences
Michael E. Goddard: The University of Melbourne

Nature Communications, 2021, vol. 12, issue 1, 1-13

Abstract: Abstract The difficulty in finding causative mutations has hampered their use in genomic prediction. Here, we present a methodology to fine-map potentially causal variants genome-wide by integrating the functional, evolutionary and pleiotropic information of variants using GWAS, variant clustering and Bayesian mixture models. Our analysis of 17 million sequence variants in 44,000+ Australian dairy cattle for 34 traits suggests, on average, one pleiotropic QTL existing in each 50 kb chromosome-segment. We selected a set of 80k variants representing potentially causal variants within each chromosome segment to develop a bovine XT-50K genotyping array. The custom array contains many pleiotropic variants with biological functions, including splicing QTLs and variants at conserved sites across 100 vertebrate species. This biology-informed custom array outperformed the standard array in predicting genetic value of multiple traits across populations in independent datasets of 90,000+ dairy cattle from the USA, Australia and New Zealand.

Date: 2021
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DOI: 10.1038/s41467-021-21001-0

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