Journal article
Genome-wide fine-mapping identifies pleiotropic and functional variants that predict many traits across global cattle populations
R Xiang, IM MacLeod, HD Daetwyler, G de Jong, E O’Connor, C Schrooten, AJ Chamberlain, ME Goddard
Nature Communications | NATURE RESEARCH | Published : 2021
Open access
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..
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Awarded by Australian Research Council
Funding Acknowledgements
Australian Research Council's Discovery Projects (DP160101056 and DP200100499) supported R.X. and M.E.G. DairyBio, a joint venture project between Agriculture Victoria (Melbourne, Australia), Dairy Australia (Melbourne, Australia) and the Gardiner Foundation (Melbourne, Australia), funded computing resources used in the analysis. The authors also thank the University of Melbourne, Australia for supporting this research. No funding bodies participated in the design of the study nor analysis, or interpretation of data nor in writing the manuscript. DataGene and CRV provided access to the reference data used in this study. DairyNZ provided access to the validation data used in this study. We thank Gert Nieuwhof, Kon Konstantinov and Timothy P. Hancock (DataGene) and staff from DairyNZ for preparation and provision of data. We thank Dr. Mekonnen Haile-Mariam for deriving the deregressed phenotypes from international MACE and Dr. Sunduimijid Bolormaa for sequence variant data imputation. We thank Dr. Majid Khansefid for the curation of validation datasets.