Journal article
Quantifying the contribution of sequence variants with regulatory and evolutionary significance to 34 bovine complex traits
R Xiang, I Van Den Berg, IM MacLeod, BJ Hayes, CP Prowse-Wilkins, M Wang, S Bolormaa, Z Liu, SJ Rochfort, CM Reich, BA Mason, CJ Vander Jagt, HD Daetwyler, MS Lund, AJ Chamberlain, ME Goddard
Proceedings of the National Academy of Sciences of the United States of America | NATL ACAD SCIENCES | Published : 2019
Abstract
Many genome variants shaping mammalian phenotype are hypothesized to regulate gene transcription and/or to be under selection. However, most of the evidence to support this hypothesis comes from human studies. Systematic evidence for regulatory and evolutionary signals contributing to complex traits in a different mammalian model is needed. Sequence variants associated with gene expression (expression quantitative trait loci [eQTLs]) and concentration of metabolites (metabolic quantitative trait loci [mQTLs]) and under histone-modification marks in several tissues were discovered from multiomics data of over 400 cattle. Variants under selection and evolutionary constraint were identified usi..
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Funding Acknowledgements
Australian Research Council's Discovery Projects (DP160101056) supported R.X. and M.E.G. Dairy Futures Cooperative Research Centre supported the generation of the Holstein and Jersey transcriptome data. DairyBio (a joint venture project between Agriculture Victoria and Dairy Australia) funded the generation of the mammary ChIP-seq data. I.v.d.B. was supported by the Center for Genomic Selection in Animals and Plants, funded by Innovation Fund Denmark Grant 0603-00519B. No funding bodies participated in the design of the study; nor the collection, analysis, or interpretation of data; nor the writing of the manuscript. We thank DataGene and CRV for access to data used in this study and Gert Nieuwhof, Kon Konstantinov, Timothy P. Hancock (Datagene) and Chris Schrooten (CRV) for preparation and provision of data. We thank partners from the 1000 Bull Genomes project for the data access. We thank Dr. Majid Khansefid for the discussion of aseQTL analysis.