Bayesian genome-wide analysis of cattle traits using variants with functional and evolutionary significance

R Xiang; EJ Breen; CP Prowse-Wilkins; AJ Chamberlain; ME Goddard

Journal article

Bayesian genome-wide analysis of cattle traits using variants with functional and evolutionary significance

R Xiang, EJ Breen, CP Prowse-Wilkins, AJ Chamberlain, ME Goddard

Animal Production Science | CSIRO PUBLISHING | Published : 2021

DOI: 10.1071/AN21061

Abstract

Context: Functional genomics studies have highlighted genomic regions with regulatory and evolutionary significance. Such information independent of association analysis may benefit fine-mapping and genomic selection of economically important traits. However, systematic evaluation of the use of functional information in mapping, and genomic selection of cattle traits, is lacking. Also, single-nucleotide polymorphisms (SNPs) from the high-density (HD) panel are known to tag informative variants, but the performance of genomic prediction using HD SNPs together with variants supported by different functional genomics is unknown. Aims: We selected six sets of functionally important variants and ..

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University of Melbourne Researchers

Ruidong Xiang Author

Mike Goddard Author

Related Projects (1)

The extent, causes and implications of pleiotropy among complex traits

The project seeks to understand how a DNA mutation can affect many characters or traits. Many traits are called complex because they are con..

Grants

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.