Ashraf, Bilal and Hunter, Darren C. and Bérénos, Camillo and Ellis, Philip A. and Johnston, Susan E. and Pilkington, Jill G. and Pemberton, Josephine M. and Slate, Jon (2022) 'Genomic prediction in the wild: A case study in Soay sheep.', Molecular Ecology, 31 (24). pp. 6541-6555.
Genomic prediction, the technique whereby an individual's genetic component of their phenotype is estimated from its genome, has revolutionised animal and plant breeding and medical genetics. However, despite being first introduced nearly two decades ago, it has hardly been adopted by the evolutionary genetics community studying wild organisms. Here, genomic prediction is performed on eight traits in a wild population of Soay sheep. The population has been the focus of a >30 year evolutionary ecology study and there is already considerable understanding of the genetic architecture of the focal Mendelian and quantitative traits. We show that the accuracy of genomic prediction is high for all traits, but especially those with loci of large effect segregating. Five different methods are compared, and the two methods that can accommodate zero-effect and large-effect loci in the same model tend to perform best. If the accuracy of genomic prediction is similar in other wild populations, then there is a real opportunity for pedigree-free molecular quantitative genetics research to be enabled in many more wild populations; currently the literature is dominated by studies that have required decades of field data collection to generate sufficiently deep pedigrees. Finally, some of the potential applications of genomic prediction in wild populations are discussed.
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|Publisher Web site:||https://doi.org/10.1111/mec.16262|
|Publisher statement:||This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.|
|Date accepted:||25 October 2021|
|Date deposited:||20 January 2022|
|Date of first online publication:||10 November 2021|
|Date first made open access:||20 January 2022|
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