Hunter, D. C. and Ashraf, B. and Bérénos, C. and Ellis, P. A. and Johnston, S. E. and Wilson, A. J. and Pilkington, J. G. and Pemberton, J. M. and Slate, J. (2022) 'Using genomic prediction to detect microevolutionary change of a quantitative trait.', Proceedings of the Royal Society B: Biological Sciences, 289 (1974). 0330.
Abstract
Detecting microevolutionary responses to natural selection by observing temporal changes in individual breeding values is challenging. The collection of suitable datasets can take many years and disentangling the contributions of the environment and genetics to phenotypic change is not trivial. Furthermore, pedigree-based methods of obtaining individual breeding values have known biases. Here, we apply a genomic prediction approach to estimate breeding values of adult weight in a 35-year dataset of Soay sheep (Ovis aries). Comparisons are made with a traditional pedigree-based approach. During the study period, adult body weight decreased, but the underlying genetic component of body weight increased, at a rate that is unlikely to be attributable to genetic drift. Thus cryptic microevolution of greater adult body weight has probably occurred. Genomic and pedigree-based approaches gave largely consistent results. Thus, using genomic prediction to study microevolution in wild populations can remove the requirement for pedigree data, potentially opening up new study systems for similar research.
Item Type: | Article |
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Full text: | (VoR) Version of Record Available under License - Creative Commons Attribution 4.0. Download PDF (680Kb) |
Status: | Peer-reviewed |
Publisher Web site: | https://doi.org/10.1098/rspb.2022.0330 |
Publisher statement: | © 2022 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited. |
Date accepted: | 12 April 2022 |
Date deposited: | 30 June 2022 |
Date of first online publication: | 11 May 2022 |
Date first made open access: | 30 June 2022 |
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