Using genomic prediction to detect microevolutionary change of a quantitative trait
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. Furt...
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Published in | Proceedings of the Royal Society. B, Biological sciences Vol. 289; no. 1974; p. 20220330 |
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Main Authors | , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
England
The Royal Society
11.05.2022
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Subjects | |
Online Access | Get full text |
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Summary: | 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 (
. 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. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 One paper of a special feature ‘Wild Quantitative Genomics: the genomic basis of fitness variation in natural populations’ edited by Susan Johnston, Nancy Chen and Emily Josephs. Electronic supplementary material is available online at https://doi.org/10.6084/m9.figshare.c.5958626. |
ISSN: | 0962-8452 1471-2954 |
DOI: | 10.1098/rspb.2022.0330 |