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The correlation of substitution effects across populations and generations in the presence of nonadditive functional gene action
Allele substitution effects at quantitative trait loci (QTL) are part of the basis of quantitative genetics theory and applications such as association analysis and genomic prediction. In the presence of nonadditive functional gene action, substitution effects are not constant across populations. We...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8664574/ https://www.ncbi.nlm.nih.gov/pubmed/34718531 http://dx.doi.org/10.1093/genetics/iyab138 |
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author | Legarra, Andres Garcia-Baccino, Carolina A. Wientjes, Yvonne C. J. Vitezica, Zulma G. |
author_facet | Legarra, Andres Garcia-Baccino, Carolina A. Wientjes, Yvonne C. J. Vitezica, Zulma G. |
author_sort | Legarra, Andres |
collection | PubMed |
description | Allele substitution effects at quantitative trait loci (QTL) are part of the basis of quantitative genetics theory and applications such as association analysis and genomic prediction. In the presence of nonadditive functional gene action, substitution effects are not constant across populations. We develop an original approach to model the difference in substitution effects across populations as a first order Taylor series expansion from a “focal” population. This expansion involves the difference in allele frequencies and second-order statistical effects (additive by additive and dominance). The change in allele frequencies is a function of relationships (or genetic distances) across populations. As a result, it is possible to estimate the correlation of substitution effects across two populations using three elements: magnitudes of additive, dominance, and additive by additive variances; relationships (Nei’s minimum distances or Fst indexes); and assumed heterozygosities. Similarly, the theory applies as well to distinct generations in a population, in which case the distance across generations is a function of increase of inbreeding. Simulation results confirmed our derivations. Slight biases were observed, depending on the nonadditive mechanism and the reference allele. Our derivations are useful to understand and forecast the possibility of prediction across populations and the similarity of GWAS effects. |
format | Online Article Text |
id | pubmed-8664574 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-86645742021-12-13 The correlation of substitution effects across populations and generations in the presence of nonadditive functional gene action Legarra, Andres Garcia-Baccino, Carolina A. Wientjes, Yvonne C. J. Vitezica, Zulma G. Genetics Investigation Allele substitution effects at quantitative trait loci (QTL) are part of the basis of quantitative genetics theory and applications such as association analysis and genomic prediction. In the presence of nonadditive functional gene action, substitution effects are not constant across populations. We develop an original approach to model the difference in substitution effects across populations as a first order Taylor series expansion from a “focal” population. This expansion involves the difference in allele frequencies and second-order statistical effects (additive by additive and dominance). The change in allele frequencies is a function of relationships (or genetic distances) across populations. As a result, it is possible to estimate the correlation of substitution effects across two populations using three elements: magnitudes of additive, dominance, and additive by additive variances; relationships (Nei’s minimum distances or Fst indexes); and assumed heterozygosities. Similarly, the theory applies as well to distinct generations in a population, in which case the distance across generations is a function of increase of inbreeding. Simulation results confirmed our derivations. Slight biases were observed, depending on the nonadditive mechanism and the reference allele. Our derivations are useful to understand and forecast the possibility of prediction across populations and the similarity of GWAS effects. Oxford University Press 2021-08-27 /pmc/articles/PMC8664574/ /pubmed/34718531 http://dx.doi.org/10.1093/genetics/iyab138 Text en © The Author(s) 2021. Published by Oxford University Press on behalf of Genetics Society of America. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Investigation Legarra, Andres Garcia-Baccino, Carolina A. Wientjes, Yvonne C. J. Vitezica, Zulma G. The correlation of substitution effects across populations and generations in the presence of nonadditive functional gene action |
title | The correlation of substitution effects across populations and generations in the presence of nonadditive functional gene action |
title_full | The correlation of substitution effects across populations and generations in the presence of nonadditive functional gene action |
title_fullStr | The correlation of substitution effects across populations and generations in the presence of nonadditive functional gene action |
title_full_unstemmed | The correlation of substitution effects across populations and generations in the presence of nonadditive functional gene action |
title_short | The correlation of substitution effects across populations and generations in the presence of nonadditive functional gene action |
title_sort | correlation of substitution effects across populations and generations in the presence of nonadditive functional gene action |
topic | Investigation |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8664574/ https://www.ncbi.nlm.nih.gov/pubmed/34718531 http://dx.doi.org/10.1093/genetics/iyab138 |
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