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Detecting the dominance component of heritability in isolated and outbred human populations
Inconsistencies between published estimates of dominance heritability between studies of human genetic isolates and human outbred populations incite investigation into whether such differences result from particular trait architectures or specific population structures. We analyse simulated datasets...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6303332/ https://www.ncbi.nlm.nih.gov/pubmed/30575761 http://dx.doi.org/10.1038/s41598-018-36050-7 |
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author | Herzig, Anthony F. Nutile, Teresa Ruggiero, Daniela Ciullo, Marina Perdry, Hervé Leutenegger, Anne-Louise |
author_facet | Herzig, Anthony F. Nutile, Teresa Ruggiero, Daniela Ciullo, Marina Perdry, Hervé Leutenegger, Anne-Louise |
author_sort | Herzig, Anthony F. |
collection | PubMed |
description | Inconsistencies between published estimates of dominance heritability between studies of human genetic isolates and human outbred populations incite investigation into whether such differences result from particular trait architectures or specific population structures. We analyse simulated datasets, characteristic of genetic isolates and of unrelated individuals, before analysing the isolate of Cilento for various commonly studied traits. We show the strengths of using genetic relationship matrices for variance decomposition over identity-by-descent based methods in a population isolate and that heritability estimates in isolates will avoid the downward biases that may occur in studies of samples of unrelated individuals; irrespective of the simulated distribution of causal variants. Yet, we also show that precise estimates of dominance in isolates are demonstrably problematic in the presence of shared environmental effects and such effects should be accounted for. Nevertheless, we demonstrate how studying isolates can help determine the existence or non-existence of dominance for complex traits, and we find strong indications of non-zero dominance for low-density lipoprotein level in Cilento. Finally, we recommend future study designs to analyse trait variance decomposition from ensemble data across multiple population isolates. |
format | Online Article Text |
id | pubmed-6303332 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-63033322018-12-28 Detecting the dominance component of heritability in isolated and outbred human populations Herzig, Anthony F. Nutile, Teresa Ruggiero, Daniela Ciullo, Marina Perdry, Hervé Leutenegger, Anne-Louise Sci Rep Article Inconsistencies between published estimates of dominance heritability between studies of human genetic isolates and human outbred populations incite investigation into whether such differences result from particular trait architectures or specific population structures. We analyse simulated datasets, characteristic of genetic isolates and of unrelated individuals, before analysing the isolate of Cilento for various commonly studied traits. We show the strengths of using genetic relationship matrices for variance decomposition over identity-by-descent based methods in a population isolate and that heritability estimates in isolates will avoid the downward biases that may occur in studies of samples of unrelated individuals; irrespective of the simulated distribution of causal variants. Yet, we also show that precise estimates of dominance in isolates are demonstrably problematic in the presence of shared environmental effects and such effects should be accounted for. Nevertheless, we demonstrate how studying isolates can help determine the existence or non-existence of dominance for complex traits, and we find strong indications of non-zero dominance for low-density lipoprotein level in Cilento. Finally, we recommend future study designs to analyse trait variance decomposition from ensemble data across multiple population isolates. Nature Publishing Group UK 2018-12-21 /pmc/articles/PMC6303332/ /pubmed/30575761 http://dx.doi.org/10.1038/s41598-018-36050-7 Text en © The Author(s) 2018 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Herzig, Anthony F. Nutile, Teresa Ruggiero, Daniela Ciullo, Marina Perdry, Hervé Leutenegger, Anne-Louise Detecting the dominance component of heritability in isolated and outbred human populations |
title | Detecting the dominance component of heritability in isolated and outbred human populations |
title_full | Detecting the dominance component of heritability in isolated and outbred human populations |
title_fullStr | Detecting the dominance component of heritability in isolated and outbred human populations |
title_full_unstemmed | Detecting the dominance component of heritability in isolated and outbred human populations |
title_short | Detecting the dominance component of heritability in isolated and outbred human populations |
title_sort | detecting the dominance component of heritability in isolated and outbred human populations |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6303332/ https://www.ncbi.nlm.nih.gov/pubmed/30575761 http://dx.doi.org/10.1038/s41598-018-36050-7 |
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