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Estimating heritability and genetic correlations from large health datasets in the absence of genetic data
Typically, estimating genetic parameters, such as disease heritability and between-disease genetic correlations, demands large datasets containing all relevant phenotypic measures and detailed knowledge of family relationships or, alternatively, genotypic and phenotypic data for numerous unrelated i...
Autores principales: | , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6890770/ https://www.ncbi.nlm.nih.gov/pubmed/31796735 http://dx.doi.org/10.1038/s41467-019-13455-0 |
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author | Jia, Gengjie Li, Yu Zhang, Hanxin Chattopadhyay, Ishanu Boeck Jensen, Anders Blair, David R. Davis, Lea Robinson, Peter N. Dahlén, Torsten Brunak, Søren Benson, Mikael Edgren, Gustaf Cox, Nancy J. Gao, Xin Rzhetsky, Andrey |
author_facet | Jia, Gengjie Li, Yu Zhang, Hanxin Chattopadhyay, Ishanu Boeck Jensen, Anders Blair, David R. Davis, Lea Robinson, Peter N. Dahlén, Torsten Brunak, Søren Benson, Mikael Edgren, Gustaf Cox, Nancy J. Gao, Xin Rzhetsky, Andrey |
author_sort | Jia, Gengjie |
collection | PubMed |
description | Typically, estimating genetic parameters, such as disease heritability and between-disease genetic correlations, demands large datasets containing all relevant phenotypic measures and detailed knowledge of family relationships or, alternatively, genotypic and phenotypic data for numerous unrelated individuals. Here, we suggest an alternative, efficient estimation approach through the construction of two disease metrics from large health datasets: temporal disease prevalence curves and low-dimensional disease embeddings. We present eleven thousand heritability estimates corresponding to five study types: twins, traditional family studies, health records-based family studies, single nucleotide polymorphisms, and polygenic risk scores. We also compute over six hundred thousand estimates of genetic, environmental and phenotypic correlations. Furthermore, we find that: (1) disease curve shapes cluster into five general patterns; (2) early-onset diseases tend to have lower prevalence than late-onset diseases (Spearman’s ρ = 0.32, p < 10(–16)); and (3) the disease onset age and heritability are negatively correlated (ρ = −0.46, p < 10(–16)). |
format | Online Article Text |
id | pubmed-6890770 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-68907702019-12-05 Estimating heritability and genetic correlations from large health datasets in the absence of genetic data Jia, Gengjie Li, Yu Zhang, Hanxin Chattopadhyay, Ishanu Boeck Jensen, Anders Blair, David R. Davis, Lea Robinson, Peter N. Dahlén, Torsten Brunak, Søren Benson, Mikael Edgren, Gustaf Cox, Nancy J. Gao, Xin Rzhetsky, Andrey Nat Commun Article Typically, estimating genetic parameters, such as disease heritability and between-disease genetic correlations, demands large datasets containing all relevant phenotypic measures and detailed knowledge of family relationships or, alternatively, genotypic and phenotypic data for numerous unrelated individuals. Here, we suggest an alternative, efficient estimation approach through the construction of two disease metrics from large health datasets: temporal disease prevalence curves and low-dimensional disease embeddings. We present eleven thousand heritability estimates corresponding to five study types: twins, traditional family studies, health records-based family studies, single nucleotide polymorphisms, and polygenic risk scores. We also compute over six hundred thousand estimates of genetic, environmental and phenotypic correlations. Furthermore, we find that: (1) disease curve shapes cluster into five general patterns; (2) early-onset diseases tend to have lower prevalence than late-onset diseases (Spearman’s ρ = 0.32, p < 10(–16)); and (3) the disease onset age and heritability are negatively correlated (ρ = −0.46, p < 10(–16)). Nature Publishing Group UK 2019-12-03 /pmc/articles/PMC6890770/ /pubmed/31796735 http://dx.doi.org/10.1038/s41467-019-13455-0 Text en © The Author(s) 2019 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 Jia, Gengjie Li, Yu Zhang, Hanxin Chattopadhyay, Ishanu Boeck Jensen, Anders Blair, David R. Davis, Lea Robinson, Peter N. Dahlén, Torsten Brunak, Søren Benson, Mikael Edgren, Gustaf Cox, Nancy J. Gao, Xin Rzhetsky, Andrey Estimating heritability and genetic correlations from large health datasets in the absence of genetic data |
title | Estimating heritability and genetic correlations from large health datasets in the absence of genetic data |
title_full | Estimating heritability and genetic correlations from large health datasets in the absence of genetic data |
title_fullStr | Estimating heritability and genetic correlations from large health datasets in the absence of genetic data |
title_full_unstemmed | Estimating heritability and genetic correlations from large health datasets in the absence of genetic data |
title_short | Estimating heritability and genetic correlations from large health datasets in the absence of genetic data |
title_sort | estimating heritability and genetic correlations from large health datasets in the absence of genetic data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6890770/ https://www.ncbi.nlm.nih.gov/pubmed/31796735 http://dx.doi.org/10.1038/s41467-019-13455-0 |
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