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Apparent latent structure within the UK Biobank sample has implications for epidemiological analysis
Large studies use genotype data to discover genetic contributions to complex traits and infer relationships between those traits. Co-incident geographical variation in genotypes and health traits can bias these analyses. Here we show that single genetic variants and genetic scores composed of multip...
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/PMC6338768/ https://www.ncbi.nlm.nih.gov/pubmed/30659178 http://dx.doi.org/10.1038/s41467-018-08219-1 |
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author | Haworth, Simon Mitchell, Ruth Corbin, Laura Wade, Kaitlin H. Dudding, Tom Budu-Aggrey, Ashley Carslake, David Hemani, Gibran Paternoster, Lavinia Smith, George Davey Davies, Neil Lawson, Daniel J. J. Timpson, Nicholas |
author_facet | Haworth, Simon Mitchell, Ruth Corbin, Laura Wade, Kaitlin H. Dudding, Tom Budu-Aggrey, Ashley Carslake, David Hemani, Gibran Paternoster, Lavinia Smith, George Davey Davies, Neil Lawson, Daniel J. J. Timpson, Nicholas |
author_sort | Haworth, Simon |
collection | PubMed |
description | Large studies use genotype data to discover genetic contributions to complex traits and infer relationships between those traits. Co-incident geographical variation in genotypes and health traits can bias these analyses. Here we show that single genetic variants and genetic scores composed of multiple variants are associated with birth location within UK Biobank and that geographic structure in genotype data cannot be accounted for using routine adjustment for study centre and principal components derived from genotype data. We find that major health outcomes appear geographically structured and that coincident structure in health outcomes and genotype data can yield biased associations. Understanding and accounting for this phenomenon will be important when making inference from genotype data in large studies. |
format | Online Article Text |
id | pubmed-6338768 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-63387682019-01-22 Apparent latent structure within the UK Biobank sample has implications for epidemiological analysis Haworth, Simon Mitchell, Ruth Corbin, Laura Wade, Kaitlin H. Dudding, Tom Budu-Aggrey, Ashley Carslake, David Hemani, Gibran Paternoster, Lavinia Smith, George Davey Davies, Neil Lawson, Daniel J. J. Timpson, Nicholas Nat Commun Article Large studies use genotype data to discover genetic contributions to complex traits and infer relationships between those traits. Co-incident geographical variation in genotypes and health traits can bias these analyses. Here we show that single genetic variants and genetic scores composed of multiple variants are associated with birth location within UK Biobank and that geographic structure in genotype data cannot be accounted for using routine adjustment for study centre and principal components derived from genotype data. We find that major health outcomes appear geographically structured and that coincident structure in health outcomes and genotype data can yield biased associations. Understanding and accounting for this phenomenon will be important when making inference from genotype data in large studies. Nature Publishing Group UK 2019-01-18 /pmc/articles/PMC6338768/ /pubmed/30659178 http://dx.doi.org/10.1038/s41467-018-08219-1 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 Haworth, Simon Mitchell, Ruth Corbin, Laura Wade, Kaitlin H. Dudding, Tom Budu-Aggrey, Ashley Carslake, David Hemani, Gibran Paternoster, Lavinia Smith, George Davey Davies, Neil Lawson, Daniel J. J. Timpson, Nicholas Apparent latent structure within the UK Biobank sample has implications for epidemiological analysis |
title | Apparent latent structure within the UK Biobank sample has implications for epidemiological analysis |
title_full | Apparent latent structure within the UK Biobank sample has implications for epidemiological analysis |
title_fullStr | Apparent latent structure within the UK Biobank sample has implications for epidemiological analysis |
title_full_unstemmed | Apparent latent structure within the UK Biobank sample has implications for epidemiological analysis |
title_short | Apparent latent structure within the UK Biobank sample has implications for epidemiological analysis |
title_sort | apparent latent structure within the uk biobank sample has implications for epidemiological analysis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6338768/ https://www.ncbi.nlm.nih.gov/pubmed/30659178 http://dx.doi.org/10.1038/s41467-018-08219-1 |
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