Apparent latent structure within the UK Biobank sample has implications for epidemiological analysis

Simon Haworth; Ruth Mitchell; Laura Corbin; Kaitlin Wade; Tom Dudding; Ashley Budu-Aggrey; David Carslake; Gibran Hemani; Lavinia Paternoster; George Davey Smith; Neil Davies; Daniel Lawson; Nicholas Timpson

doi:10.1038/s41467-018-08219-1

Apparent latent structure within the UK Biobank sample has implications for epidemiological analysis

Simon Haworth, Ruth Mitchell, Laura Corbin, Kaitlin Wade, Tom Dudding, Ashley Budu-Aggrey, David Carslake, Gibran Hemani, Lavinia Paternoster, George Davey Smith, Neil Davies, Daniel Lawson, Nicholas Timpson

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Abstract

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.

Original language	English
Article number	333
Number of pages	9
Journal	Nature Communications
Volume	10
Issue number	1
Early online date	18 Jan 2019
DOIs	https://doi.org/10.1038/s41467-018-08219-1
Publication status	Published - 1 Dec 2019

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Haworth, S., Mitchell, R., Corbin, L., Wade, K., Dudding, T., Budu-Aggrey, A., Carslake, D., Hemani, G., Paternoster, L., Davey Smith, G., Davies, N., Lawson, D., & Timpson, N. (2019). Apparent latent structure within the UK Biobank sample has implications for epidemiological analysis. Nature Communications, 10(1), Article 333. https://doi.org/10.1038/s41467-018-08219-1

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AU - Haworth, Simon

AU - Mitchell, Ruth

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AU - Budu-Aggrey, Ashley

AU - Carslake, David

AU - Hemani, Gibran

AU - Paternoster, Lavinia

AU - Davey Smith, George

AU - Davies, Neil

AU - Lawson, Daniel

AU - Timpson, Nicholas

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AB - 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.

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Apparent latent structure within the UK Biobank sample has implications for epidemiological analysis

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Latent structure in UK Biobank (10-2018)

Professor George Davey Smith

Professor Gibran Hemani

Professor Daniel John Lawson

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Apparent latent structure within the UK Biobank sample has implications for epidemiological analysis

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