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Nat Commun. 2019 Jan 18;10(1):333. doi: 10.1038/s41467-018-08219-1.

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

Author information

1
Medical Research Council Integrative Epidemiology Unit, Department of Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK.
2
Medical Research Council Integrative Epidemiology Unit, Department of Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK. n.j.timpson@bristol.ac.uk.
3
Avon Longitudinal Study of Parents and Children, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK. n.j.timpson@bristol.ac.uk.

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.

PMID:
30659178
PMCID:
PMC6338768
DOI:
10.1038/s41467-018-08219-1
[Indexed for MEDLINE]
Free PMC Article

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