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

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@article{125ed0d59d2447f4bcb747148fba5aed,
title = "Apparent latent structure within the UK Biobank sample has implications for epidemiological analysis",
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.",
author = "Simon Haworth and Ruth Mitchell and Laura Corbin and Kaitlin Wade and Tom Dudding and Ashley Budu-Aggrey and David Carslake and Gibran Hemani and Lavinia Paternoster and {Davey Smith}, George and Neil Davies and Daniel Lawson and Nicholas Timpson",
year = "2019",
month = "12",
day = "1",
doi = "10.1038/s41467-018-08219-1",
language = "English",
volume = "10",
journal = "Nature Communications",
issn = "2041-1723",
publisher = "Springer Nature",
number = "1",

}

RIS - suitable for import to EndNote

TY - JOUR

T1 - Apparent latent structure within the UK Biobank sample has implications for epidemiological analysis

AU - Haworth, Simon

AU - Mitchell, Ruth

AU - Corbin, Laura

AU - Wade, Kaitlin

AU - Dudding, Tom

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

PY - 2019/12/1

Y1 - 2019/12/1

N2 - 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.

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.

UR - http://www.scopus.com/inward/record.url?scp=85060158876&partnerID=8YFLogxK

U2 - 10.1038/s41467-018-08219-1

DO - 10.1038/s41467-018-08219-1

M3 - Article

C2 - 30659178

AN - SCOPUS:85060158876

VL - 10

JO - Nature Communications

JF - Nature Communications

SN - 2041-1723

IS - 1

M1 - 333

ER -