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Adjusting for Heritable Covariates Can Bias Effect Estimates in Genome-Wide Association Studies

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Aschard, Hugues, Vilhjálmsson, Bjarni J., Joshi, Amit D., Price, Alkes L., Kraft, Peter·2015·The American Journal of Human Genetics

DOI 10.1016/j.ajhg.2014.12.021PMC PMC4320269

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Abstract

In recent years, a number of large-scale genome-wide association studies have been published for human traits adjusted for other correlated traits with a genetic basis. In most studies, the motivation for such an adjustment is to discover genetic variants associated with the primary outcome independently of the correlated trait. In this report, we contend that this objective is fulfilled when the tested variants have no effect on the covariate or when the correlation between the covariate and the outcome is fully explained by a direct effect of the covariate on the outcome. For all other scenarios, an unintended bias is introduced with respect to the primary outcome as a result of the adjustment, and this bias might lead to false positives. Here, we illustrate this point by providing examples from published genome-wide association studies, including large meta-analysis of waist-to-hip ratio and waist circumference adjusted for body mass index (BMI), where genetic effects might be biased as a result of adjustment for body mass index. Using both theory and simulations, we explore this phenomenon in detail and discuss the ramifications for future genome-wide association studies of correlated traits and diseases.

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Authors: Aschard, H., Vilhjálmsson, B. J., Joshi, A. D., Price, A. L., & Kraft, P.
Affiliations: Harvard University (US)
Corresponding author: Aschard, Hugues
ORCID: Aschard, Hugues
Vilhjálmsson, Bjarni J.
Joshi, Amit D.
Price, Alkes L.
Kraft, Peter
Venue: The American Journal of Human Genetics
Publisher: Elsevier BV
Published: 2015-02
Volume: 96
Issue: 2
Pages: 329-339
ISSN: 0002-9297 (ISSN-L)
DOI: 10.1016/j.ajhg.2014.12.021
PMC: PMC4320269
OpenAlex: W1996682563
Cited by: 287
References: 40
Citations by year: 2026: 16 · 2025: 25 · 2024: 30 · 2023: 27 · 2022: 30 · 2021: 33 · 2020: 26 · 2019: 24 · 2018: 26 · 2017: 20 · 2016: 22 · 2015: 8
FWCI: 17.13
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/open-access/userlicense/1.0/ (vor, 185 d)
Locations: The American Journal of Human Genetics (publishedVersion)
Europe PMC (PubMed Central) (publishedVersion)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
HAL (submittedVersion)
Funders: National Human Genome Research Institute (R03HG006720)
National Cancer Institute (U19CA148065)
National Institute of Environmental Health Sciences (R21ES020754)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
Keywords: genome-wide association study, correlated traits, false positives, waist-to-hip ratio, waist circumference, body mass index, confounding
MeSH terms: Computer Simulation, Humans, Models, Genetic, Genetic Variation, Body Mass Index, Quantitative Trait, Heritable, Waist-Hip Ratio, Waist Circumference, Genome-Wide Association Study

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Adjusting for Heritable Covariates Can Bias Effect Estimates in Genome-Wide Association Studies

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