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Adjusting for heritable covariates can bias effect estimates in genome-wide association studies.

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.

SUBMITTER: Aschard H

PROVIDER: S-EPMC4320269 | biostudies-literature | 2015 Feb

REPOSITORIES: biostudies-literature

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Adjusting for heritable covariates can bias effect estimates in genome-wide association studies.

Aschard Hugues H Vilhjálmsson Bjarni J BJ Joshi Amit D AD Price Alkes L AL Kraft Peter P

American journal of human genetics 20150129 2

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 th ...[more]

PMID: 25640676

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Project description:Until recently, genome-wide association studies (GWAS) have been restricted to research groups with the budget necessary to genotype hundreds, if not thousands, of samples. Replacing individual genotyping with genotyping of DNA pools in Phase I of a GWAS has proven successful, and dramatically altered the financial feasibility of this approach. When conducting a pool-based GWAS, how well SNP allele frequency is estimated from a DNA pool will influence a study's power to detect associations. Here we address how to control the variance in allele frequency estimation when DNAs are pooled, and how to plan and conduct the most efficient well-powered pool-based GWAS.By examining the variation in allele frequency estimation on SNP arrays between and within DNA pools we determine how array variance [var(e(array))] and pool-construction variance [var(e(construction))] contribute to the total variance of allele frequency estimation. This information is useful in deciding whether replicate arrays or replicate pools are most useful in reducing variance. Our analysis is based on 27 DNA pools ranging in size from 74 to 446 individual samples, genotyped on a collective total of 128 Illumina beadarrays: 24 1M-Single, 32 1M-Duo, and 72 660-Quad.For all three Illumina SNP array types our estimates of var(e(array)) were similar, between 3-4 × 10-4 for normalized data. Var(e(construction)) accounted for between 20-40% of pooling variance across 27 pools in normalized data.We conclude that relative to var(e(array)), var(e(construction)) is of less importance in reducing the variance in allele frequency estimation from DNA pools; however, our data suggests that on average it may be more important than previously thought. We have prepared a simple online tool, PoolingPlanner (available at http://www.kchew.ca/PoolingPlanner/), which calculates the effective sample size (ESS) of a DNA pool given a range of replicate array values. ESS can be used in a power calculator to perform pool-adjusted calculations. This allows one to quickly calculate the loss of power associated with a pooling experiment to make an informed decision on whether a pool-based GWAS is worth pursuing.

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Adjusting for heritable covariates can bias effect estimates in genome-wide association studies.

Publications

Adjusting for heritable covariates can bias effect estimates in genome-wide association studies.

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