Comparison of weighting approaches for genetic risk scores in gene-environment interaction studies

BMC Genetics

Methodology Article

Christopher Carlsten¹  Katja Ickstadt²  Tamara Schikowski³  Ursula Krämer³  Anke Hüls⁴  Holger Schwender⁵ 

[1] Department of Medicine, University of British Columbia, Vancouver, BC, Canada;Institute for Heart and Lung Health, Vancouver, BC, Canada;School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada;Faculty of Statistics, TU Dortmund University, Dortmund, Germany;IUF-Leibniz Research Institute for Environmental Medicine, Düsseldorf, Germany;IUF-Leibniz Research Institute for Environmental Medicine, Düsseldorf, Germany;Faculty of Statistics, TU Dortmund University, Dortmund, Germany;Mathematical Institute, Heinrich Heine University, Düsseldorf, Germany;

关键词: Polygenic approach;    Training dataset;    Internal weights;    External weights;    Simulation study;    Power;    Type I error;   

DOI  :  10.1186/s12863-017-0586-3

 received in 2017-09-18, accepted in 2017-12-07,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundWeighted genetic risk scores (GRS), defined as weighted sums of risk alleles of single nucleotide polymorphisms (SNPs), are statistically powerful for detection gene-environment (GxE) interactions. To assign weights, the gold standard is to use external weights from an independent study. However, appropriate external weights are not always available. In such situations and in the presence of predominant marginal genetic effects, we have shown in a previous study that GRS with internal weights from marginal genetic effects (“GRS-marginal-internal”) are a powerful and reliable alternative to single SNP approaches or the use of unweighted GRS. However, this approach might not be appropriate for detecting predominant interactions, i.e. interactions showing an effect stronger than the marginal genetic effect.MethodsIn this paper, we present a weighting approach for such predominant interactions (“GRS-interaction-training”) in which parts of the data are used to estimate the weights from the interaction terms and the remaining data are used to determine the GRS. We conducted a simulation study for the detection of GxE interactions in which we evaluated power, type I error and sign-misspecification. We compared this new weighting approach to the GRS-marginal-internal approach and to GRS with external weights.ResultsOur simulation study showed that in the absence of external weights and with predominant interaction effects, the highest power was reached with the GRS-interaction-training approach. If marginal genetic effects were predominant, the GRS-marginal-internal approach was more appropriate. Furthermore, the power to detect interactions reached by the GRS-interaction-training approach was only slightly lower than the power achieved by GRS with external weights. The power of the GRS-interaction-training approach was confirmed in a real data application to the Traffic, Asthma and Genetics (TAG) Study (N = 4465 observations).ConclusionWhen appropriate external weights are unavailable, we recommend to use internal weights from the study population itself to construct weighted GRS for GxE interaction studies. If the SNPs were chosen because a strong marginal genetic effect was hypothesized, GRS-marginal-internal should be used. If the SNPs were chosen because of their collective impact on the biological mechanisms mediating the environmental effect (hypothesis of predominant interactions) GRS-interaction-training should be applied.

【 授权许可】

CC BY   
© The Author(s). 2017