Detection of gene-environment interactions in the presence of linkage disequilibrium and noise by using genetic risk scores with internal weights from elastic net regression

BMC Genetics

Methodology Article

Katja Ickstadt¹  Tamara Schikowski²  Ursula Krämer²  Anke Hüls³ 

[1] Faculty of Statistics, TU Dortmund University, Dortmund, Germany;IUF-Leibniz Research Institute for Environmental Medicine, Auf’m Hennekamp 50, 40225, Düsseldorf, Germany;IUF-Leibniz Research Institute for Environmental Medicine, Auf’m Hennekamp 50, 40225, Düsseldorf, Germany;Faculty of Statistics, TU Dortmund University, Dortmund, Germany;

关键词: Polygenic approach;    Penalized regression model;    Lasso;    Ridge regression;    Linkage disequilibrium;    Noise;   

DOI  :  10.1186/s12863-017-0519-1

 received in 2017-01-20, accepted in 2017-05-23,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundFor the analysis of gene-environment (GxE) interactions commonly single nucleotide polymorphisms (SNPs) are used to characterize genetic susceptibility, an approach that mostly lacks power and has poor reproducibility. One promising approach to overcome this problem might be the use of weighted genetic risk scores (GRS), which are defined as weighted sums of risk alleles of gene variants. The gold-standard is to use external weights from published meta-analyses.MethodsIn this study, we used internal weights from the marginal genetic effects of the SNPs estimated by a multivariate elastic net regression and thereby provided a method that can be used if there are no external weights available. We conducted a simulation study for the detection of GxE interactions and compared power and type I error of single SNPs analyses with Bonferroni correction and corresponding analysis with unweighted and our weighted GRS approach in scenarios with six risk SNPs and an increasing number of highly correlated (up to 210) and noise SNPs (up to 840).ResultsApplying weighted GRS increased the power enormously in comparison to the common single SNPs approach (e.g. 94.2% vs. 35.4%, respectively, to detect a weak interaction with an OR ≈ 1.04 for six uncorrelated risk SNPs and n = 700 with a well-controlled type I error). Furthermore, weighted GRS outperformed the unweighted GRS, in particular in the presence of SNPs without any effect on the phenotype (e.g. 90.1% vs. 43.9%, respectively, when 20 noise SNPs were added to the six risk SNPs). This outperforming of the weighted GRS was confirmed in a real data application on lung inflammation in the SALIA cohort (n = 402). However, in scenarios with a high number of noise SNPs (>200 vs. 6 risk SNPs), larger sample sizes are needed to avoid an increased type I error, whereas a high number of correlated SNPs can be handled even in small samples (e.g. n = 400).ConclusionIn conclusion, weighted GRS with weights from the marginal genetic effects of the SNPs estimated by a multivariate elastic net regression were shown to be a powerful tool to detect gene-environment interactions in scenarios of high Linkage disequilibrium and noise.

【 授权许可】

CC BY   
© The Author(s). 2017

【 图 表 】

Fig. 6