Abstract
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.
Highlights
For 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
We investigated the detection of gene-environment interactions in a simulation study and in a real data application in which we compare the performance of weighted genetic risk scores (GRS) to unweighted GRS and to the common single six independent genetic risk factors (SNPs) analysis with Bonferroni correction
Weighted and unweighted GRS vs. single SNPs analysis In a first step, we evaluated the performance of weighted and unweighted GRS to the common single SNPs analysis with Bonferroni correction in scenarios with a moderate number of correlated SNPs and noise SNPs
Summary
For 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. Genome wide association studies (GWAS) made us aware that for many diseases, the genetic influences are exceedingly complex and cannot be explained by simple Mendelian modes of inheritance only Both genetic and environmental factors may contribute to susceptibility, which clarifies the importance of analyzing gene-environment (GxE) interactions that can be defined as “a different effect of environmental exposure in disease risk in persons with different genotypes” or, equivalently, “a different effect of a genotype on disease risk in persons with different environmental exposures” [1]. Aschard [9] showed that if interactions tend to go in the same direction, the genetic risk score (GRS)-based test can outperform other approaches [9] Since this assumption might probably be true for SNPs of the same pathway, one promising approach might be to calculate pathway specific weighted GRS which are defined as weighted sum of risk alleles of gene variants related to each pathway to construct score variables representing the allelic profile of each participant
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
