Abstract
In the area of genetic epidemiology, genetic risk predictive modeling is becoming an important area of translational success. As an increasing number of genetic variants are successfully discovered, the use of multiple genetic variants in constructing a genetic risk score (GRS) for modeling has been widely applied using a variety of approaches. Previously, we compared the performance of a simple, additive GRS with weighted GRS approaches, but our initial simulation experiment assumed very simple models without many of the complications found in real genetic studies. In particular, interactions between variants and linkage disequilibrium (LD) (indirect mapping) remain important and challenging problems for GRS modeling. In the present study, we applied two simulation strategies to mimic various types of epistasis to evaluate their impact on the performance of the GRS models. We simulated a range of models demonstrating statistical interaction and linkage disequilibrium. Three genetic risk models were compared in terms of power, type I error, C-statistic and AIC, including a simple count GRS (SC-GRS), an odds ratio weighted GRS (OR-GRS) and an explained variance weighted GRS (EV-GRS). Simulation factors of interest included allele frequencies, effect sizes, strengths of interaction, degrees of LD and heritability. We extensively examined the extent to how these interactions could influence the performance of genetic risk models. Our results show that the weighted methods outperform simple count method in general even if interaction or LD is present, with well controlled type I error.
Highlights
In recent years, Genome-Wide Association Studies (GWAS) and candidate gene studies have identified a large number of genetic variants with varying effect size that are associated with complex diseases (McCarthy et al, 2008) and drug response/pharmacogenomic traits (Ritchie, 2012)
In Figure 1j, when the effect sizes among single nucleotide polymorphism (SNP) vary, it is clear that the weighted methods are consistently preferable to the unweighted one
There is no significant difference between the two weighted methods, OR- and explained variance weighted (EV-)genetic risk score (GRS)
Summary
Genome-Wide Association Studies (GWAS) and candidate gene studies have identified a large number of genetic variants with varying effect size that are associated with complex diseases (McCarthy et al, 2008) and drug response/pharmacogenomic traits (Ritchie, 2012). One popular approach for incorporating identified genetic variants is by constructing a genetic risk score (GRS) for modeling using a variety of approaches, such as an additive simple count and weighted GRS (Carayol et al, 2010; Paynter et al, 2010) The applicability of these cumulative risk scores as predictive models for disease has been proposed and brought anecdotal successes in the real genetic studies (Hess et al, 2006; Meigs et al, 2008; Klein et al, 2009; Manolio, 2010). The primary goal of GRS is beyond the initial detection of risk alleles, and typically only involves variants with previously established associations, ignoring interactions may largely limit the success of risk prediction model for complex disease and pharmacogenomic studies
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