Improving Power by Conditioning on Major SNPs in Genetic Association Studies (GWAS) #

Mahfuzur Rahman Khokan, Kaustubh Adhikari

Poster session

Abstract #

In the area of statistical genetics, classical genome-wide association studies (GWAS) assess the association between a biological characteristic and genetic variants, working with one variant at a time in a regression model, and reporting the most significant associations. However, in many cases, there are known databases of major genetic variants that have a substantial effect on the trait. In such situations, it makes sense statistically to condition on these major variants to improve power in detecting associations with new variants, but this is not a common practice in GWAS applications. In this study, we show theoretically and computationally how conducting a joint analysis of the genetic variants in a multivariate regression model, where the estimated effect of a new variant is conditioned upon some major variants, can improve the performance of the model in terms of reducing the standard error and improving the power. The amount of gain of power will depend on the correlation between the response and the covariates, as well as the correlation between the covariates. We further show that conditional results can sometimes be obtained from publicly available summary statistics reported for univariate associations in published GWAS studies, even when the individual-level data are unavailable. A prominent example of such a trait is skin colour, for which there are many studies consistently identifying a handful of major genes. We analysed a dataset for over 6,500 mixed-ethnicity Latin Americans to see how the conditioning process can improve the detection power of GWAS studies and identify new genetic variants in such a situation.