Abstract
BackgroundPredisposition to complex diseases is explained in part by genetic variation, and complex diseases are frequently comorbid, consistent with pleiotropic genetic variation influencing comorbidity. Genome Wide Association (GWA) studies typically assess association between SNPs and a single-disease phenotype. Fisher meta-analysis combines evidence of association from single-disease GWA studies, assuming that each study is an independent test of the same hypothesis. The Rank Product (RP) method overcomes limitations posed by Fisher assumptions, though RP was not designed for GWA data.MethodsWe modified RP to accommodate GWA data, and we call it modRP. Using p-values output from GWA studies, we aggregate evidence for association between SNPs and related phenotypes. To assess significance, RP randomly samples the observed ranks to develop the null distribution of the RP statistic, and then places the observed RPs into the null distribution. ModRP eliminates the effect of linkage disequilibrium and controls for differences in power at tested SNPs, to meet RP assumptions in application to GWA data.ResultsAfter validating modRP based on both positive and negative control studies, we searched for pleiotropic influences on comorbid substance use disorders in a novel study, and found two SNPs to be significantly associated with comorbid cocaine, opium, and nicotine dependence. Placing these SNPs into biological context, we developed a protein network modeling the interaction of cocaine, nicotine, and opium with these variants.ConclusionsModRP is a novel approach to identifying pleiotropic genetic influences on comorbid complex diseases. It can be used to assess association for related phenotypes where raw data is unavailable or inappropriate for analysis using other approaches. The method is conceptually simple and produces statistically significant, biologically relevant results.
Highlights
Predisposition to complex diseases is explained in part by genetic variation, and complex diseases are frequently comorbid, consistent with pleiotropic genetic variation influencing comorbidity
Extending Genome Wide Association (GWA) to assess pleiotropic influences on comorbidity is a reasonable next-step in complex disease analysis
We report here a modified Rank Product (RP) method that ensures that we meet the assumptions of RP in application to GWA by explicitly disrupting linkage disequilibrium (LD) and by grouping SNPs based on minor allele frequency (MAF)
Summary
Predisposition to complex diseases is explained in part by genetic variation, and complex diseases are frequently comorbid, consistent with pleiotropic genetic variation influencing comorbidity. Genome Wide Association (GWA) studies typically assess association between SNPs and a single-disease phenotype. Fisher metaanalysis combines evidence of association from single-disease GWA studies, assuming that each study is an independent test of the same hypothesis. Genome Wide Association (GWA) studies typically assess evidence of association between individual variants (e.g. SNPs) and a single-disease phenotype. Extending GWA to assess pleiotropic influences on comorbidity is a reasonable next-step in complex disease analysis. Approaches to GWA for comorbid phenotypes that combine raw data may not be possible if the raw data are unavailable or are inappropriate to combine (e.g., due to differences in data types or analytical methods). We validated modRP based on available control studies, found a novel, statistically significant, biologically relevant association between two SNPs and comorbid substance dependence phenotypes, providing a model for this gene-environment interaction and demonstrating the usefulness of the approach
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