GWAS of Correlated Traits

Korte et al has written an interesting paper which extends the analysis of correlated traits to genome-wide association studies (GWAS). (Korte A, Vilhjálmsson BJ, Segura V, Platt A, Long Q,Nordborg M. A mixed-model approach for genome-wide association studies of correlated traits in structured populations. Nat Genet. 2012 Aug 19. doi: 10.1038/ng.2376. [Epub ahead of print] PubMed PMID: 22902788.)

GWA studies have commonly employed a simple statistical model in which a single locus is tested for association with a single phenotype (usually in an additive model). There have been few attempts so far, if any to utilize correlated phenotypes and perhaps improve power of the studies. 

In the 2011 meeting of American Society of Human Genetics at Montreal, Canada, I have used one, relatively simple, method to combine results from two correlated traits with resulting increase in power of GWAS. (Qayyum R et al. Correlated meta-analysis of genome-wide association studies of agonist-mediated native platelet aggregation in African Americans). What we did was to conduct two separate GWAS studies of correlated platelet aggregation phenotypes and then combined the results using meta-analysis. However, the resulting p-values were adjusted for correlation between the two phenotypes using tetrachoric correlation. We further conformed our findings using the simulations from correlated distributions, confirming our findings. 

Korte et al, use linear mixed model approach to not only adjust for population stratification but also for correlation between phenotypes. They use ASReml and R for this analysis and provide R scripts on their website. Using their techniques, Korte et al unveil additional SNPs in a GWAS of LDL and triglycerides.