A Rapid Genome-wide Gene-based Association Test with Multivariate Traits - Basu, Saonli (Hum Hered, 2013)
OBJECTIVES: A gene-based genome-wide association study (GWAS) provides a powerful alternative to the traditional single SNP association analysis due to its substantial reduction in the multiple testing burden and possible gain in power due to modeling multiple SNPs within a gene. A gene-based association analysis on multivariate traits is often of interest, but imposes substantial analytical as well as computational challenges to implement it at a genome-wide level. METHODS: We have proposed a rapid implementation of multivariate multiple linear regression approach (RMMLR) in unrelated individuals as well as in families. Our approach allows for covariates. Moreover the asymptotic distribution of the test statistic is not heavily influenced by the linkage disequilibrium (LD) among the SNPs and hence can be used efficiently to perform a gene-based GWAS. We have developed corresponding R package to implement such multivariate gene-based GWAS with this RMMLR approach. RESULTS: We compare through extensive simulation several approaches for both single and multivariate traits. Our RMMLR maintains correct type-I error level even for set of SNPs in strong LD. It also has substantial gain in power to detect a gene when it is associated with a subset of the traits. We have also studied their performance on Minnesota Center for Twin Family Research dataset. CONCLUSIONS: In our overall comparison, our RMMLR approach provides an efficient and powerful tool to perform a gene-based GWAS with single or multivariate traits and maintains the type I error appropriately.