OBJECTIVES: A gene-based genome-wide association study (GWAS) provides a powerful alternative to the traditional single single nucleotide polymorphism (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 it imposes substantial analytical as well as computational challenges to implement it at a genome-wide level. METHODS: We propose a rapid implementation of the multivariate multiple linear regression (RMMLR) approach 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 a corresponding R package to implement such multivariate gene-based GWAS with this RMMLR approach. RESULTS: Through extensive simulation, we compared several approaches for both single and multivariate traits. Our RMMLR approach maintained a correct type I error level even for sets of SNPs in strong LD. It also demonstrated a substantial gain in power to detect a gene when it is associated with a subset of the traits. We also studied performances of the approaches on the 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.
OBJECTIVES: A gene-based genome-wide association study (GWAS) provides a powerful alternative to the traditional single single nucleotide polymorphism (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 it imposes substantial analytical as well as computational challenges to implement it at a genome-wide level. METHODS: We propose a rapid implementation of the multivariate multiple linear regression (RMMLR) approach 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 a corresponding R package to implement such multivariate gene-based GWAS with this RMMLR approach. RESULTS: Through extensive simulation, we compared several approaches for both single and multivariate traits. Our RMMLR approach maintained a correct type I error level even for sets of SNPs in strong LD. It also demonstrated a substantial gain in power to detect a gene when it is associated with a subset of the traits. We also studied performances of the approaches on the 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.
Authors: Alkes L Price; Nick J Patterson; Robert M Plenge; Michael E Weinblatt; Nancy A Shadick; David Reich Journal: Nat Genet Date: 2006-07-23 Impact factor: 38.330
Authors: Paul F O'Reilly; Clive J Hoggart; Yotsawat Pomyen; Federico C F Calboli; Paul Elliott; Marjo-Riitta Jarvelin; Lachlan J M Coin Journal: PLoS One Date: 2012-05-02 Impact factor: 3.240
Authors: Sophie Van der Sluis; Conor V Dolan; Jiang Li; Youqiang Song; Pak Sham; Danielle Posthuma; Miao-Xin Li Journal: Bioinformatics Date: 2014-11-26 Impact factor: 6.937
Authors: James Smith; Frances Rapport; Tracey A O'Brien; Stephanie Smith; Vanessa J Tyrrell; Emily V A Mould; Janet C Long; Hossai Gul; Jeremy Cullis; Jeffrey Braithwaite Journal: BMC Health Serv Res Date: 2020-05-21 Impact factor: 2.655
Authors: Aaron M Holleman; K Alaine Broadaway; Richard Duncan; Andrei Todor; Lynn M Almli; Bekh Bradley; Kerry J Ressler; Debashis Ghosh; Jennifer G Mulle; Michael P Epstein Journal: Sci Rep Date: 2019-05-17 Impact factor: 4.379