title: A rapid gene-based genome-wide association test with multivariate traits.
abstract: OBJECTIVE
  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.affiliation: Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minn., USA.
authors.forename: Saonli
authors.initials: S
authors.lastname: Basu
authors.forename: Yiwei
authors.initials: Y
authors.lastname: Zhang
authors.forename: Debashree
authors.initials: D
authors.lastname: Ray
authors.forename: Michael B
authors.initials: MB
authors.lastname: Miller
authors.forename: William G
authors.initials: WG
authors.lastname: Iacono
authors.forename: Matt
authors.initials: M
authors.lastname: McGue
mesh_terms: D003198:Computer Simulation; D005796:Genes / Q000235:genetics*; D055106:Genome-Wide Association Study / Q000379:methods*; D005838:Genotype; D006801:Humans; D016014:Linear Models; D008957:Models, Genetic; D020412:Multifactorial Inheritance / Q000235:genetics*; D015999:Multivariate Analysis; D020641:Polymorphism, Single Nucleotide / Q000235:genetics