title: An efficient genome-wide association test for multivariate phenotypes based on the Fisher combination function.
abstract: BACKGROUND
  In genome-wide association studies (GWAS) for complex diseases, the association between a SNP and each phenotype is usually weak. Combining multiple related phenotypic traits can increase the power of gene search and thus is a practically important area that requires methodology work. This study provides a comprehensive review of existing methods for conducting GWAS on complex diseases with multiple phenotypes including the multivariate analysis of variance (MANOVA), the principal component analysis (PCA), the generalizing estimating equations (GEE), the trait-based association test involving the extended Simes procedure (TATES), and the classical Fisher combination test. We propose a new method that relaxes the unrealistic independence assumption of the classical Fisher combination test and is computationally efficient. To demonstrate applications of the proposed method, we also present the results of statistical analysis on the Study of Addiction: Genetics and Environment (SAGE) data.
  
  
  RESULTS
  Our simulation study shows that the proposed method has higher power than existing methods while controlling for the type I error rate. The GEE and the classical Fisher combination test, on the other hand, do not control the type I error rate and thus are not recommended. In general, the power of the competing methods decreases as the correlation between phenotypes increases. All the methods tend to have lower power when the multivariate phenotypes come from long tailed distributions. The real data analysis also demonstrates that the proposed method allows us to compare the marginal results with the multivariate results and specify which SNPs are specific to a particular phenotype or contribute to the common construct.
  
  
  CONCLUSIONS
  The proposed method outperforms existing methods in most settings and also has great applications in GWAS on complex diseases with multiple phenotypes such as the substance abuse disorders.
authors.affiliation: School of Nursing, University of Michigan, Ann Arbor, Michigan, USA. jjyang@umich.edu.
authors.forename: James J
authors.initials: JJ
authors.lastname: Yang
authors.affiliation: Department of Public Health Sciences, Henry Ford Health System, Detroit, Michigan, USA. jli4@hfhs.org.
authors.forename: Jia
authors.initials: J
authors.lastname: Li
authors.affiliation: Center for Health Policy and Health Services Research, Henry Ford Health System, Detroit, Michigan, USA. kwillia5@hfhs.org.
authors.forename: L Keoki
authors.initials: LK
authors.lastname: Williams
authors.affiliation: Department of Systems, Populations and Leadership, University of Michigan, Ann Arbor, Michigan, USA. buu@umich.edu.
authors.forename: Anne
authors.initials: A
authors.lastname: Buu
mesh_terms: D003198:Computer Simulation; D030541:Databases, Genetic; D059647:Gene-Environment Interaction; D055106:Genome-Wide Association Study / Q000379:methods*; D006801:Humans; D008958:Models, Molecular; D015999:Multivariate Analysis; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide; D025341:Principal Component Analysis