title: Efficient and Accurate Multiple-Phenotype Regression Method for High Dimensional Data Considering Population Structure.
abstract: A typical genome-wide association study tests correlation between a single phenotype and each genotype one at a time. However, single-phenotype analysis might miss unmeasured aspects of complex biological networks. Analyzing many phenotypes simultaneously may increase the power to capture these unmeasured aspects and detect more variants. Several multivariate approaches aim to detect variants related to more than one phenotype, but these current approaches do not consider the effects of population structure. As a result, these approaches may result in a significant amount of false positive identifications. Here, we introduce a new methodology, referred to as GAMMA for generalized analysis of molecular variance for mixed-model analysis, which is capable of simultaneously analyzing many phenotypes and correcting for population structure. In a simulated study using data implanted with true genetic effects, GAMMA accurately identifies these true effects without producing false positives induced by population structure. In simulations with this data, GAMMA is an improvement over other methods which either fail to detect true effects or produce many false positive identifications. We further apply our method to genetic studies of yeast and gut microbiome from mice and show that GAMMA identifies several variants that are likely to have true biological mechanisms.
authors: Bioinformatics Interdepartmental Ph.D. Program, University of California, Los Angeles, California.
authors: Jong Wha J
authors: JW
authors: Joo
authors: Computer Science Department, University of California, Los Angeles, California.
authors: Eun Yong
authors: EY
authors: Kang
authors: Department of Medicine, University of California, Los Angeles, California.
authors: Elin
authors: E
authors: Org
authors: Computer Science Department, University of California, Los Angeles, California.
authors: Nick
authors: N
authors: Furlotte
authors: Department of Medicine, University of California, Los Angeles, California.
authors: Brian
authors: B
authors: Parks
authors: Computer Science Department, University of California, Los Angeles, California.
authors: Farhad
authors: F
authors: Hormozdiari
authors: Department of Medicine, University of California, Los Angeles, California.|Department of Microbiology, Immunology and Molecular Genetics, University of California, Los Angeles, California.|Department of Human Genetics, University of California, Los Angeles, California 90095.
authors: Aldons J
authors: AJ
authors: Lusis
authors: Bioinformatics Interdepartmental Ph.D. Program, University of California, Los Angeles, California eeskin@cs.ucla.edu.|Computer Science Department, University of California, Los Angeles, California.|Department of Human Genetics, University of California, Los Angeles, California 90095.
authors: Eleazar
authors: E
authors: Eskin
mesh_terms: D000465:Algorithms; D000818:Animals; D055106:Genome-Wide Association Study / Q000379:methods*; D006801:Humans; D051379:Mice; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide; D011153:Population / Q000235:genetics; D012680:Sensitivity and Specificity; D015003:Yeasts / Q000235:genetics
keywords: mixed models; multivariate analysis; population structure