title: Simultaneous Modeling of Disease Status and Clinical Phenotypes To Increase Power in Genome-Wide Association Studies.
abstract: Genome-wide association studies have identified thousands of variants implicated in dozens of complex diseases. Most studies collect individuals with and without disease and search for variants with different frequencies between the groups. For many of these studies, additional disease traits are also collected. Jointly modeling clinical phenotype and disease status is a promising way to increase power to detect true associations between genetics and disease. In particular, this approach increases the potential for discovering genetic variants that are associated with both a clinical phenotype and a disease. Standard multivariate techniques fail to effectively solve this problem, because their case-control status is discrete and not continuous. Standard approaches to estimate model parameters are biased due to the ascertainment in case-control studies. We present a novel method that resolves both of these issues for simultaneous association testing of genetic variants that have both case status and a clinical covariate. We demonstrate the utility of our method using both simulated data and the Northern Finland Birth Cohort data.
authors.affiliation: Department of Computer Science, University of California, Los Angeles, California.
authors.forename: Michael
authors.initials: M
authors.lastname: Bilow
authors.affiliation: Department of Statistics, Pontificia Universidad Católica de Chile, Santiago, Chile.|CEDYTEC, Facultad de Ingeniería, Ciencia y Tecnología, Universidad Bernardo O'Higgins, Santiago, Chile.
authors.forename: Fernando
authors.initials: F
authors.lastname: Crespo
authors.affiliation: Bioinformatics Program, University of California, Los Angeles, California.
authors.forename: Zhicheng
authors.initials: Z
authors.lastname: Pan
authors.affiliation: Department of Computer Science, University of California, Los Angeles, California.|Department of Human Genetics, University of California, Los Angeles, California.
authors.forename: Eleazar
authors.initials: E
authors.lastname: Eskin
authors.affiliation: Department of Statistics, Pontificia Universidad Católica de Chile, Santiago, Chile susana@mat.puc.cl.
authors.forename: Susana
authors.initials: S
authors.lastname: Eyheramendy
mesh_terms: D030342:Genetic Diseases, Inborn / Q000235:genetics* / Q000473:pathology; D020022:Genetic Predisposition to Disease; D055106:Genome-Wide Association Study / Q000379:methods*; D006801:Humans; D008957:Models, Genetic; D010641:Phenotype
keywords: covariates; multivariate analysis