title: Uncovering Local Trends in Genetic Effects of Multiple Phenotypes via Functional Linear Models.
abstract: Recent technological advances equipped researchers with capabilities that go beyond traditional genotyping of loci known to be polymorphic in a general population. Genetic sequences of study participants can now be assessed directly. This capability removed technology-driven bias toward scoring predominantly common polymorphisms and let researchers reveal a wealth of rare and sample-specific variants. Although the relative contributions of rare and common polymorphisms to trait variation are being debated, researchers are faced with the need for new statistical tools for simultaneous evaluation of all variants within a region. Several research groups demonstrated flexibility and good statistical power of the functional linear model approach. In this work we extend previous developments to allow inclusion of multiple traits and adjustment for additional covariates. Our functional approach is unique in that it provides a nuanced depiction of effects and interactions for the variables in the model by representing them as curves varying over a genetic region. We demonstrate flexibility and competitive power of our approach by contrasting its performance with commonly used statistical tools and illustrate its potential for discovery and characterization of genetic architecture of complex traits using sequencing data from the Dallas Heart Study.
authors.affiliation: Department of Biostatistics, University of Kentucky, Lexington, USA.
authors.forename: Olga A
authors.initials: OA
authors.lastname: Vsevolozhskaya
authors.affiliation: National Institute of Environmental Health Sciences, National Institutes of Health, USA.
authors.forename: Dmitri V
authors.initials: DV
authors.lastname: Zaykin
authors.affiliation: Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, USA.
authors.forename: David A
authors.initials: DA
authors.lastname: Barondess
authors.affiliation: Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, USA.
authors.forename: Xiaoren
authors.initials: X
authors.lastname: Tong
authors.affiliation: Department of Statistics, Michigan State University, East Lansing, USA.
authors.forename: Sneha
authors.initials: S
authors.lastname: Jadhav
authors.affiliation: Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, USA.
authors.forename: Qing
authors.initials: Q
authors.lastname: Lu
mesh_terms: D001741:Black or African American / Q000235:genetics; D000075803:Angiopoietin-Like Protein 4; D042682:Angiopoietins / Q000235:genetics; D005260:Female; D056726:Genetic Association Studies; D005838:Genotype; D006321:Heart; D006630:Hispanic or Latino / Q000235:genetics; D006801:Humans; D016014:Linear Models; D008297:Male; D008957:Models, Genetic; D010641:Phenotype; D011110:Polymorphism, Genetic / Q000235:genetics; D011795:Surveys and Questionnaires; D013781:Texas; D014280:Triglycerides / Q000097:blood; D044465:White People / Q000235:genetics
chemical_list: C474772:ANGPTL4 protein, human; D000075803:Angiopoietin-Like Protein 4; D042682:Angiopoietins; D014280:Triglycerides
keywords: functional analysis; genome-wide association studies; multivariate analysis; pleiotropy; qualitative traits; quantitative traits; sequencing studies