title: A comparison study of multivariate fixed models and Gene Association with Multiple Traits (GAMuT) for next-generation sequencing.
abstract: In this paper, extensive simulations are performed to compare two statistical methods to analyze multiple correlated quantitative phenotypes: (1) approximate F-distributed tests of multivariate functional linear models (MFLM) and additive models of multivariate analysis of variance (MANOVA), and (2) Gene Association with Multiple Traits (GAMuT) for association testing of high-dimensional genotype data. It is shown that approximate F-distributed tests of MFLM and MANOVA have higher power and are more appropriate for major gene association analysis (i.e., scenarios in which some genetic variants have relatively large effects on the phenotypes); GAMuT has higher power and is more appropriate for analyzing polygenic effects (i.e., effects from a large number of genetic variants each of which contributes a small amount to the phenotypes). MFLM and MANOVA are very flexible and can be used to perform association analysis for (i) rare variants, (ii) common variants, and (iii) a combination of rare and common variants. Although GAMuT was designed to analyze rare variants, it can be applied to analyze a combination of rare and common variants and it performs well when (1) the number of genetic variants is large and (2) each variant contributes a small amount to the phenotypes (i.e., polygenes). MFLM and MANOVA are fixed effect models that perform well for major gene association analysis. GAMuT can be viewed as an extension of sequence kernel association tests (SKAT). Both GAMuT and SKAT are more appropriate for analyzing polygenic effects and they perform well not only in the rare variant case, but also in the case of a combination of rare and common variants. Data analyses of European cohorts and the Trinity Students Study are presented to compare the performance of the two methods.
authors.affiliation: Biostatistics and Bioinformatics Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health (NIH), Bethesda, MD, USA.
authors.forename: Chi-Yang
authors.initials: CY
authors.lastname: Chiu
authors.affiliation: Laboratory of Epidemiology and Biometry, National Institute on Alcohol, Abuse and Alcoholism, NIH, Bethesda, MD, USA.
authors.forename: Jeesun
authors.initials: J
authors.lastname: Jung
authors.affiliation: Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, MD, USA.
authors.forename: Yifan
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA.
authors.forename: Daniel E
authors.initials: DE
authors.lastname: Weeks
authors.affiliation: Computational and Statistical Genomics Branch, National Human Genome Research Institute, NIH, Bethesda, MD, USA.
authors.forename: Alexander F
authors.initials: AF
authors.lastname: Wilson
authors.affiliation: Computational and Statistical Genomics Branch, National Human Genome Research Institute, NIH, Bethesda, MD, USA.
authors.forename: Joan E
authors.initials: JE
authors.lastname: Bailey-Wilson
authors.affiliation: Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA.
authors.forename: Christopher I
authors.initials: CI
authors.lastname: Amos
authors.affiliation: Epidemiology Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health (NIH), Bethesda, MD, USA.
authors.forename: James L
authors.initials: JL
authors.lastname: Mills
authors.affiliation: Department of Biostatistics, School of Public Health, The University of Michigan, Ann Arbor, MI, USA.
authors.forename: Michael
authors.initials: M
authors.lastname: Boehnke
authors.affiliation: Human Genetics Center, University of Texas-Houston, Houston, TX, USA.
authors.forename: Momiao
authors.initials: M
authors.lastname: Xiong
authors.affiliation: Biostatistics and Bioinformatics Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health (NIH), Bethesda, MD, USA.
authors.forename: Ruzong
authors.initials: R
authors.lastname: Fan
mesh_terms: D000704:Analysis of Variance; D056726:Genetic Association Studies; D005819:Genetic Markers / Q000235:genetics*; D014644:Genetic Variation / Q000235:genetics*; D015894:Genome, Human; D005838:Genotype; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D006801:Humans; D008055:Lipids / Q000032:analysis / Q000235:genetics*; D008957:Models, Genetic; D020412:Multifactorial Inheritance / Q000235:genetics*; D010641:Phenotype; D040641:Quantitative Trait Loci
chemical_list: D005819:Genetic Markers; D008055:Lipids
keywords: association mapping; common variants; complex traits; functional data analysis; multivariate analysis of variance (MANOVA); multivariate functional linear models (MFLM); quantitative trait loci; rare variants