title: Performance of random forests and logic regression methods using mini-exome sequence data.
abstract: Machine learning approaches are an attractive option for analyzing large-scale data to detect genetic variants that contribute to variation of a quantitative trait, without requiring specific distributional assumptions. We evaluate two machine learning methods, random forests and logic regression, and compare them to standard simple univariate linear regression, using the Genetic Analysis Workshop 17 mini-exome data. We also apply these methods after collapsing multiple rare variants within genes and within gene pathways. Linear regression and the random forest method performed better when rare variants were collapsed based on genes or gene pathways than when each variant was analyzed separately. Logic regression performed better when rare variants were collapsed based on genes rather than on pathways.
authors.affiliation: Inherited Disease Research Branch, National Human Genome Research Institute, National Institutes of Health, Baltimore, MD 21224, USA.
authors.forename: Yoonhee
authors.initials: Y
authors.lastname: Kim
authors.affiliation: Inherited Disease Research Branch, National Human Genome Research Institute, National Institutes of Health, Baltimore, MD 21224, USA.
authors.forename: Qing
authors.initials: Q
authors.lastname: Li
authors.affiliation: Inherited Disease Research Branch, National Human Genome Research Institute, National Institutes of Health, Baltimore, MD 21224, USA.
authors.forename: Cheryl D
authors.initials: CD
authors.lastname: Cropp
authors.affiliation: Inherited Disease Research Branch, National Human Genome Research Institute, National Institutes of Health, Baltimore, MD 21224, USA.
authors.forename: Heejong
authors.initials: H
authors.lastname: Sung
authors.affiliation: Inherited Disease Research Branch, National Human Genome Research Institute, National Institutes of Health, Baltimore, MD 21224, USA.
authors.forename: Juanliang
authors.initials: J
authors.lastname: Cai
authors.affiliation: Inherited Disease Research Branch, National Human Genome Research Institute, National Institutes of Health, Baltimore, MD 21224, USA.
authors.forename: Claire L
authors.initials: CL
authors.lastname: Simpson
authors.affiliation: Inherited Disease Research Branch, National Human Genome Research Institute, National Institutes of Health, Baltimore, MD 21224, USA.
authors.forename: Brian
authors.initials: B
authors.lastname: Perry
authors.affiliation: Clinical Sciences Section, National Institute of Arthritis and Musculoskeletal and Skin Disease, National Institutes of Health, Bethesda, MD 20892, USA.
authors.forename: Abhijit
authors.initials: A
authors.lastname: Dasgupta
authors.affiliation: Center for Information Technology, National Institutes of Health, Bethesda, MD 20892, USA.
authors.forename: James D
authors.initials: JD
authors.lastname: Malley
authors.affiliation: Inherited Disease Research Branch, National Human Genome Research Institute, National Institutes of Health, Baltimore, MD 21224, USA.
authors.forename: Alexander F
authors.initials: AF
authors.lastname: Wilson
authors.affiliation: Inherited Disease Research Branch, National Human Genome Research Institute, National Institutes of Health, Baltimore, MD 21224, USA.
authors.forename: Joan E
authors.initials: JE
authors.lastname: Bailey-Wilson