title: Regression and data mining methods for analyses of multiple rare variants in the Genetic Analysis Workshop 17 mini-exome data.
abstract: Group 14 of Genetic Analysis Workshop 17 examined several issues related to analysis of complex traits using DNA sequence data. These issues included novel methods for analyzing rare genetic variants in an aggregated manner (often termed collapsing rare variants), evaluation of various study designs to increase power to detect effects of rare variants, and the use of machine learning approaches to model highly complex heterogeneous traits. Various published and novel methods for analyzing traits with extreme locus and allelic heterogeneity were applied to the simulated quantitative and disease phenotypes. Overall, we conclude that power is (as expected) dependent on locus-specific heritability or contribution to disease risk, large samples will be required to detect rare causal variants with small effect sizes, extreme phenotype sampling designs may increase power for smaller laboratory costs, methods that allow joint analysis of multiple variants per gene or pathway are more powerful in general than analyses of individual rare variants, population-specific analyses can be optimal when different subpopulations harbor private causal mutations, and machine learning methods may be useful for selecting subsets of predictors for follow-up in the presence of extreme locus heterogeneity and large numbers of potential predictors.
authors: Inherited Disease Research Branch, National Human Genome Research Institute, National Institutes of Health, Baltimore, MD 21224, USA. jebw@mail.nih.gov
authors: Joan E
authors: JE
authors: Bailey-Wilson
authors: Jennifer S
authors: JS
authors: Brennan
authors: Shelley B
authors: SB
authors: Bull
authors: Robert
authors: R
authors: Culverhouse
authors: Yoonhee
authors: Y
authors: Kim
authors: Yuan
authors: Y
authors: Jiang
authors: Jeesun
authors: J
authors: Jung
authors: Qing
authors: Q
authors: Li
authors: Claudia
authors: C
authors: Lamina
authors: Ying
authors: Y
authors: Liu
authors: Reedik
authors: R
authors: Mägi
authors: Yue S
authors: YS
authors: Niu
authors: Claire L
authors: CL
authors: Simpson
authors: Libo
authors: L
authors: Wang
authors: Yildiz E
authors: YE
authors: Yilmaz
authors: Heping
authors: H
authors: Zhang
authors: Zhaogong
authors: Z
authors: Zhang
mesh_terms: D001185:Artificial Intelligence; D003627:Data Interpretation, Statistical; D057225:Data Mining; D059472:Exome; D020022:Genetic Predisposition to Disease / Q000235:genetics*; D014644:Genetic Variation; D016045:Human Genome Project; D006801:Humans; D015201:Meta-Analysis as Topic; D017720:Molecular Epidemiology / Q000379:methods*; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*; D012044:Regression Analysis; D017421:Sequence Analysis