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Regression and data mining methods for analyses of multiple rare variants in the Genetic Analysis Workshop 17 mini‐exome data
Author(s) -
BaileyWilson Joan E.,
Brennan Jennifer S.,
Bull Shelley B.,
Culverhouse Robert,
Kim Yoonhee,
Jiang Yuan,
Jung Jeesun,
Li Qing,
Lamina Claudia,
Liu Ying,
Mägi Reedik,
Niu Yue S.,
Simpson Claire L.,
Wang Libo,
Yilmaz Yildiz E.,
Zhang Heping,
Zhang Zhaogong
Publication year - 2011
Publication title -
genetic epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.301
H-Index - 98
eISSN - 1098-2272
pISSN - 0741-0395
DOI - 10.1002/gepi.20657
Subject(s) - locus (genetics) , biology , heritability , genetic heterogeneity , computational biology , genetics , exome , population , missing heritability problem , allele , exome sequencing , phenotype , genotype , gene , genetic variants , medicine , environmental health
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. Genet. Epidemiol . 35:S92–S100, 2011. © 2011 Wiley Periodicals, Inc.