title: Use of principal components to aggregate rare variants in case-control and family-based association studies in the presence of multiple covariates.
abstract: Rare variants may help to explain some of the missing heritability of complex diseases. Technological advances in next-generation sequencing give us the opportunity to test this hypothesis. We propose two new methods (one for case-control studies and one for family-based studies) that combine aggregated rare variants and common variants located within a region through principal components analysis and allow for covariate adjustment. We analyzed 200 replicates consisting of 209 case subjects and 488 control subjects and compared the results to weight-based and step-up aggregation methods. The principal components and collapsing method showed an association between the gene FLT1 and the quantitative trait Q1 (P<10-30) in a fraction of the computation time of the other methods. The proposed family-based test has inconclusive results. The two methods provide a fast way to analyze simultaneously rare and common variants at the gene level while adjusting for covariates. However, further evaluation of the statistical efficiency of this approach is warranted.
authors.affiliation: Department of Epidemiology and Biostatistics and Institute for Human Genetics, University of California San Francisco, 1450 Third Street, Box 3110, San Francisco, CA 94148-3110, USA.
authors.forename: Rémi
authors.initials: R
authors.lastname: Kazma
authors.affiliation: Department of Epidemiology and Biostatistics and Institute for Human Genetics, University of California San Francisco, 1450 Third Street, Box 3110, San Francisco, CA 94148-3110, USA.
authors.forename: Thomas J
authors.initials: TJ
authors.lastname: Hoffmann
authors.affiliation: Department of Epidemiology and Biostatistics and Institute for Human Genetics, University of California San Francisco, 1450 Third Street, Box 3110, San Francisco, CA 94148-3110, USA.
authors.forename: John S
authors.initials: JS
authors.lastname: Witte