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Testing for an Unusual Distribution of Rare Variants

På svenska

Author

  • Benjamin M. Neale
  • Manuel A. Rivas
  • Benjamin F. Voight
  • David Altshuler
  • Bernie Devlin
  • Marju Orho-Melander
  • Sekar Kathiresan
  • Shaun M. Purcell
  • Kathryn Roeder
  • Mark J. Daly

Summary, in English

Technological advances make it possible to use high-throughput sequencing as a primary discovery tool of medical genetics, specifically for assaying rare variation. Still this approach faces the analytic challenge that the influence of very rare variants can only be evaluated effectively as a group. A further complication is that any given rare variant could have no effect, could increase risk, or could be protective. We propose here the C-alpha test statistic as a novel approach for testing for the presence of this mixture of effects across a set of rare variants. Unlike existing burden tests, C-alpha, by testing the variance rather than the mean, maintains consistent power when the target set contains both risk and protective variants. Through simulations and analysis of case/control data, we demonstrate good power relative to existing methods that assess the burden of rare variants in individuals.

Publishing year

2011

Language

English

Publication/Series

PLoS Genetics

Volume

7

Issue

3

Document type

Journal article

Publisher

Public Library of Science (PLoS)

Topic

  • Endocrinology and Diabetes

Status

Published

Research group

  • Genomics, Diabetes and Endocrinology

ISBN/ISSN/Other

  • ISSN: 1553-7404