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Testing for an Unusual Distribution of Rare Variants

Neale, Benjamin M.; Rivas, Manuel A.; Voight, Benjamin F.; Altshuler, David; Devlin, Bernie; Orho-Melander, Marju LU ; Kathiresan, Sekar; Purcell, Shaun M.; Roeder, Kathryn and Daly, Mark J. (2011) In PLoS Genetics 7(3).
Abstract
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.... (More)
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. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
PLoS Genetics
volume
7
issue
3
publisher
Public Library of Science
external identifiers
  • wos:000288996600004
  • scopus:79953752624
ISSN
1553-7404
DOI
10.1371/journal.pgen.1001322
language
English
LU publication?
yes
id
d5229f51-63cd-495a-9a1f-c37323d3f98c (old id 1964828)
date added to LUP
2011-05-23 14:26:39
date last changed
2017-11-19 03:03:14
@article{d5229f51-63cd-495a-9a1f-c37323d3f98c,
  abstract     = {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.},
  author       = {Neale, Benjamin M. and Rivas, Manuel A. and Voight, Benjamin F. and Altshuler, David and Devlin, Bernie and Orho-Melander, Marju and Kathiresan, Sekar and Purcell, Shaun M. and Roeder, Kathryn and Daly, Mark J.},
  issn         = {1553-7404},
  language     = {eng},
  number       = {3},
  publisher    = {Public Library of Science},
  series       = {PLoS Genetics},
  title        = {Testing for an Unusual Distribution of Rare Variants},
  url          = {http://dx.doi.org/10.1371/journal.pgen.1001322},
  volume       = {7},
  year         = {2011},
}