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Meta-analysis of gene-level tests for rare variant association.

Liu, Dajiang J; Peloso, Gina M; Zhan, Xiaowei; Holmen, Oddgeir L; Zawistowski, Matthew; Feng, Shuang; Nikpay, Majid; Auer, Paul L; Goel, Anuj and Zhang, He, et al. (2014) In Nature Genetics 46(2). p.200-200
Abstract
The majority of reported complex disease associations for common genetic variants have been identified through meta-analysis, a powerful approach that enables the use of large sample sizes while protecting against common artifacts due to population structure and repeated small-sample analyses sharing individual-level data. As the focus of genetic association studies shifts to rare variants, genes and other functional units are becoming the focus of analysis. Here we propose and evaluate new approaches for performing meta-analysis of rare variant association tests, including burden tests, weighted burden tests, variable-threshold tests and tests that allow variants with opposite effects to be grouped together. We show that our approach... (More)
The majority of reported complex disease associations for common genetic variants have been identified through meta-analysis, a powerful approach that enables the use of large sample sizes while protecting against common artifacts due to population structure and repeated small-sample analyses sharing individual-level data. As the focus of genetic association studies shifts to rare variants, genes and other functional units are becoming the focus of analysis. Here we propose and evaluate new approaches for performing meta-analysis of rare variant association tests, including burden tests, weighted burden tests, variable-threshold tests and tests that allow variants with opposite effects to be grouped together. We show that our approach retains useful features from single-variant meta-analysis approaches and demonstrate its use in a study of blood lipid levels in ∼18,500 individuals genotyped with exome arrays. (Less)
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Nature Genetics
volume
46
issue
2
pages
200 - 200
publisher
Nature Publishing Group
external identifiers
  • pmid:24336170
  • wos:000331208300019
  • scopus:84895808047
ISSN
1546-1718
DOI
10.1038/ng.2852
language
English
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yes
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3237f8fe-027c-4523-9813-a79a888fe10f (old id 4223713)
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http://www.ncbi.nlm.nih.gov/pubmed/24336170?dopt=Abstract
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2014-01-04 18:05:41
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2017-11-19 03:00:54
@article{3237f8fe-027c-4523-9813-a79a888fe10f,
  abstract     = {The majority of reported complex disease associations for common genetic variants have been identified through meta-analysis, a powerful approach that enables the use of large sample sizes while protecting against common artifacts due to population structure and repeated small-sample analyses sharing individual-level data. As the focus of genetic association studies shifts to rare variants, genes and other functional units are becoming the focus of analysis. Here we propose and evaluate new approaches for performing meta-analysis of rare variant association tests, including burden tests, weighted burden tests, variable-threshold tests and tests that allow variants with opposite effects to be grouped together. We show that our approach retains useful features from single-variant meta-analysis approaches and demonstrate its use in a study of blood lipid levels in ∼18,500 individuals genotyped with exome arrays.},
  author       = {Liu, Dajiang J and Peloso, Gina M and Zhan, Xiaowei and Holmen, Oddgeir L and Zawistowski, Matthew and Feng, Shuang and Nikpay, Majid and Auer, Paul L and Goel, Anuj and Zhang, He and Peters, Ulrike and Farrall, Martin and Orho-Melander, Marju and Kooperberg, Charles and McPherson, Ruth and Watkins, Hugh and Willer, Cristen J and Hveem, Kristian and Melander, Olle and Kathiresan, Sekar and Abecasis, Gonçalo R},
  issn         = {1546-1718},
  language     = {eng},
  number       = {2},
  pages        = {200--200},
  publisher    = {Nature Publishing Group},
  series       = {Nature Genetics},
  title        = {Meta-analysis of gene-level tests for rare variant association.},
  url          = {http://dx.doi.org/10.1038/ng.2852},
  volume       = {46},
  year         = {2014},
}