Your browser has javascript turned off or blocked. This will lead to some parts of our website to not work properly or at all. Turn on javascript for best performance.

The browser you are using is not supported by this website. All versions of Internet Explorer are no longer supported, either by us or Microsoft (read more here: https://www.microsoft.com/en-us/microsoft-365/windows/end-of-ie-support).

Please use a modern browser to fully experience our website, such as the newest versions of Edge, Chrome, Firefox or Safari etc.

Default user image.

Olle Melander

Principal investigator

Default user image.

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

Author

  • Dajiang J Liu
  • Gina M Peloso
  • Xiaowei Zhan
  • Oddgeir L Holmen
  • Matthew Zawistowski
  • Shuang Feng
  • Majid Nikpay
  • Paul L Auer
  • Anuj Goel
  • He Zhang
  • Ulrike Peters
  • Martin Farrall
  • Marju Orho-Melander
  • Charles Kooperberg
  • Ruth McPherson
  • Hugh Watkins
  • Cristen J Willer
  • Kristian Hveem
  • Olle Melander
  • Sekar Kathiresan
  • Gonçalo R Abecasis

Summary, in English

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.

Department/s

  • Diabetes - Cardiovascular Disease
  • Cardiovascular Research - Hypertension
  • EXODIAB: Excellence in Diabetes Research in Sweden
  • EpiHealth: Epidemiology for Health

Publishing year

2014

Language

English

Pages

200-200

Publication/Series

Nature Genetics

Volume

46

Issue

2

Document type

Journal article

Publisher

Nature Publishing Group

Topic

  • Endocrinology and Diabetes
  • Cardiac and Cardiovascular Systems

Status

Published

Research group

  • Diabetes - Cardiovascular Disease
  • Cardiovascular Research - Hypertension

ISBN/ISSN/Other

  • ISSN: 1546-1718