How should we use information about HWE in the meta-analyses of genetic association studies?

Background: It is often recommended that control groups in meta-analyses of genetic association studies are checked for Hardy-Weinberg equilibrium (HWE) as a surrogate for assessing study quality. However, tests for HWE have low power and there is currently no consensus about how to handle studies t...

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Main Authors: Cosetta Minelli, John R. Thompson, Keith R. Abrams, Ammarin Thakkinstian, John Attia
Other Authors: National Heart and Lung Institute
Format: Article
Published: 2018
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Online Access:https://repository.li.mahidol.ac.th/handle/123456789/19774
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spelling th-mahidol.197742018-07-12T09:46:39Z How should we use information about HWE in the meta-analyses of genetic association studies? Cosetta Minelli John R. Thompson Keith R. Abrams Ammarin Thakkinstian John Attia National Heart and Lung Institute University of Leicester Mahidol University University of Newcastle, Australia Medicine Background: It is often recommended that control groups in meta-analyses of genetic association studies are checked for Hardy-Weinberg equilibrium (HWE) as a surrogate for assessing study quality. However, tests for HWE have low power and there is currently no consensus about how to handle studies that deviate significantly from HWE. Methods: We identified 72 papers describing 114 meta-analyses of 1603 primary gene-disease comparisons. Based on these studies and on related simulations, we evaluated four different strategies for handling studies that appear not to be in HWE: (i) include them in the meta-analysis; (ii) exclude them if the test for HWE results in P < 0.05; (iii) exclude them if a measure of the size of departure from HWE is large and (iv) exclude them if (ii) and (iii). Results: Of the 72 papers, 26 did not report information on HWE, with a trend toward increased reporting with time. HWE was evaluated through testing, with only three papers assessing the size of departure. On re-analysis, 9% of the 1603 primary comparisons showed significant deviation from HWE. The chance of an extreme departure from HWE was inversely related to the sample size of the study. Simulations suggest that there is no advantage in excluding studies that appear not to be in HWE. Conclusions: Meta-analyses should report both the magnitude and the statistical significance of departures from HWE. Studies that appear to deviate from HWE should be investigated further for weaknesses in their design, but these studies should not be excluded unless there are other grounds for doubting the quality of the study. © The Author 2007; all rights reserved. 2018-07-12T02:46:39Z 2018-07-12T02:46:39Z 2008-02-01 Article International Journal of Epidemiology. Vol.37, No.1 (2008), 136-146 10.1093/ije/dym234 14643685 03005771 2-s2.0-38949183612 https://repository.li.mahidol.ac.th/handle/123456789/19774 Mahidol University SCOPUS https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=38949183612&origin=inward
institution Mahidol University
building Mahidol University Library
continent Asia
country Thailand
Thailand
content_provider Mahidol University Library
collection Mahidol University Institutional Repository
topic Medicine
spellingShingle Medicine
Cosetta Minelli
John R. Thompson
Keith R. Abrams
Ammarin Thakkinstian
John Attia
How should we use information about HWE in the meta-analyses of genetic association studies?
description Background: It is often recommended that control groups in meta-analyses of genetic association studies are checked for Hardy-Weinberg equilibrium (HWE) as a surrogate for assessing study quality. However, tests for HWE have low power and there is currently no consensus about how to handle studies that deviate significantly from HWE. Methods: We identified 72 papers describing 114 meta-analyses of 1603 primary gene-disease comparisons. Based on these studies and on related simulations, we evaluated four different strategies for handling studies that appear not to be in HWE: (i) include them in the meta-analysis; (ii) exclude them if the test for HWE results in P < 0.05; (iii) exclude them if a measure of the size of departure from HWE is large and (iv) exclude them if (ii) and (iii). Results: Of the 72 papers, 26 did not report information on HWE, with a trend toward increased reporting with time. HWE was evaluated through testing, with only three papers assessing the size of departure. On re-analysis, 9% of the 1603 primary comparisons showed significant deviation from HWE. The chance of an extreme departure from HWE was inversely related to the sample size of the study. Simulations suggest that there is no advantage in excluding studies that appear not to be in HWE. Conclusions: Meta-analyses should report both the magnitude and the statistical significance of departures from HWE. Studies that appear to deviate from HWE should be investigated further for weaknesses in their design, but these studies should not be excluded unless there are other grounds for doubting the quality of the study. © The Author 2007; all rights reserved.
author2 National Heart and Lung Institute
author_facet National Heart and Lung Institute
Cosetta Minelli
John R. Thompson
Keith R. Abrams
Ammarin Thakkinstian
John Attia
format Article
author Cosetta Minelli
John R. Thompson
Keith R. Abrams
Ammarin Thakkinstian
John Attia
author_sort Cosetta Minelli
title How should we use information about HWE in the meta-analyses of genetic association studies?
title_short How should we use information about HWE in the meta-analyses of genetic association studies?
title_full How should we use information about HWE in the meta-analyses of genetic association studies?
title_fullStr How should we use information about HWE in the meta-analyses of genetic association studies?
title_full_unstemmed How should we use information about HWE in the meta-analyses of genetic association studies?
title_sort how should we use information about hwe in the meta-analyses of genetic association studies?
publishDate 2018
url https://repository.li.mahidol.ac.th/handle/123456789/19774
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