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...
Saved in:
Main Authors: | , , , , |
---|---|
Other Authors: | |
Format: | Article |
Published: |
2018
|
Subjects: | |
Online Access: | https://repository.li.mahidol.ac.th/handle/123456789/19774 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Mahidol University |
id |
th-mahidol.19774 |
---|---|
record_format |
dspace |
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 |
_version_ |
1763491958535225344 |