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Assessing the quality of published genetic association studies in meta-analyses: the quality of genetic studies (Q-Genie) tool

BACKGROUND: Advances in genomics technology have led to a dramatic increase in the number of published genetic association studies. Systematic reviews and meta-analyses are a common method of synthesizing findings and providing reliable estimates of the effect of a genetic variant on a trait of inte...

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Autores principales: Sohani, Zahra N., Meyre, David, de Souza, Russell J., Joseph, Philip G., Gandhi, Mandark, Dennis, Brittany B., Norman, Geoff, Anand, Sonia S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4431044/
https://www.ncbi.nlm.nih.gov/pubmed/25975208
http://dx.doi.org/10.1186/s12863-015-0211-2
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author Sohani, Zahra N.
Meyre, David
de Souza, Russell J.
Joseph, Philip G.
Gandhi, Mandark
Dennis, Brittany B.
Norman, Geoff
Anand, Sonia S.
author_facet Sohani, Zahra N.
Meyre, David
de Souza, Russell J.
Joseph, Philip G.
Gandhi, Mandark
Dennis, Brittany B.
Norman, Geoff
Anand, Sonia S.
author_sort Sohani, Zahra N.
collection PubMed
description BACKGROUND: Advances in genomics technology have led to a dramatic increase in the number of published genetic association studies. Systematic reviews and meta-analyses are a common method of synthesizing findings and providing reliable estimates of the effect of a genetic variant on a trait of interest. However, summary estimates are subject to bias due to the varying methodological quality of individual studies. We embarked on an effort to develop and evaluate a tool that assesses the quality of published genetic association studies. Performance characteristics (i.e. validity, reliability, and item discrimination) were evaluated using a sample of thirty studies randomly selected from a previously conducted systematic review. RESULTS: The tool demonstrates excellent psychometric properties and generates a quality score for each study with corresponding ratings of ‘low’, ‘moderate’, or ‘high’ quality. We applied our tool to a published systematic review to exclude studies of low quality, and found a decrease in heterogeneity and an increase in precision of summary estimates. CONCLUSION: This tool can be used in systematic reviews to inform the selection of studies for inclusion, to conduct sensitivity analyses, and to perform meta-regressions. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12863-015-0211-2) contains supplementary material, which is available to authorized users.
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spelling pubmed-44310442015-05-15 Assessing the quality of published genetic association studies in meta-analyses: the quality of genetic studies (Q-Genie) tool Sohani, Zahra N. Meyre, David de Souza, Russell J. Joseph, Philip G. Gandhi, Mandark Dennis, Brittany B. Norman, Geoff Anand, Sonia S. BMC Genet Methodology Article BACKGROUND: Advances in genomics technology have led to a dramatic increase in the number of published genetic association studies. Systematic reviews and meta-analyses are a common method of synthesizing findings and providing reliable estimates of the effect of a genetic variant on a trait of interest. However, summary estimates are subject to bias due to the varying methodological quality of individual studies. We embarked on an effort to develop and evaluate a tool that assesses the quality of published genetic association studies. Performance characteristics (i.e. validity, reliability, and item discrimination) were evaluated using a sample of thirty studies randomly selected from a previously conducted systematic review. RESULTS: The tool demonstrates excellent psychometric properties and generates a quality score for each study with corresponding ratings of ‘low’, ‘moderate’, or ‘high’ quality. We applied our tool to a published systematic review to exclude studies of low quality, and found a decrease in heterogeneity and an increase in precision of summary estimates. CONCLUSION: This tool can be used in systematic reviews to inform the selection of studies for inclusion, to conduct sensitivity analyses, and to perform meta-regressions. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12863-015-0211-2) contains supplementary material, which is available to authorized users. BioMed Central 2015-05-15 /pmc/articles/PMC4431044/ /pubmed/25975208 http://dx.doi.org/10.1186/s12863-015-0211-2 Text en © Sohani et al.; licensee BioMed Central. 2015 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Methodology Article
Sohani, Zahra N.
Meyre, David
de Souza, Russell J.
Joseph, Philip G.
Gandhi, Mandark
Dennis, Brittany B.
Norman, Geoff
Anand, Sonia S.
Assessing the quality of published genetic association studies in meta-analyses: the quality of genetic studies (Q-Genie) tool
title Assessing the quality of published genetic association studies in meta-analyses: the quality of genetic studies (Q-Genie) tool
title_full Assessing the quality of published genetic association studies in meta-analyses: the quality of genetic studies (Q-Genie) tool
title_fullStr Assessing the quality of published genetic association studies in meta-analyses: the quality of genetic studies (Q-Genie) tool
title_full_unstemmed Assessing the quality of published genetic association studies in meta-analyses: the quality of genetic studies (Q-Genie) tool
title_short Assessing the quality of published genetic association studies in meta-analyses: the quality of genetic studies (Q-Genie) tool
title_sort assessing the quality of published genetic association studies in meta-analyses: the quality of genetic studies (q-genie) tool
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4431044/
https://www.ncbi.nlm.nih.gov/pubmed/25975208
http://dx.doi.org/10.1186/s12863-015-0211-2
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