Literature DB >> 19367188

An empirical comparison of meta-analyses of published gene-disease associations versus consortium analyses.

A Cecile J W Janssens1, Angela M González-Zuloeta Ladd, Sandra López-Léon, John P A Ioannidis, Ben A Oostra, Muin J Khoury, Cornelia M van Duijn.   

Abstract

PURPOSE: Consortia of investigators currently compile sufficiently large sample sizes to investigate the effects of low-risk susceptibility genetic variants. It is not clear how the results obtained by consortia compare with those derived from meta-analyses of published studies.
METHODS: We performed meta-analyses of published data for 16 genetic polymorphisms investigated by the Breast Cancer Association Consortium, and compared sample sizes, heterogeneity, and effect sizes. PubMed, Web of Science, and Human Genome Epidemiology Network databases were searched for breast cancer case-control association studies.
RESULTS: We found that meta-analyses of published data and consortium analyses were based on substantially different data. Published data by non-consortium teams amounted on average to 26.9% of all available data (range 3.0 -50.0%). Both approaches showed statistically significant decreased breast cancer risks for CASP8 D302H. The meta-analyses of published data demonstrated statistically significant results for five other genes and the consortium analyses for two other genes, but the strength of this evidence, evaluated on the basis of the Venice criteria, was not strong.
CONCLUSIONS: Because both approaches identified the same gene out of 16 candidates, the methods can be complimentary. The expense and complexity of consortium-based studies should be considered vis-à-vis the potential methodological limitations of synthesis of published studies.

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Year:  2009        PMID: 19367188     DOI: 10.1097/GIM.0b013e3181929237

Source DB:  PubMed          Journal:  Genet Med        ISSN: 1098-3600            Impact factor:   8.822


  4 in total

Review 1.  A systematic review evaluating the methodological aspects of meta-analyses of genetic association studies in cancer research.

Authors:  Stefania Boccia; Emma De Feo; Paola Gallì; Francesco Gianfagna; Rosarita Amore; Gualtiero Ricciardi
Journal:  Eur J Epidemiol       Date:  2010-09-10       Impact factor: 8.082

2.  Assessment of cumulative evidence for the association between glutathione S-transferase polymorphisms and lung cancer: application of the Venice interim guidelines.

Authors:  Scott M Langevin; John P A Ioannidis; Paolo Vineis; Emanuela Taioli
Journal:  Pharmacogenet Genomics       Date:  2010-10       Impact factor: 2.089

3.  A systematic review of cancer GWAS and candidate gene meta-analyses reveals limited overlap but similar effect sizes.

Authors:  Christine Q Chang; Ajay Yesupriya; Jessica L Rowell; Camilla B Pimentel; Melinda Clyne; Marta Gwinn; Muin J Khoury; Anja Wulf; Sheri D Schully
Journal:  Eur J Hum Genet       Date:  2013-07-24       Impact factor: 4.246

4.  A systematic analysis of the association studies between CASP8 D302H polymorphisms and breast cancer risk.

Authors:  Yinliang Zhang; Wei Li; Yi Hong; Guoying Wu; Kan He; Dahai Liu
Journal:  J Genet       Date:  2017-06       Impact factor: 1.166

  4 in total

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