Literature DB >> 18779388

Evaluation of the potential excess of statistically significant findings in published genetic association studies: application to Alzheimer's disease.

Fotini K Kavvoura1, Matthew B McQueen, Muin J Khoury, Rudolph E Tanzi, Lars Bertram, John P A Ioannidis.   

Abstract

The authors evaluated whether there is an excess of statistically significant results in studies of genetic associations with Alzheimer's disease reflecting either between-study heterogeneity or bias. Among published articles on genetic associations entered into the comprehensive AlzGene database (www.alzgene.org) through January 31, 2007, 1,348 studies included in 175 meta-analyses with 3 or more studies each were analyzed. The number of observed studies (O) with statistically significant results (P = 0.05 threshold) was compared with the expected number (E) under different assumptions for the magnitude of the effect size. In the main analysis, the plausible effect size of each association was the summary effect presented in the respective meta-analysis. Overall, 19 meta-analyses (all with eventually nonsignificant summary effects) had a documented excess of O over E: Typically single studies had significant effects pointing in opposite directions and early summary effects were dissipated over time. Across the whole domain, O was 235 (17.4%), while E was 164.8 (12.2%) (P < 10(-6)). The excess showed a predilection for meta-analyses with nonsignificant summary effects and between-study heterogeneity. The excess was seen for all levels of statistical significance and also for studies with borderline P values (P = 0.05-0.10). The excess of significant findings may represent significance-chasing biases in a setting of massive testing.

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Year:  2008        PMID: 18779388      PMCID: PMC3695656          DOI: 10.1093/aje/kwn206

Source DB:  PubMed          Journal:  Am J Epidemiol        ISSN: 0002-9262            Impact factor:   4.897


  48 in total

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Review 4.  The case of the misleading funnel plot.

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5.  New models of collaboration in genome-wide association studies: the Genetic Association Information Network.

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6.  Operating characteristics of a rank correlation test for publication bias.

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Journal:  Nature       Date:  2007-06-28       Impact factor: 49.962

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Authors:  John P A Ioannidis; Nikolaos A Patsopoulos; Evangelos Evangelou
Journal:  PLoS One       Date:  2007-09-05       Impact factor: 3.240

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  19 in total

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Journal:  Ann Intern Med       Date:  2009-10-20       Impact factor: 25.391

5.  Distinguishing true from false positives in genomic studies: p values.

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Review 6.  Clinical outcome prediction by microRNAs in human cancer: a systematic review.

Authors:  Viswam S Nair; Lauren S Maeda; John P A Ioannidis
Journal:  J Natl Cancer Inst       Date:  2012-03-06       Impact factor: 13.506

7.  Whole genome association analysis shows that ACE is a risk factor for Alzheimer's disease and fails to replicate most candidates from Meta-analysis.

Authors:  Jennifer Webster; Eric M Reiman; Victoria L Zismann; Keta D Joshipura; John V Pearson; Diane Hu-Lince; Matthew J Huentelman; David W Craig; Keith D Coon; Thomas Beach; Kristen C Rohrer; Alice S Zhao; Doris Leung; Leslie Bryden; Lauren Marlowe; Mona Kaleem; Diego Mastroeni; Andrew Grover; Joseph Rogers; Reinhard Heun; Frank Jessen; Heike Kölsch; Christopher B Heward; Rivka Ravid; Michael L Hutton; Stacey Melquist; Ron C Petersen; Richard J Caselli; Andreas Papassotiropoulos; Dietrich A Stephan; John Hardy; Amanda Myers
Journal:  Int J Mol Epidemiol Genet       Date:  2009-07-26

8.  CYP2D6 inhibition and breast cancer recurrence in a population-based study in Denmark.

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Review 10.  Gut microbiota-derived metabolite trimethylamine-N-oxide and multiple health outcomes: an umbrella review and updated meta-analysis.

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