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Assessment of systematic effects of methodological characteristics on candidate genetic associations

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Abstract

Candidate genetic association studies have been found to have a low replication rate in the past. Here, we aimed to assess whether aspects of reported methodological characteristics in genetic association studies may be related to the magnitude of effects observed. An observational, literature-based investigation of 511 case–control studies of genetic association studies indexed in 2007, was undertaken. Meta-regression analyses were used to assess the relationship between 23 reported methodological characteristics and the magnitude of genetic associations. The 511 studies had been conducted in 52 countries and were published in 220 journals (median impact factor 5.1). The multivariate meta-regression model of methodological characteristics plus disease category accounted for 17.2 % of the between-study variance in the magnitude of the reported genetic associations. Our findings are consistent with the view that better conducted and better reported genetic association research may lead to less inflated results.

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Acknowledgments

Diana Fox and Zahra Montazeri for administrative support. Knowledge Translation Branch, Canadian Institutes of Health Research (Grant Ref No: 200606KRS-162113-KRS-CECA-102805); Biotechnology, Genomics and Population Health Branch, Public Health Agency of Canada. National Guard Health Affairs, Saudi Arabia. Julian Little holds a Canada Research Chair in Human Genome Epidemiology. The funders had no role in the decision to submit the article or in its preparation.

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Authors and Affiliations

  1. Department of Epidemiology and Community Medicine, University of Ottawa, 451 Smth Road, Ottawa, ON, K1H 8M5, Canada

    Badr Aljasir, Alex Yurkiewich, David Moher & Julian Little

  2. National Guard Health Affairs, Western Region, Jeddah, Saudi Arabia

    Badr Aljasir

  3. Department of Medicine, Stanford Prevention Research Center, Stanford University School of Medicine, Stanford, CA, USA

    John P. A. Ioannidis

  4. Department of Health Research and Policy, Stanford University School of Medicine, Stanford, CA, USA

    John P. A. Ioannidis

  5. Department of Statistics, Stanford University School of Humanities and Sciences, Stanford, CA, USA

    John P. A. Ioannidis

  6. Ottawa Hospital Research Institute, Ottawa, ON, Canada

    David Moher

  7. MRC Biostatistics Unit, Cambridge, UK

    Julian P. T. Higgins

  8. Office for Biotechnology, Genomics and Population Health, Public Health Agency of Canada, Toronto, ON, Canada

    Paul Arora

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  1. Badr Aljasir
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Correspondence to Julian Little.

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Aljasir, B., Ioannidis, J.P.A., Yurkiewich, A. et al. Assessment of systematic effects of methodological characteristics on candidate genetic associations. Hum Genet 132, 167–178 (2013). https://doi.org/10.1007/s00439-012-1237-4

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