Abstract
Meta-analyses and Individual Patient Data (IPD) meta-analyses of genetic association studies are a powerful tool to summarize the scientific evidences, however their application present considerable potential and several pitfalls. We reviewed systematically all published meta-analyses and IPD meta-analyses of genetic association studies in the field of cancer research, searching for relevant studies on the Medline, Embase, and HuGE Reviews Archive databases until January 2009. The association between selected predictors of methodological quality and the year of publication was also evaluated. 144 meta-analyses involving 299 gene-disease associations, and 25 IPD meta-analyses on 83 gene-disease were included. Overall quality of the reports showed a substantial improvement over time, as authors have become more inclusive of primary papers published in all languages since 2005 (P-value = 0.087), as well as statistical heterogeneity and publication bias were evaluated more systematically. Only 35.4% of the meta-analyses, however, adopted a comprehensive bibliographic search strategy to identify the primary reports, 63.9% did not specify the language of the included studies, 39.8% did not test for Hardy–Weinberg Equilibrium (HWE), while 62.2 and 75.9% of the meta-analyses and IPD meta-analyses, respectively, did not declare the scientific rationale for the genetic model chosen. Additionally, the HWE assessment showed a substantial decreasing trend over time (P-value = 0.031) while publication bias was more often evaluated when statistical heterogeneity was actually present (P-value = 0.007). Although we showed a general methodological improvement over time, guidelines on conducting and reporting meta-analyses of genetic association studies are needed to enhance their methodological quality.
Similar content being viewed by others
References
Lin BK, Clyne M, Walsh M, et al. Tracking the epidemiology of human genes in the literature: the HuGE Published Literature database. Am J Epidemiol. 2006;164:1–4.
Ioannidis JP. Genetic associations: false or true? Trends Mol Med. 2003;9:135–8.
Ioannidis JP, Gwinn M, Little J, et al. A road map for efficient and reliable human genome epidemiology. Nat Genet. 2006;38:3–5.
Hattersley AT, McCarthy MI. What makes a good genetic association study? Lancet. 2005;366:1315–23.
Ioannidis JP, Boffetta P, Little J, et al. Assessment of cumulative evidence on genetic associations: interim guidelines. Int J Epidemiol. 2008;37:120–32.
Little J, Higgins JP, Ioannidis JP, et al. Strengthening the reporting of genetic association studies (STREGA): an extension of the strobe statement. Ann Intern Med. 2009;150:206–15.
Higgins JP, Little J, Ioannidis JP, et al. Turning the pump handle: evolving methods for integrating the evidence on gene-disease association. Am J Epidemiol. 2007;166:863–6.
Sagoo GS, Little J, Higgins JP. Systematic Reviews of Genetic Association Studies. Plos med. 2009;6:e1000028.
Attia J, Thakkinstian A, D’Este C. Meta-analyses of molecular association studies: Methodologic lessons for genetic epidemiology. J Clin Epidemiol. 2003;56:297–303.
Minelli C, Thompson JR, Abrams KR, Thakkinstian A, Attia J. The quality of meta-analyses of genetic association studies: a review with recommendations. Am J Epidemiol. 2009;170:1333–43.
Suh Y, Vijg J. SNP discovery in associating genetic variation with human disease phenotypes. Mutat Res. 2005;573:41–53.
Krontiris TG, Devlin B, Karp DD, Robert NJ, Risch N. An association between the risk of cancer and mutations in the HRAS1 minisatellite locus. N Engl J Med. 1993;329:517–23.
Marcus PM, Hayes RB, Vineis P, et al. Cigarette smoking, N-acetyltransferase 2 acetylation status, and bladder cancer risk: a case-series meta-analysis of a gene-environment interaction. Cancer Epidemiol Biomarkers Prev. 2000;9:461–7.
Patsopoulos NA, Analatos AA, Ioannidis JP. Relative citation impact of various study designs in the health sciences. JAMA. 2005;293:2362–6.
Chalmers I. The Cochrane collaboration: preparing, maintaining, and disseminating systematic reviews of the effects of health care. Ann N Y Acad Sci. 1993;703:156–63.
Ioannidis JP. Meta-analysis in public health: potentials and problems. Ital J Public Health. 2006;3:9–13.
Moher D, Cook DJ, Eastwood S, Olkin I, Rennie D, et al. Improving the quality of reports of meta-analyses of randomised controlled trials: The QUOROM statement. Quality of Reporting of Meta-analyses. Lancet. 1999;354:1896e900.
Liberati A, Altman DG, Tetzlaff J, et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. J Clin Epidemiol. 2009;62:e1–34.
Boyle P, Ferlay J. Cancer incidence and mortality in Europe, 2004. Ann Oncol. 2005;16:481–8.
Hopewell S, Clarke M, Lefebvre C, Scherer R. Handsearching versus electronic searching to identify reports of randomized trials. Cochrane Database Syst Rev. 2007;2:MR000001.
Lemeshow AR, Blum RE, Berlin JA, et al. Searching one or two databases was insufficient for meta-analysis of observational studies. J Clin Epidemiol. 2005;58(9):867–73.
Seminara D, Khoury MJ, O’Brien TR, et al. The emergence of networks in human genome epidemiology: challenges and opportunities. Epidemiology. 2007;18:1–8.
Janssens AC, González-Zuloeta Ladd AM, López-Léon S, et al. An empirical comparison of meta-analyses of published gene-disease associations versus consortium analyses. Genet Med. 2009;11:153–62.
Pan Z, Trikalinos TA, Kavvoura FK, Lau J, Ioannidis JP. Local literature bias in genetic epidemiology: an empirical evaluation of the Chinese literature. PloS Med. 2005;2:e334.
Sanderson S, Tatt ID, Higgins JP. Tools for assessing quality and susceptibility to bias in observational studies in epidemiology: a systematic review and annotated bibliography. Int J Epidemiol. 2007;36:666–76.
Herbison P, Hay-Smith J, Gillespie WJ. Adjustment of meta-analyses on the basis of quality scores should be abandoned. J Clin Epidemiol. 2006;59:1249–56.
Egger M, Davey Smith G, Altman DG. Systematic Rewiers in Health Care. Meta–analysis in context. London, UK: BMJ Publishing Group; 2001.
Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. BMJ. 2003;327:557–60.
Lau J, Ioannidis JP, Terrin N, Schmid CH, Olkin I. The case of the misleading funnel plot. BMJ. 2006;333:597–600.
Ioannidis JP, Trikalinos TA. The appropriateness of asymmetry tests for publication bias in meta-analyses: a large survey. CMAJ. 2007;176:1091–6.
Attia J, Ioannidis JP, Thakkinstian A, et al. How to use an article about genetic association: A: background concepts. JAMA. 2009;301:74–81.
Acknowledgments
We thank Benedetto Simone for the English style revision of the final draft of the manuscript and Jessica Rowell and Alessia Melegaro for data collection.
Author information
Authors and Affiliations
Corresponding author
Additional information
This is an invited commentary to article doi:10.1007/s10654-010-9492-y
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Boccia, S., De Feo, E., Gallì, P. et al. A systematic review evaluating the methodological aspects of meta-analyses of genetic association studies in cancer research. Eur J Epidemiol 25, 765–775 (2010). https://doi.org/10.1007/s10654-010-9503-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10654-010-9503-z