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      Replication validity of genetic association studies

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          Abstract

          The rapid growth of human genetics creates countless opportunities for studies of disease association. Given the number of potentially identifiable genetic markers and the multitude of clinical outcomes to which these may be linked, the testing and validation of statistical hypotheses in genetic epidemiology is a task of unprecedented scale. Meta-analysis provides a quantitative approach for combining the results of various studies on the same topic, and for estimating and explaining their diversity. Here, we have evaluated by meta-analysis 370 studies addressing 36 genetic associations for various outcomes of disease. We show that significant between-study heterogeneity (diversity) is frequent, and that the results of the first study correlate only modestly with subsequent research on the same association. The first study often suggests a stronger genetic effect than is found by subsequent studies. Both bias and genuine population diversity might explain why early association studies tend to overestimate the disease protection or predisposition conferred by a genetic polymorphism. We conclude that a systematic meta-analytic approach may assist in estimating population-wide effects of genetic risk factors in human disease.

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          Most cited references21

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          Meta-analysis in clinical trials

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            Quantitative synthesis in systematic reviews.

            Joseph Lau (1997)
            The final common pathway for most systematic reviews is a statistical summary of the data, or meta-analysis. The complex methods used in meta-analyses should always be complemented by clinical acumen and common sense in designing the protocol of a systematic review, deciding which data can be combined, and determining whether data should be combined. Both continuous and binary data can be pooled. Most meta-analyses summarize data from randomized trials, but other applications, such as the evaluation of diagnostic test performance and observational studies, have also been developed. The statistical methods of meta-analysis aim at evaluating the diversity (heterogeneity) among the results of different studies, exploring and explaining observed heterogeneity, and estimating a common pooled effect with increased precision. Fixed-effects models assume that an intervention has a single true effect, whereas random-effects models assume that an effect may vary across studies. Meta-regression analyses, by using each study rather than each patient as a unit of observation, can help to evaluate the effect of individual variables on the magnitude of an observed effect and thus may sometimes explain why study results differ. It is also important to assess the robustness of conclusions through sensitivity analyses and a formal evaluation of potential sources of bias, including publication bias and the effect of the quality of the studies on the observed effect.
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              Publication bias in clinical research

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                Author and article information

                Journal
                Nature Genetics
                Nat Genet
                Springer Science and Business Media LLC
                1061-4036
                1546-1718
                November 2001
                October 15 2001
                November 2001
                : 29
                : 3
                : 306-309
                Article
                10.1038/ng749
                11600885
                b88b1acb-5f3c-4d68-b8a5-8fe47caf8320
                © 2001

                http://www.springer.com/tdm

                History

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