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      Synopsis and meta-analysis of genetic association studies in osteoporosis for the focal adhesion family genes: the CUMAGAS-OSTEOporosis information system

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          Abstract

          Background

          Focal adhesion (FA) family genes have been studied as candidate genes for osteoporosis, but the results of genetic association studies (GASs) are controversial. To clarify these data, a systematic assessment of GASs for FA genes in osteoporosis was conducted.

          Methods

          We developed Cumulative Meta-Analysis of GAS-OSTEOporosis (CUMAGAS-OSTEOporosis), a web-based information system that allows the retrieval, analysis and meta-analysis (for allele contrast, recessive, dominant, additive and codominant models) of data from GASs on osteoporosis with the capability of update. GASs were identified by searching the PubMed and HuGE PubLit databases.

          Results

          Data from 72 studies involving 13 variants of 6 genes were analyzed and catalogued in CUMAGAS-OSTEOporosis. Twenty-two studies produced significant associations with osteoporosis risk under any genetic model. All studies were underpowered (<50%). In four studies, the controls deviated from the Hardy-Weinberg equilibrium. Eight variants were chosen for meta-analysis, and significance was shown for the variants collagen, type I, α 1 ( COL1A1) G2046T (all genetic models), COL1A1 G-1997T (allele contrast and dominant model) and integrin β-chain β 3 ( ITGB3) T176C (recessive and additive models). In COL1A1 G2046T, subgroup analysis has shown significant associations for Caucasians, adults, females, males and postmenopausal women. A differential magnitude of effect in large versus small studies (that is, indication of publication bias) was detected for the variant COL1A1 G2046T.

          Conclusion

          There is evidence of an implication of FA family genes in osteoporosis. CUMAGAS-OSTEOporosis could be a useful tool for current genomic epidemiology research in the field of osteoporosis.

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          Most cited references42

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          Do multiple outcome measures require p-value adjustment?

          Background Readers may question the interpretation of findings in clinical trials when multiple outcome measures are used without adjustment of the p-value. This question arises because of the increased risk of Type I errors (findings of false "significance") when multiple simultaneous hypotheses are tested at set p-values. The primary aim of this study was to estimate the need to make appropriate p-value adjustments in clinical trials to compensate for a possible increased risk in committing Type I errors when multiple outcome measures are used. Discussion The classicists believe that the chance of finding at least one test statistically significant due to chance and incorrectly declaring a difference increases as the number of comparisons increases. The rationalists have the following objections to that theory: 1) P-value adjustments are calculated based on how many tests are to be considered, and that number has been defined arbitrarily and variably; 2) P-value adjustments reduce the chance of making type I errors, but they increase the chance of making type II errors or needing to increase the sample size. Summary Readers should balance a study's statistical significance with the magnitude of effect, the quality of the study and with findings from other studies. Researchers facing multiple outcome measures might want to either select a primary outcome measure or use a global assessment measure, rather than adjusting the p-value.
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            Common genetic variation and human traits.

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              Heterogeneity testing in meta-analysis of genome searches.

              Genome searches for identifying susceptibility loci for the same complex disease often give inconclusive or inconsistent results. Genome Search Meta-analysis (GSMA) is an established non-parametric method to identify genetic regions that rank high on average in terms of linkage statistics (e.g., lod scores) across studies. Meta-analysis typically aims not only to obtain average estimates, but also to quantify heterogeneity. However, heterogeneity testing between studies included in GSMA has not been developed yet. Heterogeneity may be produced by differences in study designs, study populations, and chance, and the extent of heterogeneity might influence the conclusions of a meta-analysis. Here, we propose and explore metrics that indicate the extent of heterogeneity for specific loci in GSMA based on Monte Carlo permutation tests. We have also developed software that performs both the GSMA and the heterogeneity testing. To illustrate the concept, the proposed methodology was applied to published data from meta-analyses of rheumatoid arthritis (4 scans) and schizophrenia (20 scans). In the first meta-analysis, we identified 11 bins with statistically low heterogeneity and 8 with statistically high heterogeneity. The respective numbers were 9 and 6 for the schizophrenia meta-analysis. For rheumatoid arthritis, bins 6.2 (the HLA region that is a well-documented susceptibility locus for the disease) and 16.3 (16q12.2-q23.1) had both high average ranks and low between-study heterogeneity. For schizophrenia, this was seen for bin 3.2 (3p25.3-p22.1) and heterogeneity was still significantly low after adjusting for its high average rank. Concordance was high between the proposed metrics and between weighted and unweighted analyses. Data from genome searches should be synthesized and interpreted considering both average ranks and heterogeneity between studies. 2004 Wiley-Liss, Inc.
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                Author and article information

                Journal
                BMC Med
                BMC Medicine
                BioMed Central
                1741-7015
                2011
                26 January 2011
                : 9
                : 9
                Affiliations
                [1 ]Department of Biomathematics, University of Thessaly School of Medicine, Larissa, Greece
                [2 ]Center for Clinical Evidence Synthesis, Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Tufts University School of Medicine, Boston, MA, USA
                [3 ]Department of Orthopedics, University of Thessaly School of Medicine, Larissa, Greece
                Article
                1741-7015-9-9
                10.1186/1741-7015-9-9
                3040157
                21269451
                37351822-0aaa-4d95-93bd-4b15fbec6ea4
                Copyright ©2011 Zintzaras et al; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 21 December 2010
                : 26 January 2011
                Categories
                Research Article

                Medicine
                Medicine

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