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Bioinformatics (Oxford, England), Vol. 21, No. 18. (15 September 2005), pp. 3672-3673.
Abstract
SUMMARY: Heterogeneity and genome search meta-analysis (HEGESMA) is a comprehensive software for performing genome scan meta-analysis, a quantitative method to identify genetic regions (bins) with consistently increased linkage score across multiple genome scans, and for testing the heterogeneity of the results of each bin across scans. The program provides as an output the average of ranks and three heterogeneity statistics, as well as corresponding significance levels. Statistical inferences are based on Monte Carlo permutation tests. The program allows both unweighted and ...
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Computational biology and chemistry, Vol. 32, No. 1. (February 2008), pp. 38-46.
Abstract
The combination of results from different large-scale datasets of multidimensional biological signals (such as gene expression profiling) presents a major challenge. Methodologies are needed that can efficiently combine diverse datasets, but can also test the extent of diversity (heterogeneity) across the combined studies. We developed METa-analysis of RAnked DISCovery datasets (METRADISC), a generalized meta-analysis method for combining information across discovery-oriented datasets and for testing between-study heterogeneity for each biological variable of interest. The method is based on non-parametric Monte Carlo permutation ...
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BMJ (Clinical research ed.), Vol. 336, No. 7658. (21 June 2008), pp. 1413-1415.
Abstract
10.1136/bmj.a117 ...
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American journal of epidemiology, Vol. 170, No. 3. (1 August 2009), pp. 269-279.
by Muin J. Khoury, Lars Bertram, Paolo Boffetta, et al.Adam S. Butterworth, Stephen J. Chanock, Siobhan M. Dolan, Isabel Fortier, Montserrat Garcia-Closas, Marta Gwinn, Julian P. Higgins, A. Cecile Janssens, James Ostell, Ryan P. Owen, Roberta A. Pagon, Timothy R. Rebbeck, Nathaniel Rothman, Jonine L. Bernstein, Paul R. Burton, Harry Campbell, Anand Chockalingam, Helena Furberg, Julian Little, Thomas R. O'Brien, Daniela Seminara, Paolo Vineis, Deborah M. Winn, Wei Yu, John P. Ioannidis
Abstract
Genome-wide association studies (GWAS) have led to a rapid increase in available data on common genetic variants and phenotypes and numerous discoveries of new loci associated with susceptibility to common complex diseases. Integrating the evidence from GWAS and candidate gene studies depends on concerted efforts in data production, online publication, database development, and continuously updated data synthesis. Here the authors summarize current experience and challenges on these fronts, which were discussed at a 2008 multidisciplinary workshop sponsored by the Human Genome ...
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American Journal of Medical Genetics Part B: Neuropsychiatric Genetics, Vol. 9999, No. 9999. (2009), n/a.
by Evangelos Evangelou, Demetrius M. Maraganore, Grazia Annesi, et al.Laura Brighina, Alexis Brice, Alexis Elbaz, Carlo Ferrarese, Georgios M. Hadjigeorgiou, Rejko Krueger, Jean-Charles Lambert, Suzanne Lesage, Katerina Markopoulou, George D. Mellick, Bram Meeus, Nancy L. Pedersen, Aldo Quattrone, Christine Van Broeckhoven, Manu Sharma, Peter A. Silburn, Eng-King Tan, Karin Wirdefeldt, John P. A. Ioannidis, For
Abstract
Early genome-wide association (GWA) studies on Parkinson's disease (PD) have not been able to yield conclusive, replicable signals of association, perhaps due to limited sample size. We aimed to investigate whether association signals derived from the meta-analysis of the first two GWA investigations might be replicable in different populations. We examined six single-nucleotide polymorphisms (SNPs) (rs1000291, rs1865997, rs2241743, rs2282048, rs2313982, and rs3018626) that had reached nominal significance with at least two of three different strategies proposed in a previous analysis of ...
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Nat Genet, Vol. 29, No. 3. (November 2001), pp. 306-309.
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 ...
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International journal of epidemiology (18 April 2008)
Abstract
BACKGROUND: Several approaches are available for evaluating heterogeneity in meta-analysis. Sensitivity analyses are often used, but these are often implemented in various non-standardized ways. METHODS: We developed and implemented sequential and combinatorial algorithms that evaluate the change in between-study heterogeneity as one or more studies are excluded from the calculations. The algorithms exclude studies aiming to achieve either the maximum or the minimum final I(2) below a desired pre-set threshold. We applied these algorithms in databases of meta-analyses of binary outcome ...
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American journal of epidemiology, Vol. 168, No. 4. (15 August 2008)
Abstract
The author evaluated the implications of nominal statistical significance for changing the credibility of null versus alternative hypotheses across a large number of observational associations for which formal statistical significance (p < 0.05) was claimed. Calculation of the Bayes factor (B) under different assumptions was performed on 272 observational associations published in 2004-2005 and a data set of 50 meta-analyses on gene-disease associations (752 studies) for which statistically significant associations had been claimed (p < 0.05). Depending on the formulation of ...
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Pharmacogenomics, Vol. 10, No. 2. (February 2009), pp. 191-201.
Abstract
The advent of genome-wide association studies has allowed considerable progress in the identification and robust replication of common gene variants that confer susceptibility to common diseases and other phenotypes of interest. These genetic effect sizes are almost invariably moderate to small in magnitude and single studies, even if large, are underpowered to detect them with confidence. Meta-analysis of many genome-wide association studies improves the power to detect more associations, and to investigate the consistency or heterogeneity of these associations across diverse ...
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Nature Reviews Genetics, Vol. 10, No. 5. (01 May 2009), pp. 318-329.
Abstract
Studies using genome-wide platforms have yielded an unprecedented number of promising signals of association between genomic variants and human traits. This Review addresses the steps required to validate, augment and refine such signals to identify underlying causal variants for well-defined phenotypes. These steps include: large-scale exact replication across both similar and diverse populations; fine mapping and resequencing; determination of the most informative markers and multiple independent informative loci; incorporation of functional information; and improved phenotype mapping of the implicated genetic ...
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Lancet neurology, Vol. 5, No. 11. (November 2006), pp. 917-923.
by A. Elbaz, L. M. Nelson, H. Payami, et al.J. P. Ioannidis, B. K. Fiske, G. Annesi, A. Carmine Belin, S. A. Factor, C. Ferrarese, G. M. Hadjigeorgiou, D. S. Higgins, H. Kawakami, R. Krüger, K. S. Marder, R. P. Mayeux, G. D. Mellick, J. G. Nutt, B. Ritz, A. Samii, C. M. Tanner, C. Van Broeckhoven, S. K. Van Den Eeden, K. Wirdefeldt, C. P. Zabetian, M. Dehem, J. S. Montimurro, A. Southwick, R. M. Myers, T. A. Trikalinos
Abstract
BACKGROUND: A genome-wide association study identified 13 single-nucleotide polymorphisms (SNPs) significantly associated with Parkinson's disease. Small-scale replication studies were largely non-confirmatory, but a meta-analysis that included data from the original study could not exclude all SNP associations, leaving relevance of several markers uncertain. METHODS: Investigators from three Michael J Fox Foundation for Parkinson's Research-funded genetics consortia-comprising 14 teams-contributed DNA samples from 5526 patients with Parkinson's disease and 6682 controls, which were genotyped for the 13 SNPs. Most (88%) participants were of ...
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Human genetics, Vol. 123, No. 1. (February 2008), pp. 1-14.
Abstract
Meta-analysis offers the opportunity to combine evidence from retrospectively accumulated or prospectively generated data. Meta-analyses may provide summary estimates and can help in detecting and addressing potential inconsistency between the combined datasets. Application of meta-analysis in genetic associations presents considerable potential and several pitfalls. In this review, we present basic principles of meta-analytic methods, adapted for human genome epidemiology. We describe issues that arise in the retrospective or the prospective collection of relevant data through various sources, common traps to consider ...
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Journal of the National Cancer Institute, Vol. 101, No. 1. (7 January 2009), pp. 24-36.
by Paolo Vineis, Maurizio Manuguerra, Fotini K. Kavvoura, et al.Simonetta Guarrera, Alessandra Allione, Fabio Rosa, Alessandra Di Gregorio, Silvia Polidoro, Federica Saletta, John P. Ioannidis, Giuseppe Matullo
Abstract
BACKGROUND: Several genes encoding for DNA repair molecules implicated in maintaining genomic integrity have been proposed as cancer-susceptibility genes. Although efforts have been made to create synopses for specific fields that summarize the data from genetic association studies, such an overview is not available for genes involved in DNA repair. METHODS: We have created a regularly updated database of studies addressing associations between DNA repair gene variants (excluding highly penetrant mutations) and different types of cancer. Using 1087 datasets and publicly ...
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Nature genetics, Vol. 40, No. 7. (26 July 2008), pp. 827-834.
Abstract
In an effort to pinpoint potential genetic risk factors for schizophrenia, research groups worldwide have published over 1,000 genetic association studies with largely inconsistent results. To facilitate the interpretation of these findings, we have created a regularly updated online database of all published genetic association studies for schizophrenia ('SzGene'). For all polymorphisms having genotype data available in at least four independent case-control samples, we systematically carried out random-effects meta-analyses using allelic contrasts. Across 118 meta-analyses, a total of 24 genetic variants ...
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American journal of epidemiology, Vol. 164, No. 7. (1 October 2006), pp. 609-614.
Abstract
Accumulated evidence from searching for candidate gene-disease associations of complex diseases can offer some insights as the field moves toward discovery-oriented approaches with massive genome-wide testing. Meta-analyses of 50 non-human lymphocyte antigen gene-disease associations with documented overall statistical significance (752 studies) show summary odds ratios with a median of 1.43 (interquartile range, 1.28-1.65). Many different biases may operate in this field, for both single studies and meta-analyses, and these biases could invalidate some of these seemingly "validated" associations. Studies with a ...
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PLoS ONE, Vol. 2, No. 2. (2007)
Abstract
BACKGROUND: Genome-wide association studies hold substantial promise for identifying common genetic variants that regulate susceptibility to complex diseases. However, for the detection of small genetic effects, single studies may be underpowered. Power may be improved by combining genome-wide datasets with meta-analytic techniques. METHODOLOGY/PRINCIPAL FINDINGS: Both single and two-stage genome-wide data may be combined and there are several possible strategies. In the two-stage framework, we considered the options of (1) enhancement of replication data and (2) enhancement of first-stage data, and then, ...
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PLoS ONE, Vol. 2, No. 9. (2007)
Abstract
BACKGROUND: Meta-analysis is the systematic and quantitative synthesis of effect sizes and the exploration of their diversity across different studies. Meta-analyses are increasingly applied to synthesize data from genome-wide association (GWA) studies and from other teams that try to replicate the genetic variants that emerge from such investigations. Between-study heterogeneity is important to document and may point to interesting leads. METHODOLOGY/PRINCIPAL FINDINGS: To exemplify these issues, we used data from three GWA studies on type 2 diabetes and their replication efforts ...
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PLoS Med, Vol. 6, No. 2. (3 February 2009), e1000022.
by Julian Little, Julian P. T. Higgins, John P. A. Ioannidis, et al.David Moher, France Gagnon, Erik von Elm, Muin J. Khoury, Barbara Cohen, George Davey-Smith, Jeremy Grimshaw, Paul Scheet, Marta Gwinn, Robin E. Williamson, Guang Y. Zou, Kim Hutchings, Candice Y. Johnson, Valerie Tait, Miriam Wiens, Jean Golding, Cornelia van Duijn, John McLaughlin, Andrew Paterson, George Wells, Isabel Fortier, Matthew Freedman, Maja Zecevic, Richard King, Claire Infante-Rivard, Alex Stewart, Nick Birkett
Abstract
Julian Little and colleagues present the STREGA recommendations, which are aimed at improving the reporting of genetic association studies. ...
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Proceedings of the National Academy of Sciences of the United States of America, Vol. 105, No. 2. (15 January 2008), pp. 617-622.
Abstract
Many gene-disease associations proposed to date have not been consistently replicated across different populations. Nonreplication often reflects false positives in the original claims. However, occasionally, nonreplication may be due to heterogeneity due to biases or even genuine diversity of the genetic effects in different populations. Here, we propose methods for estimating the required sample size to replicate an association across many studies with different amounts of between-study heterogeneity, when data are summarized through metaanalysis. We demonstrate thresholds of between-study heterogeneity (tau(0)(2)) ...
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PLoS ONE, Vol. 3, No. 7. (23 July 2008), e2778.
Abstract
Appraisal of the scientific impact of researchers, teams and institutions with productivity and citation metrics has major repercussions. Funding and promotion of individuals and survival of teams and institutions depend on publications and citations. In this competitive environment, the number of authors per paper is increasing and apparently some co-authors don't satisfy authorship criteria. Listing of individual contributions is still sporadic and also open to manipulation. Metrics are needed to measure the networking intensity for a single scientist or group of ...
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BMC Medicine, Vol. 5 (25 October 2007), 30.
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American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics (24 March 2008)
Abstract
Genome-wide testing platforms are increasingly used to promote "agnostic" approaches to the discovery of gene variants associated with the risk of many common diseases and quantitative traits. The early track record of genome-wide association (GWA) studies suggests that some proposed associations are replicated quite consistently with large-scale subsequent evidence from multiple studies, others have a more inconsistent replication record, some have failed to be replicated by independent investigators and many more early proposed associations await further replication. An important question is ...
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Journal of Clinical Epidemiology, Vol. In Press, Corrected Proof
Abstract
Objective To examine trends in and determinants of the number of authors in clinical studies.Study Design and Setting We analyzed determinants of the number of authors in 633 articles of randomized trials and 313 articles of nonrandomized studies included in large meta-analyses (seven and six topics, respectively). Analyses were adjusted for topic. We also evaluated 310 randomly sampled case reports that had an abstract and described a single case.Results After adjusting for topic and other determinants, for both randomized trials and ...
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Journal of evaluation in clinical practice, Vol. 14, No. 5. (October 2008), pp. 951-957.
Abstract
Statistical tests of heterogeneity and bias, in particular publication bias, are very popular in meta-analyses. These tests use statistical approaches whose limitations are often not recognized. Moreover, it is often implied with inappropriate confidence that these tests can provide reliable answers to questions that in essence are not of statistical nature. Statistical heterogeneity is only a correlate of clinical and pragmatic heterogeneity and the correlation may sometimes be weak. Similarly, statistical signals may hint to bias, but seen in isolation they ...
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PLoS Med, Vol. 5, No. 10. (7 October 2008), e201.
Abstract
John Ioannidis and colleagues argue that the current system of publication in biomedical research provides a distorted view of the reality of scientific data. ...
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PLoS Genet, Vol. 2, No. 8. (August 2006)
Abstract
The most simple and commonly used approach for genetic associations is the case-control study design of unrelated people. This design is susceptible to population stratification. This problem is obviated in family-based studies, but it is usually difficult to accumulate large enough samples of well-characterized families. We addressed empirically whether the two designs give similar estimates of association in 93 investigations where both unrelated case-control and family-based designs had been employed. Estimated odds ratios differed beyond chance between the two designs in ...
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FASEB J, Vol. 19, No. 14. (December 2005), pp. 1943-1944.
Abstract
Printed articles increasingly rely on online supplements to store critical scientific information, but such data may eventually become unavailable. We checked the current availability of online supplementary scientific information published in six top-cited scientific journals (Science, Nature, Cell, New England Journal of Medicine, Lancet, Proceedings of the National Academy of Sciences USA). Here we show that in 4.7% and 9.6% of articles with online supplementary material, some of the supplements became unavailable within 2 and 5 years of their publication, respectively. ...
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Nature genetics, Vol. 41, No. 2. (28 February 2009), pp. 149-155.
by John P. Ioannidis, David B. Allison, Catherine A. Ball, et al.Issa Coulibaly, Xiangqin Cui, Aedín C. Culhane, Mario Falchi, Cesare Furlanello, Laurence Game, Giuseppe Jurman, Jon Mangion, Tapan Mehta, Michael Nitzberg, Grier P. Page, Enrico Petretto, Vera van Noort
Abstract
Given the complexity of microarray-based gene expression studies, guidelines encourage transparent design and public data availability. Several journals require public data deposition and several public databases exist. However, not all data are publicly available, and even when available, it is unknown whether the published results are reproducible by independent scientists. Here we evaluated the replication of data analyses in 18 articles on microarray-based gene expression profiling published in Nature Genetics in 2005-2006. One table or figure from each article was independently ...
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JAMA : the journal of the American Medical Association, Vol. 301, No. 2. (14 January 2009), pp. 191-197.
Abstract
In the first article of this series, we reviewed the basic genetics concepts necessary to understand genetic association studies. In this second article, we enumerate the major issues in judging the validity of these studies, framed as critical appraisal questions. Was the disease phenotype properly defined and accurately recorded by someone blind to the genetic information? Have any potential differences between disease and nondisease groups, particularly ethnicity, been properly addressed? In genetic studies, one potential cause of spurious associations is differences ...
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JAMA : the journal of the American Medical Association, Vol. 301, No. 3. (21 January 2009), pp. 304-308.
Abstract
In the first 2 articles of this series, we reviewed the basic genetics concepts necessary to understand genetic association studies, and we enumerated the major issues in judging the validity of these studies. In this third article, we review the issues relating to the applicability of the results in the clinical situation. How large and precise are the associations? Many genetic effects are expected to be smaller in magnitude than traditional risk factors. Does the genetic association improve predictive power beyond ...
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Molecular psychiatry, Vol. 11, No. 1. (January 2006), pp. 29-36.
Abstract
Autism and autism-spectrum disorders exhibit high heritability, although specific susceptibility genes still remain largely elusive. We performed a heterogeneity-based genome search meta-analysis (HEGESMA) of nine genome scans on autism or autism-spectrum disorders. Each genome scan was separated in 30 cM bins and the maximum linkage statistic from each bin was ranked. Significance for each bin's average rank and for between-scan heterogeneity (dis-similarity in the average ranks) was obtained through Monte Carlo tests. For autism, data from 771 affected sibpairs were synthesized ...
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JAMA : the journal of the American Medical Association, Vol. 301, No. 1. (7 January 2009), pp. 74-81.
Abstract
This is the first in a series of 3 articles serving as an introduction to clinicians wishing to read and critically appraise genetic association studies. We summarize the key concepts in genetics that clinicians must understand to review these studies, including the structure of DNA, transcription and translation, patterns of inheritance, Hardy-Weinberg equilibrium, and linkage disequilibrium. We review the types of DNA variation, including single-nucleotide polymorphisms (SNPs), insertions, and deletions, and how these can affect protein function. We introduce the idea ...
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Nature Reviews Genetics, Vol. 9, No. 5. (01 May 2008), pp. 356-369.
Abstract
The past year has witnessed substantial advances in understanding the genetic basis of many common phenotypes of biomedical importance. These advances have been the result of systematic, well-powered, genome-wide surveys exploring the relationships between common sequence variation and disease predisposition. This approach has revealed over 50 disease-susceptibility loci and has provided insights into the allelic architecture of multifactorial traits. At the same time, much has been learned about the successful prosecution of association studies on such a scale. This Review ...
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Eur J Hum Genet, Vol. 13, No. 7. (13 April 2005), pp. 840-848.
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BMC Medical Research Methodology, Vol. 8 (20 May 2008), 31.
by Ajay Yesupriya, Evangelos Evangelou, Fotini K. Kavvoura, et al.Nikolaos A. Patsopoulos, Melinda Clyne, Matthew C. Walsh, Bruce K. Lin, Wei Yu, Marta Gwinn, John P. A. Ioannidis, Muin J. Khoury
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PLoS Med, Vol. 2, No. 8. (30 August 2005), e124.
Abstract
There is increasing concern that most current published research findings are false. The probability that a research claim is true may depend on study power and bias, the number of other studies on the same question, and, importantly, the ratio of true to no relationships among the relationships probed in each scientific field. In this framework, a research finding is less likely to be true when the studies conducted in a field are smaller; when effect sizes are smaller; when there ...
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Am. J. Epidemiol. (8 September 2008), kwn206.
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 ...
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