STrengthening the REporting of Genetic Association Studies (STREGA): An Extension of the STROBE Statement
University of Ottawa1, University of Ioannina2, University of Bern3, Centers for Disease Control and Prevention4, PLOS5, University of Bristol6, Ottawa Hospital Research Institute7, University of Texas Health Science Center at Houston8, University of Western Ontario9, Erasmus University Rotterdam10, Cancer Care Ontario11, McGill University12, Harvard University13
TL;DR: The STREGA recommendations are presented, which are aimed at improving the reporting of genetic association studies and are designed to improve the quality of studies.
Abstract: Making sense of rapidly evolving evidence on genetic associations is crucial to making genuine advances in human genomics and the eventual integration of this information in the practice of medicine and public health. Assessment of the strengths and weaknesses of this evidence, and hence the ability to synthesize it, has been limited by inadequate reporting of results. The STrengthening the REporting of Genetic Association studies (STREGA) initiative builds on the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement and provides additions to 12 of the 22 items on the STROBE checklist. The additions concern population stratification, genotyping errors, modelling haplotype variation, Hardy-Weinberg equilibrium, replication, selection of participants, rationale for choice of genes and variants, treatment effects in studying quantitative traits, statistical methods, relatedness, reporting of descriptive and outcome data, and the volume of data issues that are important to consider in genetic association studies. The STREGA recommendations do not prescribe or dictate how a genetic association study should be designed but seek to enhance the transparency of its reporting, regardless of choices made during design, conduct, or analysis.
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TL;DR: In virtually all medical domains, diagnostic and prognostic multivariable prediction models are being developed, validated, updated, and implemented with the aim to assist doctors and individuals in estimating probabilities and potentially influence their decision making.
Abstract: The TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) Statement includes a 22-item checklist, which aims to improve the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. This explanation and elaboration document describes the rationale; clarifies the meaning of each item; and discusses why transparent reporting is important, with a view to assessing risk of bias and clinical usefulness of the prediction model. Each checklist item of the TRIPOD Statement is explained in detail and accompanied by published examples of good reporting. The document also provides a valuable reference of issues to consider when designing, conducting, and analyzing prediction model studies. To aid the editorial process and help peer reviewers and, ultimately, readers and systematic reviewers of prediction model studies, it is recommended that authors include a completed checklist in their submission. The TRIPOD checklist can also be downloaded from www.tripod-statement.org.
2,982 citations
TL;DR: The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used, and is best used in conjunction with the TRIPod explanation and elaboration document.
Abstract: Prediction models are developed to aid health-care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health-care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org).
1,973 citations
TL;DR: The nature of the prediction in diagnosis is estimating the probability that a specific outcome or disease is present (or absent) within an individual, at this point in timethat is, the moment of prediction (T= 0), and prognostic prediction involves a longitudinal relationship.
Abstract: Prediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org).
1,615 citations
TL;DR: In this article, the authors performed a meta-analysis on the entire body of work assessing the relationship between 5-HTTLPR, stress and depression and concluded that there is no evidence supporting the presence of genetic moderation.
Abstract: The principal function of the serotonin transporter is to remove serotonin from the synapse, returning it to the presynaptic neuron where the neurotransmitter can be degraded or re-released at a later time. A polymorphism in the promoter region of the serotonin transporter gene (5-HTTLPR) has been found to affect the transcription rate of the gene, with the short (s) allele transcriptionally less efficient that the alternate long (l) allele. In 2003, Caspi and colleagues examined the relationship between 5-HTTLPR, stress and depression using a prospective, longitudinal birth cohort and found that subjects carrying the less functional 5-HTTLPR s allele reported greater sensitivity to stress1.
This study has been cited over 2000 times in the scientific literature and generated a great deal of excitement and controversy around the potential of gene × environment interaction studies2. To date, there have been 55 follow-up studies, exploring whether 5-HTTLPR moderates the relationship between stress and depression, with some studies supporting the association between the 5-HTTLPR s allele and greater stress sensitivity and others not. Two recent meta-analyses have assessed a subset of these studies and concluded that there is no evidence supporting the presence of genetic moderation3, 4.
Since their publication, these meta-analyses have been criticized for only including a subset of the studies investigating the relationship between 5-HTTLPR stress and depression5–9. In fact, while 56 primary data studies have assessed whether 5-HTTLPR moderates the relationship between stress and depression, the Munafo and Risch meta-analyses included only 5 and 14 of those studies respectively10–48. Further, Uher and McGuffin have demonstrated that the larger, Risch meta-analysis included a significantly greater proportion of negative replication studies than positive replication studies8.
There are multiple reasons that the studies included in the meta-analyses were limited. First, the primary study data needed for traditional meta-analysis was often not available, either in the original publications or in follow-up email inquiries to study authors. For instance, Munafo and colleagues reported that 15 studies met criteria for inclusion in their meta-analysis. However, they were only able to obtain the primary study data needed for inclusion for five of those studies. There is no evidence that the studies that were able to be included in the meta-analyses were of higher “quality” than those not included.
Another reason why many studies were not included in the Risch and Munafo meta-analysis is that both meta-analyses focused exclusively on studies that explored an interaction between 5-HTTLPR and stressful life events (SLEs) in the development of depression. The original Caspi article, however, not only reported an interaction between 5-HTTLPR and SLEs, but also an interaction between 5-HTTLPR and childhood maltreatment stress. Nine studies have attempted to replicate this interaction with childhood maltreatment, but these studies were not included in the meta-analyses.
Some observers have noted that the SLE study design may have limited power to detect genetic moderation effects because they are susceptible to biases introduced by impaired recall of stressors by subjects and highly variable stressors between subjects9, 45. A newer class of studies has attempted to bypass these potential problems by focusing on specific populations that have experienced a substantial, specific stressor. These studies test whether 5-HTTLPR moderates the relationship between a specific stressor and depression. Eighteen studies have employed such specific stressor designs, but like the childhood maltreatment studies, these studies were excluded from the previous meta-analysis.
In this study, rather than focus on a limited of studies, we sought to perform a meta-analysis on the entire body of work assessing the relationship between 5-HTTLPR, stress and depression. Unfortunately, different types of studies have generally used different study designs to explore this question, rendering it very difficult to combine the studies into a single traditional meta-analysis. An approach useful in situations where equivalent raw data are not available across all studies, is to combine the studies at the level of significance tests 49. The Liptak-Stouffer Z-score method is a well-validated method for combining p-values across studies that has been utilized widely across genomics and biostatistics 50–56. In this study, we utilize the Liptak-Stouffer Z-score method to combine the results from studies investigating whether the 5-HTTLPR variant moderates the relationship between stress and depression.
1,304 citations
TL;DR: The EQUATOR Network as mentioned in this paper is an international initiative that aims to enhance the reliability and value of the published health research literature by providing resources, education and training to facilitate good research reporting and assists in the development, dissemination and implementation of robust reporting guidelines.
Abstract: Growing evidence demonstrates widespread deficiencies in the reporting of health research studies. The EQUATOR Network is an international initiative that aims to enhance the reliability and value of the published health research literature. EQUATOR provides resources, education and training to facilitate good research reporting and assists in the development, dissemination and implementation of robust reporting guidelines. This paper presents a collection of tools and guidelines available on the EQUATOR website (http://www.equator-network.org) that have been developed to increase the accuracy and transparency of health research reporting.
962 citations
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TL;DR: The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) initiative developed recommendations on what should be included in an accurate and complete report of an observational study, resulting in a checklist of 22 items (the STROBE statement) that relate to the title, abstract, introduction, methods, results, and discussion sections of articles.
Abstract: Much biomedical research is observational. The reporting of such research is often inadequate, which hampers the assessment of its strengths and weaknesses and of a study's generalisability. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Initiative developed recommendations on what should be included in an accurate and complete report of an observational study. We defined the scope of the recommendations to cover three main study designs: cohort, case-control, and cross-sectional studies. We convened a 2-day workshop in September 2004, with methodologists, researchers, and journal editors to draft a checklist of items. This list was subsequently revised during several meetings of the coordinating group and in e-mail discussions with the larger group of STROBE contributors, taking into account empirical evidence and methodological considerations. The workshop and the subsequent iterative process of consultation and revision resulted in a checklist of 22 items (the STROBE Statement) that relate to the title, abstract, introduction, methods, results, and discussion sections of articles. 18 items are common to all three study designs and four are specific for cohort, case-control, or cross-sectional studies. A detailed Explanation and Elaboration document is published separately and is freely available on the Web sites of PLoS Medicine, Annals of Internal Medicine, and Epidemiology. We hope that the STROBE Statement will contribute to improving the quality of reporting of observational studies.
15,454 citations
TL;DR: The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Initiative developed recommendations on what should be included in an accurate and complete report of an observational study, resulting in a checklist of 22 items that relate to the title, abstract, introduction, methods, results, and discussion sections of articles.
Abstract: Much biomedical research is observational. The reporting of such research is often inadequate, which hampers the assessment of its strengths and weaknesses and of a study’s generalizability. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Initiative developed recommendations on what should be included in an accurate and complete report of an observational study. We defined the scope of the recommendations to cover three main study designs: cohort, case-control and cross-sectional studies. We convened a two-day workshop, in September 2004, with methodologists, researchers and journal editors to draft a checklist of items. This list was subsequently revised during several meetings of the coordinating group and in e-mail discussions with the larger group of STROBE contributors, taking into account empirical evidence and methodological considerations. The workshop and the subsequent iterative process of consultation and revision resulted in a checklist of 22 items (the STROBE Statement) that relate to the title, abstract, introduction, methods, results and discussion sections of articles. Eighteen items are common to all three study designs and four are specific for cohort, case-control, or cross-sectional studies. A detailed Explanation and Elaboration document is published separately and is freely available on the web sites of PLoS Medicine, Annals of Internal Medicine and Epidemiology. We hope that the STROBE Statement will contribute to improving the quality of reporting of observational studies.
13,974 citations
"STrengthening the REporting of Gene..." refers methods in this paper
...In the Table, we present the STREGA recommendations, an extension to the STROBE checklist (55) for genetic association studies....
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...The epidemiology community has recently developed the Strengthening the Reporting of Observational studies in Epidemiology (STROBE) Statement for cross-sectional, case–control, and cohort studies (55, 56)....
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TL;DR: This study has demonstrated that careful use of a shared control group represents a safe and effective approach to GWA analyses of multiple disease phenotypes; generated a genome-wide genotype database for future studies of common diseases in the British population; and shown that, provided individuals with non-European ancestry are excluded, the extent of population stratification in theBritish population is generally modest.
Abstract: There is increasing evidence that genome-wide association ( GWA) studies represent a powerful approach to the identification of genes involved in common human diseases. We describe a joint GWA study ( using the Affymetrix GeneChip 500K Mapping Array Set) undertaken in the British population, which has examined similar to 2,000 individuals for each of 7 major diseases and a shared set of similar to 3,000 controls. Case-control comparisons identified 24 independent association signals at P < 5 X 10(-7): 1 in bipolar disorder, 1 in coronary artery disease, 9 in Crohn's disease, 3 in rheumatoid arthritis, 7 in type 1 diabetes and 3 in type 2 diabetes. On the basis of prior findings and replication studies thus-far completed, almost all of these signals reflect genuine susceptibility effects. We observed association at many previously identified loci, and found compelling evidence that some loci confer risk for more than one of the diseases studied. Across all diseases, we identified a large number of further signals ( including 58 loci with single-point P values between 10(-5) and 5 X 10(-7)) likely to yield additional susceptibility loci. The importance of appropriately large samples was confirmed by the modest effect sizes observed at most loci identified. This study thus represents a thorough validation of the GWA approach. It has also demonstrated that careful use of a shared control group represents a safe and effective approach to GWA analyses of multiple disease phenotypes; has generated a genome-wide genotype database for future studies of common diseases in the British population; and shown that, provided individuals with non-European ancestry are excluded, the extent of population stratification in the British population is generally modest. Our findings offer new avenues for exploring the pathophysiology of these important disorders. We anticipate that our data, results and software, which will be widely available to other investigators, will provide a powerful resource for human genetics research.
9,244 citations
TL;DR: A new statistical method is presented, applicable to genotype data at linked loci from a population sample, that improves substantially on current algorithms and performs well in absolute terms, suggesting that reconstructing haplotypes experimentally or by genotyping additional family members may be an inefficient use of resources.
Abstract: Current routine genotyping methods typically do not provide haplotype information, which is essential for many analyses of fine-scale molecular-genetics data. Haplotypes can be obtained, at considerable cost, experimentally or (partially) through genotyping of additional family members. Alternatively, a statistical method can be used to infer phase and to reconstruct haplotypes. We present a new statistical method, applicable to genotype data at linked loci from a population sample, that improves substantially on current algorithms; often, error rates are reduced by >50%, relative to its nearest competitor. Furthermore, our algorithm performs well in absolute terms, suggesting that reconstructing haplotypes experimentally or by genotyping additional family members may be an inefficient use of resources.
7,482 citations
"STrengthening the REporting of Gene..." refers background in this paper
...Acknowledgment: The authors thank Kyle Vogan and Allen Wilcox for their participation in the workshop and for their comments; Michele Cargill (Affymetrix) and Aaron del Duca (DNA Genotek) for their participation in the workshop as observers; and the Public Population Project in Genomics (P3G), hosted by the University of Montreal and supported by Genome Canada and Genome Quebec....
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...Publication guidelines for improvement studies in health care: evolution of the SQUIRE Project....
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...Information on common patterns of genetic variation revealed by the International Haplotype Map (HapMap) Project (108) can be applied in the analysis of genomewide association studies to infer genotypic variation at markers not typed directly in these studies (121, 122)....
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TL;DR: The dbSNP database is a general catalog of genome variation to address the large-scale sampling designs required by association studies, gene mapping and evolutionary biology, and is integrated with other sources of information at NCBI such as GenBank, PubMed, LocusLink and the Human Genome Project data.
Abstract: In response to a need for a general catalog of genome variation to address the large-scale sampling designs required by association studies, gene mapping and evolutionary biology, the National Center for Biotechnology Information (NCBI) has established the dbSNP database [S.T.Sherry, M.Ward and K.Sirotkin (1999) Genome Res., 9, 677–679]. Submissions to dbSNP will be integrated with other sources of information at NCBI such as GenBank, PubMed, LocusLink and the Human Genome Project data. The complete contents of dbSNP are available to the public at website: http://www.ncbi.nlm.nih.gov/SNP. The complete contents of dbSNP can also be downloaded in multiple formats via anonymous FTP at ftp:// ncbi.nlm.nih.gov/snp/.
6,449 citations