The rapidly evolving evidence on genetic associations is crucial to integrating human genomics into the practice of medicine and public health [1,2]. Genetic factors are likely to affect the occurrence of numerous common diseases, and therefore identifying and characterizing the associated risk (or protection) will be important in improving the understanding of etiology and potentially for developing interventions based on genetic information. The number of publications on the associations between genes and diseases has increased tremendously; with more than 34 000 published articles, the annual number has more than doubled between 2001 and 2008 [3,4]. Articles on genetic associations have been published in about 1500 journals and in several languages.
Despite the many similarities between genetic association studies and “classical” observational epidemiologic studies (that is, cross-sectional, case-control, and cohort) of lifestyle and environmental factors, genetic association studies present several specific challenges including an unprecedented volume of new data [5,6] and the likelihood of very small individual effects. Genes may operate in complex pathways with gene-environment and gene-gene interactions . Moreover, the current evidence base on gene-disease associations is fraught with methodological problems [8–10]. Inadequate reporting of results, even from well-conducted studies, hampers assessment of a study's strengths and weaknesses, and hence the integration of evidence .
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.
Although several commentaries on the conduct, appraisal and/or reporting of genetic association studies have so far been published [12–39], their recommendations differ. For example, some papers suggest that replication of findings should be part of the publication [12,13,16,17,23,26,34–36] whereas others consider this suggestion unnecessary or even unreasonable [21,40–44]. In many publications, the guidance has focused on genetic association studies of specific diseases [14,15,17,19,22,23,25,26,31–38] or the design and conduct of genetic association studies [13–15,17,19,20,22,23,25,30–32,35,36] rather than on the quality of the reporting.
Despite increasing recognition of these problems, the quality of reporting genetic association studies needs to be improved [45–49]. For example, an assessment of a random sample of 315 genetic association studies published from 2001 to 2003 found that most studies provided some qualitative descriptions of the study participants (for example, origin and enrolment criteria), but reporting of quantitative descriptors, such as age and sex, was variable . In addition, completeness of reporting of methods that allow readers to assess potential biases (for example, number of exclusions or number of samples that could not be genotyped) varied . Only some studies described methods to validate genotyping or mentioned whether research staff were blinded to outcome. The same problems persisted in a smaller sample of studies published in 2006 . Lack of transparency and incomplete reporting have raised concerns in a range of health research fields [11,50–53] and poor reporting has been associated with biased estimates of effects in clinical intervention studies .
The main goal of this article is to propose and justify a set of guiding principles for reporting results of genetic association studies. 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]. Given the relevance of general epidemiologic principles for genetic association studies, we propose recommendations in an extension of the STROBE Statement called the STrengthening the REporting of Genetic Association studies (STREGA) Statement. The recommendations of the STROBE Statement have a strong foundation because they are based on empirical evidence on the reporting of observational studies, and they involved extensive consultations in the epidemiologic research community . We have sought to identify gaps and areas of controversy in the evidence regarding potential biases in genetic association studies. With the recommendations, we have indicated available empirical or theoretical work that has demonstrated or suggested that a methodological feature of a study can influence the direction or magnitude of the association observed. We acknowledge that for many items, no such evidence exists. The intended audience for the reporting guideline is broad and includes epidemiologists, geneticists, statisticians, clinician scientists, and laboratory-based investigators who undertake genetic association studies. In addition, it includes “users” of such studies who wish to understand the basic premise, design, and limitations of genetic association studies in order to interpret the results. The field of genetic associations is evolving very rapidly with the advent of genome-wide association investigations, high-throughput platforms assessing genetic variability beyond common single nucleotide polymorphisms (SNPs) (for example, copy number variants, rare variants), and eventually routine full sequencing of samples from large populations. Our recommendations are not intended to support or oppose the choice of any particular study design or method. Instead, they are intended to maximize the transparency, quality and completeness of reporting of what was done and found in a particular study.