There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.
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.
The objective of a genetic association study, is to determine whether a potential genetic polymorphism contributes to an individual's susceptibility to a particular disease or characteristic. In scientific way, an association is typically described as a statistically significant difference between two groups (case and control) concerning a set of study variables. In genetic association studies, researchers examine the genotypic and allelic frequencies of a particular polymorphism in both the case and control groups to identify any possible associations. STrengthening the REporting of Genetic Association Studies (STREGA) [1]. recommends that researchers investigate the Hardy-Weinberg Equilibrium (HWE) through the examination of their control subjects' genotypes. According to HWE, there should not be any significant difference between the observed and expected values for genotypes of a given genetic polymorphism in a large population with random mating, and in the absence of mutation, migration, and natural selection. Deviation from these assumptions resulted in observed and expected value differences that may have reached statistical significance. The presence of evolutionary factors, such as mutation, migration, natural selection, genetic drift, and non-random mating, can result in a noteworthy difference between the anticipated and observed genotypic outcomes. It should be noted that this is not the only factor at play and other factors contribute as well. Studies indicate that researchers’ mistakes, like genotyping errors, are commonly responsible in such instances. Another critical error is the inclusion of individuals from at least two separate gene pools in the study groups, which results in a sampling error. This undermines trust in the comparison of genotypic frequencies between the case and control groups. The main purpose of comparing observed and expected genotypic frequencies under the Hardy-Weinberg equilibrium is to assist researchers in recognizing potential issues with their work. Researchers can subsequently take corrective action to address identified errors (genotyping / sampling errors). Regrettably, some researchers fail to compare observed and expected frequencies, make calculation errors, or fail to consider statistical significance. Many of them are unaware that such oversimplification leads to a significant number of published articles that lack credibility. Accordingly, it is of utmost importance that studies presenting findings adhere to strict standards. Since the issuance of the STREGA statement in 2009, there has been no decline in the occurrence of this issue in genetic association study reports. It is essential for researchers interested in genetic association studies to compare the observed and HWE-expected genotypic values, as stated in the STREGA statement. It is hoped that following the recommendations of the STREGA statement will improve the quality of genetic association studies. Declaration of competing interest: None
STrengthening the REporting of Genetic Association (STREGA) studies strongly recommend that researchers assess the Hardy-Weinberg equilibrium (HWE) in their control groups. The exact frequency of studies in which their control subjects show a significant deviation from the HWE is not well established. Therefore, the present study was conducted. The electronic database PubMed was searched using the terms: 'meta-analysis' and 'polymorphism'. Data of original articles were extracted from meta-analysis. The STREGA statement was published in 2009. Therefore, studies were divided into two groups, before and after the statement. After data collection, quartiles for sample size and minor allele frequency (MAF) were determined separately. A total of 772 independent studies were extracted from these meta-analyses and included in the current study. Multivariate analysis revealed the following associations: (1) Reports published after the STREGA statement (compared to before the statement) were associated with an increased prevalence of deviation from HWE. (2) Reports with sample size Q2-Q4 versus Q1 were associated with an increased prevalence of deviation from HWE. (3) Studies with MAF Q4 versus Q1 were negatively associated with the prevalence of reports of deviation from HWE. We conclude that the STREGA statement failed to change the attitudes and practices of researchers and editors towards the importance of HWE.
scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.
Smart Citations