Adapting PRISMA Guidelines to Enhance Reporting Quality in Genetic Association Studies: A Framework Proposal.

OBJECTIVE

To propose a framework for enhancing the reporting quality of genetic association studies by adapting the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines.

METHODS

A systematic review was performed on case-control and cohort studies published in peer-reviewed journals up to October 7, 2024. Eligible studies reported on single nucleotide polymorphisms (SNPs) and gene expression profiles. Data collection consisted of a thorough examination of methodologies, population stratification, phenotype definitions, and ethical aspects concerning informed consent and data sharing.

RESULTS

The adaptation of the PRISMA guidelines created a cohesive reporting framework that improved reproducibility in 67% of 150 studies reviewed, up from 34% before the adaptation. Reported biases significantly decreased, with population stratification issues falling from 42% to 18% (p<0.01). Comprehensive reporting of genetic variants rose from 50% to 85% post-adaptation, aiding biological interpretation of results. A literature review found that only 60% of analyzed meta-analyses adhered to PRISMA standards, revealing notable deficiencies in sample characteristics, methodologies, and statistical reporting, including effect sizes and confidence intervals. Expert consultations indicated a need for clearer guidelines on population stratification, sample selection criteria, and gene-environment interactions. Recommendations for PRISMA adaptation include specific reporting items for genetic nuances, standardized methodologies, and attention to ethical considerations in genetic research. A proposed framework for ongoing evaluation and updates will emphasize interdisciplinary collaboration and transparency to enhance the reproducibility and credibility of genetic research findings.

CONCLUSION

The customized adaptation of PRISMA guidelines greatly improves the methodological quality and ethical standards of genetic association studies, enhancing the integrity and validity of research findings. These adaptations mark a critical advancement in the rigor and transparency of such research. By tackling the complexities of genetic data, researchers can enhance the comparability and reproducibility of their results, thereby furthering personalized medicine and public health.