Background: Completion of the human genome project and advances in genotyping technology has resulted in an increase in publication of genetic association studies. Systematic reviews and meta-analyses are a common method of providing synthesized effect of a genetic variant on a trait of interest, but, summary estimates are subject to bias due to the varying methodological quality of individual studies. Objective: To quantify bias introduced from individual studies and inform meta-analyses, we developed, validated, and empirically evaluated a tool that assesses the quality of genetic association studies. Methods: We used published guidelines and recommendations to create a list of items with potential impact on quality. We chose final items in consultation with five experts. Evaluation of the tool was performed in two parts. Firstly, four reviewers rated 30 studies randomly selected from a published meta-analysis. Their ratings were used to assess construct validity, reliability, and item discrimination of the tool. We report G-coefficients as measures of inter-rater reliability, internal consistency, and overall reliability of the tool, as well as item-total correlations and Cronbach’s alpha to assess the discriminative ability of each item. Secondly, a systematic review of published meta-analyses was conducted. The tool was applied to 50 randomly selected meta-analyses from the literature and studies of ‘poor’ quality were excluded to assess whether application of our tool improves precision of estimates and reduces heterogeneity. Results: The tool demonstrates excellent psychometric properties and generates a quality score for each study with corresponding ratings of ‘low’, ‘moderate’, or ‘high’ quality. When applied to individual meta-analyses to exclude studies of low quality, we found a decrease in heterogeneity and an increase in precision of summary estimates. Conclusion: Integration of our tool into meta-analyses to inform selection of studies for inclusion, conduct sensitivity analyses, and perform meta-regressions, can help improve the state of evidence in the field of genetic epidemiology, which is currently plagued with irreproducible findings.