Final answer:
To mitigate false positives in GWAS, a commonly employed method is increasing the study's sample size. This approach improves the statistical power of the study and lowers the risk of findings being influenced by random chance.
Step-by-step explanation:
The method commonly employed to mitigate false positives in GWAS (Genome-Wide Association Studies) is increasing sample size. This strategy enhances the statistical power of the study, allowing for a more accurate detection of genuine associations between genetic variants and the traits or diseases being studied. By having a larger sample size, researchers can reduce the impact of random chance on the results, thus lowering the probability of false positive findings. It's crucial for the sample size to be large enough to represent the population adequately. An inadequate sample size leads to a greater risk of both Type I (false positive) and Type II (false negative) errors.
Beyond just increasing the sample size, proper matching of the case and control groups, stringent statistical significance thresholds (like the Bonferroni correction), replication of findings in independent cohorts, and in-depth bioinformatics analyses are also critical in mitigating false positives in GWAS studies.