Alleviating misclassified germline variants in underrepresented populations: a strategy using popmax

Purpose Germline variant interpretation often depends on population-matched control cohorts. This is not feasible for population-groups that are underrepresented in current population reference databases. Methods We classify germline variants with population-matched controls for two ancestrally diverse cohorts of patients: 132 early-onset or familial CRC patients from Singapore (SG), and 100 early-onset CRC patients from the United States (US). The effects of using a population-mismatched control cohort are simulated by swapping the control cohorts used for each patient cohort, with or without the popmax computational strategy. Results Population-matched classifications revealed a combined 62 pathogenic or likely pathogenic (P/LP) variants in 34 genes across both cohorts. Using a population-mismatched control cohort resulted in misclassification of non-P/LP variants as P/LP, driven by the absence of ancestry-specific rare variants in the control cohort. Popmax was more effective in alleviating misclassifications for the SG cohort than the US cohort. Conclusion Underrepresented population-groups can suffer from higher rates of false positive P/LP results. Popmax can partially alleviate these misclassifications, but its efficacy still depends on the degree with which the population-groups is represented in the control cohort.