A genome-first approach to characterize DICER1 pathogenic variant prevalence, penetrance and cancer, thyroid, and other phenotypes in 2 population-scale cohorts

Abstract

Purpose: Population-scale, exome-sequenced cohorts with linked electronic health records (EHR) permit genome-first exploration of phenotype. Phenotype and cancer risk are well characterized in children with a pathogenic DICER1 (HGNC ID:17098) variant. Here, the prevalence, penetrance, and phenotype of pathogenic germline DICER1 variants in adults were investigated in 2 population-scale cohorts. Methods: Variant pathogenicity was classified using published DICER1 ClinGen criteria in the UK Biobank (469,787 exomes; unrelated: 437,663) and Geisinger (170,503 exomes; unrelated: 109,789) cohorts. In the UK Biobank cohort, cancer diagnoses in the EHR, cancer, and death registry were queried. For the Geisinger cohort, the Geisinger Cancer Registry and EHR were queried. Results: In the UK Biobank, there were 46 unique pathogenic DICER1 variants in 57 individuals (1:8242; 95% CI: 1:6362-1:10,677). In Geisinger, there were 16 unique pathogenic DICER1 variants (including 1 microdeletion) in 21 individuals (1:8119; 95% CI: 1:5310-1:12,412). Cohorts were well powered to find larger effect sizes for common cancers. Cancers were not significantly enriched in DICER1 heterozygotes; however, there was a ∼4-fold increased risk for thyroid disease in both cohorts. There were multiple ICD10 codes enriched >2-fold in both cohorts. Conclusion: Estimates of pathogenic germline DICER1 prevalence, thyroid disease penetrance, and cancer phenotype from genomically ascertained adults are determined in 2 large cohorts.

Keywords

• DICER1
• DICER1 syndrome
• Health care population
• Penetrance
• Prevalence