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.