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. 2024:2:101846.
doi: 10.1016/j.gimo.2024.101846. Epub 2024 Apr 11.

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

Jung Kim  1 , Jeremy Haley  2 , Jessica N Hatton  1 , Uyenlinh L Mirshahi  2 , H Shanker Rao  2 , Mark F Ramos  1 , Diane Smelser  2 , Gretchen Urban  2 , Kris Ann P Schultz  3   4   5 , David J Carey  2 , Douglas R Stewart  1
Affiliations

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

Jung Kim et al. Genet Med Open. 2024.

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 was investigated in two 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 (1:8,242;95%CI:1:6,362-1:10,677). In Geisinger, there were 16 unique pathogenic DICER1 variants (including one microdeletion) in 21 individuals (1:8,119;95%CI:1:5,310-1:12,412). 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 two large cohorts.

Keywords: DICER1; DICER1 syndrome; healthcare population; penetrance; prevalence.

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Conflict of interest statement

Conflict of Interest Statement The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Risk of thyroid phenotypes in DICER1 heterozygotes in UK Biobank and Geisinger. Hypothyroidism (E03), nontoxic goiter (E04), thyrotoxicosis (E05), and all thyroid codes (E01-E07)-adjusted odd ratios, 95% CI, and adjusted P value were calculated using logistic regression with age at enrollment (only for UK Biobank), sex, race, BMI, and smoking status.
Figure 2
Figure 2
Cumulative incidence of thyroid phenotypes. Reverse Kaplan-Meier plots in DICER1 heterozygotes compared with controls for (A) UK Biobank and (B) Geisinger (adjusted for relatedness). Thyroid ICD10 codes, including E01-E07 (but excluding E03.2, E03.3, and E06.4), are considered events. The log-rank test P values from Cox-proportional hazard model were adjusted for age at enrollment (only for UK Biobank), sex, race, BMI, smoking status. For (B), left-truncation bias correction was applied using first encounter date.
Figure 3
Figure 3
Risk of other phenotypes in DICER1 heterozygotes. ICD10 codes in (A) UK Biobank and (B) Geisinger enriched in DICER1 heterozygotes vs controls adjusted odd ratios and 95% CI were calculated using logistic regression with age at enrollment (only for UK Biobank), sex, race, BMI, smoking status. Red font denotes statistical significance after Bonferroni correction (P < .002 for UK Biobank and P < .0004 for Geisinger).
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