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PurposeCurrent penetrance estimates of Birt-Hogg-Dubé (BHD)-associated cutaneous, pulmonary, and renal manifestations are based on clinically ascertained families. In a healthcare system population, we used a genetics-first approach to estimate the prevalence of pathogenic/likely pathogenic (P/LP) variants in the FLCN gene, which cause BHD, and the penetrance of BHD-related phenotypes.MethodsExomes from 135,990 patient-participants in Geisinger’s MyCode® cohort were assessed for P/LP FLCN variants. BHD-related phenotypes were evaluated from electronic health records. Association between FLCN P/LP variants and BHD-related phenotypes were assessed by Firth’s logistic regression.ResultsP/LP FLCN variants were identified in 35 (1 in 3,234) individuals, 68.6% of whom had BHD-related phenotype(s) including: cystic lung disease (65.7%); pneumothoraces (17.1%); cutaneous manifestations (8.6%); and renal cancer (2.9%). Only four (11.4%) were clinically diagnosed with BHD.ConclusionIn this healthcare population, the frequency of P/LP FLCN variants is 60 times higher than the previously reported prevalence. Although most variant-positive individuals had BHD-related phenotypes in the EHR, a minority were clinically diagnosed with BHD, likely because cutaneous manifestations, pneumothoraces, and renal cancer were observed at lower frequencies than clinical cohorts. Improved clinical recognition of cystic lung disease and education concerning its association with FLCN variants could prompt evaluation for BHD.

Post-zygotic mutations incurred during DNA replication, DNA repair, and other cellular processes lead to somatic mosaicism. Somatic mosaicism is an established cause of various diseases, including cancers. However, detecting mosaic variants in DNA from non-cancerous somatic tissues poses significant challenges, particularly if the variants only are present in a small fraction of cells. Here, the Brain Somatic Mosaicism Network conducted a coordinated, multi-institutional study to: (i) examine the ability of existing methods to detect simulated somatic single nucleotide variants (SNVs) in DNA mixing experiments; (ii) generate multiple replicates of whole genome sequencing data from the dorsolateral prefrontal cortex, other brain regions, dura mater, and dural fibroblasts of a single neurotypical individual; (iii) devise strategies to discover somatic SNVs; and (iv) apply various approaches to validate somatic SNVs. These efforts led to the identification of 43 bona fide somatic SNVs that ranged in variant allele fractions from ~0.005 to ~0.28. Guided by these results, we devised best practices for calling mosaic SNVs from 250X whole genome sequencing data in the accessible portion of the human genome that achieve 90% specificity and sensitivity. Finally, we demonstrated that analysis of multiple bulk DNA samples from a single individual allows the reconstruction of early developmental cell lineage trees. Thus, this study provides a unified set of best practices to detect somatic SNVs in non-cancerous tissues. The data and methods are freely available to the scientific community and should serve as a guide to assess the contributions of somatic SNVs to neuropsychiatric diseases.