336: Measuring disease likelihood in genomic ascertainment

Sapp JC et al., The American Journal of Human Genetics - A longitudinal study of recipients of medically actionable secondary genomic findings develops a Bayesian approach that integrates variant, family genotypic, and phenotypic data to estimate the probability that a secondary finding represents a true clinicomolecular diagnosis, with a detailed analysis of BRCA1/BRCA2 families and implications for screening policy and clinical management. Key terms: secondary findings, BRCA1, BRCA2, Bayesian risk assessment, population genomic screening. Study Highlights:The team enrolled 227 secondary findings recipients and completed genotyping and deep phenotyping for 163 probands, using cascade testing and variant reclassification. They piloted a Bayesian method combining prior population prevalence, variant pathogenicity, and family genotype–phenotype data to estimate clinicomolecular diagnosis (CMD) probabilities for BRCA1/2 families. CMD probabilities varied widely (26.2% to >99.9%) and over half of BRCA1/2 families met NCCN diagnostic testing criteria, indicating underuse of diagnostic testing. Conclusion:In opportunistic secondary findings contexts the posterior probability that a patient has the implicated monogenic disease can differ substantially from variant pathogenicity; integrating familial genotypic and phenotypic data via Bayesian methods refines risk estimates and should guide shared decision-making, management strategies, and policy for population genomic screening. Music:Enjoy the music based on this article at the end of the episode. Article title:Measuring disease likelihood in genomic ascertainment First author:Sapp JC Journal:The American Journal of Human Genetics DOI:10.1016/j.ajhg.2026.03.009 Reference:Sapp JC, Lewis KL, Modlin EW, et al. Measuring disease likelihood in genomic ascertainment. The American Journal of Human Genetics. 2026;113:1–12. doi:10.1016/j.ajhg.2026.03.009 License:This episode is based on an open-access article published under the Creative Commons Attribution 4.0 International License (CC BY 4.0) – https://creativecommons.org/licenses/by/4.0/ Support:Base by Base – Stripe donations: https://donate.stripe.com/7sY4gz71B2sN3RWac5gEg00 Official website https://basebybase.com On PaperCast Base by Base you’ll discover the latest in genomics, functional genomics, structural genomics, and proteomics. Episode link: https://basebybase.com/episodes/measuring-disease-likelihood-genomic-ascertainment QC:This episode was checked against the original article PDF and publication metadata for the episode release published on 2026-04-07. QC Scope:- article metadata and core scientific claims from the narration- excludes analogies, intro/outro, and music- transcript coverage: Audited transcript sections describing the Bayesian CMD approach, the BRCA1/BRCA2 findings, the Family 8334 case, NCCN criteria implications, and study design/limitations.- transcript topics: ACMG secondary findings context and selection bias; Bayesian probability model for CMD; Cascade testing and family data integration; BRCA1 vs BRCA2 variant distribution and penetrance; NCCN criteria and clinical testing underutilization; Study design and recruitment (163 probands from 41 sources) QC Summary:- factual score: 10/10- metadata score: 10/10- supported core claims: 5- claims flagged for review: 0- metadata checks passed: 4- metadata issues found: 0 Metadata Audited:- article_doi- article_title- article_journal- license Factual Items Audited:- CMD probability range across BRCA1/BRCA2 families: 26.2% to 100%- Baseline posterior probability for BRCA2-related CMD: 58.2%- Posterior CMD probability for family 8334: 99.2%- Average CMD...