A pedigree-based prediction model identifies carriers of deleterious de novo mutations in families with Li-Fraumeni syndrome
Year: 2020
Type: preprint
Abstract: De novo mutations (DNMs) are increasingly recognized as rare disease causal factors. Identifying DNM carriers will allow researchers to study the likely distinct molecular mechanisms of DNMs. We developed Famdenovo to predict DNM status (DNM or familial mutation [FM]) of deleterious autosomal dominant germline mutations for any syndrome. We introduce Famdenovo.TP53 for Li-Fraumeni syndrome (LFS) and analyze 324 LFS family pedigrees from four US cohorts: a validation set of 186 pedigrees and a discovery set of 138 pedigrees. The concordance index for Famdenovo.TP53 prediction was 0.95 (95% CI: [0.92, 0.98]). Forty individuals (95% CI: [30, 50]) were predicted as DNM carriers, increasing the total number from 42 to 82. We compared clinical and biological features of FM versus DNM carriers: (1) cancer and mutation spectra along with parental ages were similarly distributed; (2) ascertainment criteria like early-onset breast cancer (age 20–35 yr) provides a condition for an unbiased estimate of the DNM rate: 48% (23 DNMs vs. 25 FMs); and (3) hotspot mutation R248W was not observed in DNMs, although it was as prevalent as hotspot mutation R248Q in FMs. Furthermore, we introduce Famdenovo.BRCA for hereditary breast and ovarian cancer syndrome and apply it to a small set of family data from the Cancer Genetics Network. In summary, we introduce a novel statistical approach to systematically evaluate deleterious DNMs in inherited cancer syndromes. Our approach may serve as a foundation for future studies evaluating how new deleterious mutations can be established in the germline, such as those in TP53 .
Source: Genome Research
Language: English
Topic: Genomics and Rare Diseases
Subfield: Genetics
Domain: Life Sciences
Open Access status: bronze
Awards: Precision Assessment and Delivery of Cancer Risks in BRCA 1/2 Mutation Cancers, Cancer Genetic Network (CGN), Consistent variable selection in p>>n settings, Statistical methods for genomic analysis of heterogeneous tumors, G4032302005, A Mutational Model for Childhood Cancer, G4763935553, G9000161106, G9000292079, Experience-driven development of reliable stimulus representations in dynamic cortical networks, Innate Immune Mechanisms Controlling Flavivirus Neurovirulence, Cancer Center Support Grant, NLM Research Training Program in Biomedical Informatics and Data Science for Predoctoral and Postdoctoral Fellows, Statistical methods and tools for cancer risk prediction in families with germline mutations in TP53
OpenAlex ID: W3050921496
