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Bayesian approach to determining penetrance of pathogenic SDH variants

  • Diana E Benn
  • , Ying Zhu
  • , Katrina A Andrews
  • , Mathilda Wilding
  • , Emma L Duncan
  • , Trisha Dwight
  • , Richard W Tothill
  • , John Burgess
  • , Ashley Crook
  • , Anthony J Gill
  • , Rodney J Hicks
  • , Edward Kim
  • , Catherine Luxford
  • , Helen Marfan
  • , Anne Louise Richardson
  • , Bruce Robinson
  • , Arran Schlosberg
  • , Rachel Susman
  • , Lyndal Tacon
  • , Alison Trainer
  • Katherine Tucker, Eamonn R Maher, Michael Field, Roderick J Clifton-Bligh
  • From the George Institute for Global Health, University of New South Wales Sydney (V.P., M.J.J., B.N., S. Bompoint), the Royal North Shore Hospital (V.P.), Concord Repatriation General Hospital (M.J.J.), and the Charles Perkins Centre, University of Sydney (B.N.), Sydney, and the Kolling Institute of Medical Research, Sydney Medical School, University of Sydney, Royal North Shore Hospital, St. Leonards, NSW (C.P.) - all in Australia; Imperial College London (B.N.) and the Department of Renal Medicine, UCL M
  • University of Cambridge and NIHR Cambridge Biomedical Research Centre and Cancer Research UK Cambridge Centre and Cambridge University Hospitals NHS Foundation Trust
  • Royal North Shore Hospital
  • Queensland University of Technology
  • Department of Children and Adolescents Oncology
  • University of Tasmania
  • University of Sydney
  • Royal Brisbane and Women's Hospital
  • Prince of Wales Hospital

Research output: Contribution to journalArticlepeer-review

Abstract

BACKGROUND: Until recently, determining penetrance required large observational cohort studies. Data from the Exome Aggregate Consortium (ExAC) allows a Bayesian approach to calculate penetrance, in that population frequencies of pathogenic germline variants should be inversely proportional to their penetrance for disease. We tested this hypothesis using data from two cohorts for succinate dehydrogenase subunits A, B and C (SDHA-C) genetic variants associated with hereditary pheochromocytoma/paraganglioma (PC/PGL).

METHODS: Two cohorts were 575 unrelated Australian subjects and 1240 unrelated UK subjects, respectively, with PC/PGL in whom genetic testing had been performed. Penetrance of pathogenic SDHA-C variants was calculated by comparing allelic frequencies in cases versus controls from ExAC (removing those variants contributed by The Cancer Genome Atlas).

RESULTS: Pathogenic SDHA-C variants were identified in 106 subjects (18.4%) in cohort 1 and 317 subjects (25.6%) in cohort 2. Of 94 different pathogenic variants from both cohorts (seven in SDHA, 75 in SDHB and 12 in SDHC), 13 are reported in ExAC (two in SDHA, nine in SDHB and two in SDHC) accounting for 21% of subjects with SDHA-C variants. Combining data from both cohorts, estimated lifetime disease penetrance was 22.0% (95% CI 15.2% to 30.9%) for SDHB variants, 8.3% (95% CI 3.5% to 18.5%) for SDHC variants and 1.7% (95% CI 0.8% to 3.8%) for SDHA variants.

CONCLUSION: Pathogenic variants in SDHB are more penetrant than those in SDHC and SDHA. Our findings have important implications for counselling and surveillance of subjects carrying these pathogenic variants.

Original languageEnglish
Pages (from-to)729-734
Number of pages6
JournalJournal of Medical Genetics
Volume55
Issue number11
Early online date10 Sept 2018
DOIs
Publication statusPublished - 26 Oct 2018

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • Algorithms
  • Alleles
  • Australia
  • Bayes Theorem
  • Genetic Association Studies/methods
  • Genetic Predisposition to Disease
  • Genetic Variation
  • Genotype
  • Humans
  • Isoenzymes
  • Models, Genetic
  • Penetrance
  • Phenotype
  • Succinate Dehydrogenase/genetics
  • United Kingdom

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