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Variant-Specific Mendelian Risk Prediction Model.

Julie-Alexia Dias1,2, Eunchan Bae3, Theodore Huang1,2

  • 1Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.

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|December 9, 2025
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Summary
This summary is machine-generated.

This study introduces a new cancer risk prediction model that accounts for specific pathogenic sequence variant (PSV) penetrances, improving accuracy for BRCA1/2 variants. The Fam3PRO-variant model offers more precise risk predictions, even with incomplete family history data.

Keywords:
Mendelian risk predictioncancer risk managementcancer susceptibilitypathogenic sequence variants

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Area of Science:

  • Genetics and Genomics
  • Cancer Epidemiology
  • Biostatistics

Background:

  • Pathogenic sequence variants (PSVs) increase cancer risk, necessitating accurate prediction models.
  • Existing Mendelian risk models often assume uniform gene-level penetrance, which may not reflect reality for genes like BRCA1/2.
  • Cancer risk can vary significantly between different PSVs within the same gene.

Purpose of the Study:

  • To extend Mendelian risk prediction models by incorporating PSV-specific penetrances.
  • To evaluate the performance and clinical utility of the proposed Fam3PRO-variant model.
  • To assess the impact of underreporting in family history data on model accuracy.

Main Methods:

  • Developed the Fam3PRO-variant model, an extension of Fam3PRO, incorporating PSV-specific penetrances for BRCA1/2 variants.
  • Classified BRCA1/2 PSVs into cancer-specific risk regions: breast cancer clustering region (BCCR), ovarian cancer clustering region (OCCR), and 'other'.
  • Conducted simulations and evaluated the model on two large cohorts (CGN and CCGCRN), assessing calibration, discrimination, accuracy, PPV, NPV, sensitivity, and specificity.

Main Results:

  • The Fam3PRO-variant model demonstrated high calibration, discrimination, and accuracy in predicting region-specific BRCA1/2 carrier status.
  • The model remained robust against underreporting in family history data, providing more accurate region-specific predictions than gene-level models.
  • Clinical utility assessment showed high specificity and NPV at the region-specific level, comparable to the existing gene-level model.

Conclusions:

  • Mendelian risk prediction models can be effectively enhanced with PSV-specific penetrances, even with data limitations like underreporting.
  • The Fam3PRO-variant model provides more precise region-specific PSV carrier probabilities, improving cancer risk prediction and prevention strategies.
  • This approach offers a valuable tool for personalized cancer risk assessment and management.