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Area of Science:

  • Genetics
  • Epidemiology
  • Biostatistics

Background:

  • The BOADICEA model predicts breast cancer risk by assessing genetic susceptibility, including rare variants and a polygenic component (polygenic risk score, PRS).
  • The current BOADICEA version uses a 313 single nucleotide polymorphism (SNP) PRS.
  • Evaluating alternative PRS and their integration into BOADICEA is crucial for refining risk prediction.

Purpose of the Study:

  • To assess methods for incorporating the existing 313 SNP PRS and alternative PRS into the BOADICEA breast cancer risk model.
  • To compare different approaches for estimating key parameters related to the polygenic component within the BOADICEA framework.

Main Methods:

  • Employed logistic regression and a retrospective likelihood (RL) approach to estimate parameters, including the proportion of the polygenic component explained by the PRS (α2).
  • Constrained age-specific log-odds ratios (log-OR) as a function of age-dependent polygenic relative risk for logistic regression.
  • The RL approach additionally modeled the unmeasured polygenic component.

Main Results:

  • Evaluated 11 PRS, including variations of the 313 SNP PRS.
  • Logistic regression underestimated α compared to RL estimates.
  • RL estimates for α were consistent with proportionality to the odds ratio (OR) per 1 standard deviation (SD), with the constant estimated using the 313 SNP PRS.
  • Minor changes in PRS SNPs can significantly alter mean estimates.

Conclusions:

  • BOADICEA can be adapted to various PRS while maintaining model consistency.
  • The described methods enhance the comprehensiveness of breast cancer risk assessment.