# Simulation study from empirical data with penetrance

#### BayesMendel Lab

## Goal

Here we apply the penetrance package to simulated families where the data-generating penetrance function is known and based on existing penetrance estimates.

## Simulated Data

The data-generating distribution of the age-specific penetrances is based on existing penetrance estimates for Colorectal cancer in carriers of any pathogenic variant in MLH1 from the PanelPRO Database.

The families were simulated using the PedUtils Rpackage.

    dat <- test_fam2

## Simple simulation

Then we run the estimation using the default settings.

    # Set the random seed
    set.seed(2024)

    # Set the prior
    prior_params <- list(
        asymptote = list(g1 = 1, g2 = 1),
        threshold = list(min = 5, max = 30),
        median = list(m1 = 2, m2 = 2),
        first_quartile = list(q1 = 6, q2 = 3)
    )

    # Set the allele frequency for MLH1 based on PanelPRO Database
    prevMLH1 <- 0.0004453125

    # We use the default baseline (non-carrier) penetrance
    print(baseline_data_default)

    # We run the estimation procedure with one chain and 20k iterations
    out_sim <- penetrance(
        pedigree  = dat, twins = NULL, n_chains = 1, n_iter_per_chain = 20000,
        ncores = 2, baseline_data = baseline_data_default , prev = prevMLH1,
        prior_params = prior_params, burn_in = 0.1, median_max = TRUE,
        ageImputation = FALSE, removeProband = FALSE
    )

## References

Lee G, Liang JW, Zhang Q, Huang T, Choirat C, Parmigiani G, Braun D. Multi-syndrome, multi-gene risk modeling for individuals with a family history of cancer with the novel R package PanelPRO. Elife. 2021;10:e68699. doi:10.7554/eLife.6869