An R package for the estimation of age-specific penetrance for complex family-based studies in a format compatible with with the PanelPRO R package.
Accurate estimation of age-specific penetrance is essential for assessing disease risk in individuals with pathogenic genetic variants (PGVs). Penetrance refers to the probability that an individual carrying a specific genetic variant will develop the associated trait, such as cancer. Estimating this probability is a crucial step in clinical decision-making and personalized risk assessment for hereditary (cancer) syndromes.
The package leverages Mendelian inheritance models, which are widely used in family-based genetic studies to assess how genetic variants are passed down through generations. These models typically involve a —an individual for whom family history and genetic data are collected. The proband serves as the starting point for mapping out the family’s genetic structure, including relationships and phenotypic traits, such as cancer diagnoses. Family data, including cancer occurrence, ages of diagnosis, and genetic test results, are collected for the proband and their relatives. Using this data, Mendelian models compute the likelihood of certain genetic configurations and disease outcomes based on inheritance patterns.
The core methodology in the package relies on a four-parameter Weibull distribution to model age-specific penetrance. Estimation is performed using a Bayesian framework with Markov Chain Monte Carlo (MCMC) methods, allowing the package to provide robust and flexible penetrance estimates. Through this approach, the package models the likelihood of cancer occurrence across family members, even when some genotypic information is missing or incomplete, which is common in real-world studies.
The package also incorporates prior knowledge into the estimation process, enabling users to specify default, custom, or study-based prior distributions. By employing the Elston-Stewart peeling algorithm, the package efficiently calculates likelihoods across family pedigrees, ensuring scalability and accuracy, even in large datasets.
By providing user-friendly functions for data input, prior specification, and estimation, the package equips researchers and clinicians with a powerful tool for estimating cancer risk in complex family-based studies. This empowers informed decision-making and preventive strategies in hereditary cancer syndromes, where understanding the genetic basis of risk is critical for patient care.
To install, use
git clone git@github.com:nicokubi/penetrance.gitOpen the source directory as new R project and install the package with
devtools::install()or directly in R studio
devtools::install_github("https://github.com/nicokubi/penetrance")This following is a quick-start guide for basic usage of the package. For greater detail on options, please refer to the other articles.
The primary function in the package is penetrance. The
package workflow includes three main parts: user input, including family
data in the form of pedigrees and specification for the penetrance
estimation, the estimation of the posterior distribution using the MCMC
approach, and the outputting of the results in the form of the samples
from the approximated posterior distribution, i.e. the estimated
penetrance function.
library(penetrance)The user must specify the pedigree argument as a data
frame that contains the family data (see test_family_1).
The family data must be in the correct format with the following
columns:
ID: A numeric value representing the unique
identifier for each individual. There should be no duplicated
entries.
Sex: A numeric value where 0 indicates
female and 1 indicates male. Unknown sex needs to be coded
as NA.
MotherID: A numeric value representing the unique
identifier for an individual’s mother.
FatherID: A numeric value representing the unique
identifier for an individual’s father.
isProband: A numeric value where 1
indicates the individual is a proband and 0
otherwise.
CurAge: A numeric value indicating the age of
censoring (current age if the person is alive or age at death if the
person is deceased). Allowed ages range from 1 to
94. Unknown ages can be left empty or coded as
NA.
isAff: A numeric value indicating the affection
status of cancer, with 1 for diagnosed individuals and
0 otherwise. Missing entries are not supported.
Age: A numeric value indicating the age of cancer
diagnosis, encoded as NA if the individual was not
diagnosed. Allowed ages range from 1 to 94.
Unknown ages can be left empty or coded as NA.
geno: A column for germline testing or tumor marker
testing results. Positive results should be coded as 1,
negative results as 0, and unknown results as
NA or left empty.
There are a few ways in which a user can specify how the estimation approach is run. Available options are:
#' @param pedigree A data frame containing the pedigree data in the required format. It should include the following columns:
#' - `PedigreeID`: A numeric value representing the unique identifier for each family. There should be no duplicated entries.
#' - `ID`: A numeric value representing the unique identifier for each individual. There should be no duplicated entries.
#' - `Sex`: A numeric value where `0` indicates female and `1` indicates male. Unknown sex needs to be coded as `NA`.
#' - `MotherID`: A numeric value representing the unique identifier for an individual's mother.
#' - `FatherID`: A numeric value representing the unique identifier for an individual's father.
#' - `isProband`: A numeric value where `1` indicates the individual is a proband and `0` otherwise.
#' - `CurAge`: A numeric value indicating the age of censoring (current age if the person is alive or age at death if the person is deceased). Allowed ages range from `1` to `94`. Unknown ages can be left empty or coded as `NA`.
#' - `isAff`: A numeric value indicating the affection status of cancer, with `1` for diagnosed individuals and `0` otherwise. Missing entries are not supported.
#' - `Age`: A numeric value indicating the age of cancer diagnosis, encoded as `NA` if the individual was not diagnosed. Allowed ages range from `1` to `94`. Unknown ages can be left empty or coded as `NA`.
#' - `geno`: A column for germline testing or tumor marker testing results. Positive results should be coded as `1`, negative results as `0`, and unknown results as `NA` or left empty.
#' @param twins A list specifying identical twins or triplets in the family. For example, to indicate that "ora024" and "ora027" are identical twins, and "aey063" and "aey064" are identical twins, use the following format: `twins <- list(c("ora024", "ora027"), c("aey063", "aey064"))`.
#' @param n_chains Integer, the number of chains for parallel computation. Default is 1.
#' @param n_iter_per_chain Integer, the number of iterations for each chain. Default is 10000.
#' @param ncores Integer, the number of cores for parallel computation. Default is 6.
#' @param baseline_data Data for the baseline risk estimates (probability of developing cancer), such as population-level risk from a cancer registry. Default data, for exemplary purposes, is for Colorectal cancer from the SEER database.
#' @param max_age Integer, the maximum age considered for analysis. Default is 94.
#' @param remove_proband Logical, indicating whether to remove probands from the analysis. Default is FALSE.
#' @param age_imputation Logical, indicating whether to perform age imputation. Default is FALSE.
#' @param median_max Logical, indicating whether to use the baseline median age or `max_age` as an upper bound for the median proposal. Default is TRUE.
#' @param BaselineNC Logical, indicating that the non-carrier penetrance is assumed to be the baseline penetrance. Default is TRUE.
#' @param var Numeric vector, variances for the proposal distribution in the Metropolis-Hastings algorithm. Default is `c(0.1, 0.1, 2, 2, 5, 5, 5, 5)`.
#' @param burn_in Numeric, the fraction of results to discard as burn-in (0 to 1). Default is 0 (no burn-in).
#' @param thinning_factor Integer, the factor by which to thin the results. Default is 1 (no thinning).
#' @param imp_interval Integer, the interval at which age imputation should be performed when age_imputation = TRUE.
#' @param distribution_data Data for generating prior distributions.
#' @param prev Numeric, prevalence of the carrier status. Default is 0.0001.
#' @param sample_size Optional numeric, sample size for distribution generation.
#' @param ratio Optional numeric, ratio parameter for distribution generation.
#' @param prior_params List, parameters for prior distributions.
#' @param risk_proportion Numeric, proportion of risk for distribution generation.
#' @param summary_stats Logical, indicating whether to include summary statistics in the output. Default is TRUE.
#' @param rejection_rates Logical, indicating whether to include rejection rates in the output. Default is TRUE.
#' @param density_plots Logical, indicating whether to include density plots in the output. Default is TRUE.
#' @param plot_trace Logical, indicating whether to include trace plots in the output. Default is TRUE.
#' @param penetrance_plot Logical, indicating whether to include penetrance plots in the output. Default is TRUE.
#' @param penetrance_plot_pdf Logical, indicating whether to include PDF plots in the output. Default is TRUE.
#' @param plot_loglikelihood Logical, indicating whether to include log-likelihood plots in the output. Default is TRUE.
#' @param plot_acf Logical, indicating whether to include autocorrelation function (ACF) plots for posterior samples. Default is TRUE.
#' @param probCI Numeric, probability level for credible intervals in penetrance plots. Must be between 0 and 1. Default is 0.95.
#' @param sex_specific Logical, indicating whether to use sex-specific parameters in the analysis. Default is TRUE.Penetrance provides the user with a flexible approach to prior specification, balancing customization with an easy-to-use workflow. In addition to providing default prior distributions, the package allows users to customize the priors by including existing penetrance estimates or prior knowledge. The following settings for the prior distribution specification are available:
The PenEstim function takes baseline age-specific
probabilitie of developing cancer as as input
baseline_data. In the default setting with
BaselineNC = TRUE this baseline is assumed to reflect the
non-carrier penetrance. For rare mutations this is considered a
reasonable assumption. The baseline_data must be a data frame with
baseline data for females and males.
Furthermore the specification of prevalence of the carrier status
(prev) is required. For rare autosomal dominant conditions,
prevalence = 2 * allele frequency.
The PenEsim also includes an option for automatic age imputation
AgeImputation. We apply an age imputation as part of the
MCMC routine. The imputation of ages is performed based on the
individual’s affected status (\(aff\)),
sex (\(sex\)), and their degree of
relationship to the proband who is a carrier of the PV. For greater
detail on the age imputation approach see documentation.
For the likelihood calculation monozygous twins can be specified
using the twins arguement.
twins <- list(c("ora024", "ora027"), c("aey063", "aey064"))