lfsproC.cs: Calculate the posterior probability of p53 mutations for...

In wwylab/LFSPRO: TP53 germline mutation carrier estimation and cancer risk predictions

Calculate the posterior probability of p53 mutations for cancer-specific risk prediction model on the basis of family history

Description

lfsproC.cs is used to calculate posterior probability of p53 mutation based on cancer-specific risk prediction model specifically integrated counselees' family cancer history.

Usage

  lfsproC.cs(family.data, ind.data, counselee.id, penetrance.all, allef, nloci, mRate, mut.info) 

Arguments

family.data	Family data that describe how the individuals are related, for ONE FAMILY ONLY.
ind.data	A comprehensive dataset that describes each individual in details, including age, gender (1 for male, 2 for female), mutation status (0 for wildtype, 1 for mutation, NA if unknown), vital status (A if alive, D if dead), and cancer history (cancer type and age at diagnosis). Both data1 and data2 can be generated from family and cancer datasets using combinedata and convert.data. See the example below.
counselee.id	Individual id for the counselee. If you want to estimate multiple samples at the same time, just set counselee.id as a vector of IDs for all.
penetrance.all	Penetrance data. See lfspenet.2010 for details.
allef	List. Allele frequency for each locus/gene. If there is only one gene and two alleles in the gene (allele frequency is 0.1 and 0.9), allef = list(c(0.1,0.9)), If there are two genes,two alleles (allele frequency is 0.1 and 0.9) for gene 1 and three alleles (allele frequncy is 0.2, 0.2 and 0.6) for gene 2, allef = list(c(0.1,0.9),c(0.2,0.2,0.6)). We set it as allef=list(c(0.9994,0.0006)) by default.
nloci	Number of loci/genes in the model. It's set to be 1 here.
mRate	Mutation rate. We set it as 0.00012 by default.
mut.info	Set to TRUE (default) if you want to use known genetic testing results of family members in the calculation of mutation probabilities. Set to FALSE otherwise.

Value

A data frame of posterior probabilities of having wild type, one allele mutated and two allels mutated in TP53 for each counselee.

Author(s)

Seung Jun Shin, Nam Nguyen, Wenyi Wang

References

Shin, S. J., Yuan, Y., Strong, L. C., Bojadzieva, J., and Wang, W. (2018). Bayesian Semiparametric Estimation of Cancer-Specific Age-at-Onset Penetrance With Application to Li-Fraumeni Syndrome. Journal of the American Statistical Association, 1–12. https://doi.org/10.1080/01621459.2018.1482749

See Also

lfsClassic, lfsChompret2009, lfsChompret2015, lfspro

Examples

fam.cancer.data <- combinedata(fam.data, cancer.data)
data.obj <- convert.data(fam.cancer.data)
data.obj1 <- data.obj[[1]]
data.obj2 <- data.obj[[2]]

allef <- list(c(0.9997,0.0003))
nloci <-1
mRate <- 6e-05
cid <- c(5,6,9)
lfsproC.cs(data.obj1[[1]], data.obj2[[1]], cid, lfspenet.cs, allef, nloci, mRate)

wwylab/LFSPRO documentation built on Feb. 1, 2023, 1:05 a.m.