lfsproC {LFSPRO}R Documentation

Estimating TP53 mutation probability for families with Li-Fraumeni Syndrome for combined family and cancer data

Description

We use Mendelian risk prediction model to estimate the probability for the counselee as a TP53 mutation carrier on the basis of his/her family cancer history.

Usage

lfsproC(fam.cancer.data, penetrance.all, counselee.id, allef, nloci, mRate)

Arguments

fam.cancer.data

Combined family and cancer information data for ONE FAMILY ONLY. See fam.cancer.data for details.

penetrance.all

Penetrance data. See LFSpenet.2010 for details.

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 the samples you want to estimate.

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 suggeste to use list(c(1-maf,maf)). maf = 0.0001.

nloci

Number of loci/genes in the model. It is set to be 1 here.

mRate

Mutation rate. We suggest to use 5e-4.

Value

The TP53 mutation carrier probability for each counselee.

Author(s)

Gang Peng, Wenyi Wang

References

Peng, G., Bojadzieva, J., Ballinger, M., Thomas, D., Strong, L. and Wang, W. Estimating TP53 Mutation Carrier Probability in Families with Li-Fraumeni Syndrome Using LFSPRO. Submitted.

Chen, S., Wang, W., Broman, K. and Parmigiani, G. (2004) BayesMendel: An R Environment for Mendelian Risk Prediction. Statistical Application in Genetics and Molecular Biology, 3(1): Article 21.

See Also

LFSClassic, LFSChompret, lfspro, peelingRC

Examples

# convert cancer type to specific number and check the cancer type
num.cancer <- nrow(cancer.data)
cancer.type.num <- rep(-1, num.cancer)
for(i in 1:num.cancer){
  tmp <- LFSpro.cancer.type[cancer.data$cancer.type[i]]
  if(is.na(tmp)){
    print(paste("Cannot find cancer ", cancer.data$cancer.type[i], 
                  " in the LFSpro predefined cancer type", sep = ""))
    print("LFSpro predefined cancer types are: ")
    print(cancer.type.all)
    print("Please check the input cancer information data.")
      
    num.counselee <- nrow(counselee.id)
    pp <- rep(-1, num.counselee)
      
    rlt <- data.frame(cbind(counselee.id, pp),check.names = FALSE)
    colnames(rlt) <- c("fam.id", "id", "pp")
    return(rlt)
  }
  cancer.type.num[i] <- tmp
}
  
cancer.data$cancer.type <- cancer.type.num
  
allef <- allef.g <- list(c(0.9997,0.0003))
mRate.g <- 6e-05
fam.cancer.data <- CombineData(fam.data, cancer.data)
lfsproC(fam.cancer.data[[1]], LFSpenet.2010, 0, allef.g, 1,mRate.g)

[Package LFSPRO version 1.0.5 Index]