lfsproC {LFSPRO} | R Documentation |
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.
lfsproC(fam.cancer.data, penetrance.all, counselee.id, allef, nloci, mRate)
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. |
The TP53 mutation carrier probability for each counselee.
Gang Peng, Wenyi Wang
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.
LFSClassic
, LFSChompret
, lfspro
, peelingRC
# 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)