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Famdenovo

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Overview
DescriptionEstimate the probability of a germline mutation to be de novo using family history data.
Development Information
GitHub wwylab/Famdenovo
LanguageR
Current version0.1.0
PlatformsPlatform independent
LicenseGPL v3
StatusActive
NewsA new version is now available
References
Citation Clinical investigation of de novo mutations in TP53 using Famdenovo. Gao F, et al.
Help and Support
Contact Wenyi Wang 
Discussion Issues On GitHub 

Famdenovo

Famdenovo is an algorithm that calculates the probability of a germline mutation to be de novo based on family history data. We demonstrate the utility of Famdenovo using germline TP53 mutations, which is a main cause for the Li-Fraumeni Syndrome , as de novo TP53 mutations account for 7%-20% of the LFS patients.1,2

Download

Source File: Famdenovo_0.1.1.tar.gz . The source file is compatible with windows, linux and mac os.

For the details about how to install and use Famdenovo, please check the Manual .

Installation

After downloading the R package, type the following command in the command line:

R CMD INSTALL Famdenovo_0.1.1.tar.gz

or, type the following command in R Console to install the package:

install.packages("Famdenovo_0.1.1.tar.gz", repos = NULL)

Or, you can install from GitHub:

library(devtools)
devtools:::install_github("wwylab/Famdenovo/Famdenovo_0.1.1")

Check here for details of R package installation.

Input Data Illustration

Famdenovo requires three data sets as input: family, cancer, mutation, person.id, mutation, and gene.

Family Information Data

The input should be a data frame. The family data should include the following columns with the corresponding column names:


Example: For the pedigree shown below, it can be stored in the pedigree information data file on its right:

Cancer Information Data

The input should be a data frame. The cancer data should include the following columns with the corresponding column names:


Example:

Mutation Information Data

The input should be a data frame. The mutation data should include the following columns with the corresponding column names:


Example:

Person ID

The input should be either character string(s) or numrical value(s) of the person(s) you want to analyze.

Gene

The input should be character string(s). The default value is “TP53”. We will add other genes in the future.

Package Illustration

Function “Famdenovo” calculates the probability of the de novo status for a germline mutation in familial diseases.

Famdenovo(family, cancer, mutation, person.id, gene = "TP53")

The following example briefly illustrates how to use the package.

library(Famdenovo)
# Test case 1
data(TP53.test1.family)
data(TP53.test1.cancer)
data(TP53.test1.mutation)
person.id <- c(2201, 2203, 3201, 3202, 4203)
output1 <- Famdenovo(TP53.test1.family, TP53.test1.cancer, person.id, TP53.test1.mutation); output1
# Test case 2
data(TP53.test2.family)
data(TP53.test2.cancer)
person.id <- c(1, 11, 12, 13)
output2 <- Famdenovo(TP53.test2.family, TP53.test2.cancer, person.id); output2
# Compare with known denovo/familial status
data(TP53.test2.counselees)
valid.person.id <- person.id[person.id %in% unique(TP53.test2.counselees$id)]
TP53.test2.counselees[TP53.test2.counselees$id == valid.person.id, c("id", "cou.state")]
merge(TP53.test2.counselees[TP53.test2.counselees$id == valid.person.id, c("id", "cou.state")], output2, all.x=T)

Output

Currently we apply Famdenovo to the cancer gene TP53. The output of Famdenovo is the probability of a TP53 mutation being de novo.

# Test case 1
[1] "The following ids are not carriers: 3202, 4203"
    id  prob.denovo
1 2201 0.0001205471
2 2203 0.0001105599
3 3201 0.0126455556
# Test case 2
[1] "Warning: Famdenovo output is only applicatble to mutation carriers"
  id prob.denovo
1  1 0.999994634
2 11 0.003633958
3 12 0.003774075
4 13 0.100231438
# Compare with known denovo/familial status
  id  cou.state prob.denovo
1  1    denovo  0.999994634
2 12  familial  0.003774075
3 13  familial  0.100231438


  1. Gonzalez KD, Buzin CH, Noltner KA, et al. High frequency of de novo mutations in Li-Fraumeni syndrome. J Med Genet 46:689-93, 2009. http://dx.doi.org/10.1136/jmg.2008.058958 [return]
  2. Peng G, Bojadzieva J, Ballinger ML, Thomas DM, Strong LC, Wang W., Estimating TP53 Mutation Carrier Probability in Families with Li-Fraumeni Syndrome Using LFSPRO. Cancer Epidemiology, Biomarker and Prevention Jan 2017. https://doi.org/10.1158/1055-9965.EPI-16-0695 [return]