forrel

Introduction

The goal of forrel is to provide forensic pedigree computations and relatedness inference from genetic marker data. Some of its functionality is derived from paramlink, which is no longer actively developed. ‘forrel’ is part of the ped suite, a collection of packages for pedigree analysis with pedtools as the central package for handling pedigrees and marker data.

The most important analyses currently supported by forrel are:

• Likelihood ratios for relationship testing
• Simulation of marker genotypes and complete DNA profiles. Unconditional or conditional on known genotypes
• Pairwise relatedness inference: Estimation of IBD (kappa) coefficients from marker data
• Visualisation of IBD coefficients in the IBD triangle
• Power analysis for relationship testing: Exclusion power (EP) and inclusion power (IP)
• Tailor-made functions for power analysis in family reunion cases:
  • missingPersonPlot()
  • missingPersonEP()
  • missingPersonIP()
  • MPPsims()
  • powerPlot()
• Import of pedigree data and frequency databases from the Familias software.

Installation

To get the latest version, install from GitHub as follows:

 # First install devtools if needed
if(!require(devtools)) install.packages("devtools")

# Install forrel from GitHub
devtools::install_github("magnusdv/forrel")

An example

In this short introduction, we first demonstrate simulation of marker data for a pair of siblings. Then - pretending the relationship is unknown to us - we estimate the relatedness between the brothers using the simulated data. If all goes well, the estimate should be close to the expected value for siblings.

library(forrel)
#> Loading required package: pedtools

Create the pedigree

We start by creating and plotting a pedigree with two brothers, named bro1 and bro2.

bros = c("bro1", "bro2")
x = nuclearPed(children = bros)
plot(x)

Marker simulation

Now let us simulate the genotypes of 100 independent SNPs for the brothers. Each SNP has alleles 1 and 2, with equal frequencies by default. This is an example of unconditional simulation, since we don’t give any genotypes to condition on. Unconditional simulation is performed by simple gene dropping, i.e., by drawing random alleles independently for the parents, followed by a “Mendelian coin toss” in each parent-child transmission.

x = markerSim(x, N = 100, ids = bros, alleles = 1:2, seed = 1234)
#> Unconditional simulation of 100 autosomal markers.
#> Individuals: bro1, bro2
#> Allele frequencies:
#>    1   2
#>  0.5 0.5
#> Mutation model: No 
#> 
#> Simulation finished.
#> Number of calls to the likelihood function: 0.
#> Total time used: 0.12 seconds.

Note 1: The seed argument is passed onto the random number generator. If you use the same seed, you should get exactly the same results.
Note 2: To suppress the informative messages printed during simulation, add verbose = FALSE to the function call.

The pedigree x now has 100 markers attached to it. The genotypes of the first few markers are shown when printing x to the screen:

x
#>    id fid mid sex <1> <2> <3> <4> <5>
#>     1   *   *   1 -/- -/- -/- -/- -/-
#>     2   *   *   2 -/- -/- -/- -/- -/-
#>  bro1   1   2   1 1/1 1/2 1/1 2/1 2/2
#>  bro2   1   2   1 1/1 2/1 1/1 2/1 2/2
#> Only 5 (out of 100) markers are shown. See `?print.ped` for options.

Estimation of IBD coefficients

The IBDestimate() function estimates the coefficients of identity-by-descent (IBD) between pairs of individuals, from the available marker data.

k = IBDestimate(x, ids = bros)
k
#>    ID1  ID2   N        k0        k1        k2
#> 1 bro1 bro2 100 0.1484924 0.5513491 0.3001585

The theoretical expectation for non-inbred full siblings is (κ0, κ1, κ2) = (0.25, 0.5, 0.25). To get a visual sense of how close our estimate is, it is instructive to plot it in the IBD triangle:

showInTriangle(k, labels = TRUE)