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Forensic pedigree analysis and relatedness inference. Part of the pedsuite ecosystem for pedigree analysis in R.

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# forrel <img src="man/figures/logo.png" align="right" height=140/>

[![CRAN
status](https://www.r-pkg.org/badges/version/forrel)](https://CRAN.R-project.org/package=forrel)
[![](https://cranlogs.r-pkg.org/badges/grand-total/forrel?color=yellow)](https://cran.r-project.org/package=forrel)
[![](https://cranlogs.r-pkg.org/badges/last-month/forrel?color=yellow)](https://cran.r-project.org/package=forrel)

## Introduction

The goal of **forrel** is to provide forensic pedigree computations and
relatedness inference from genetic marker data. The **forrel** package
is part of the **pedsuite**, a collection of R packages for pedigree
analysis.

The most important analyses currently supported by **forrel** are:

- Likelihood ratio (LR) computations for kinship testing
  - `quickLR()`
  - `kinshipLR()`
- Pairwise relatedness inference: Estimation of IBD coefficients (both
  $\kappa$ and $\Delta$) from marker data
  - `ibdEstimate()`
  - `ibdBootstrap()`
- Check and visualise relationships in pedigree data
  - `checkPairwise()`
- Simulation of marker genotypes, possibly conditional on known
  genotypes
  - `markerSim()`
  - `profileSim()`
  - `markerSimParametric()`
  - `profileSimParametric()`
- Power analysis for relationship testing
  - `LRpower()`
  - `exclusionPower()`
- Tailor-made functions for power analysis in missing person cases
  - `missingPersonPlot()`
  - `missingPersonEP()`
  - `missingPersonIP()`
  - `MPPsims()`
  - `powerPlot()`
- Predict DNA profiles for untyped pedigree members
  - `rankProfiles()`

#### Related pedsuite packages

- [**pedtools**](https://github.com/magnusdv/pedtools): Tools for
  pedigree data management.
- [**dvir**](https://github.com/magnusdv/dvir): Disaster victim
  identification.
- [**pedFamilias**](https://github.com/magnusdv/pedFamilias):
  Import/export files used by the Familias software.
- [**pedprobr**](https://github.com/magnusdv/pedprobr): Probability
  computations in pedigrees.
- [**pedmut**](https://github.com/magnusdv/pedmut): Mutation modelling
  for pedigree analysis.
- [**KLINK**](https://github.com/magnusdv/KLINK): Kinship analysis with
  linked markers.

## Installation

To get the current official version of **forrel**, install from CRAN as
follows:

``` r
install.packages("forrel")
```

Alternatively, you can obtain the latest development version from
GitHub:

``` r
# install.packages("remotes") # if needed
remotes::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.

``` r
library(forrel)
#> Loading required package: pedtools
```

**Create the pedigree**

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

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

**Marker simulation**

Now let us simulate the genotypes of 100 independent SNPs for all four
family members. 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.

``` r
x = markerSim(x, N = 100, alleles = 1:2, seed = 1234)
#> Unconditional simulation of 100 autosomal markers.
#> Individuals: 1, 2, bro1, bro2
#> Allele frequencies:
#>    1   2
#>  0.5 0.5
#> Mutation model: No 
#> 
#> Simulation finished.
#> Calls to `likelihood()`: 0.
#> Total time used: 0.07 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:

``` r
x
#>    id fid mid sex <1> <2> <3> <4> <5>
#>     1   *   *   1 1/2 1/2 1/1 2/2 2/2
#>     2   *   *   2 1/1 1/2 1/1 1/1 2/2
#>  bro1   1   2   1 1/1 1/2 1/1 1/2 2/2
#>  bro2   1   2   1 1/1 1/2 1/1 1/2 2/2
#> Only 5 (out of 100) markers are shown.
```

**Conditional simulation**

Suppose one of the brothers is homozygous 1/1 and that we want to
simulate genotypes for the other brother. This is achieved with the
following code, where after first attaching a marker to the pedigree,
specifying the known genotype, we condition on it by referencing it in
`markerSim()`.

``` r
y = nuclearPed(children = c("bro1", "bro2")) |> 
  addMarker(bro1 = "1/1", alleles = 1:2, name = "snp1") |> 
  markerSim(N = 100, ids = "bro2", partialmarker = "snp1", 
            seed = 321, verbose = FALSE)
y
#>    id fid mid sex <1> <2> <3> <4> <5>
#>     1   *   *   1 -/- -/- -/- -/- -/-
#>     2   *   *   2 -/- -/- -/- -/- -/-
#>  bro1   1   2   1 1/1 1/1 1/1 1/1 1/1
#>  bro2   1   2   1 2/2 1/2 1/1 1/1 1/1
#> Only 5 (out of 100) markers are shown.
```

Note that the previous code also demonstrates how **pedsuite** is well
adapted to the R pipe `|>`.

**Estimation of IBD coefficients**

The `ibdEstimate()` function estimates the coefficients of
*identity-by-descent* (IBD) between pairs of individuals, from the
available marker data. Let us try with the simulated genotypes we just
generated:

``` r
k = ibdEstimate(y, ids = c("bro1", "bro2"))
#> Estimating 'kappa' coefficients
#> Initial search value: (0.333, 0.333, 0.333)
#> Pairs of individuals: 1
#>   bro1 vs. bro2: estimate = (0.28, 0.54, 0.18), iterations = 10
#> Total time: 0.00512 secs
k
#>    id1  id2   N      k0      k1      k2
#> 1 bro1 bro2 100 0.28001 0.53998 0.18001
```

To get a visual sense of the estimate, it is instructive to plot it in
the IBD triangle:

``` r
showInTriangle(k, labels = TRUE)
```

Reassuringly, the estimate is close to the theoretical expectation for
non-inbred full siblings,
$(\kappa_0, \kappa_1, \kappa_2) = (0.25, 0.5, 0.25)$, corresponding to
the point marked `S` in the triangle.

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