pedprobr

Pedigree probabilities in R

https://github.com/magnusdv/pedprobr

Repository

Pedigree probabilities in R

README.Rmd

---
output: github_document
---



```{r, echo = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  fig.align = "center"
)
```

# pedprobr 


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



## Introduction

The main content of **pedprobr** is an implementation of the Elston-Stewart algorithm for pedigree likelihoods given marker genotypes. It is part of the [pedsuite](https://magnusdv.github.io/pedsuite/), a collection of packages for pedigree analysis in R.

The **pedprobr** package does much of the hard work in several other pedsuite packages:

* [**forrel**](https://github.com/magnusdv/forrel): relatedness analysis and forensic pedigree analysis
* [**dvir**](https://github.com/magnusdv/dvir): disaster victim identification
* [**KLINK**](https://github.com/magnusdv/KLINK): kinship testing with linked markers
* [**paramlink2**](https://github.com/magnusdv/paramlink2): parametric linkage analysis
* [**pedbuildr**](https://github.com/magnusdv/pedbuildr): pedigree reconstruction
* [**segregatr**](https://github.com/magnusdv/segregatr): medical segregation analysis


The workhorse of **pedprobr** is the `likelihood()` function, which supports a variety of situations:

* autosomal and X-linked markers
* a single marker or two linked markers
* complex inbred pedigrees
* pedigrees with inbred founders
* mutation models


## Installation
To get the current official version of **pedprobr**, install from CRAN as follows:
```{r, eval = FALSE}
install.packages("pedprobr")
```

Alternatively, get the latest development version from GitHub:
```{r, eval = FALSE}
# install.packages("devtools") # install devtools if needed
devtools::install_github("magnusdv/pedprobr")
```

## Getting started
```{r}
library(pedprobr)
```

To set up a simple example, we first use **pedtools** utilities to create a pedigree where two brothers are genotyped with a single SNP marker. The marker has alleles `a` and `b`, with frequencies 0.2 and 0.8 respectively, and both brothers are heterozygous `a/b`.
```{r pedplot, fig.height=2.7, fig.width=2.5}
# Pedigree with SNP marker
x = nuclearPed(nch = 2) |> 
  addMarker(geno = c(NA, NA, "a/b", "a/b"), afreq = c(a = 0.2, b = 0.8), name = "M1")

# Plot with genotypes
plot(x, marker = "M1")
```

The pedigree likelihood, i.e., the probability of the genotypes given the pedigree, is obtained as follows:
```{r}
likelihood(x)
```

## Genotype probability distributions
Besides `likelihood()`, other important functions in **pedprobr** are:

* `oneMarkerDistribution()`: the joint genotype distribution at a single marker, for any subset of pedigree members
* `twoMarkerDistribution()`: the joint genotype distribution at two linked markers, for a single person

In both cases, the distributions are computed conditionally on any known genotypes at the markers in question.

To illustrate `oneMarkerDistribution()` we continue our example from above, and consider the following question: **What is the joint genotype distribution of the parents, conditional on the genotypes of the children?** 

The answer is found as follows:
```{r}
oneMarkerDistribution(x, ids = 1:2, verbose = F)
```

The output confirms the intuitive result that the parents cannot both be homozygous for the same allele. The most likely combination is that one parent is heterozygous `a/b`, while the other is homozygous `b/b`.

The argument `output` controls how the output of `oneMarkerDistribution()` is formatted. Instead of the default matrix (or multidimensional array, if more than 2 individuals), we can also get the distribution in table format:
```{r}
oneMarkerDistribution(x, ids = 1:2, verbose = F, output = "table")
```
A third possibility is `output = "sparse"`, which gives a table similar to the above, but with only the rows with non-zero probability.

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