README

As of version 2.1.0, the shiny app is integrated as part of the package. To run it locally, simply run the command

ibdsim2::launchApp()

Introduction

The purpose of ibdsim2 is to simulate and analyse the gene flow in pedigrees. In particular, such simulations can be used to study distributions of chromosomal segments shared identical-by-descent (IBD) by pedigree members. In each meiosis, the recombination process is simulated using sex specific recombination rates in the human genome (Halldorsson et al., 2019), or with recombination maps provided by the user. Additional features include calculation of realised relatedness coefficients, distribution plots of IBD segments, and estimation of two-locus relatedness coefficients.

ibdsim2 is part of the pedsuite collection of packages for pedigree analysis in R. A detailed presentation of these packages, including a separate chapter on ibdsim2, is available in the book Pedigree analysis in R (Vigeland, 2021).

Installation

install.packages("ibdsim2")

# install.packages("devtools") # if needed
devtools::install_github("magnusdv/ibdsim2")

Example 1: A simple simulation

The most important function in ibdsim2 is ibdsim(), which simulates the recombination process in a given pedigree. In this example we demonstrate this for in a family quartet, and show how to visualise the result.

library(ibdsim2)

The main input to ibdsim() is a pedigree and a recombination map. In our case we use pedtools::nuclearPed() to create the pedigree, and we load chromosome 1 of the built-in map of human recombination.

# Pedigree with two siblings
x = nuclearPed(2)

# Recombination map
chr1 = loadMap("decode19", chrom = 1)

sim = ibdsim(x, N = 1, map = chr1, seed = 1, verbose = F)

The output of ibdsim() is a matrix (or a list of matrices, if N > 1). Here are the first few rows of the simulation we just made:

head(sim)
#>      chrom   startMB     endMB   startCM     endCM 1:p 1:m 2:p 2:m 3:p 3:m 4:p 4:m
#> [1,]     1  1.431813  4.578604  0.000000  8.166722   1   2   3   4   1   3   2   3
#> [2,]     1  4.578604  9.307115  8.166722 17.248719   1   2   3   4   1   4   2   3
#> [3,]     1  9.307115 19.734735 17.248719 39.094436   1   2   3   4   2   4   2   3
#> [4,]     1 19.734735 22.758328 39.094436 43.760248   1   2   3   4   2   4   1   3
#> [5,]     1 22.758328 41.436476 43.760248 66.998786   1   2   3   4   2   4   1   4
#> [6,]     1 41.436476 61.366417 66.998786 86.491160   1   2   3   4   2   4   1   3

Each row of the matrix corresponds to a segment of the genome, and describes the allelic state of the pedigree in that segment. Each individual has two columns, one with the paternal allele (marked by the suffix “:p”) and one with the maternal (suffix “:m”). The founders (the parents in our case) are assigned alleles 1, 2, 3 and 4.

The function haploDraw() interprets the founder alleles as colours and draws the resulting haplotypes onto the pedigree. See ?haploDraw for an explanation of pos and other arguments.

haploDraw(x, sim, pos = c(2, 4, 2, 4))

Example 2: Distributions of IBD segments

In this example we will compare the distributions of counts/lengths of IBD segments between the following pairwise relationships:

Note that GR and HS have the same relatedness coefficients kappa = (1/2, 1/2, 0), meaning that they are genetically indistinguishable in the context of unlinked loci. In contrast, HU has kappa = (3/4, 1/4, 0).

For simplicity we create a pedigree containing all the three relationships we are interested in.

x = halfSibPed() |> addSon(5)
plot(x)

ids = list(GR = c(2,7), 
           HS = 4:5, 
           HU = c(4,7))

Next, we use ibdsim() to produce 500 simulations of the underlying IBD pattern in the entire pedigree.

s = ibdsim(x, N = 500, map = "decode19", seed = 1234)
#> Simulation parameters:
#> Simulations  : 500
#> Chromosomes  : 1-22
#> Genome length: 2753.93 Mb
#>                2602.29 cM (male)
#>                4180.42 cM (female)
#> Recomb model : chi
#> Target indivs: 1-7
#> Skip recomb  : -
#> Total time used: 2.94 secs

The plotSegmentDistribution() function, with the option type = "ibd1" analyses the IBD segments in each simulation, and makes a nice plot. Note that the names of the ids list are used in the legend.

plotSegmentDistribution(s, type = "ibd1", ids = ids, shape = 1:3)

We conclude that the three distributions are almost completely disjoint. Hence the three relationships can typically be distinguished on the basis of their IBD segments, if these can be determined accurately enough.