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The goal of ibdfindr is to detect genomic regions shared identical by descent (IBD) between two individuals, using SNP genotypes. It does so by fitting a continuous-time hidden Markov model (HMM) to the data. The current model has only two states, non-IBD and IBD1, and is therefore primarily intended for unilineal relationships.
ibdfindr was introduced in Vigeland et al. (2026), where it was applied to identify a victim of the 1956 Marcinelle mining disaster in Belgium.
To get the latest official release of ibdfindr, install from CRAN as follows:
install.packages("ibdfindr")For the development version, install from GitHub:
remotes::install_github("magnusdv/ibdfindr")library(ibdfindr)As an example we consider the built-in dataset
cousinsDemo, which contains almost 4000 SNP genotypes for
two related individuals. The data were simulated assuming a relationship
of first cousins. (For details on how the data was generated, the source
code is available in the repository’s folder data-raw.)
head(cousinsDemo)#> CHROM MARKER MB CM A1 A2 FREQ1 ID1 ID2
#> 1 1 rs9442372 1.083324 0.0000000 A G 0.3890 A/G G/G
#> 2 1 rs4648727 1.844830 0.1803812 A C 0.3628 A/C A/C
#> 3 1 rs10910082 2.487496 1.2670505 C T 0.3740 C/T C/T
#> 4 1 rs6695131 3.084360 1.9782286 C T 0.5719 C/T C/T
#> 5 1 rs3765703 3.675872 4.8403127 G T 0.4058 G/G T/T
#> 6 1 rs7367066 3.887191 5.7748481 C T 0.6739 C/C C/CThe function findIBD() conveniently wraps the key steps
of the package:
fitHMM())findSegments())ibdPosteriors())ibd = findIBD(cousinsDemo)
#> Individuals: ID1, ID2
#> Chromosome type: autosomal
#> Markers: 3915 input, 0 removed, 3915 used
#> Fitting HMM parameters...
#> Optimising `k1` and `a` jointly; method: L-BFGS-B
#> k1 = 0.237, a = 7.148
#> Finding IBD segments...
#> 14 segments (total length: 597.21 cM)
#> Calculating IBD posteriors...
#> Analysis complete in 1.09 secsFor details of the different steps, see the documentation of the
individual functions: fitHMM(),
findSegments(), and ibdPosteriors().
To visualise the results we pass the output to
plotIBD(). This plots the posterior probabilities on a
background (grey points) showing the identity-by-state (IBS) status at
each marker, i.e. whether the individuals have 0, 1 or 2 alleles in
common. Inferred IBD regions are shown as red segments at the bottom of
each chromosome panel.
plotIBD(ibd)
We may also inspect the identified segments as a data frame, showing the start and end positions, and the number of markers in each segment:
ibd$segments
#> chrom startCM endCM n
#> 1 1 113.000492 153.08623 54
#> 2 1 186.630319 223.39542 46
#> 3 2 67.118524 108.35905 50
#> 4 2 156.120361 227.62362 90
#> 5 4 59.474215 89.85522 41
#> 6 4 106.931772 131.69280 35
#> 7 6 102.041153 136.17734 43
#> 8 7 118.702115 149.11283 38
#> 9 8 72.976430 106.82971 55
#> 10 13 72.049460 127.22724 63
#> 11 14 1.808552 58.07117 67
#> 12 14 73.201009 116.00254 49
#> 13 18 10.592411 83.28443 101
#> 14 19 72.014020 99.15558 34The brothersX dataset contains genotypes for two
brothers typed with 246 X-chromosomal SNPs. The analysis below indicates
that they share 3 IBD segments on the X chromosome.
ibdX = findIBD(brothersX)
#> Individuals: ID1, ID2
#> Chromosome type: X (male/male)
#> Markers: 246 input, 0 removed, 246 used
#> Fitting HMM parameters...
#> Optimising `k1` and `a` jointly; method: L-BFGS-B
#> k1 = 0.502, a = 6.711
#> Finding IBD segments...
#> 3 segments (total length: 88.05 cM)
#> Calculating IBD posteriors...
#> Analysis complete in 0.0866 secs
plotIBD(ibdX)
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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