The program hmmIBD implements a hidden Markov model (HMM) for detecting genomic regions that are identical by descent (IBD) for pairs of haploid samples. It was written to find large IBD regions in sequenced haploid P. falciparum genomes, but it can be applied to other organisms (including phased diploids) and can find shorter IBD regions as well. The program takes as input a file of genotype calls for a set of samples, assumed to be from a single population. As of version 2.0.0, the program will also accept a second file of genotype calls, which are treated as coming from a different population with different allele frequencies. For a single population, all pairwise comparisons are made between the samples (unless otherwise specified with a -b or -g flag.) For two populations, all comparisons are made between samples from different populations.

The details of the model and program are described in a manuscript in preparation.

Under the HMM, each variant site is assumed to be in one of two hidden states, IBD or not-IBD. To calculate the probability of each state, estimates of the allele frequencies for every variant are required. By default, they are calculated from the input data, but a separate file of allele frequencies can be supplied by the user (preferable if analyzing a subset of the data).

The model has two free parameters, (1) the fraction of the genome that is IBD, and (2) the number of generations during which recombination has been breaking down IBD blocks. (Note: the former is generally estimated more accurately than the latter, and is relatively robust to the latter.) The program fits for optimal values of these parameters by an iterative estimation-maximization procedure. Iterations of the fit are capped at a user-settable maximum (default = 5). To accurately determine the IBD fraction for large shared chromosome segments, only a few iterations are needed, while for smaller, older blocks of IBD, the fit may continue to improve for 15 or more iterations.

The Viterbi algorithm calculates the best single set of state assignments given data under the HMM and outputs that set. The forward-backward algorithm sums the fraction of the genome that is IBD over all possible state assignments given data under the HMM, weighting each by the probability of that set of states. If you are interested in the IBD fraction, rather than precisely which parts of the genome are IBD, this is probably the output you want (see fract_sites_IBD in the Output section).