ROHMM -- A Flexible Hidden Markov Model Framework To Detect Runs of
Homozygosity From Genotyping Data
Runs of long homozygous stretches (ROH) are considered to be the result
of consanguinity and usually contain recessive deleterious disease
causing mutations (Szpiech et al., 2013). Several algorithms have been
developed to detect ROHs. Here, we developed a simple, alternative
strategy by examining X chromosome non-pseudoautosomal region to detect
the ROHs from next generation sequencing data utilizing the genotype
probabilities and the Hidden Markov Model algorithm as a tool, namely
ROHMM. It is implemented purely in java and contains both command-line
and a graphical user interface. We tested ROHMM on simulated data as
well as real population data from 1000G Project and a clinical sample.
Our results have shown that ROHMM can perform robustly producing highly
accurate homozygosity estimations under all conditions thereby meeting
and even exceeding the performance of its natural competitors.