ROHMM-A flexible hidden Markov model framework to detect runs of homozygosity from genotyping data

Runs of long homozygous (ROH) stretches are considered to be the result of consanguinity and usually contain recessive deleterious disease-causing mutations. 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 a command line and a graphical user interface. We tested ROHMM on simulated data as well as real population data from the 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.

Automated summary

A new strategy was developed in this study to detect Runs of long homozygous (ROH) by examining the X chromosome non-pseudoautosomal region, utilizing genotype probabilities and the hidden Markov model algorithm. Testing on simulated and real population data demonstrated that this method, named ROHMM, performed robustly and with high accuracy, surpassing its natural competitors.