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


Çelik G., TUNCALI T.

Human Mutation, vol.43, no.2, pp.158-168, 2022 (Journal Indexed in SCI Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 43 Issue: 2
  • Publication Date: 2022
  • Doi Number: 10.1002/humu.24316
  • Title of Journal : Human Mutation
  • Page Numbers: pp.158-168

Abstract

© 2021 Wiley Periodicals LLCRuns 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.