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 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 43 Issue: 2
  • Publication Date: 2022
  • Doi Number: 10.1002/humu.24316
  • Journal Name: Human Mutation
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, BIOSIS, EMBASE, MEDLINE
  • Page Numbers: pp.158-168
  • Keywords: hidden Markov model, homozygosity mapping, population genetics, runs of homozygosity, whole-exome sequencing, whole-genome sequencing
  • Ankara Yıldırım Beyazıt University Affiliated: Yes

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.