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ROHMM -- A Flexible Hidden Markov Model Framework To Detect Runs of Homozygosity From Genotyping Data
  • Gökalp Çelik,
Gökalp Çelik
Ankara Yildirim Beyazit University

Corresponding Author:[email protected]

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Ankara University Faculty of Medicine
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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.
06 Sep 2021Submitted to Human Mutation
11 Sep 2021Submission Checks Completed
11 Sep 2021Assigned to Editor
14 Sep 2021Reviewer(s) Assigned
07 Oct 2021Review(s) Completed, Editorial Evaluation Pending
09 Oct 2021Editorial Decision: Revise Minor
27 Nov 20211st Revision Received
30 Nov 2021Submission Checks Completed
30 Nov 2021Assigned to Editor
30 Nov 2021Reviewer(s) Assigned
11 Dec 2021Review(s) Completed, Editorial Evaluation Pending
15 Dec 2021Editorial Decision: Accept
28 Dec 2021Published in Human Mutation. 10.1002/humu.24316