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Evaluation of vicinity-based hidden Markov models for genotype imputation.

Su Wang1, Miran Kim2, Xiaoqian Jiang3

  • 1Center for Precision Health, School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, 77030, USA.

BMC Bioinformatics
|August 29, 2022
PubMed
Summary

Vicinity-based hidden Markov models (HMMs) offer an accurate and cost-effective method for genotype imputation. These locality-based approaches efficiently estimate untyped genetic variants using nearby typed markers, enhancing genetic studies.

Keywords:
Forward–Backward algorithmGenotype imputationHidden Markov modelsViterbi algorithm

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Decreasing DNA sequencing costs increase genetic variation knowledge.
  • Whole-genome sequencing for large samples remains costly.
  • In-silico genotype imputation with genotyping-by-arrays is a cost-effective alternative.

Purpose of the Study:

  • Assess the accuracy of vicinity-based hidden Markov models (HMMs) for genotype imputation.
  • Evaluate the impact of HMM parameters on imputation accuracy.
  • Determine the effectiveness of locality-based imputation for common and uncommon variants.

Main Methods:

  • Utilized vicinity-based HMMs, imputing untyped variants using nearby typed variants within small windows (e.g., 1 centimorgan).
  • Employed a comprehensive benchmark set to assess imputation accuracy.
  • Analyzed the influence of various HMM parameters on imputation performance.

Main Results:

  • Vicinity-based HMMs demonstrate high accuracy in imputing both common and uncommon genetic variants.
  • Locality-based imputation, as implemented by vicinity-based HMMs, proves effective.
  • Identified optimal parameter settings for vicinity-based HMMs.

Conclusions:

  • Locality-based imputation models are effective for genotype imputation.
  • Identified parameter settings can guide future imputation method development.
  • Vicinity-based HMMs provide a framework for restructuring and parallelizing imputation methods.