Evaluation of vicinity-based hidden Markov models for genotype imputationopen access
- Authors
- Wang, Su; Kim, Miran; Jiang, Xiaoqian; Harmanci, Arif Ozgun
- Issue Date
- Aug-2022
- Publisher
- BMC
- Keywords
- Genotype imputation; Hidden Markov models; Forward-Backward algorithm; Viterbi algorithm
- Citation
- BMC BIOINFORMATICS, v.23, no.1, pp.1 - 26
- Indexed
- SCIE
SCOPUS
- Journal Title
- BMC BIOINFORMATICS
- Volume
- 23
- Number
- 1
- Start Page
- 1
- End Page
- 26
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/172593
- DOI
- 10.1186/s12859-022-04896-4
- ISSN
- 1471-2105
- Abstract
- Background: The decreasing cost of DNA sequencing has led to a great increase in our knowledge about genetic variation. While population-scale projects bring important insight into genotype-phenotype relationships, the cost of performing whole-genome sequencing on large samples is still prohibitive. In-silico genotype imputation coupled with genotyping-by-arrays is a cost-effective and accurate alternative for genotyping of common and uncommon variants. Imputation methods compare the genotypes of the typed variants with the large population-specific reference panels and estimate the genotypes of untyped variants by making use of the linkage disequilibrium patterns. Most accurate imputation methods are based on the Li-Stephens hidden Markov model, HMM, that treats the sequence of each chromosome as a mosaic of the haplotypes from the reference panel. Results: Here we assess the accuracy of vicinity-based HMMs, where each untyped variant is imputed using the typed variants in a small window around itself (as small as 1 centimorgan). Locality-based imputation is used recently by machine learning-based genotype imputation approaches. We assess how the parameters of the vicinity-based HMMs impact the imputation accuracy in a comprehensive set of benchmarks and show that vicinity-based HMMs can accurately impute common and uncommon variants. Conclusions: Our results indicate that locality-based imputation models can be effectively used for genotype imputation. The parameter settings that we identified can be used in future methods and vicinity-based HMMs can be used for re-structuring and parallelizing new imputation methods. The source code for the vicinity-based HMM implementations is publicly available at https://github.com/harmancilab/LoHaMMer.
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