Evaluation of vicinity-based hidden Markov models for genotype imputation
DC Field | Value | Language |
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dc.contributor.author | Wang, Su | - |
dc.contributor.author | Kim, Miran | - |
dc.contributor.author | Jiang, Xiaoqian | - |
dc.contributor.author | Harmanci, Arif Ozgun | - |
dc.date.accessioned | 2022-10-25T07:44:57Z | - |
dc.date.available | 2022-10-25T07:44:57Z | - |
dc.date.created | 2022-10-06 | - |
dc.date.issued | 2022-08 | - |
dc.identifier.issn | 1471-2105 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/172593 | - |
dc.description.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. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | BMC | - |
dc.title | Evaluation of vicinity-based hidden Markov models for genotype imputation | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Miran | - |
dc.identifier.doi | 10.1186/s12859-022-04896-4 | - |
dc.identifier.scopusid | 2-s2.0-85136840420 | - |
dc.identifier.wosid | 000847359300001 | - |
dc.identifier.bibliographicCitation | BMC BIOINFORMATICS, v.23, no.1, pp.1 - 26 | - |
dc.relation.isPartOf | BMC BIOINFORMATICS | - |
dc.citation.title | BMC BIOINFORMATICS | - |
dc.citation.volume | 23 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 1 | - |
dc.citation.endPage | 26 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Biochemistry & Molecular Biology | - |
dc.relation.journalResearchArea | Biotechnology & Applied Microbiology | - |
dc.relation.journalResearchArea | Mathematical & Computational Biology | - |
dc.relation.journalWebOfScienceCategory | Biochemical Research Methods | - |
dc.relation.journalWebOfScienceCategory | Biotechnology & Applied Microbiology | - |
dc.relation.journalWebOfScienceCategory | Mathematical & Computational Biology | - |
dc.subject.keywordPlus | LINKAGE DISEQUILIBRIUM | - |
dc.subject.keywordPlus | GENOME-WIDE | - |
dc.subject.keywordPlus | IDENTITY | - |
dc.subject.keywordPlus | DESCENT | - |
dc.subject.keywordPlus | SCALE | - |
dc.subject.keywordPlus | ASSOCIATION | - |
dc.subject.keywordPlus | VARIANTS | - |
dc.subject.keywordPlus | ANCESTRY | - |
dc.subject.keywordPlus | ACCURACY | - |
dc.subject.keywordPlus | LOCI | - |
dc.subject.keywordAuthor | Genotype imputation | - |
dc.subject.keywordAuthor | Hidden Markov models | - |
dc.subject.keywordAuthor | Forward-Backward algorithm | - |
dc.subject.keywordAuthor | Viterbi algorithm | - |
dc.identifier.url | https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-022-04896-4 | - |
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