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

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 eac

SUBMITTER: Wang S 

PROVIDER: S-EPMC9422108 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature