Assessing Genotype Imputation Methods for Low-Coverage Sequencing Data in Populations With Differing Relatedness and Inbreeding Levels.

ABSTRACT: Low-coverage sequencing (LCS) followed by genotype imputation has become a cost-efficient approach for obtaining whole-genome SNPs. Several imputation methods for LCS data have been developed over the last decade. However, comparisons of their accuracy in inferring missing genotypes and their effectiveness for downstream analysis such as population genetics have not been comprehensively studied. In the present study, we assessed the imputation performance of five different tools: GLIMPSE2, GeneImp, QUILT2, STITCH and Beagle5.4, using populations simulated by SLiM4 that represent different levels of genetic relatedness and inbreeding. Imputation accuracy was calculated at the level of variant, haplotype and sample. The effectiveness of using imputed genotypes in recovering genetic structure

SUBMITTER: Vi T 

PROVIDER: S-EPMC12550480 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature