Empirical versus estimated accuracy of imputation: optimising filtering thresholds for sequence imputation.

ABSTRACT:

Background

Genotype imputation is a cost-effective method for obtaining sequence genotypes for downstream analyses such as genome-wide association studies (GWAS). However, low imputation accuracy can increase the risk of false positives, so it is important to pre-filter data or at least assess the potential limitations due to imputation accuracy. In this study, we benchmarked three different imputation programs (Beagle 5.2, Minimac4 and IMPUTE5) and compared the empirical accuracy of imputation with the software estimated accuracy of imputation (Rsq_soft). We also tested the accuracy of imputation in cattle for autosomal and X chromosomes, SNP and INDEL, when imputing from either low-density or high-density genotypes.

Results

The accuracy of imputing sequence varia

SUBMITTER: Nguyen TV 

PROVIDER: S-EPMC11566673 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature