A fast data-driven method for genotype imputation, phasing and local ancestry inference: MendelImpute.jl.

ABSTRACT:

Motivation

Current methods for genotype imputation and phasing exploit the volume of data in haplotype reference panels and rely on hidden Markov models (HMMs). Existing programs all have essentially the same imputation accuracy, are computationally intensive and generally require prephasing the typed markers.

Results

We introduce a novel data-mining method for genotype imputation and phasing that substitutes highly efficient linear algebra routines for HMM calculations. This strategy, embodied in our Julia program MendelImpute.jl, avoids explicit assumptions about recombination and population structure while delivering similar prediction accuracy, better memory usage and an order of magnitude or better run-times compared to the fastest competing method. MendelImpute operate

SUBMITTER: Chu BB 

PROVIDER: S-EPMC8665755 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature