A Fast Data-Driven Method for Genotype Imputation, Phasing, and Local Ancestry Inference: MendelImpute.jl

Abstract

Current methods for genotype imputation and phasing exploit the sheer volume of data in haplotype reference panels and rely on hidden Markov models. Existing programs all have essentially the same imputation accuracy, are computationally intensive, and generally require pre-phasing the typed markers. We propose a novel data-mining method for genotype imputation and phasing that substitutes highly efficient linear algebra routines for hidden Markov model 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 operates on both dosage data and unphased genotype data and simultaneously imputes missing genotypes and phase at both the typed and untyped SNPs. Finally, MendelImpute naturally extends to global and local ancestry estimation and lends itself to new strategies for data compression and hence faster data transport and sharing.

SNVstory: A dockerized algorithm for rapid and accurate inference of sub-continental ancestry

“…Taken together, these curated variant datasets enable an alternative class of models to be used to predict ancestry based upon samples labeled with known ancestry [18][19][20][21][22][23][24][25][26][27][28] . However, many methods suffer shortcomings, including not having discrete ancestry labels beyond the main continental groups or, for those methods using the 1kGP, not considering that many subjects are within the same families and, therefore, fail to satisfy the principle of independent and identically distributed data.…”