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

Benjamin B Chu1, Eric M Sobel1,2, Rory Wasiolek1

  • 1Department of Computational Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA.

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Summary

We developed MendelImpute.jl, a novel data-mining method for genotype imputation and phasing. This approach offers similar accuracy to existing methods but is significantly faster and more memory-efficient.

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Area of Science:

  • Genomics
  • Computational Biology
  • Bioinformatics

Background:

  • Current genotype imputation and phasing methods rely on Hidden Markov Models (HMMs).
  • These HMM-based methods are computationally intensive and often require prephasing of typed markers.
  • Existing programs exhibit similar imputation accuracy, highlighting a need for more efficient approaches.

Purpose of the Study:

  • Introduce a novel data-mining method for genotype imputation and phasing.
  • Develop a computationally efficient alternative to HMM-based methods.
  • Improve memory usage and run-time performance for genotype data analysis.

Main Methods:

  • Utilize highly efficient linear algebra routines, replacing traditional HMM calculations.
  • Implement the method in a Julia program named MendelImpute.jl.
  • Process both dosage and unphased genotype data simultaneously.

Main Results:

  • Achieve similar prediction accuracy to existing methods.
  • Demonstrate superior memory usage and an order of magnitude faster run-times.
  • Perform simultaneous imputation of missing genotypes and phasing of typed and untyped SNPs.
  • Extendable to global and local ancestry estimation and data compression.

Conclusions:

  • MendelImpute.jl offers a significant advancement in genotype imputation and phasing efficiency.
  • The method provides a faster and more memory-efficient alternative for genomic data analysis.
  • Potential applications include improved ancestry estimation and data handling strategies.