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  3. Minimizing Reference Bias With An Imputed Personalized Reference.
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Minimizing reference bias with an imputed personalized reference.

Kavya Vaddadi1, Mao-Jan Lin1, Sina Majidian1

  • 1Department of Computer Science, Whiting School of Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA.

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|March 5, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces an impute-first alignment framework for DNA sequencing, creating personalized diploid references to significantly reduce errors and improve variant-calling accuracy compared to traditional methods.

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Area of Science:

  • Genomics
  • Bioinformatics

Background:

  • Pangenome indexes minimize reference bias in sequencing data.
  • Personalized diploid references offer further bias reduction by matching donor alleles.

Purpose of the Study:

  • To present a novel impute-first alignment framework combining genotype imputation and read alignment.
  • To enhance variant-calling accuracy and reduce errors in whole-genome sequencing.

Main Methods:

  • Genotype individuals using a subsample of sequencing reads.
  • Impute a personalized diploid reference using a reference panel and imputation algorithm.
  • Index the personalized reference and align reads using linear or graph aligners.

Main Results:

  • Achieved a higher variant-calling F1 score (99.77%) on HG002 compared to existing methods.
  • Demonstrated substantial error reduction: 38.73% vs. linear, 14.97% vs. graph, and 6.05% vs. personalized graph aligners.
  • The impute-first approach produced accurate, phased diploid references even from low-coverage data.

    • Conclusions:

    • The impute-first framework provides an advantageous alternative to pangenome references for whole-genome sequencing.
    • This approach fully considers linkage disequilibrium and is compatible with both linear and graph reference representations.
    • Highest accuracy was achieved using a standard linear aligner (BWA-MEM).