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Efficient phasing and imputation of low-coverage sequencing data using large reference panels.

Simone Rubinacci1,2, Diogo M Ribeiro1,2, Robin J Hofmeister1,2

  • 1Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.

Nature Genetics
|January 8, 2021
PubMed
Summary
This summary is machine-generated.

We developed GLIMPSE, a cost-effective method for whole-genome sequencing imputation. This approach significantly improves accuracy and reduces computational costs, making large-scale genomic studies more accessible.

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Area of Science:

  • Genomics
  • Population Genetics
  • Bioinformatics

Background:

  • Low-coverage whole-genome sequencing (WGS) with imputation is a cost-effective genotyping strategy.
  • Current imputation methods are computationally intensive and do not effectively utilize large reference panels, limiting their competitiveness with SNP arrays.

Purpose of the Study:

  • To introduce GLIMPSE, a novel method for phasing and imputing low-coverage sequencing data using modern reference panels.
  • To demonstrate GLIMPSE's performance across various coverages and human populations.
  • To highlight the potential of low-coverage imputation for disease and population genetics studies.

Main Methods:

  • Development of the GLIMPSE algorithm for phasing and imputation of low-coverage WGS data.
  • Evaluation of GLIMPSE performance across different sequencing coverages and diverse human populations.
  • Comparison of GLIMPSE with existing imputation methods and SNP arrays.

Main Results:

  • GLIMPSE achieves imputation of a human genome for under US$1 in computational cost.
  • The method significantly outperforms existing imputation techniques in both accuracy and cost-effectiveness.
  • 1× coverage WGS with GLIMPSE enables effective gene expression association studies and surpasses dense SNP arrays in rare variant burden tests.

Conclusions:

  • GLIMPSE offers a computationally efficient and accurate solution for imputing low-coverage sequencing data.
  • This advancement has the potential to revolutionize the design and execution of large-scale genomic studies.
  • Low-coverage imputation is a viable and promising approach for future genetic research.