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Related Concept Videos

Next-generation Sequencing03:00

Next-generation Sequencing

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The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features....
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Rare Event Detection Using Error-corrected DNA and RNA Sequencing
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Improving HLA typing imputation accuracy and eplet identification with local next-generation sequencing training

Romain Lhotte1,2,3, Véronique Letort2, Cédric Usureau1

  • 1Immunology and Histocompatibility Laboratory, Saint-Louis Hospital, Paris, France.

HLA
|April 8, 2024
PubMed
Summary

HaploSFHI accurately imputes high-resolution Human Leukocyte Antigen (HLA) typing from lower-resolution data, improving donor-recipient compatibility assessments. This method enhances cost-effectiveness in HLA typing by identifying optimal candidates for sequencing.

Keywords:
HLAepletshistocompatibilityimputationnext‐generation sequencing (NGS)

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Area of Science:

  • Immunogenetics
  • Computational Biology
  • Transplantation Science

Background:

  • Accurate Human Leukocyte Antigen (HLA) typing is crucial for donor-recipient compatibility, especially at the eplet level.
  • Second field HLA typings, required for eplet-level assessment, are not always available and imputation from lower-resolution data is necessary.
  • Existing computational tools for HLA imputation often rely on ambiguous data, limiting prediction accuracy.

Purpose of the Study:

  • To develop and validate a novel computational tool, HaploSFHI, for imputing second field HLA typings from low- or intermediate-resolution data.
  • To compare the performance of HaploSFHI against existing imputation tools using extensive datasets.
  • To provide guidance on optimizing HLA typing strategies for cost-effectiveness and improved incompatibility assessment.

Main Methods:

  • Gathered Next-Generation Sequencing (NGS) typing data from 61,393 individuals across 17 French laboratories for multiple HLA loci.
  • Developed HaploSFHI, a modified iterative maximum likelihood algorithm, to impute high-resolution HLA typings.
  • Validated HaploSFHI performance against HaploStats, HLA-EMMA, and HLA-Upgrade using French and independent European test sets.

Main Results:

  • HaploSFHI demonstrated superior accuracy in imputing second field HLA typings across all tested loci compared to reference tools.
  • HaploSFHI was the only tool capable of imputing DQA1, and it provided DRB3/4/5 imputations alongside HaploStats.
  • Analysis identified common imputation errors and haplotype variability, informing strategies for selecting individuals for confirmatory sequencing.

Conclusions:

  • HaploSFHI significantly improves the accuracy of HLA imputation, particularly for loci like DQA1 and DRB3/4/5.
  • The tool's performance is attributed to the use of local, non-ambiguous data, offering a reliable method for estimating high-resolution HLA typings.
  • HaploSFHI facilitates cost-effective HLA typing by guiding the selection of individuals for whom sequencing is most beneficial for incompatibility assessment.