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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Interpretable Clinical Genomics with a Likelihood Ratio Paradigm.

Peter N Robinson1, Vida Ravanmehr2, Julius O B Jacobsen3

  • 1The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Institute for Systems Genomics, University of Connecticut, Farmington, CT 06032, USA.

American Journal of Human Genetics
|August 7, 2020
PubMed
Summary

LIRICAL enhances rare disease genomic diagnostics by providing robust likelihood ratio estimates for diagnoses and phenotypes. This approach improves diagnostic accuracy and interpretability, aiding clinicians in identifying genetic causes of rare diseases.

Keywords:
Human Phenotype Ontologyexome sequencinggenome sequencingliklihood ratiophenotype-driven genomic diagnostics

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Area of Science:

  • Genomics
  • Medical Genetics
  • Bioinformatics

Background:

  • Human Phenotype Ontology (HPO) analysis is standard for rare disease genomic diagnostics.
  • Current methods lack robust prediction strength estimates and phenotype contribution measures.
  • Genomic diagnostic success rates remain limited (25%-50%), necessitating improved approaches.

Purpose of the Study:

  • To develop a novel genomic diagnostic approach using the likelihood ratio (LR) framework.
  • To provide estimates for posttest probability of diagnoses and LR for HPO phenotypes.
  • To assess the predicted pathogenicity of observed genotypes.

Main Methods:

  • Implemented the Likelihood Ratio Interpretation of Clinical AbnormaLities (LIRICAL) framework.
  • Utilized LR to quantify diagnostic predictions and phenotype contributions.
  • Evaluated LIRICAL on 384 case reports covering 262 Mendelian diseases.

Main Results:

  • LIRICAL achieved 92.9% accuracy in placing the correct diagnosis within the top three ranks.
  • The mean posttest probability for correct diagnoses was 67.3%.
  • Simulations demonstrated LIRICAL's robustness against genomic and phenomic noise.

Conclusions:

  • LIRICAL offers accurate and clinically interpretable results for phenotype-driven genomic diagnostics.
  • The LR framework provides enhanced confidence in diagnostic predictions.
  • This method improves the utility of HPO-based analysis in rare disease diagnosis.