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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
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Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
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geneEX: An Integrated Phenotype-Driven Algorithm for Rapid Identification of Causative Variants in Monogenic

Junyu Zhang1,2, Dongyun Liu3,4, Mei Chen5

  • 1Reproductive Medicine Center, Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China.

Molecular Genetics & Genomic Medicine
|September 22, 2025
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Summary

This study introduces geneEX, an algorithm that uses large language models to improve the accuracy and efficiency of identifying pathogenic variants in rare genetic disorders by analyzing clinical phenotypes. geneEX automates variant prioritization, aiding in the diagnosis of monogenic diseases.

Keywords:
phenotype‐drivenprioritization rankingrare disease diagnosis

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Accurate identification of pathogenic variants is critical for diagnosing monogenic genetic disorders.
  • Next-Generation Sequencing (NGS) has improved diagnostic efficiency but challenges remain in pinpointing causative variants due to data complexity.
  • Current diagnostic methods face limitations in speed and accuracy for complex genetic data analysis.

Purpose of the Study:

  • To develop an innovative phenotype-driven algorithm, geneEX, for enhanced identification of pathogenic variants in rare genetic disorders.
  • To improve the efficiency and accuracy of diagnosing monogenic diseases through automated data analysis and interpretation.
  • To leverage large language model technology for precise phenotype extraction and gene association.

Main Methods:

  • Developed geneEX, a phenotype-driven algorithm integrating large language model technology.
  • Implemented semantic vector representation for automatic Human Phenotype Ontology (HPO) acquisition and HPO-associated gene identification.
  • Enabled semantic matching between patient free-text phenotypes and disease phenotypes for improved pathogenic gene discovery.
  • Algorithm ranks candidate causative variants for rapid identification in rare genetic disorders.

Main Results:

  • geneEX demonstrated strong performance in ranking pathogenic variants on both virtual and clinical datasets.
  • Supplementary matching of free-text phenotypes significantly improved the precision of candidate variant prioritization.
  • The algorithm effectively automates the process from clinical samples to pathogenic variant identification.

Conclusions:

  • geneEX achieves automated HPO acquisition and full-process identification of pathogenic variants.
  • Integrating free-text phenotypic descriptions enhances the accuracy of pathogenic gene identification.
  • This approach significantly boosts the precision and efficiency of identifying pathogenic variants in rare genetic disorders, supporting monogenic disease diagnosis.