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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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Related Experiment Video

Updated: Sep 9, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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DRIVE-KG: Enhancing variant-phenotype association discovery in understudied complex diseases using heterogeneous

Ananya Rajagopalan1, Tram Anh Nguyen1, Lindsay A Guare1

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Summary
This summary is machine-generated.

We developed DRIVE-KG, a knowledge graph integrating multi-omics data to uncover novel SNP-endometriosis links and improve patient classification for this understudied women's health condition.

Keywords:
Complex DiseasesDiscoveryEndometriosisKnowledge GraphMulti-OmicsWomen’s Health

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Area of Science:

  • Genomics and Bioinformatics
  • Computational Biology
  • Women's Health Research

Background:

  • Endometriosis is a prevalent women's health condition affecting 10% of reproductive-aged women.
  • Limited genetic characterization of endometriosis exists, with current GWAS explaining only 11% of its heritability.
  • Multi-omics data integration is challenging due to sparsity and high dimensionality.

Purpose of the Study:

  • To present DRIVE-KG, a novel heterogeneous knowledge graph for integrating multi-omics data.
  • To identify novel single nucleotide polymorphism (SNP)-endometriosis associations.
  • To improve clinical prediction of endometriosis status using graph-based machine learning.

Main Methods:

  • Constructed a heterogeneous graph (DRIVE-KG) integrating data from dbSNP, NCBI Human Gene, Omics Pred, GTEx, and Open Targets.
  • Utilized a link prediction model to identify SNP-phenotype associations.
  • Employed a graph convolutional network (GCN) for patient-level endometriosis classification using data from 1,441 participants.

Main Results:

  • Uncovered 66 high-confidence, previously unreported SNP-endometriosis associations.
  • Identified links between novel variants and obesity, lipid metabolism, and depressive disorders, aligning with emerging endometriosis hypotheses.
  • Achieved an AUPRC of 0.738 for endometriosis classification using GCN, outperforming a genetic risk score (0.679).

Conclusions:

  • Heterogeneous multi-omics data integration via DRIVE-KG is valuable for discovery and clinical prediction in understudied diseases.
  • DRIVE-KG facilitates the identification of novel genetic associations for endometriosis.
  • Graph-based approaches offer improved clinical prediction for endometriosis compared to traditional methods.