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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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Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...
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Pleiotropy is the phenomenon in which a single gene impacts multiple, seemingly unrelated phenotypic traits. For example, defects in the SOX10 gene cause Waardenburg Syndrome Type 4, or WS4, which can cause defects in pigmentation, hearing impairments, and an absence of intestinal contractions necessary for elimination. This diversity of phenotypes results from the expression pattern of SOX10 in early embryonic and fetal development. SOX10 is found in neural crest cells that form melanocytes,...
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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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The human genome is over 99.9% identical between individuals, yet genetic differences exist at millions of bases. The human genome contains approximately 3 million variant positions per individual, many of which are heterozygous, contributing to genetic diversity and individual traits. Genetic variations include single-nucleotide polymorphisms (SNPs), insertions, deletions, and copy number variations (CNVs).SNPs, the most common variation, involve single-base changes in DNA. These can be...
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Gregor Mendel's work (1822 - 1884) was primarily focused on pea plants. Through his initial experiments, he determined that every gene in a diploid cell has two variants called alleles inherited from each parent. He suggested that amongst these two alleles, one allele is dominant in character and the other recessive. The combination of alleles determines the phenotype of a gene in an organism.
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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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Integrating multi-omics data with DRIVE-KG advances understanding of endometriosis. This knowledge graph identifies novel genetic associations and improves patient classification, offering new avenues for women's health research.

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Area of Science:

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

Background:

  • Endometriosis is a prevalent yet understudied women's health condition affecting 10% of reproductive-age women.
  • Limited genetic characterization of endometriosis, with current GWAS explaining only 11% of heritability, necessitates integrative approaches.
  • Graph representations offer a powerful framework for harmonizing diverse biological data.

Purpose of the Study:

  • To present DRIVE-KG, a novel knowledge graph for integrating multi-omics data to study complex diseases like endometriosis.
  • To develop and evaluate machine learning models for disease risk inference and patient-level classification using DRIVE-KG.
  • To uncover novel single nucleotide polymorphism (SNP)-phenotype associations and improve diagnostic capabilities for endometriosis.

Main Methods:

  • Constructed a heterogeneous graph (DRIVE-KG) integrating diverse multi-omics datasets.
  • Trained a link prediction model to identify SNP-phenotype associations (endometriosis, obesity).
  • Developed a graph convolutional network (GCN) for patient-level classification of endometriosis/adenomyosis.

Main Results:

  • Identified 66 high-confidence candidate SNP-endometriosis associations, enriched for obesity and depressive disorder traits.
  • Discovered novel genetic signals for endometriosis, distinct from known obesity associations.
  • Achieved an F1 score of 0.752 for endometriosis/adenomyosis classification using GCN, outperforming genetic risk scores (0.698).
  • GCN demonstrated meaningful stratification of disease severity and adenomyosis signal.

Conclusions:

  • Heterogeneous integration of multi-omics data via DRIVE-KG is effective for discovery and clinical prediction in understudied diseases.
  • DRIVE-KG facilitates uncovering novel genetic insights into endometriosis etiology.
  • The developed GCN model shows potential for improving endometriosis/adenomyosis diagnosis and stratification.