KGWAS: novel genetics discovery enabled by massive functional genomics knowledge graph

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Double-stranded

Mike Kaz

14 episodes

6 days ago

Exploring the fascinating world of genetics and its impact on human health! We break down complex science topics like DNA, genome wide association studies, CRISPR gene editing, and epigenetics into accessible conversations. Join us for weekly episodes covering cutting-edge biology research and how your genome shapes your health. Subscribe for the latest genetic discoveries explained simply!

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KGWAS: novel genetics discovery enabled by massive functional genomics knowledge graph

Double-stranded

59 minutes 47 seconds

1 month ago

KGWAS: novel genetics discovery enabled by massive functional genomics knowledge graph

KGWAS is a novel geometric deep learning method that leverages a massive functional knowledge graph across variants and genes to significantly improve detection power in small-cohort GWASs. Thank you, Kexin Huang and Martin Jinye Zhang, for joining me on this one.Preprint:https://www.medrxiv.org/content/10.11...GitHub Page:https://github.com/snap-stanford/KGWASOfficial Website:https://kgwas.stanford.edu/

📖 Video Chapters:0:00 - Introduction to Rare Disease Genetics Problem0:54 - Welcome & Paper Overview1:22 - What is KGWAS? The Elevator Pitch2:41 - Why Finding More GWAS Hits Matters4:46 - Origin Story: Collaboration with GSK6:09 - Knowledge Graph Architecture Overview7:36 - Building the Variant-Gene-Pathway Network9:55 - Handling Linkage Disequilibrium (LD) Challenges13:55 - Training the Large-Scale Graph Neural Network14:16 - Model Training Requirements and Scalability15:22 - Open Source Availability and Accessibility16:05 - Scaling to Whole Genome Analysis17:12 - Figure 2: Validation Through Simulations18:24 - UK Biobank Downsampling Experiments20:04 - Precision-Recall Performance Results21:00 - Figure 3: Real-World Disease Applications21:38 - Ulcerative Colitis Case Study Example22:48 - Experimental Validation of Ulcerative Colitis Discovery24:03 - Myasthenia Gravis Case Study Analysis26:10 - Knowledge Graph Component Analysis and Ablation Studies27:54 - Tissue-Specific vs Context-Agnostic Approaches32:03 - Figure 4: Network Interpretation and Attention Mechanisms36:30 - Alzheimer's Disease Network Visualization40:01 - Drug Development Applications and Implications41:15 - Post-GWAS Era Applications and End-to-End Solutions43:01 - Figure 5: Compatibility with Existing GWAS Tools48:16 - Population Diversity and Cross-Ancestry Applications49:41 - Future Directions and Technical Improvements52:16 - Input/Output Requirements and Computational Resources54:28 - Website Demo and Interactive Features56:59 - PhD Student Advice and Research Philosophy59:36 - Closing Remarks