Finnish? this mutation → brain cancer

The Duffy antigen gene (DARC) mutation and GATA1 transcription factor binding site explain how a single T-46C promoter variant causes the Duffy-negative blood type and natural resistance to Plasmodium vivax malaria.Paper discussed in the video:Disruption of a GATA motif in the Duffy gene promoter abolishes erythroid gene expression in Duffy–negative individualshttps://www.nature.com/articles/ng0695-224Tools I use to make these type of videos:https://e2g.stanford.edu/

Rs12936231, one tiny DNA switch on chromosome 17 raises your risk of asthma, type 1 diabetes, and 3 other autoimmune diseases at once. We trace the real biobank data live and reveal how a weakened CTCF boundary explains it all.Paper discussed in the video:Allele-Specific Chromatin Remodeling in the ZPBP2/GSDMB/ORMDL3 Locus Associated with the Risk of Asthma and Autoimmune Diseasehttps://doi.org/10.1016/j.ajhg.2009.08.007Tools I use to make these type of videos:https://platform.opentargets.org/https://e2g.stanford.edu/https://github.com/statgen/phewebhttps://smart.embl.de/smart/change_mode.cgi

A single DNA letter (rs55705857) makes Finns 6x more likely to get brain cancer. We trace it live from FinnGen PheWAS to AlphaFold to reveal how it hijacks the MYC oncogene.Paper discussed in the video:A noncoding single-nucleotide polymorphism at 8q24 drives IDH1-mutant glioma formation https://www.science.org/doi/full/10.1126/science.abj2890Tools I use to make these type of videos:https://platform.opentargets.org/https://e2g.stanford.edu/https://github.com/statgen/pheweb

In this deep dive, we break down the journey of the PIM Kinase target. We explore the complex PIM1 molecular mechanism, examine why first-generation pan-PIM inhibitors like AZD-1208 faced safety issues and a lack of monotherapy efficacy, and reveal the final clinical verdict of the Terminated and Completed Phase 1 studies.📖 Video Chapters:0:00 - Intro1:05 - Biobanks Associations for PIM122:18 - PIM1 Gene39:00 - PIM1 Protein52:52 - PIM1 Known Drugs and Clinical Trials

Disclaimer: This video is for educational purposes only and should not be used for medical diagnosis or treatment decisions. Always consult with healthcare professionals for medical advice.In this episode, we explore the association between the PEAR1 gene and various platelet disorders. Using data and research, we aim to explain the significance of platelet count and the effects of thrombocytopenia and low platelets in these conditions. This includes genetic analysis of the genome.#genetics #blood #platelets

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

This podcast was made possible by CIUT-FM 89.5, University of Toronto Community Radio. For more content visit https://ciut.fm/Link to the paper:https://www.nature.com/articles/s41586-025-08916-0Join me in a conversation with two of the authors, Lakshmi Mahendrawada and Steven Hahn.

This podcast was made possible by CIUT-FM 89.5, University of Toronto Community Radio. For more content, visit https://ciut.fm/Join us for this comprehensive masterclass episode as we explore the fascinating world of K562 cells with Dr. Tom Karagiannis, who has worked with these remarkable cells for almost three decades. From his first encounter with them as a young researcher in 1996 during the exciting era of targeted therapy development, Dr. Karagiannis shares both the scientific breakthroughs and practical realities of working with these "weeds" of the lab.📖 Video Chapters:0:00 - The 1917 Origin Story: A Woman's Legacy2:40 - Interview Begins: Dr. Tom Karagiannis on K562 Basics5:42 - The 1990s Gleevec Revolution: Why K562 Cells Became Essential7:10 - The Lozio Discovery: From Patient to Immortal Cell Line8:48 - What Makes K562 Cells Special: Continuous Growth vs Normal Cells10:36 - Cell Culture Evolution: From Glass Pipettes to Modern Labs12:18 - Historical Context: HeLa Cells to Cancer Virus Research14:26 - The Philadelphia Chromosome: Understanding the Genetic Driver17:06 - Gleevec Revolution: The First Targeted Cancer Therapy19:48 - K562 Cell Genetics: The Near-Triploid Puzzle21:00 - Cell Differentiation: Research Applications and Advantages24:08 - Transferrin Receptor Research: Dr. Tom's Early Work28:37 - K562 Cells as "Weeds": Why They're So Easy to Grow33:08 - Evolution of Cancer Research: From Targeted Therapy to CAR-T35:42 - Personal Research Stories: Genetic Discoveries and Insights36:03 - Genetic Discoveries: Novel Chromosomes and Cellular Evolution42:27 - Drug Resistance Studies: Creating Doxorubicin-Resistant Cell Lines44:12 - Epigenetic Research: HDAC Inhibitors and Combination Therapies49:16 - Combination Therapy Mechanisms: How HDAC Inhibitors Enhance Chemotherapy50:40 - Drug Resistance Reality: Gleevec Limitations and Cancer Stem Cells53:54 - K562's Continued Value: Why Genetic Drift Doesn't Diminish Research Utility57:27 - PhD Advice

Dr. Andrew Paterson is a Senior Scientist in Genetics & Genome Biology at SickKids and a Professor at the Dalla Lana School of Public Health, University of Toronto. His work focuses on uncovering the genetic basis of diabetes and its complications.In this episode, we discuss the real promise—and current limits—of GWAS in diabetes research, why the X chromosome is still often ignored, how datasets like GTEx and UK Biobank are shaping the future of precision medicine, and what functional follow-up really looks like. If you're curious about the path from variant to function to treatment, this one’s for you.📖 Video Chapters:0:00 - Highlights of the episode2:07 - Welcome to Dr. Paterson & What Diabetes Really Is3:11 - Monogenic, Type 1 & Type 2 — Mapping the Diabetes Family5:11 - Life Before Insulin: Why Early Type 1 Was Fatal6:46 - Where Each Diabetes Type Sits on the Genetic Spectrum8:02 - HLA Breakthroughs: Early Genetic Clues to Type 18:45 - Childhood & Ancestry Risk: Who Gets Diabetes?9:40 - Industrialization and the Type 2 Surge10:55 - Do the Stats Match Patients in Clinic?11:40 - Why Dr. Paterson Chose Diabetes & Genetics14:02 - Genetics vs Confounding: Tracing Causal Pathways 16:17 - How Geneticists Link Variants to Disease17:57 - 1 Billion SNPs & Linkage Disequilibrium 10118:45 - SNP Arrays: From $1,000 to $20 Per Genome20:07 - Whole-Genome Sequencing vs Genotyping Chips20:58 - Cleaning Genotype Data: QC Essentials21:49 - Regression Basics: Testing SNP–Trait Links22:57 - Blood or Saliva? Where GWAS DNA Comes From25:00 - The DCCT Trial & Its Landmark Design26:44 - Conventional vs Intensive Diabetes Therapy28:00 - C-Peptide: Residual Beta-Cell Function Insights30:28 - Genetics, Environment & the Polygenic Reality32:07 - Biobanks Power the Modern GWAS Boom33:43 - Mega-Datasets: UK Biobank & Million Veteran Program34:59 - Landmark 2010 Discovery: Gene for Glycemic Control36:40 - Mining DCCT’s Longitudinal A1C Data with GWAS38:44 - Diabetes-Specific Variant & DCCT’s Selective Cohort40:00 - Conventional vs Intensive Therapy in the DCCT41:50 - From Finger-Prick Meters to Continuous Glucose Monitors42:48 - Breakthrough #2: COL4A3 Variant That Protects Kidneys45:35 - Meta-GWAS Uncovers a Protective Missense Allele47:49 - COL4A3 Minor Allele: Reduced DKD Risk48:56 - RAST Trial & Renal Biopsy Insights49:52 - GBM Thickness Links Genetics to Pathology50:45 - Diabetes-Specific Gene-Environment Interaction52:12 - Type 2 Diabetes: Power & Confounders52:55 - Variant-to-Function Workflow: Starting the Hunt53:45 - Tools: GWAS Catalog & GTEx for Functional Clues55:39 - GTEx & eQTLs — Turning GWAS Hits into Gene-Expression Clues56:46 - pQTL Goldmines: UK Biobank, deCODE & Plasma Proteomics57:36 - From Association to Biology: How Long Does Functional Follow-Up Take?58:49 - Modeling COL4A3 in Diabetic Mice: A Roadmap for Mechanistic Proof60:05 - Genetics at the Top of the Drug-Discovery Funnel61:01 - Why Genetic Targets Double a Drug’s Odds of Success62:35 - Stop Skipping the Sex Chromosomes! X & Y in GWAS Explained1:03:35 - Cracking the X-Chromosome Problem1:04:30 - Imputation & Meta-Analysis Fixes1:05:59 - Quality Concerns: Why Researchers Hesitate1:06:55 - Polygenic Scores Need X-Linked Signals1:07:33 - Is the Field Getting Better?1:08:30 - Publishing Standards & Acronym Debates1:09:12 - So… What Does This Mean for the Average Person?1:10:53 - Precision Screening: Genetics Guides Cancer Checks1:11:28 - From Promise to Practice: Genes in Everyday Care1:12:03 - Career Wisdom for Aspiring Researchers

If you’ve ever analyzed sequencing data, chances are you’ve used tools developed by Phil Ewels—whether you knew it or not. He’s the creator of MultiQC, a key developer behind Nextflow and nf-core, and a major advocate for making bioinformatics workflows scalable, reproducible, and user-friendly.In this episode, we dive into:🔬 How MultiQC became the standard for bioinformatics quality control⚙️ The philosophy behind nf-core and the trade-offs of workflow standardization🤖 The role of AI in bioinformatics—will machines build pipelines for us?🌍 The future of open-source collaboration and making workflows more accessibleIf you’ve ever struggled with workflow management, questioned reproducibility, or wondered where bioinformatics is heading, this is the episode for you.🎧 Tune in now to hear my conversation with Phil Ewels.🤝 Connect with Phil here: https://phil.ewels.co.uk/🐦 Tell me what you think of the episode on X: https://x.com/Mike__Kazemi