The Genetics Podcast
EP 238: Uncovering epistatic interactions in complex disease with machine learning with Bin Yu of UC Berkeley
Published: 7 May 2026 at 12:00 Europe/London
Listen on
Episode notes
This week on The Genetics Podcast, Patrick is joined by Dr. Bin Yu, Chancellor’s Distinguished Professor at UC Berkeley. They discuss how different statistical approaches, from linear models to random forests, can be used to study complex genetic traits, recent findings on epistasis in cardiomyopathy, and how improving robustness and reproducibility can lead to more reliable scientific conclusions.
Show Notes
0:00 Intro to The Genetics Podcast
01:00 Welcome to Bin
01:47 Linear models as the foundation of genetic analysis
05:34 Using random forests and stability to identify gene–gene interactions beyond linear models
11:05 How iterative feature weighting in random forests improves detection of gene interactions
13:10 Using GWAS to prioritize features in high-dimensional genetic data
15:06 Applying stable interaction models to hypertrophic cardiomyopathy in UK Biobank
20:47 Biological insights from gene–gene interactions in cardiomyopathy and evidence for indirect epistasis
23:25 Scaling discovery of epistatic interactions with better data and integrated experimental validation
27:21 The predictability, computability, and stability (PCS) framework for data science
30:06 How Bin’s early life during the Chinese Cultural Revolution shaped her
32:54 Balancing AI-driven productivity with human reasoning and scientific thinking
35:23 Developing the ability to read people through observation, listening, and real-world interaction
38:03 Closing remarks
Find out more:
