This week on The Genetics Podcast, Patrick is joined by Jonathan Marchini, Head of Statistical Genetics and Machine Learning at the Regeneron Genetics Center. They discuss Jonathan’s pioneering role in developing computational methods from the HapMap era through to today, how those innovations underpin large-scale imputation and analysis, and why exomes with imputation remain more powerful than whole genomes for discovery at Regeneron.
Show Notes:
0:00 Intro to The Genetics Podcast
00:59 Welcome to Jonathan
01:47 Jonathan’s career path from teaching in rural Tanzania to genomics research at Oxford
04:43 Lessons from the HapMap era and the birth of imputation
08:30 Ongoing challenges with data sharing and usable tools
10:30 Handling massive genetic datasets at Regeneron and developing new computational methods to scale
15:26 Key discoveries from the million-exome paper
18:04 Pushing computational limits in meta-analysis
19:50 Polygenic risk scores in the clinic and their role in trial design
23:38 Why Regeneron prioritizes exomes with imputation over whole genomes and what that means for discovery
27:58 Where AI truly adds value in genomics and where simpler models still win
32:27 Interpreting rare variants, the promise of protein models, and why better phenotyping is key
35:31 Closing remarks and opportunities at Regeneron
Find out more
Regeneron Genetics Center (https://www.regeneron.com/science/genetics-center)
Million exome paper (https://www.nature.com/articles/s41586-024-07556-0)
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