The Genetics Podcast
EP 160: Artificial Intelligence, GWAS in Drug Discovery, and Career Insights with Dr. Eric Fauman, Executive Director and Head of Computational Biology in the Internal Medicine Research Unit at Pfizer
November 7, 2024
In this episode, we welcome Dr. Eric Fauman, Executive Director and Head of Computational Biology in the Internal Medicine Research Unit at Pfizer. Eric and Patrick discuss facilitating efficient identification of potential drug targets and the role of artificial intelligence in genetics research and drug discovery. Please note that Eric has kindly shared some interesting research that was mentioned in the podcast. It is pasted at the end of the show notes.
0:00 Introduction

1:30 The power of social media: How Eric published 10 papers based on ideas that he discussed on Twitter

5:50 Explanation of The Table of Everything, an internal database at Pfizer that catalogs nearly 20,000 human genes and their associated diseases and traits

13:20 How Eric’s team works to correlate genome-wide association study (GWAS) results to real biological phenotypes and outcomes

18:10 Introduction to protein quantitative trait locus (PQTL), including its importance in biological and genetic data

25:10 Examining the evolving bottlenecks in drug development and the challenges of validating genetic targets 

28:30 Navigating the gap between genetic hits and biological understanding, and how AI or functional studies could bridge this in target discovery

32:20 Linus Pauling's mentorship of Eric and how he might react to AlphaFold2’s breakthroughs in structural biology

35:15 Eric's take on using AI and how he's experimenting with it on trusted datasets

41:00 An introduction to Mendelian randomization, as well as its strengths and limitations

47:00 How Eric uses the TOP Model (Talent, Opportunity, and Passion) to guide this career choices and path

52:00 Diversity and collaboration in genetics research and implementation

55:00 Closing remarks

Resources mentioned throughout the episode:
Mendelian Randomization with Proxy Biomarkers
Paper: Mendelian randomisation with proxy exposures: challenges and opportunities, I Rahu, R Tambets, EB Fauman, Kaur Alasoo (2024)
Explores proxy biomarkers as a method to assess in vivo activity of a protein target.

Trait Colocalization and Causal Genes
Paper: Integrative analysis of metabolite GWAS illuminates the molecular basis of pleiotropy and genetic correlation, CJ Smith, N Sinnott-Armstrong, A Cichońska, H Julkunen, EB Fauman, Jonathon Pritchard, Elife 11, e79348
Demonstrates how traits with opposing effects on a genetic variant may suggest a causal gene sits between them

Metabolite Profiling in Human Knockouts
Paper: McGregor TL, Hunt KA, Yee E, et al. Characterising a healthy adult with a rare HAO1 knockout to support a therapeutic strategy for primary hyperoxaluria, Elife. 2020;9. Published 2020 Mar 24.

Community Workshop on Effector Gene Standards
Presentation: Watch on YouTube

TOP Model for Career Guidance
Article: Grab the Helm: How to Take Charge of Your Purpose, Passion, Progress

The Table of Everything
Overview: Read more on Pfizer’s site

UK Biobank Protein QTL Study
Paper: Sun, B.B., Chiou, J., Traylor, M. et al. Plasma proteomic associations with genetics and health in the UK Biobank,Nature, 622, 329–338 (2023).

Eric’s First GWAS Contribution
Paper: Shin SY, Fauman EB, Petersen AK, et al. An atlas of genetic influences on human blood metabolites, Nat Genet.2014;46(6):543-550.

Every Gene Ever Annotated (EGEA)
Public Resource: View annotations on GitHub
 
 
Nine reasons not to use eQTLs to identify causal genes from GWAS:
Random Sequences Can Create Regulatory Elements
Enhancer Variants and Buffering in Important Genes
eQTL Data Limitations vs. Proximity Information