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
Listen on
Episode 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
Explores proxy biomarkers as a method to assess in vivo activity of a protein target.
Trait Colocalization and Causal Genes
Demonstrates how traits with opposing effects on a genetic variant may suggest a causal gene sits between them
Metabolite Profiling in Human Knockouts
Community Workshop on Effector Gene Standards
Presentation: Watch on YouTube
TOP Model for Career Guidance
The Table of Everything
Overview: Read more on Pfizer’s site
UK Biobank Protein QTL Study
Eric’s First GWAS Contribution
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
- “~83% of random promoter sequences yielded measurable expression” - de Boer CG, Nat Biotechnol, 2020
- “Recently evolved enhancers are formed predominantly by exaptation of ancestral DNA” - Villar D, Cell, 2015
- “Extensive co-regulation of neighboring genes complicates the use of eQTLs in target gene prioritization” - Tambets R, et al., HGG Adv., 2024
Enhancer Variants and Buffering in Important Genes
- “eQTLs at GWAS loci are more likely to point to genes with low enhancer redundancy not associated with disease” - Wang X, Goldstein DB, Am J Hum Genet., 2020
- “GWAS and eQTL studies are systematically biased toward different types of variants” - Mostafavi H, et al., Nat Genet., 2023
- “CNVs are buffered by post-transcriptional regulation in 23%-33% of proteins significantly enriched in protein complex members” - Gonçalves E, et al., Cell Systems, 2017
eQTL Data Limitations vs. Proximity Information
- “cis-eQTL target genes are relatively poor indicators of ‘true positive’ causal genes” - Stacey D, et al., NAR., 2018
- “When molecular QTL colocalization evidence was removed, we saw similar classification results” - Mountjoy E, et al., Nat Genet., 2021
- “Key predictive features included coding or transcript-altering SNVs, distance to gene, and open chromatin-based metrics” - Forgetta V, et al., Hum Genet., 2022