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
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0:00 Introduction1:30 The power of social media: How Eric published 10 papers based on ideas that he discussed on Twitter5:50 Explanation of The Table of Everything, an internal database at Pfizer that catalogs nearly 20,000 human genes and their associated diseases and traits13:20 How Eric’s team works to correlate genome-wide association study (GWAS) results to real biological phenotypes and outcomes18:10 Introduction to protein quantitative trait locus (PQTL), including its importance in biological and genetic data25:10 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 discovery32:20 Linus Pauling's mentorship of Eric and how he might react to AlphaFold2’s breakthroughs in structural biology35:15 Eric's take on using AI and how he's experimenting with it on trusted datasets41:00 An introduction to Mendelian randomization, as well as its strengths and limitations47:00 How Eric uses the TOP Model (Talent, Opportunity, and Passion) to guide this career choices and path52:00 Diversity and collaboration in genetics research and implementation55:00 Closing remarksResources mentioned throughout the episode:Mendelian Randomization with Proxy BiomarkersPaper: 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 GenesPaper: 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 themMetabolite Profiling in Human KnockoutsPaper: 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 StandardsPresentation: Watch on YouTube)TOP Model for Career GuidanceArticle: Grab the Helm: How to Take Charge of Your Purpose, Passion, Progress)The Table of EverythingOverview: Read more on Pfizer’s site)UK Biobank Protein QTL StudyPaper: 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 ContributionPaper: 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

• “~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