In a recent episode of The Genetics Podcast, host Patrick Short sits down with Eric Fauman, Executive Director and Head of Computational Biology in Pfizer's Internal Medicine Research Unit. With over 26 years at Pfizer and a background in protein crystallography and genetics, Eric's work combines computational biology and genetics to drive drug discovery, particularly through the development of tools and datasets that bring genetic insights into the drug development process.
Making use of social media in science
Eric opens by sharing how his Twitter presence has facilitated unexpected and valuable collaborations, leading to over 10 co-authored publications. Through his popular “Who’s That Causal Gene?” series, he highlights causal genes identified through genetic studies, sparking conversation and partnerships across the scientific community. One example includes a collaboration on the HAO1 gene with David Von Heel, where a Twitter interaction led to a deeper investigation into HAO1's metabolic implications.
Simplifying genetic data for researchers
Another key topic in the conversation is Eric's brainchild, the “Table of Everything,” a comprehensive internal tool at Pfizer that consolidates human genetic data into a single, accessible format. Initially developed to simplify data interpretation for geneticists and bench scientists alike, the tool maps all known Genome-Wide Association Studies (GWAS) hits to the nearest gene, highlighting likely causal genes for a wide range of traits and diseases. Eric says that by making genetic data more user-friendly, it helps researchers to quickly identify gene-trait associations, which can lead to faster and more informed drug target selection.
The role of PQTLs in drug discovery
Eric shares his enthusiasm for protein quantitative trait loci (PQTLs), which identify genetic variants associated with protein levels in blood. Unlike expression QTLs (eQTLs), which often don’t correlate well with disease-related traits, PQTLs provide more direct insights into protein function. As an example, he discusses the IL6 receptor, where a specific genetic variant impacting the IL6R gene directly correlates with autoimmune disease risk. PQTLs can offer clues about causative genes and highlight potential intervention points, making them incredibly valuable for drug target discovery.
Making sense of Mendelian randomization for real-world impact
Eric goes on to explain the concept of Mendelian randomization (MR), a technique that uses genetic variants as natural experiments to establish causal links between traits and disease. Although MR is a useful tool, he does say that not all MR studies are created equal, and cautions that high-throughput studies lacking biological context may come out with misleading conclusions. He also talks about the importance of interpreting MR results with a grounding in biology, offering an example where MR helped clarify the role of histidine ammonia lyase in vitamin D metabolism.
The intersection of AI and drug discovery
The episode also includes a conversation about artificial intelligence, where Eric shares his experiments with large language models (LLMs). While he finds these tools helpful for reviewing and summarizing existing knowledge, he believes they still fall short in generating new insights in complex fields like biology. But one important use is that of a “harsh reviewer” that can spot inconsistencies and provide critical feedback on his own work, which shows that there is plenty of potential for AI to support research in other ways too.
Overcoming bottlenecks in translating genetic insights to treatments
Reflecting on the challenges of drug development, Eric discusses bottlenecks in translating genetic hits into viable drug targets. He notes that while there’s now an explosion of genetic data, the functional understanding of many genes is still limited. He believes the field needs more high-throughput methods for functional characterization, like Perturb-seq, to bridge the gap between genetic association and practical therapeutic knowledge.
Finding the intersection of talent, opportunity, and passion
To close the discussion, Eric offers career advice based on his TOP (Talent, Opportunity, Passion) model, which encourages individuals to seek work that combines their skills, enthusiasm, and the needs of others. He emphasizes the importance of aligning personal strengths and passions with organizational goals to find fulfilling and impactful roles. He also highlights the value of building a personal “brand” around your expertise and interests, which can lead to unexpected opportunities and collaborations.
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