How to Identify the Blind Spots in Your Biotech's Genomic Data Before They Cost You a Drug Target
Show notes
Most drug discovery genomic data comes from a thin slice of the world, and that bias follows every decision downstream.
Your team can run a Mendelian randomization study on 35,000 patients and still walk away with a single signal that doesn't even apply to the population you care about. If your phenotype definitions are fuzzy, more data won't save you.
Erika Kvikstad is a computational biologist who led precision medicine for cardiovascular disease at Bristol-Myers Squibb, working on therapies including Camzyos for hypertrophic cardiomyopathy. She now works independently on genomic data equity, focused on how reference populations shape everything from target discovery to clinical trial recruitment.
You'll get a practical look at how to evaluate real-world data vendors, why heart failure is nearly impossible to define cleanly from billing codes, and where statistical power breaks down even with tens of thousands of patients. Erika also explains how her team used AI to reconstruct missing imaging data and validate cardiomyopathy diagnoses at scale.
This episode covers GWAS studies, Mendelian randomization, UK Biobank, proteome-wide analysis, and the practical gap between biobank-scale data and disease-specific cohorts. It's built for data and analytics leaders working in life sciences who need to understand where genomic bias enters their pipeline, not just that it exists.
Clarification
Around 57:58–58:24, in discussing the proteome-wide Mendelian randomization study, Erika moved quickly between two related findings. BTN3A2 was identified as a candidate associated with ischemic stroke and potential immune-modulatory biology. Separately, single-cell expression data helped contextualize other candidate signals, including some with enriched expression in cardiomyocyte populations. Cardiomyocyte-enriched expression was not a specific finding for BTN3A2.
Chapter Markers
00:00 Whose genome are we designing drugs for
01:34 Erika's path from academic genomics to BMS
03:48 Building the precision medicine strategy at BMS
06:37 Ross shares his own HCM diagnosis
07:09 Why heart failure resists clean definition
11:11 How medication use reclassifies patients
14:35 Imaging as a biomarker, and its data gaps
20:23 Data infrastructure gaps across regions
22:44 What to look for when evaluating a data vendor
27:35 Consortia and biobanked specimens for rare mutations
29:52 Cardiovascular data infrastructure versus oncology
32:29 Where statistical power breaks down
37:07 UK Biobank's strengths and its limits
40:01 Bridging broad biobanks with disease-specific cohorts
44:32 How reference population bias propagates downstream
48:53 Where genomic bias hits hardest in the pipeline
53:18 Inside a proteome-wide Mendelian randomization study
59:42 Choosing the right computational tool for the question
1:06:38 Building globally representative genomic infrastructure
1:08:04 Ross's takeaways on bias and statistical power
Useful Links & Resources
- Erika on LinkedIn: https://www.linkedin.com/in/erikakvikstad
- UK Biobank: https://www.ukbiobank.ac.uk
- Alliance for Genomic Discovery: https://alliancegenomicdiscovery.org
- SHaRe Registry (DCM Foundation): https://dcmfoundation.org
Connect With the Show
- Ross Katz on LinkedIn: https://www.linkedin.com/in/b-ross-katz/
- (Ross Katz on X: https://x.com/brosskatz
- CorrDyn LinkedIn: https://www.linkedin.com/company/corrdyn/
Have you run into genomic reference bias in your own work? Tell us what it looked like and how your team caught it.
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