How can we make genetic testing and the interpretation of genetic variations more fair and accurate
When we think about genetic research and its role in precision medicine, one of its most compelling promises lies in its universality. The promise is that we all share the same fundamental blueprint – our DNA. Regardless of background, we each have a life code that can be read and interpreted.
Precision medicine, therefore, holds the potential to interpret genetic variation for anyone and everyone, making personalized care accessible to all. This vision paves the way for equitable healthcare – a future where everyone benefits equally from genomic advancements. However, our recent study, “Using multiplexed functional data to reduce variant classification inequities in underrepresented populations,” recently published in Genome Medicine, highlights a persistent and glaring issue: the inequity in variant classification, particularly among underrepresented populations.
Accurate and precise variant classification and interpretation is mission critical to clinical genetics by determining whether a genetic change is benign (not clinically consequential) or pathogenic (may have a potential clinical impact) and, therefore, if it contributes to the manifestation of a clinical disease either now or in the future. Yet, the process is inherently biased. The databases we use to interpret variants – those sprawling collections of genomic data – are overwhelmingly derived from populations of European-like genetic ancestry. This imbalance leads to a disproportionate number of variants of uncertain significance (VUS) in individuals of non-European-like genetic ancestry. For families waiting on a diagnosis, a VUS can feel like a roadblock, leaving them in diagnostic limbo.
In our work, we aimed to break through that roadblock by leveraging multiplexed assays of variant effects (MAVEs). MAVEs are unique in their experimental design: don’t just test a few variants; test them ALL. MAVEs enable researchers to test thousands – or even millions – of genetic variants simultaneously for their downstream functional effects. This comprehensive approach generates experimental data at a scale previously unimaginable, creating a functional “lookup table” for variant effects.
We demonstrate that not only can MAVEs potentially reclassify more than 70% of VUS, but we were able to reclassify more VUS in individuals of non-European-like genetic ancestry than those of European-like genetic ancestry. This is unprecedented. There is no other experimental technique or effort in general in genomics that is not only leading to equitable advances in genomics for all but also compensating for current disparities!
For instance, a VUS identified in a patient of African ancestry can be compared against functional data generated by MAVEs, sidestepping the need for population-matched genomic data that may not yet exist.
However, this advancement doesn’t exist in isolation; it raises ethical and policy questions. How do we ensure that this functional data is accessible to clinicians and researchers worldwide, especially in resource-limited settings? What safeguards should we implement to ensure this data isn’t misused, particularly in ways that could exacerbate existing disparities? As we scale up MAVEs, integrating these datasets into clinical practice must be done equitably, thoughtfully, quickly, and with standardization to allow scalability.
MAVEs are a powerful tool, but they are not a panacea. They are a step toward a more inclusive genomic landscape – a landscape where no one is left behind due to their ancestry. Geneticists, ethicists, policymakers and patient advocates must come together to ensure these technologies fulfill their promise of equitable care. Here at the Human Genome Sequencing Center, founded and directed by my Ph.D. mentor, Dr. Richard Gibbs, we are committed to hyper-accelerating human genetics and genomics at scale for everyone. By addressing the gaps in our systems with innovative tools like MAVEs, we not only advance science but also uphold the principles of justice and equity that should be central to medicine.
By Moez Dawood, M.D./Ph.D. student, Baylor College of Medicine
