Posted By: The American Journal of Human Genetics, AJHG
Each month, the editors of The American Journal of Human Genetics interview an author of a recently published paper. This month, we check in with Julie-Alexia (X; LinkedIn) to discuss her recent paper “Evaluating multi-ancestry genome-wide association methods: statistical power, population structure, and practical implications.”
AJHG: What motivated you to start working on this project?
JD: As I began my PhD in statistical genetics, I wanted to start from the ground up. Much of the excitement in our field today centers on precision medicine, which utilizes genetics to refine disease risk prediction, identify novel associations, and ultimately improve health outcomes. While these downstream applications are incredibly important, they all depend on the foundation laid by genome-wide association studies (GWAS). The emergence of large and genetically varied biobanks has created discovery opportunities far beyond what was previously possible, both due to their scale and inclusion of participants from multiple ancestries. Hence, I felt it was critical to revisit a very fundamental question: how should we best analyze multi-ancestry data to maximize discovery power while minimizing bias from population stratification? Addressing this challenge felt like the natural first step for my PhD. Now, having laid that groundwork, I am turning to polygenic risk scores, which I see as the logical “next step” in translating GWAS findings into predictive tools.
AJHG: What about the paper/project most excites you?
JD: What excites me most is the potential impact this work can have on the design and interpretation of tomorrow’s large GWAS. As biobanks continue to grow in both scale and genetic variation, researchers will increasingly face the question: how to analyze multi-ancestry data in ways that are both powerful and reliable? Our study provides not just empirical results, but also a theoretical framework that clarifies why certain approaches, particularly pooled analysis, perform better under realistic conditions. I find it especially rewarding that these insights are immediately actionable, as they can inform how new studies are planned, how biobank resources are leveraged, and how discoveries are made across populations. To me, the exciting part is knowing that this work will help shape the next generation of genetic studies, ensuring that they are both more inclusive and more effective in driving discovery.
AJHG: Thinking about the bigger picture, what implications do you see from this work for the larger human genetics community?
JD: One of the key implications of this work is that it challenges the community to reconsider how we design and share data for multi-ancestry analyses. Currently, raw genetic data are difficult to share across cohorts due to privacy, regulatory, and logistical barriers, which have made meta-analysis the default approach. Our findings suggest that a pooled analysis, when feasible, offers clear advantages in power and inclusivity. Looking ahead, this may motivate the community to develop new frameworks for federated data access or consortia-wide infrastructures that enable pooled analyses without requiring direct data transfer.
Beyond data sharing, this work underscores the importance of recruiting and retaining participants from many genetic ancestries. The benefits of pooled analysis are amplified when studies include balanced ancestry representation, which in turn enhances the generalizability of discoveries and improves tools such as polygenic risk scores. Finally, these results have downstream implications for fine-mapping, functional studies, and risk prediction: more powerful GWAS create a stronger foundation for all subsequent efforts in precision medicine.
I see this study as both a methodological guide and a call to action for the human genetics community to invest in infrastructures and collaborations that maximize discovery across populations.
AJHG: What advice do you have for trainees/young scientists?
JD: My biggest piece of advice is to get involved in your field as much as possible. Methodological rigor and technical skills are essential, but science is not done alone. My progress has been driven by engaging with a variety of projects, mentors, and collaborators. Some of the most formative parts of my own training have come from working across groups and institutions: as a Special Volunteer in the Trans-Divisional Research Program at the NIH, through my involvement in PRIMED (Polygenic Risk Methods Development) with both the Cancer and Legacy groups, and as part of the Confluence Project at the University of Cambridge. At Harvard, I am lucky to be co-supervised by Peter Kraft, PhD, and Xihong Lin, PhD, while also working with Giovanni Parmigiani, PhD. These experiences have allowed me to interact with some of the pioneers of the field, which is always motivating and humbling. Personally, building these collaborative networks is essential for tackling the big, complex questions in human genetics.
AJHG: And for fun, tell us something about your life outside of the lab.
JD: Outside of the lab, I love scuba diving and skiing. However, on a daily basis, I enjoy distance running, which is my go-to time for brainstorming ideas, like my next graphical abstract.
Julie-Alexia Dias, MSc, is a Biostatistics PhD student at the Harvard T.H. Chan School of Public Health.