Posted By: HGG Advances
Each month, the editors of Human Genetics and Genomics Advances interview an early-career researcher who has published work in the journal. This month, we check in with Jayati Sharma to discuss her paper, “Genetic ancestry influences gene-environment interactions with sociocultural factors: Results from the Hispanic Community Health Study/Study of Latinos.”
HGGA: What motivated you to start working on this project?
Jayati Sharma (JS): I have been interested in how our genetics and environment interact to affect our overall physical health for a long time. Given the significant disparities in the health profiles of different minoritized populations in the United States, I have persistent questions around the basis for these disparities and how well we’re able to characterize them. I wanted to approach these questions by looking at the sociocultural environment and genetic information of individuals from diverse backgrounds, integrating this information to examine heterogeneity through statistical modeling. We specifically worked with collaborators to build on their existing framework and add genetic ancestry information into a robust framework that looked at the interplay of a genetic score for BMI, diet, and acculturation. This work also comprised my Master’s thesis in Genetic Epidemiology.
HGGA: What about this paper/project most excites you?
JS: We found several associations between continuous genetic ancestry and health-related variables (e.g., BMI, cardiovascular disease) in the overall Hispanic/Latino sample that did not persist when we stratified analyses by self-identified background group. I think this is a really important finding that highlights how population-level research can produce spurious associations driven by key confounders that may not be measured or considered in most analyses.
HGGA: What do you hope the impact of this work will be for the human genetics community?
JS: I hope this paper can demonstrate a concrete example of gene-environment heterogeneity in a Hispanic/Latino sample, even when controlling for self-identified background group and genetic ancestry. There are many ongoing questions in human genetics about how to label study participants with population labels and descriptors. This work helps add evidence that specific and finer-level detail is needed in data collection to better understand important genetic and environmental heterogeneity in general, but specifically in a Hispanic/Latino population.
HGGA: What are some of the biggest challenges you’ve faced as a young scientist?
JS: Crafting concrete and achievable research goals out of lofty ideas always feels like a challenge to me. I like to approach problems from several different angles and am always hoping I can find a way around roadblocks, which are often inevitable. I’ve had a lot of support from mentors in navigating research setbacks and finding alternate paths. Knowing how to write up a deliverable and narrative from various research findings is also a difficult task, but it’s always enlightening to see how one can transform a big research question into a compelling story as an answer.
HGGA: And for fun, what is one of the most fascinating things in genetics you’ve learned about in the past year or so?
JS: I’ve recently been reading about the use of Ancestral Recombination Graphs (ARGs) to store genetic data in a more computationally efficient way. I think it’s very interesting that ARGs can exploit knowledge of genetic relatedness and recombination structure to efficiently compress otherwise massive data while maintaining its integrity.
Jayati Sharma recently defended her PhD in Epidemiology at the Johns Hopkins Bloomberg School of Public Health.