News

By Kimberly Mann Bruch, SDSC Communications

In a venture to unravel the intricacies of genetic regulation and disease biology, UC San Diego Assistant Professor in the Halıcıoğlu Data Science Institute and the Department of Medicine Division of Biomedical Informatics Tiffany Amariuta-Bartell has been utilizing Expanse at the San Diego Supercomputer Center (SDSC) at UC San Diego to uncover hidden insights into disease mechanisms and molecular phenotypes.

Amariuta's most recent research has been focused on developing novel statistical models to decipher genetic and genomic data – particularly looking at the genetic regulation of gene expression. She uses Expanse to carry out her machine learning techniques to conduct rigorous analysis and model development.

“Expanse enables us to complete parallelized analyses of vast genomic datasets – a feat unattainable on personal computers,” she said. “SDSC plays a critical role in accelerating our research efforts as otherwise our work would not be possible.”

She said that her current work on Expanse involves a better understanding of the implications for proper disease diagnosis, risk assessment and personalized medicine for underrepresented populations.

“By leveraging large-scale biobanks and integrating ancestrally diverse data, our research on Expanse holds promise for translating scientific discoveries into tangible clinical applications,” Amariuta explained. “We are especially interested in identifying potential therapeutic targets and advancing personalized medicine – based on genetic differences and a key aspect of the work in my lab is to bridge the gap in genomics research by addressing the underrepresentation of minority populations.”

Looking ahead, Amariuta envisions a future where her laboratory’s research findings translate into real clinical change – improving patient outcomes and healthcare delivery. Her collaborations with physician scientists aim to integrate personalized genomic risk assessment tools into clinical practice, while her efforts with open-source software initiatives foster knowledge dissemination and community engagement. Additionally, as genomic technologies evolve, Amariuta said that her lab remains poised to adapt and innovate – addressing emerging questions in disease biology.

Amariuta’s published work has focused on developing machine learning models to predict the function of DNA variants, the improvement of accuracy of personalized genomic models in non-European populations, estimating the contribution of specific cell types to disease risk and  single cell multi-omic statistical models to associate cell types with disease risk.