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Tiffany Amariuta Receives NSF CAREER Award


Tiffany Amariuta, an assistant professor in the Department of Medicine and at the Halıcıoğlu Data Science Institute at University of California San Diego has been awarded the prominent NSF Faculty Early Career Development Program Award. This recognition underscores her exceptional leadership qualities in research and education, particularly within the realms of data science and biomedical informatics.

Amariuta’s joint appointment with the Division of Biomedical Informatics at the UC San Diego School of Medicine emphasizes her commitment to employing interdisciplinary methodologies in tackling intricate biological inquiries through statistical and computational approaches.

The focus of Amariuta’s project, titled “Scalable Algorithms for Regularized and Non-Linear Genetic Models of Gene Expression,” lies at the intersection of genetics, statistics and computer science. The overarching goal of her research is to advance our understanding of genetic regulation and its implications for human health. Specifically, her work aims to develop innovative algorithms that can effectively map genetic variants to target genes, shedding light on the intricate mechanisms underlying gene expression.

Genetic models of gene expression play a crucial role in elucidating the relationships between genetic variants and phenotypic traits. However, existing statistical approaches face significant challenges, including limited sample sizes, prohibitively large multiple hypothesis burdens, and model misspecification. Amariuta’s proposal seeks to overcome these obstacles by introducing novel algorithms capable of handling ancestrally and molecularly diverse datasets and resolving complex statistical issues.

One key aspect of Amariuta’s project is its focus on addressing the underrepresentation of minority populations in genetic studies. By incorporating data from understudied minorities and leveraging advanced statistical techniques, her research aims to promote diversity in genetic discovery and ensure equitable access to biomedical advancements.

Identifying important gene regulation mechanisms in minority populations

Amariuta plans to enhance the accuracy of linking genetic variation to changes in gene expression in underrepresented ancestral groups where sample sizes are restrictively low. She will do this by integrating data from multiple populations in a Bayesian approach that leads to more reliable prediction of how DNA variants regulate gene expression levels. This will reveal population-specific gene regulatory mechanisms, shedding light on differences in disease prevalences and causal mechanisms.

Developing a genome-wide variant-to-gene mapping approach

The majority of genetic regulation of gene expression is estimated to act distally (i.e., more than 10 genes away), but these relationships are difficult to detect due to large multiple testing burdens associated with conventional variant-gene association analysis. Amariuta plans to leverage gene regulatory networks and feature selection algorithms to identify candidate regulatory variants. By doing so, she will more robustly identify distal regulatory control of gene expression, which is likely to provide mechanistic hypotheses for many poorly characterized disease-associated variants.

Utilizing single-cell gene expression data

Amariuta proposes the development of new mixed models that utilize single-cell gene expression data to estimate gene expression heritability accurately. This approach promises to reveal cell-type-specific patterns of gene regulation, providing valuable insights into the complexities of genetic regulation.

Beyond advancing scientific knowledge, Amariuta’s project also encompasses a robust educational component. Her outreach efforts include a residential on-campus summer coding boot camp and research experience for high school students from under-resourced communities in San Diego County.

Additionally, she plans to develop a statistical genetics course aimed at providing students with the tools to pursue cutting-edge research in the field.

This award NSF CAREER award represents a significant milestone in the field of genetic research. Amariuta’s innovative approach to developing scalable algorithms for genetic modeling holds immense promise for advancing our understanding of human biology and disease. Through her research and educational initiatives, she aims to foster diversity, equity, and excellence in both academia and society as a whole.

Kaleigh O'Merry, Special Projects & Initiatives Analyst at the Halıcıoğlu Data Science Institute