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3:30-3:50 pm               Future of Diverse Data for Multiscale Modeling:

“Inclusive study design at every scale improves genomic analysis and clinical application for everyone”

Chani Hodonsky, Univ. of Virginia

BIO: Chani Hodonsky is a postdoctoral fellow in the Miller Lab in the University of Virginia Center for Public Health Genomics. She obtained her PhD in Epidemiology from the University of North Carolina Gillings School of Public Health in 2019, where her dissertation focused on the role of genetic variation in red blood cell traits in ancestrally diverse populations in the United States as a member of the PAGE study. Her current research looks at genetic contributions to atherosclerosis in humans, in particular the role of genetic variants on genes expressed in coronary artery tissue.

ABSTRACT: Over the past decade, genome-wide association studies (GWAS) have identified thousands of associations between regions in the genome and complex diseases, in sample sizes ranging from thousands to over a million people. More recently, improved affordability and access to RNA sequencing have allowed for identification of associations between genetic variants and the expression of genes expected to play a role in these diseases. Efforts by the Population Architecture using Genomics and Epidemiology (PAGE) study and others have demonstrated that inclusive, ancestrally diverse study design benefits both discovery efforts and identification of independent associations within known genetic loci. Publicly available resources for interpreting results of these methods provide researchers an opportunity to apply their results on the background of larger sample sizes. However, academic research and accompanying public data have and remain focused on European-ancestry populations, despite the majority of human genetic variation lying outside of Europe. Current polygenic risk scores also rely heavily on genetic associations identified in European-ancestry populations, preventing broad application of a potentially useful clinical tool and threatening to increase health disparities nationally and globally. Increased representation of ancestrally diverse study populations is necessary to improve both the discovery and interpretation of genetic associations. It is also expected to enhance accessibility of clinical applications such as risk prediction and therapeutic development strategies. Chani spent several years working as a molecular genetics technician prior to attending graduate school for epidemiology training. In combination, these experiences led her to seek out a postdoctoral position which would allow for continued study of cardiovascular genomics on both population- and tissue-specific levels.

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