PhD Student Wins Best Poster Award at CHARGE Meeting.
Shuo Li, a fifth-year student in the Biostatistics PhD program, received an award for Best Poster at the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium’s annual meeting. The conference took place April 14-17 in Rotterdam, the Netherlands.
The subject of Li’s poster was “Genome-Wide Association Study of 11,785 Individuals Identifies 7 Loci Associated with Brain-Derived Neurotrophic Factor (BDNF).”
BDNF is a human protein that plays a role in neuronal survival and growth, and is essential in learning and long-term memory. In his project, Li aimed to better understand the genetic determinants of circulating BDNF levels and the role of BDNF in age-related neurological diseases and depressive disorders, as well as in social and behavioral patterns. He says he “found BDNF heritable uncovered new genetic association with circulating BDNF,” and that the results provide a foundation for a better understanding of BDNF regulation and function.
The CHARGE Consortium facilitates a collaboration of genome-wide association studies, which examine gene activity related to cardiovascular and aging traits. It consists of 10 cohort studies, such as the Framingham Heart Study; Multi-Ethnic Study of Atherosclerosis; the Health, Aging, and Body Composition Study; and the Age, Gene, Environment, Susceptibility Study.
The consortium has become the international training ground for epidemiological study and analysis of cardiovascular disease and aging. It encourages student participation to enhance training for future scientists, including Li, who plans to become a data scientist after he receives his PhD.
Li says that his PhD program has proven invaluable to his research owing to its foundation in mathematics, statistics, programming, and data analysis. He also credits his professors for being supportive of students’ career development.
“One of the most rewarding parts is that students in the department are offered research assistanceships,” Li says. “Through the RA projects, we can work with and learn from distinguished scientists. As a result, I have become a fast learner and get the ability to continue learning new methods and software.”