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Grants Fund Statistical Methodology Development in Biomedical Research
Heping Zhang, Professor of Public Health in the
Division of Biostatistics, has received two grants from the National
Institutes of Health's National Institute on Drug Abuse to develop
statistical methods in biomedical research, particularly for genetic
and genomic analysis of substance use data. |
Dr. Heping Zhang, Professor of Public Health in the Division of Biostatistics,
has been awarded two grants totaling $1.5 million over five years from
the National Institutes of Health's National Institute on Drug
Abuse (NIDA). One of the grants is an Independent Scientist Award for
Dr. Zhang's career development, and the other is a research project.
Both grants will be used to develop statistical methods in biomedical
research, particularly for genetic and genomic analysis of substance
use data. In particular, the methods will be developed
and applied to identify candidate genes for nicotine dependence as well
as its comorbid conditions, including drug use and psychiatric conditions.
The completion of Human Genome Project and growing complexity of biomedical
studies create enormous challenges for researchers trying to understand
the complex relationships between genes, environment, and diseases. Our
ability to collect and warehouse data is advancing much faster than our
ability to develop analytic tools to understand the information. The
NIDA grants will allow Dr. Zhang and his associates to devote a substantial
level of effort to developing statistical methods and software to meet
the challenges by incorporating biological processes into the consideration
and by considering the complex relationship among comorbid substance
use and psychiatric disorders. The same methodology will also be useful
for studying the genetic mechanisms of cancer, hypertension, and other
complex diseases. For example, Dr. Zhang and his associates have established
diagnostic procedures for breast and colon cancer based on gene expression
profiles.
Those working with Dr. Zhang on this research in his Lab of Statistics
and Bioinformatics include postdoctoral associates Young-Ju Kim, Jun
Liu, Xueqing Wang, Yuanqing Ye, and Xiaoyun Zhong, fellows Musie Ghebremichael
and John Myers, and doctoral students Fenghai Duan and Rui Feng.
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