Statistical Genetics and Genomics
Program Description
Statistical genetics and genomics aims to develop novel efficient and powerful statistical, probabilistic, and computational methods for analysis of genetics and genomics data. As outlined by Dr. Francis Collins and others in their Nature (2003) report, titled "A Vision for the Future of Genomics Research: A Blueprint for the Genomic Era", Phase two of the Human Genome Project has three major components: genomics to biology, genomics to health, and genomics to society. The new experimental technologies have generated and will generate even more high-dimensional and complex genomics data sets. Analysis of these data and inferences from these data are intrinsically statistical and fall within the realm of statistical genetics and genomics.
Genomics has become a central discipline of biomedical research. Data in genomics, comparative genomics, and high-throughput biochemistry enable biologists to conduct their research in systems biology. There is a great need of new statistical methods for analyzing and integrating the ever-complex genetics and genomics data. The University of Pennsylvania has very strong research programs in life sciences, including many prominent research programs in genetics and genomics such as cancer genomics and pharmacogenomics. The statistical genetics and genomics program works closely with these research programs, accordingly.
Research in statistical genetics and genomics at Penn covers many areas. Specifically, Drs. Hongzhe Li, Jinbo Chen, Mingyao Li, and Nandita Mitra have developed statistical methods for mapping genes for complex diseases, including novel survival analysis models for incorporating age of onset information into genetic linkage and association analysis, methods for haplotype analysis for various study designs, methods of identifying functional SNPs in the linked regions, and methods for admixture mapping. Drs. Hongzhe Li and Mahlet Tadesse have developed various methods for linking high-throughput genomics data to various clinical phenotypes and methods for incorporating biological pathway information into analysis of microarray gene expression data. Dr. Kim Sellers also has developed methods for data normalization and image analysis of 2-D gel protemic data. Other areas of active research include statistical methods for identifying complex SNP-SNP interactions and methods for analysis of microarray time course gene expression data.
Program Members
- Hongzhe Lee (Li), PhD (program leader)
- Jinbo Chen, PhD
- Mingyao Li, PhD
- Nandita Mitra, PhD
- Kimberly Sellers, PhD
- Mahlet Tadesse, ScD
