Driven by advances in array based and sequencing based technology, the Human Genome Project, International HapMap Project, the 1,000 Genomes Project, and various forms of genome-wide association studies, genetics is taking an ever-more-central role in all the biomedical sciences. These advances have in turn resulted in an explosive increase in the quantity and variety of genetic data. Faculty and students at U-M Biostatistics play a leading role in a wide range of genetic studies, often in collaboration with other investigators at the U-M Center for Statistical Genetics and around the world. Specific studies seek to identify genetic variants and/or genes that play a role in human diseases such as diabetes, asthma, psoriasis, cancer, bipolar disorder, macular degeneration, that allow discrimination of different disease or tumor subtypes, and that explore human genetic variation. Faculty and students also are working to develop new statistical designs, analytic and computational methods, as well as integrative tools, to ensure the efficient generation and use of genetic data from a wide range of genetic studies, including genome-wide association studies, targeted, whole exome, and whole genome sequencing studies, and expression studies. The statistical approaches used in this area include likelihood-based and Bayesian methods, and often are computationally intensive.
Faculty: G. Abecasis, V. Baladandayuthapani, M. Boehnke, L. Fritsche, H. Jiang, H.M. Kang, J. Kang, Y. Li, J. Morrison, B. Mukherjee, L. Scott, W. Wen, M. Zawistowski, X. Zhou
Links: U-M Center for Statistical Genetics, U-M Genome Science Training Program, U-M Biostatistical Training in Cancer Research, Rogel Cancer Center, U-M Department of Human Genetics, U-M Public Health Genetics Interdepartmental Concentration, Kidney Epidemiology & Cost Center