May 18, 2007

Colloquium Speaker: Aravinda Chakravarti


Aravinda Chakravarti, Ph.D. is Director, Center for Complex Disease Genomics and Professor of Medicine, Pediatrics, Molecular Biology & Genetics, and, Biostatistics at the Johns Hopkins University School of Medicine and the Bloomberg School of Public Health. He received his doctoral degree in human genetics from the University of Texas Health Science Center in Houston in 1979 and continued postdoctoral training at the University of Washington in Seattle during 1979-80. He started his faculty career at the University of Pittsburgh (1980 - 1993), was the James H. Jewell Professor of Genetics at Case Western Reserve University (1994-2000), and the inaugural Director and Henry J. Knott Professor of the McKusick-Nathans Institute of Genetic Medicine at Johns Hopkins (2000-2007). Dr. Chakravarti is one of the Editors-in-Chief of Genome Research, and serves on the Advisory and Editorial Boards of numerous national and international journals, boards and societies. He is a past member of the NIH National Advisory Council of the National Human Genome Research Institute, Chaired the NIH Subcommittee on the 3rd 5-year Genome Project Plan, and continues to serve on several NIH panels. His research is aimed at genomic-scale analysis of the human genome, computational analysis of gene variation and function, and understanding the molecular genetic basis of common genetic disorders.


Colloquium Topic: Genes for Common, Chronic Diseases

Almost everything we know of human genetic disease has arisen from rare mutations in individual families showing single gene (Mendelian) disease inheritance. However, the vast majority of human diseases have a complex etiology with likely multiple genes underlying its transmission. Moreover, the individual component mutations are likely very common. The International HapMap Project was initiated to complete a map of common human variation so that these could be directly tested for their influence in human disease. A number of genomic, genetic and computational technologies have emerged to make large-scale analysis of human disease for common variants possible. I will discuss the history, perspectives, challenges, successes and failures for this remarkable natural experiment.