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Biography |
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Professor Doug Brutlag received his Ph.D. with Great Distinction from Stanford University in 1972 and has been a Professor of Biochemistry there since 1974. His research helped develop the field of bioinformatics, the application of computer science to molecular biology. In 1979 he was a co-founder of IntelliGenetics, one of the first firms involved in bioinformatics and in 1997 he co-founded the International Society for Computational Biology. He has served on the scientific advisory boards of many firms and organizations including the National Library of Medicine and the Max Planck Institute.
At Stanford, Professor Brutlag has chaired the Senate Committees on Academic Computing and Information Systems (C-ACIS), Distributed Information and Computer Environments (DICE), the Infrastructure Advisory Group (IAG), and the Senate Committee on Libraries (C-LIB).
Dr. Brutlag’s honors include an NIH Senior Fogarty Fellowship, Guggenheim Fellowship, Fellow of the American Association for the Advancement of Science, Fellow of the American College of Medical Informatics and Honorary Professor of Bioinformatics at Keio University, Japan. He was awarded the 1992 Computerworld-Smithsonian Award in Science.
Complete biography: http://cmgm.stanford.edu/~brutlag/
Research: http://brutlag.stanford.edu/
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Abstract |
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Linking Genes to Disease: Leveraging the Human Genome
The genes responsible for 67% of the 20,000 inherited traits and diseases in humans have already been assigned. However, the genes causing many of the most common diseases and conditions have not. Unfortunately many of remaining diseases are complex, caused by multiple genes, or by genes in several pathways, or are largely environmental. In this talk, I will describe genome-wide association studies (GWAS) that have the ability to detect genes and genetic regions linked to disease, even when multiple genes or pathways are involved and even in the case of low penetrance or heritability. The primary power of the GWAS method is that it makes no assumptions about the cause of the disease. Thus this statistical method can discover totally unsuspected mechanisms of disease. The primary weakness of the method is that it merely finds a statistical association of gene regions with traits or disease. The GWAS method, by itself, gives no indication of the molecular basis of the disease, whether it is due to changes in protein coding or splicing or regulation of transcription, translation or targeting of gene products. However by focusing GWAS studies on populations, ethnic groups, cohorts, families and trios one gets increased power of detection of gene regions involved in disease. Also by focusing on different types of genetic markers, SNPs, coding regions and regulatory regions, GWAS can give some clues to the underlying mechanism. To date, over 658 GWAS studies have linked thousands of gene regions to complex diseases and traits. Hopefully these methods will help us leverage the human genome to provide a better understanding, diagnosis and treatment of human disease.
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