Center for Computational Biology Seminar: Dr. Shamil Sunyaev, Department of Biomedical Informatics, Harvard Medical School

Seminar | March 6 | 4:30-5:30 p.m. | 125 Li Ka Shing Center

 Center for Computational Biology

Large-scale genomic data reveal mechanisms of mutagenesis and help predict complex phenotypes

Abstract:
Statistical analysis of large genomic datasets has recently emerged as a discovery tool in many areas of genetics. Two examples include studies of mutagenesis and of the relationship between genotype and phenotype. We developed a statistical model of regional variation of human mutation rate. Application of this model to population sequencing data generated strong mechanistic hypotheses on the origin of human mutation. In a separate study, we developed a method that predicts complex phenotypes, such as common human diseases, from genotypes. This new non-parametric shrinkage (NPS) method does not make any specific assumptions regarding allelic architecture. The method reliably corrects for linkage disequilibrium in summary statistics of 5 million dense genome-wide markers and consistently improves prediction accuracy over state of the art techniques.

Biography:
Shamil Sunyaev is a computational genomicist and geneticist. Research in his lab encompasses many aspects of population genetic variation including the origin of mutations, the effect of allelic variants on molecular function, population and evolutionary genetics, and genetics of human complex and Mendelian traits. He developed several computational and statistical methods widely adopted by the community. Sunyaev obtained a PhD in molecular biophysics from the Moscow Institute of Physics and Technology and completed his postdoctoral training in bioinformatics at the European Molecular Biology Laboratory (EMBL). He is an Associate Member at Broad Institute of MIT and Harvard.

 Light refreshments will be provided at reception from 4:00pm - 4:30pm, 125 LKS foyer.

 ccbadmin@berkeley.edu