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Complex Model Building for Precision Medicine Using Summary-level, Information from Big and Disparate Data Sources
Professor Nilanjan Chatterjee
Bloomberg Distinguished Professor, Bloomberg School of Public Health Department of Oncolog, School of Medicine Johns Hopkins University
Wednesday 15 August 2018, 03:00pm - 04:00pm
S16-06-118, DSAP Seminar Room

Extraction of information from rich and diverse datasets through summary-level statistics, as opposed to individual level data, can be appealing because of various practical and ethical considerations. In this talk, I will describe statistical methods for building complex models using summary-level information in two distinct applications. One involves assessment of genetic architecture of complex traits by modeling of effect-size distributions using association statistics available from large genome-wide association studies.  The other involves development of a generalized meta-analysis framework for unified model building using information on parameters from disparate, but possibly overlapping, sub-models fitted to different studies. Implications for future precision medicine efforts towards disease prevention will be discussed for both applications.