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Greenberg Lectures: Scalable Statistical Inference of Large-Scale Whole Genome Sequencing Studies
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The 2021 Bernard G. Greenberg Distinguished Lecture Series
Featuring Professor Xihong Lin, Harvard University
Lecture 1: May 20, 10-11:00 a.m.
Scalable Statistical Inference of Large-Scale Whole Genome Sequencing Studies
Big data from genome, exposome and phenome are becoming available at a rapidly increasing rate. Examples include Whole Genome Sequencing data, smartphone data, wearable devices, and Electronic Health Records (EHRs). A rapidly increasing number of large-scale national and institutional biobanks have emerged worldwide. Biobanks integrate genotype, electronic health records and lifestyle data and are the trend of health science research. In this talk, Dr. Lin will discuss several scalable and omnibus methods for analysis of large scale biobanks and population-based Whole Genome Sequencing (WGS) studies of common and rare genetic variants, including variant component tests, Aggregated Cauchy test (ACAT) and a Minimax Optimal Ridge-Type Set Test (MORST). The discussions are illustrated using ongoing large-scale whole-genome sequencing studies of the Genome Sequencing Program of the National Human Genome Research Institute and the Trans-Omics Precision Medicine Program from the National Heart, Lung and Blood Institute, and the UK Biobank and FinnGen.