MEB seminar: Analysis of Genome, Exposome and Phenome data
Presenter: Professor Xihong Lin, Departments of Biostatistics and Statistics, Harvard University
Welcome to mingle after the seminar!
Massive ‘ome data, including genome, exposome, and phenome data, are becoming available at an increasing rate with no apparent end in sight. Examples include Whole Genome Sequencing data, multiple metal data, digital phenotyping data, and Electronic Medical Records. A large number of genome-wide association studies conducted in the last ten years have identified over 1,000 common genetic variants that are associated with many complex diseases and traits. Whole genome sequencing data and different types of genomics data have become rapidly available. Two large ongoing whole genome sequencing programs (Genome Sequencing Program (GSP) of NHGRI and Trans-omics for Precision Medicine Program (TOPMed) of NHLBI) plan to sequence 300,000-350,000 whole genomes. These massive genetic and genomic data, as well as exposure and phenotype data, present many exciting opportunities as well as challenges in data analysis and result interpretation. This talk will discuss analysis strategies for some of these challenges, including rare variant analysis of whole-genome sequencing association studies; analysis of multiple phenotypes (pleiotropy), and integrative analysis of different types of genetic and genomic, environmental data.
Xihong Lin is Chair and Henry Pickering Walcott Professor of Department of Biostatistics and Coordinating Director of the Program of Quantitative Genomics at the Harvard T. H. Chan School of Public Health, and Professor of Statistics of the Faculty of Arts and Sciences of Harvard University. Dr. Lin’s research interests lie in development and application of statistical and computational methods for analysis of massive genetic and genomic, epidemiological, environmental, and medical data. She currently works on whole genome sequencing association studies, genes and environment, integrative analysis of genetic, genomic and environmental data, and statistical learning methods for massive health science data. Dr. Lin received the 2002 Mortimer Spiegelman Award from the American Public Health Association and the 2006 Presidents’ Award of the Committee of Presidents of Statistical Societies (COPSS). She is an elected fellow of American Statistical Association (ASA), Institute of Mathematical Statistics, and International Statistical Institute. Dr. Lin received the MERIT Award (R37) (2007-2015) and the Outstanding Investigator Award (OIA) (R35) (2015-2022) from the National Cancer Institute (NCI). She is the contacting PI of the Program Project (PO1) on Statistical Informatics in Cancer Research from NCI, the Analysis Center of the Genome Sequencing Program of the National Human Genome Research Institute, and the T32 training grant on interdisciplinary training in statistical genetics and computational biology. Dr. Lin was the former Chair of the COPSS (2010-2012) and a former member of the Committee of Applied and Theoretical Statistics (CATS) of the National Academy of Science. She is the former Chair of the new ASA Section of Statistical Genetics and Genomics. She was the former Coordinating Editor of Biometrics and the founding co-editor of Statistics in Biosciences, and is currently the Associate Editor of Journal of the American Statistical Association. She has served on a large number of statistical society committees, and numerous NIH and NSF review panels.
This seminar is kindly supported by funding from the Strategic Research Area in Epidemiology (SfoEpi) grant.Contact person: Weimin Ye