Xihong Lin is Professor of Biostatistics and Coordinating Director of the Program of Quantitative Genomics at the Harvard School of Public Health (HSPH). She received her PhD degree from the University of Washington in 1994 under the direction of Professor Norman Breslow. She was on the faculty of the Department of Biostatistics at the University of Michigan from 1994–2005 before she joined the HSPH in 2005. Lin received the 2002 Mortimer Spiegelman Award from the American Public Health Association, and the 2006 COPSS Presidents’ Award. She is an elected fellow of IMS, ASA and the International Statistical Institute. Lin was the former Chair of COPSS (2010–12). She is currently a member of the Committee of Applied and Theoretical Statistics of the US National Academy of Science. Lin is a recipient of the MERIT (Method to Extend Research in Time) award from the National Cancer Institute, which provides long-term support for her methodological research. She is the PI of the T32 training grant on interdisciplinary training in statistical genetics and computational biology. She has served on numerous editorial boards of statistical and genetic journals. She was the former Coordinating Editor of Biometrics, and currently the co-editor of Statistics in Biosciences, and Associate Editor of Journal of the American Statistical Association and American Journal of Human Genetics. She was a permanent member of the NIH study section of Biostatistical Methods and Study Designs, and has served on several other study sections of NIH and NSF.

Xihong Lin will deliver her IMS Medallion Lecture at the 2014 ENAR/IMS spring meeting, held March 16–19, 2014, in Baltimore, Maryland, USA. The preliminary program for the meeting is available to download from http://enar.org/meetings2014/spring2014_prelimprogram.PDF

Statistical Genetics and Genomics in the Big Data Era: Opportunities and Challenges in Research and Training

The human genome project in conjunction with the rapid advance of high throughput technology has transformed the landscape of health science research. The genetic and genomic era provides an unprecedented promise of understanding genetic underpinnings of complex diseases or traits, studying gene-environment interactions, predicting disease risk, and improving prevention and intervention, and advancing personalized medicine. 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. Massive next generation sequencing data as well as different types of ‘omics data have become rapidly available in the last few years. These big genetic and genomic data present statisticians with many exciting opportunities as well as challenges in data analysis and in interpretation of results. They also call for more interdisciplinary knowledge and research, e.g., in statistics, machine learning, data curation, molecular biology, genetic epidemiology and clinical science. In this talk, I will discuss some of these challenges, such as low-level pre-processing, analysis of rare variants in next generation sequencing association studies; integrative genomics, which integrates different types of ’oimcs data; and study of gene-environment and gene-treatment interactions. I will also discuss strategies of training next generation quantitative genomic scientists at the interface of statistical genetics and genomics, computational biology and genetic epidemiology, to meet these challenges.