Xihong Lin

Professor of Biostatistics

Department of Biostatistics

Xihong Lin’s Group Website  |  Program in Quantitative Genomics’ Website

Bio

Xihong Lin is Professor and former Chair of the Department of Biostatistics, Coordinating Director of the Program in Quantitative Genomics at the Harvard T. H. Chan School of Public Health, and Professor of the Department of Statistics at the Faculty of Arts and Sciences of Harvard University, and Associate Member of the Broad Institute of Harvard and MIT.

Dr. Lin is an elected member of the National Academy of Medicine. She received the 2002 Mortimer Spiegelman Award from the American Public Health Association, and the 2006 Committee of Presidents of Statistical Societies (COPSS) Presidents’ Award and the 2017 COPSS FN David Award. She is an elected fellow of American Statistical Association (ASA), Institute of Mathematical Statistics, and International Statistical Institute.

Dr. Lin’s research interests lie in development and application of scalable statistical and computational methods for analysis of massive data from genome, exposome and phenome, and scalable statistical inference and learning for big health and genomic data.  Examples include analytic methods and applications for large scale Whole Genome Sequencing studies, biobanks and electronic health records, whole genome variant functional annotations, genes and environment, multiple phenotype analysis, risk prediction, integrative analysis of different types of data, causal mediation analysis and causal inference, analysis of complex observational study data. Her theoretical and computational statistical research includes statistical methods for testing a large number of complex hypotheses, statistical inference for large covariance matrices, prediction models using high-dimensional data, and cloud-based statistical computing.

Dr. Lin’s statistical methodological research has been supported by the MERIT Award (R37) (2007-2015) and the Outstanding Investigator Award (OIA) (R35) (2015-2022) from the National Cancer Institute (NCI). She is the contact PI of the Harvard Analysis Center of the Genome Sequencing Program of the National Human Genome Research Institute, and the multiple PI of the U19 grant on Integrative Analysis of Lung Cancer Etiology and Risk from NCI. She is also the contact PI of the T32 training grant on interdisciplinary training in statistical genetics and computational biology.  She is the former contact PI of the Program Project (PO1) on Statistical Informatics in Cancer Research from NCI.

Dr. Lin is 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 co-launched the new Section of Statistical Genetics and Genomics of the American Statistical Association and served as a former section chair. She is the former Coordinating Editor of Biometrics and the founding co-editor of Statistics in Biosciences.  She has served on a large number of committees of many statistical societies, and numerous NIH and NSF review panels.

Selected Publications

[Full list of PubMed articles]

  • Wu, M. C., Lee, S., Cai, T., Li, Y., Boehnke, M. and Lin, X (2011) Rare Variant
    Association Testing for Sequencing Data Using the Sequence Kernel Association Test (SKAT). American Journal of Human Genetics, 89, 82-93.
  • Lee, S., Wu, M., Lin, X. (2012) Optimal tests for rare variant effects in sequencing association studies. Biostatistics, 13(4):762-775.
  • Lee, S., Abecasis, G., Boehnke, M., Lin, X. (2014) Rare-variant association analysis: Study designs and statistical tests. Am J Human Genetics, 95(1):5-23.
  • Y. T., VanderWeele, T. J., and Lin, X. (2014) Joint Analysis of SNP and
    Gene Expression Data in Genetic Association Studies of Complex Diseases. Annals
    of Applied Statistics,8(1):352-376.
  • Barnett, I. and Lin, X. (2014) Analytic P-value calculation for the higher criticism test in finite p problems. Biometrika, 101 (4), 964-970.
  • Murkerjee, R., Pillai, N.S., Lin, X. (2015) Hypothesis testing for sparse binary regression. Annals of Statistics, 43, 352-381.
  • Chen, J. Just, A. C., Schwartz, J., Hou, L, Jafari, L., Sun, Z, Baccarelli, A. and Lin, X. (2016). CpGFilter: Model-based CpG probe filtering with replicates for epigenome-wide association studies. Bioinformatics, in press.
  • Chen, H., Wang, C., Conomos, MP, Stilp, AM, Li, Z., Sofer, T., Szpiro, AA, Chen, W., Brehm, JM, Celedón, JC, Redline, SS,   Papanicolaou, GP, Thornton, TA, Laurie, CC, Rice, K. and Lin, X (2016). Control for population structure and relatedness for binary traits in genetic association studies using logistic mixed models. American Journal of Human Genetics, 98(4), pp.653-666.
  • Lin, X., Lee, S., Wu, M. C., Wang, C., Chen, H., Li, Z., Lin, X. (2016) Test for Rare Variants by Environment Interactions in Sequencing Association Studies, Biometrics, 72, 156–164.