Xihong Lin
Primary Faculty

Xihong Lin

Professor of Biostatistics



Other Positions

Coordinating Director, Program in Quantitative Genomics


Harvard T.H. Chan School of Public Health

Professor of Statistics

Statistics -Sr. Faculty

Harvard Faculty of Arts and Sciences

Professor of Biostatistics and Statistics

Statistics -Sr. Faculty

Harvard Faculty of Arts and Sciences


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 MIT and Harvard.

Dr. Lin’s research interests lie in the development and application of scalable statistical and machine learning methods for the analysis of massive and complex genetic and genomic, epidemiological and health data. Some examples of her current research include analytic methods and applications for large scale Whole Genome Sequencing studies, biobanks and Electronic Health Records, techniques and tools for whole genome variant functional annotations, analysis of the interplay of genes and environment, multiple phenotype analysis, polygenic risk prediction and heritability estimation. Additional examples include integrative analysis of different types of data, Mendelian Randomization, causal mediation analysis and causal inference, federated and transferred learning, single cell genomics, analysis of epidemiological and complex observational studies, and analysis of COVID-19 epidemic data. Dr. Lin’s theoretical and computational statistical research includes statistical methods for testing a large number of complex hypotheses, causal inference, statistical and ML methods for large matrices, prediction models using high-dimensional data, federated and transferred learning, cloud-based statistical computing, and mixed models, nonparametric and semiparametric regression, and statistical methods for epidemiological studies.

Dr. Lin’s statistical methodological research has been supported by the MERIT Award (R37) (2007-2015), the Outstanding Investigator Award (OIA) (R35) (2015-2029) from the National Cancer Institute (NCI), the R01 grant from the National Heart, Lung, and Blood Institute. She is the multiple PI of a Predictive Modeling Center of the Impact of Genomic Variation on Function (IGVF) Program of the National Human Genome Research Institute (NHGRI), 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 (P01) on Statistical Informatics in Cancer Research from NCI, and the former contact PI of the Harvard Analysis Center (U19) of the Genome Sequencing Program of the National Human Genome Research Institute.

Dr. Lin was active in the early phase of the COVID-19 pandemic. She is one of the corresponding authors of the JAMA and Nature papers on the analysis of the Wuhan COVID-19 data on transmission, public health intervention and epidemiological characteristics. She is the senior author of the 2021 Journal of the American Statistical Association Discussion paper on modeling COVID transmission dynamics in US. In Spring 2020, Dr. Lin served on the State of Massachusetts COVID-19 Task Force, and testified in the UK Parliament’s Committee of Science and Technology on COVID Responses.

Dr. Lin was elected to the National Academy of Medicine in 2018 and the National Academy of Sciences in 2023. She received the 2002 Mortimer Spiegelman Award from the American Public Health Association, the 2006 Committee of Presidents of Statistical Societies (COPSS) Presidents’ Award, the 2017 COPSS FN David Award, the 2008 Janet L. Norwood Award for Outstanding Achievement of a Woman in Statistics, the 2022 National Institute of Statistical Sciences Jerome Sacks Award for Outstanding Cross-Disciplinary Research, and the 2022 Marvin Zelen Leadership in Statistical Science Award. She is an elected fellow of American Statistical Association (ASA), Institute of Mathematical Statistics, and International Statistical Institute.

Dr. Lin is the former Chair of the Committee of Presidents of Statistical Societies (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 founding chair of the US Biostatistics Department Chair Group, and the founding co-chair of the Young Researcher Workshop of East-North American Region (ENAR) of the International Biometric Society. She co-launched the 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, numerous NIH and NSF review panels, and several National Academies committees.


Impact of individual level uncertainty of lung cancer polygenic risk score (PRS) on risk stratification.

Wang X, Zhang Z, Ding Y, Chen T, Mucci L, Albanes D, Landi MT, Caporaso NE, Lam S, Tardon A, Chen C, Bojesen SE, Johansson M, Risch A, Bickeböller H, Wichmann HE, Rennert G, Arnold S, Brennan P, McKay JD, Field JK, Shete SS, Le Marchand L, Liu G, Andrew AS, Kiemeney LA, Zienolddiny-Narui S, Behndig A, Johansson M, Cox A, Lazarus P, Schabath MB, Aldrich MC, Hung RJ, Amos CI, Lin X, Christiani DC.

Genome Med. 2024 Feb 05. 16(1):22. PMID: 38317189

Type 2 Diabetes Modifies the Association of CAD Genomic Risk Variants With Subclinical Atherosclerosis.

Hasbani NR, Westerman KE, Kwak SH, Chen H, Li X, Di Corpo D, Wessel J, Bis JC, Sarnowski C, Wu P, Bielak LF, Guo X, Heard-Costa N, Kinney GL, Mahaney MC, Montasser ME, Palmer ND, Raffield LM, Terry JG, Yanek LR, Bon J, Bowden DW, Brody JA, Duggirala R, Jacobs DR, Kalyani RR, Lange LA, Mitchell BD, Smith JA, Taylor KD, Carson AP, Curran JE, Fornage M, Freedman BI, Gabriel S, Gibbs RA, Gupta N, Kardia SLR, Kral BG, Momin Z, Newman AB, Post WS, Viaud-Martinez KA, Young KA, Becker LC, Bertoni AG, Blangero J, Carr JJ, Pratte K, Psaty BM, Rich SS, Wu JC, Malhotra R, Peyser PA, Morrison AC, Vasan RS, Lin X, Rotter JI, Meigs JB, Manning AK, de Vries PS.

Circ Genom Precis Med. 2023 Dec. 16(6):e004176. PMID: 38014529

A statistical framework for powerful multi-trait rare variant analysis in large-scale whole-genome sequencing studies.

Li X, Chen H, Selvaraj MS, Van Buren E, Zhou H, Wang Y, Sun R, McCaw ZR, Yu Z, Arnett DK, Bis JC, Blangero J, Boerwinkle E, Bowden DW, Brody JA, Cade BE, Carson AP, Carlson JC, Chami N, Chen YI, Curran JE, de Vries PS, Fornage M, Franceschini N, Freedman BI, Gu C, Heard-Costa NL, He J, Hou L, Hung YJ, Irvin MR, Kaplan RC, Kardia SLR, Kelly T, Konigsberg I, Kooperberg C, Kral BG, Li C, Loos RJF, Mahaney MC, Martin LW, Mathias RA, Minster RL, Mitchell BD, Montasser ME, Morrison AC, Palmer ND, Peyser PA, Psaty BM, Raffield LM, Redline S, Reiner AP, Rich SS, Sitlani CM, Smith JA, Taylor KD, Tiwari H, Vasan RS, Wang Z, Yanek LR, Yu B, Rice KM, Rotter JI, Peloso GM, Natarajan P, Li Z, Liu Z, Lin X.

bioRxiv. 2023 Nov 02. PMID: 37961350

Rare variants in long non-coding RNAs are associated with blood lipid levels in the TOPMed whole-genome sequencing study.

Wang Y, Selvaraj MS, Li X, Li Z, Holdcraft JA, Arnett DK, Bis JC, Blangero J, Boerwinkle E, Bowden DW, Cade BE, Carlson JC, Carson AP, Chen YI, Curran JE, de Vries PS, Dutcher SK, Ellinor PT, Floyd JS, Fornage M, Freedman BI, Gabriel S, Germer S, Gibbs RA, Guo X, He J, Heard-Costa N, Hildalgo B, Hou L, Irvin MR, Joehanes R, Kaplan RC, Kardia SL, Kelly TN, Kim R, Kooperberg C, Kral BG, Levy D, Li C, Liu C, Lloyd-Jone D, Loos RJ, Mahaney MC, Martin LW, Mathias RA, Minster RL, Mitchell BD, Montasser ME, Morrison AC, Murabito JM, Naseri T, O'Connell JR, Palmer ND, Preuss MH, Psaty BM, Raffield LM, Rao DC, Redline S, Reiner AP, Rich SS, Ruepena MS, Sheu WH, Smith JA, Smith A, Tiwari HK, Tsai MY, Viaud-Martinez KA, Wang Z, Yanek LR, Zhao W, Rotter JI, Lin X, Natarajan P, Peloso GM.

Am J Hum Genet. 2023 10 05. 110(10):1704-1717. PMID: 37802043


Improving diversity in the genomics field

The field of genomics has the exciting potential to improve human health by using an individual’s DNA to predict disease risk, tailor treatments, and more—but because genomics studies to date have overwhelmingly included people with European ancestries, they…

Using gene editing to fight deadly genetic diseases

Cutting-edge gene editing techniques hold enormous promise for tackling devastating diseases such as sickle cell disease, Huntington’s disease, and heart disease, according to experts who spoke at Harvard Chan School's annual PQG conference.

Using quantitative genomics to track and understand COVID-19

Tracking variants of SARS-CoV-2, understanding which mutations make the virus more dangerous, and figuring out where the virus originally came from were some of the topics highlighted at the 15th annual Program in Quantitative Genomics conference.