Lin Lab
The Lin Lab, led by Dr. Xihong Lin at the Harvard T.H. Chan School of Public Health, develops and applies integrative statistical, machine-learning, and generative AI methods to generate new insights into the causes, prevention, and treatment of complex diseases. By integrating whole-genome sequencing, multi-omics, and phenotype data from population cohorts and biobanks with functional genomic experimental data, our research spans the full spectrum from genetic variation to biological function, disease phenotypes, and actionable strategies for improving human health. Our work encompasses whole-genome sequencing analysis, variant functional annotation, functional genomics, disease risk prediction, causal inference, gene–environment interactions, and precision health. We also develop scalable, open-access resources and tools that empower researchers to analyze population-scale genomic health data and experimental functional genomics data and accelerate trustworthy scientific discovery.
655 Huntington Ave, Boston, MA 02115
Research Grants
The following grants and programs support the Lin Lab’s statistical methodological research:
- Statistical Methods for Analysis of Massive Genetic and Genomic Data in Cancer Research (2015-2029) – the National Cancer Institute (NCI)
- Statistical Methods for Integrative Analysis of Large-Scale Multi-Ethnic Whole Genome Sequencing Studies and Biobanks of Common Diseases – the National Heart, Lung, and Blood Institute
- Impact of Genomic Variation on Function (IGVF) – Program of the National Human Genome Research Institute (NHGRI)
- Integrative Analysis of Lung Cancer Etiology and Risk – Baylor College of Medicine
- T32 training grant on interdisciplinary training in statistical genetics and computational biology
- Emerging Statistical and Quantitative Issues in Genomic Research in Health Sciences – National Science Foundation
- Interactive Data Portals and Robust Analytics Tools to Wrap PASC Cohorts (iDRAW) – Massachusetts General Hospital
- Predictive Modeling of the Functional and Phenotypic Impacts of Genetic Variants – University of Massachusetts Medical School