Directed by Dr. Xihong Lin at the Harvard T.H. Chan School of Public Health, the Lin Lab is motivated by a desire to advance our collective knowledge in genomics and human disease research. Our Lab draws upon the diverse expertise and interdisciplinary knowledge of our team, and includes research scientists, postdoctoral researchers, software developers, and doctoral students. To support our research, we developed novel software programs including FAVOR, STAAR, and STAARpipeline. We also launched websites dedicated to our Covid-19 spread mapper research and our work on Functional Annotation Variants.

Our research interests lie predominantly 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 to improve the understanding of complex human disease.

Areas of 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 annotation and interpretation, analysis of the interplay of genes and environment, multiple phenotype analysis, polygenic risk prediction and heritability estimation. We are also interested in causal inference problems such as Mendelian Randomization and mediation analysis, federated and transferred learning, integrative analysis of whole-genome sequencing and single-cell sequencing data, analysis of epidemiological and complex observational studies, and analysis of COVID-19 epidemic data. On the phenotype side, we are interested in developing nonlinear machine learning and latent variable methods for phenotypic refinement. The Lin Lab, with Dr. Xihong Lin as the team leader, also worked on developing scalable algorithms to construct polygenic risk scores from large-scale GWAS and improve prediction accuracy in under-represented populations.