Investigator, Asst Prof (M) Center for Genomic Medicine, Mass General Research Institute

Associate Professor of Psychiatry Harvard Medical School

PhD Fudan University 2014

PhD University of Warwick 2013

biostatistics; brain imaging; data science; machine learning; neuroimaging genetics; statistical genetics

Research in the Ge Lab broadly focuses on statistical genetics and neuroimaging genetics. We develop new statistical, computational and machine learning methods to analyze and integrate large-scale genomic, neuroimaging, behavioral and electronic health records data, in order to (i) understand the genetic architecture of human complex traits and common diseases; (ii) unravel the biological basis of brain structure and function, and the genetic and neural underpinnings of mental disorders; and (iii) improve individualized prediction of disease risk, development, severity and progression.

Current research directions:

Genomic Prediction: Building reliable and accurate genomic prediction models may improve risk stratification, diagnostic accuracy, prevention of common diseases and prediction of therapeutic outcomes. We develop robust and computationally efficient algorithms to improve the predictive performance of polygenic risk scores in individuals with diverse genetic and sociocultural backgrounds and to facilitate the implementation of polygenic risk scores in clinical settings.

Statistical Genetics: We develop scalable and accurate statistical genetics methods and leverage global biobanks and electronic health records to dissect the genetic architecture of human complex traits and diseases in populations of diverse genetic ancestries, facilitate the discovery and mapping of common and rare disease-causing variants, and improve individualized prediction of disease risk and trajectories.

Neuroimaging Genetics: Neurological and psychiatric disorders often emerge from variations in brain structure and function. We develop statistical and computational techniques to explore the genetic underpinnings of individual differences in high-dimensional phenotypes derived from structural and functional brain magnetic resonance imaging (MRI) scans, and integrate large-scale neuroimaging, genetic, transcriptomic, clinical and behavioral data to understand the biological basis of brain disorders.