Research

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.

Selected Publications

Full publication list on Google Scholar

Preprint

Distinguishing different psychiatric disorders using DDx-PRS. Peyrot et al. medRxiv, 2024; doi: https://doi.org/10.1101/2024.02.02.24302228.
Genome-wide polygenic risk scores predict risk of glioma and molecular subtypes. Nakase et al. medRxiv, 2024; doi: https://doi.org/10.1101/2024.01.10.24301112.
Characterizing the phenotypic and genetic structure of psychopathology in UK Biobank. Williams et al. medRxiv, 2023; doi: https://doi.org/10.1101/2023.09.05.23295086.
Fine-mapping across diverse ancestries drives the discovery of putative causal variants underlying human complex traits and diseases. Yuan et al. medRxiv, 2023; doi: 10.1101/2023.01.07.23284293.
Genetic heterogeneity across dimensions of alcohol use behaviors. Savage et al. medRxiv, 2023; doi: https://doi.org/10.1101/2023.12.26.23300537.
The pleiotropic architecture of human impulsivity across biological scales. Mallard et al. medRxiv, 2023; doi: https://doi.org/10.1101/2023.11.28.23299133.
Genome-wide association study of treatment resistant depression highlights shared biology with metabolic traits. Kang et al. medRxiv, 2022; doi: https://doi.org/10.1101/2022.08.10.22278630.
Exploration of Alzheimer's disease MRI biomarkers using APOE4 carrier status in the UK Biobank. Du et al. medRxiv, 2021; doi: 10.1101/2021.09.09.21263324.
Findings and insights from the genetic investigation of age of first reported occurrence for complex disorders in the UK Biobank and FinnGen. Feng et al. medRxiv, 2020; doi: https://doi.org/10.1101/2020.11.20.20234302.

2024

Morphological and genetic decoding shows heterogeneous patterns of brain aging in chronic musculoskeletal pain. Zhao et al. Nature Mental Health, 2024; 2:435-449.
Whole-exome sequencing in UK Biobank reveals rare genetic architecture for depression. Tian et al. Nature Communications, 2024; 15(1):1755.
Selection, optimization and validation of ten chronic disease polygenic risk scores for clinical implementation in diverse US populations. Lennon et al. Nature Medicine, 2024; 30(2):480-487.
Shared genetic architectures of educational attainment in East Asian and European populations. Chen et al. Nature Human Behavior, 2024; 8:562-575.
Brain-based classification of youth with anxiety disorders: transdiagnostic examinations within the ENIGMA-Anxiety database using machine learning. Bruin et al. Nature Mental Health, 2024; 2:104–118.
Principles and methods for transferring polygenic risk scores across global populations. Kachuri et al. Nature Reviews Genetics, 2024; 25:8-25.

2023

Analysis across Taiwan Biobank, Biobank Japan, and UK Biobank identifies hundreds of novel loci for 36 quantitative traits. Chen et al. Cell Genomics, 2023; 3(12):100436.
Multi-ancestry study of the genetics of problematic alcohol use in over 1 million individuals. Zhou et al. Nature Medicine, 2023; 29(12):3184-3192.
Cell-type-specific Alzheimer's disease polygenic risk scores are associated with distinct disease processes in Alzheimer's disease. Yang et al. Nature Communications, 2023; 14(1):7659.
The molecular genetic landscape of human brain size variation. Seidlitz et al. Cell Reports, 2023; 42(11):113439.
Transcriptome-wide structural equation modeling of 13 major psychiatric disorders for cross-disorder risk and drug repurposing. Grotzinger et al. JAMA Psychiatry, 2023; e231808.
Language network lateralization is reflected throughout the macroscale functional organization of cortex. Labache et al. Nature Communications, 2023; 14(1):3405.
The impact of rare protein coding genetic variation on adult cognitive function. Chen et al. Nature Genetics, 2023; 55(6):927-938.
Using brain cell-type-specific protein interactomes to interpret neurodevelopmental genetic signals in schizophrenia. Hsu et al. iScience, 2023; 26(5):106701.
Genetic architecture of the inflammatory bowel diseases across East Asian and European ancestries. Liu et al. Nature Genetics, 2023; 55(5):796-806.
Multivariate genomic architecture of cortical thickness and surface area at multiple levels of analysis. Grotzinger et al. Nature Communications, 2023; 14(1):946.

2022

Collective genomic segments with differential pleiotropic patterns between cognitive dimensions and psychopathology. Lam et al. Nature Communications, 2022; 13(1):6868.
Taiwan Biobank: A rich biomedical research database of the Taiwanese population. Feng et al. Cell Genomics, 2022; 2(11):100197.
Global Biobank Meta-analysis Initiative: Powering genetic discovery across human disease. Zhou et al. Cell Genomics, 2022; 2(10):100192.
Large-scale sequencing identifies multiple genes and rare variants associated with Crohn's disease susceptibility. Sazonovs et al. Nature Genetics, 2022; 54(9):1275-1283.
Genome-wide polygenic score to predict chronic kidney disease across ancestries. Khan et al. Nature Medicine, 2022; 28(7):1412-1420.
Development and validation of a trans-ancestry polygenic risk score for type 2 diabetes in diverse populations. Ge et al. Genome Medicine, 2022; 14(1):70.
Improving polygenic prediction in ancestrally diverse populations. Ruan et al. Nature Genetics, 2022; 54(5):573-580.
Mapping genomic loci implicates genes and synaptic biology in schizophrenia. Trubetskoy et al. Nature, 2022; 604(7906):502-508.
Cross-ethnicity/race generalization failure of behavioral prediction from resting-state functional connectivity. Li et al. Science Advances, 2022; 8(11):eabj1812.

2021

Use of the PsycheMERGE Network to investigate the association between depression polygenic scores and white blood cell count. Sealock et al. JAMA Psychiatry, 2021; 78(12):1365-1374.
A comparison of ten polygenic score methods for psychiatric disorders applied across multiple cohorts. Ni et al. Biological Psychiatry, 2021; 90(9):611-620.
Individual-specific areal-level parcellations improve functional connectivity prediction of behavior. Kong et al. Cerebral Cortex, 2021; 31(10):4477-4500.
Heritability of individualized cortical network topography. Anderson et al. Proceedings of the National Academy of Sciences USA, 2021; 118(9):e2016271118.
Clinical laboratory test-wide association scan of polygenic scores identifies biomarkers of complex disease. Dennis et al. Genome Medicine, 2021; 13(1):6.
The causal role of circulating vitamin D concentrations in human complex traits and diseases: a large-scale Mendelian randomization study. Jiang et al. Scientific Reports, 2021; 11(1):184.

2020

The default network of the human brain is associated with perceived social isolation. Spreng et al. Nature Communications, 2020; 11(1):6393.
An exposure-wide and Mendelian randomization approach to identifying modifiable factors for the prevention of depression. Choi et al. American Journal of Psychiatry, 2020; 177(10):944-954.
The schizophrenia risk locus in SLC39A8 alters brain metal transport and plasma glycosylation. Mealer et al. Scientific Reports, 2020; 10(1):13162.
Transcriptional and imaging-genetic association of cortical interneurons, brain function, and schizophrenia risk. Anderson et al. Nature Communications, 2020; 11(1):2889.
The genetic architecture of the human cerebral cortex. Grasby et al. Science, 2020; 367(6484):eaay6690.

2019

Genomic relationships, novel loci, and pleiotropic mechanisms across eight psychiatric disorders. Cross-Disorder Group of the Psychiatric Genomics Consortium. Cell, 2019; 179(7):1469-1482.e11.
Penetrance and pleiotropy of polygenic risk scores for schizophrenia in 106,160 patients across four health care systems. Zheutlin et al. American Journal of Psychiatry, 2019; 176(10):846-855.
Global signal regression strengthens association between resting-state functional connectivity and behavior. Li et al. NeuroImage, 2019; 196:126-141.
The shared genetic basis of educational attainment and cerebral cortical morphology. Ge et al. Cerebral Cortex, 2019; 29(8):3471-3481.
Resting brain dynamics at different timescales capture distinct aspects of human behavior. Liégeois et al. Nature Communications, 2019; 10(1):2317.
Polygenic prediction via Bayesian regression and continuous shrinkage priors. Ge et al. Nature Communications, 2019; 10(1):1776.

Before 2019

Dissociable influences of APOE ε4 and polygenic risk of AD dementia on amyloid and cognition. Ge et al. Neurology, 2018; 90(18):e1605-e1612.
Heritability analysis with repeat measurements and its application to resting-state functional connectivity. Ge et al. Proceedings of the National Academy of Sciences USA, 2017; 114(21):5521-5526.
Phenome-wide heritability analysis of the UK Biobank. Ge et al. PLoS Genetics, 2017; 13(4):e1006711.
Novel genetic loci underlying human intracranial volume identified through genome-wide association. Adams et al. Nature Neuroscience, 2016; 19(12):1569-1582.
Multidimensional heritability analysis of neuroanatomical shape. Ge et al. Nature Communications, 2016; 7:13291.
Morphometricity as a measure of the neuroanatomical signature of a trait. Sabuncu et al. Proceedings of the National Academy of Sciences USA, 2016; 113(39):E5749-5756.
Transcriptional profiles of supragranular-enriched genes associate with corticocortical network architecture in the human brain. Krienen et al. Proceedings of the National Academy of Sciences USA, 2016; 113(4):E469-478.
A kernel machine method for detecting effects of interaction between multidimensional variable sets: an imaging genetics application. Ge et al. NeuroImage, 2015; 109:505-514.
Massively expedited genome-wide heritability analysis (MEGHA). Ge et al. Proceedings of the National Academy of Sciences USA, 2015; 112(8):2479-2484.
Achieving modulated oscillations by feedback control. Ge et al. Physical Review E, 2014; 90(2):022909.
Analysis of multiple sclerosis lesions via spatially varying coefficients. Ge et al. Annals of Applied Statistics, 2014; 8(2):1095-1118.
Genetics of the connectome. Thompson et al. NeuroImage, 2013; 80:475-488.
Increasing power for voxel-wise genome-wide association studies: the random field theory, least square kernel machines and fast permutation procedures. Ge et al. NeuroImage, 2012; 63(2):858-73.
Invariance principles allowing of non-Lyapunov functions for estimating attractor boundaries of discrete dynamical systems. Ge et al. IEEE Transactions on Automatic Control, 2012; 57(2):500-505.