Tian Ge

This study identifies plasma proteins linked to brain structure and neurological disorders using multi-omics integration and Mendelian randomization.

This paper develops rare-variant polygenic risk scores (rvPRSs) using whole-genome sequencing data from the UK Biobank, showing they improve disease risk stratification and biological interpretability beyond common-variant PRSs.

The paper models the genetic architecture of cortical expansion using GWAS data and links it to neuropsychiatric disorders.

The paper identifies 260 genes associated with sleep phenotypes using rare coding variants from large-scale sequencing data, revealing distinct genetic architectures and biological mechanisms across different sleep measurements.

This paper introduces mRBM-HBP, a Bayesian framework that integrates resting-state and task fMRI to improve cortical parcellation accuracy and individual specificity.

This paper conducts a multi-ancestry GWAS of stimulant use disorder, identifies 19 genome-wide significant loci, and finds genetic correlations with other psychiatric and substance use disorders.

This study develops a framework to calibrate polygenic risk scores and analyze misclassification and inconsistency in risk stratification.

The paper introduces DIMPLE-GWAS, a dimensionality reduction framework for GWAS of brain scans, identifying 25 latent phenotypes with higher heritability and discovering 104 new significant loci.

This paper develops and validates multi-ancestry polygenic risk scores for type 2 diabetes prediction and complications across diverse populations.

The paper identifies shared genetic loci between cortical morphology and psychiatric disorders, finding regional and global effects with directional heterogeneity.

The paper examines how intersectional sociodemographic factors affect genetic risk estimates from polygenic scores across multiple diseases.

This study identifies 1,294 genomic regions and 961 effector genes associated with externalizing disorders, revealing neural mechanisms and polygenic predictors of variance in these conditions.

This study conducts the first comprehensive genetic analysis of alcohol use disorder in a Nepalese population, identifying a novel genome-wide significant locus and highlighting the need for replication and improved polygenic prediction.

This paper improves type 2 diabetes polygenic risk scores by including rare and population-specific variants, showing better risk prediction in diverse populations.

This study identifies genetic associations for multiple sleep disorders and medications in over 1.6 million individuals, revealing shared and unique genetic factors across sleep traits.

This paper introduces rtPRS-CS, a real-time polygenic risk score method that dynamically updates predictions as new data streams in, improving accuracy for schizophrenia risk across diverse populations.

This study investigates the polygenic relationship between cannabis use and schizophrenia using data from the All of Us Research Program, finding independent associations between polygenic scores for cannabis use disorder and schizophrenia.

This study evaluates transdiagnostic polygenic risk scores for psychiatric disorders using the All of Us Research Program data, finding that a common psychiatric genetic factor PRS better predicts comorbidity burden than single-disorder scores.

This study identifies two distinct genetic factors influencing executive function and attentional control, showing unique links to psychiatric disorders and cognitive phenotypes.

PennPRS provides a cloud platform for training polygenic risk scores without individual-level data, validated with simulations and real data across 6,000 phenotypes.

The paper conducts GWAS on brain volumes, identifies genetic loci, and develops polygenic scores with cross-ancestry predictive ability and causal links to neuropsychiatric disorders.

This paper introduces SuSiEx, a cross-population fine-mapping method that improves resolution and power by integrating data across ancestries, leading to more accurate identification of causal variants in complex traits and diseases.

The paper improves polygenic prediction accuracy by using diverse datasets, showing greater benefits for underrepresented populations.

This study develops a novel polygenic risk score (PRS-CS) for glioma that outperforms existing methods, improving risk prediction and subtype classification using genome-wide association study data.

The paper investigates the impact of rare high-effect genetic variants on brain morphology and related behavioral traits in adolescents using the ABCD cohort.

This study uses EHR data to estimate TRD probability and identifies two genome-wide significant loci linked to metabolic traits, suggesting shared biology with related conditions.

Admix-kit provides tools for genetic analysis of admixed populations, enabling simulation, association testing, and polygenic scoring.

The paper derives cell-type-specific Alzheimer's disease polygenic risk scores and shows their associations with distinct pathological features in AD.

The paper identifies 979 novel genetic loci for 36 quantitative traits by analyzing data from the Taiwan Biobank, Biobank Japan, and UK Biobank, highlighting the value of diverse populations in GWAS.

This study identifies 928 genes associated with brain weight variation across three datasets and links them to neurodevelopmental processes and brain function.