Bingxin Zhao

The paper presents a connection-level genetic atlas of human cortical functional connectivity, identifying genetic loci associated with resting and task states.

PRIORITI uses language models to predict target-indication success in drug discovery, achieving high accuracy and robustness across different benchmarks.

Domain-grounded prompting lets GPT-5 produce genetic evidence for 13,022 drug target-indication pairs that predicts clinical success as well as expert curation, with wider coverage.

The paper maps phenotypic and genetic links between plasma proteins and organ imaging traits using UK Biobank data, identifying associations and causal links across multiple organs.

This paper maps genetic, proteomic, and metabolomic effects along human white-matter tracts using UK Biobank data, revealing localized associations with neurodegenerative and psychiatric traits.

The paper constructs a multi-ancestry methylome atlas of purified monocytes, identifies population-specific methylation quantitative trait loci, and performs methylome-wide association studies for 41 traits, revealing novel associations for type 2 diabetes.

The paper identifies rare protein-coding variants associated with multi-organ MRI traits using exome-wide association analysis in the UK Biobank.

The paper uses genetically imputed brain imaging phenotypes to find associations with psychiatric traits in a multi-ancestry cohort.

This study benchmarks five DNA foundation models across genomic tasks, finding that mean token embeddings improve classification performance and specialized models outperform general ones in gene expression and QTL prediction.

This study uses Mendelian randomization to identify genetic causal links between 58 diseases, 56 imaging traits, and multiple organs across 402 imaging traits and 372 clinical outcomes.

The paper evaluates proteomic and polygenic risk scores for disease prediction, finding proteomic scores outperform polygenic scores in most traits and show temporal responsiveness.

The paper investigates organ-specific and modality-specific aging clocks using proteomic and imaging data, revealing distinct biological processes and their roles in age-related diseases.

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 develops a deep learning method to quantify 3D aortic geometry in large biobanks, expanding available phenotypes for studying aortic degeneration.

This paper introduces a Bayesian Mendelian randomization framework to analyze causal effects of latent exposures on disease outcomes using GWAS summary statistics for co-regulated traits.

This study identifies 393 genomic loci associated with biological age gaps in nine human organ systems using UK Biobank data, revealing organ-specific and pleiotropic genetic effects.

This study identifies sixteen genome-wide significant loci associated with brain age gaps derived from MRI modalities and highlights potential causal relationships between brain aging and diseases.

The paper introduces BLISS, a framework for training proteome-wide association models using pQTL summary statistics, enabling multi-ancestry analysis of protein-phenotype associations.

The paper investigates the genetic impact of the X-chromosome and sex differences in the human brain, identifying 72 genome-wide significant trait-locus pairs linked to brain-related disorders.

The paper introduces BIGA, a cloud platform for analyzing cross-trait genetic architecture using GWAS summary statistics, enabling unified data analysis and resource sharing.

The paper uses genetic data to predict imaging-derived phenotypes from the UK Biobank, creating polygenic scores that identify disease risk in external cohorts.

The paper identifies 60 genetic loci associated with brain ventricular and subcortical shape features using shape measures from over 45,000 UK Biobank and ABCD study subjects.

This study identifies genetic overlaps between sleep traits and multi-organ imaging biomarkers, showing strong genetic links between sleep and psychiatric disorders.

This study identifies common genetic variants influencing intrinsic brain networks using rsfMRI data from 44,190 individuals, finding associations with cognitive and clinical traits.

The paper investigates phenotypic and genetic links between heart and brain using MRI data from 40,000 UK Biobank subjects, finding strong correlations and shared genetic influences.

This study identifies genetic loci associated with functional brain networks and areas using large-scale fMRI data, linking them to cognitive traits and brain disorders.

This study identifies common genetic variants influencing white matter microstructure using dMRI data from 42,919 individuals, revealing genetic links to neuropsychiatric disorders and other traits.

This study performs transcriptome-wide association analysis on 211 neuroimaging traits, identifies 204 genes, and shows TWAS-derived polygenic risk scores explain up to 6.97% of phenotypic variance.

This study identifies 287 novel genetic variants associated with brain volumes using GWAS in 19,629 UK Biobank participants and finds genetic correlations with cognitive and mental health traits.

This paper investigates the performance of polygenic risk scores (PRS) for complex traits, showing how prediction accuracy depends on sample size, SNP count, and true causal SNP count.