Quan Sun

StarTrail identifies rapidly changing regions and cliff genes in spatial omics data, improving boundary detection and uncovering missed biological insights.

HAUDI improves polygenic score construction for admixed populations by efficiently extending the GAUDI method to multi-way admixture with faster computation and better performance.

PEARL is a deep graph learning method for multi-omics integration that outperforms existing methods and identifies functionally important genes in Alzheimer's disease.

The paper improves risk prediction for cardiometabolic traits in American Indians by integrating polygenic scores with lifestyle and clinical context variables, enhancing model discrimination for coronary heart disease.

EPIC-unmix improves cell-type specific analysis from bulk RNA-seq by integrating single-cell reference profiles, identifying novel genes and eQTLs in Alzheimer's disease.

This study introduces VAE-PRS, a deep learning model that improves polygenic prediction for blood cell traits by capturing interaction effects and outperforming traditional methods in 14 out of 16 traits.

DREAM-GNN improves drug repositioning by integrating heterogeneous biomedical data through a dual-route graph neural network framework, outperforming existing methods in recovering repositioning candidates.

This study identifies 22 novel gene-sleep duration interaction loci for blood pressure traits across 811,405 individuals, highlighting distinct genetic influences of short and long sleep on cardiovascular health.

The paper performs GWAS on cardiovascular outcomes in CKD patients, identifying stroke-associated SNVs but not replicating CHD findings, and finding genetic correlations with published GWAS.

This study identifies 17 genetic loci associated with sleep duration and lipid levels, offering potential druggable targets for dyslipidemia in individuals with sleep disturbances.

The paper identifies eQTL and sQTL in African American blood cells, finding many rare variants undetected in European ancestry studies and creating an open database for results.

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

The paper identifies four SNPs in the LILRB3 gene associated with graft failure in African American kidney transplant recipients and links them to immune-related diseases.

The paper identifies functional CREs in iPSCs and neurons and links them to neuropsychiatric disease risk variants.

The paper demonstrates that TOPMed imputation improves the identification of rare variants associated with type 2 diabetes in Latino populations, leading to novel associations and better polygenic scores.

The paper uses evolutionary constraint scores to identify functional genomic regions and finds they are enriched for variants associated with common diseases.

DeepGWAS enhances GWAS signals for neuropsychiatric disorders using deep neural networks, improving locus detection in schizophrenia and validating results in other disorders.

GAUDI improves polygenic risk prediction in admixed populations by modeling ancestry-specific and shared effects, outperforming existing methods in simulations and real data.

The paper identifies ancestry-derived loci associated with CKD traits and risk factors in U.S. Hispanic/Latino individuals of Central American origin using admixture mapping.

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

This study examines the genetic links between baseline hematological parameters and risk of SARS-CoV-2 infection and severe COVID-19 using Mendelian randomization and GWAS data.

This study identifies 557 new gene-trait associations for hematological traits using transcriptome-wide association analysis in UK Biobank data.

The paper improves genotype imputation for CF patients using the TOPMed reference panel and a CF-specific imputation panel, enhancing marker coverage and PRS accuracy.

eSCAN improves aggregate association testing in WGS data by integrating enhancer regions, enhancing statistical power and resolution in blood cell trait analysis.

This study prioritizes functional elements of ~148,000 GWAS variants for blood cell traits using a web tool, showing prioritized variants are more likely to be experimentally validated.

The paper identifies novel genetic associations for biomarker traits in non-European UK Biobank populations, highlighting missed signals in European-centric analyses.

AFA estimates ancestral allele frequencies in admixed populations with high accuracy, showing strong correlation with gnomAD frequencies in the Hispanic Community Health Study/Study of Latinos.

SnapHiC identifies high-resolution chromatin loops from scHi-C data and demonstrates cell-type-specific chromatin architecture in mouse and human cells.