Huwenbo Shi

scEPS integrates GWAS and single-cell data to identify disease-associated cell neighborhoods by testing gene expression variance explained in disease contexts.

MultiSuSiE improves multi-ancestry fine-mapping in All of Us whole-genome sequencing data by allowing causal effect sizes to vary across ancestries, achieving higher power and lower computational costs.

The paper introduces LDSPEC to estimate correlations between causal disease effects of proximal SNPs, finding significant correlations that vary with distance, allele frequency, LD, and functional annotations, implicating stabilizing selection.

The paper investigates the link between genetic associations and regulatory function, finding limited evidence that baseline gene expression explains GWAS associations.

The paper develops a combined SNP-to-gene linking strategy (cS2G) to improve the identification of disease-causing genes and assess disease omnigenicity using GWAS data.

This paper introduces PolyPred and PolyPred+ to improve cross-population polygenic risk score accuracy by addressing LD, MAF, and causal effect size differences using fine-mapping and non-European training data.

The paper partitions gene-level heritability by allele frequency to identify disease-relevant genes with heritability from rare variants, finding 370 gene-trait pairs not detected by existing methods.

The paper introduces GCSC regression to identify gene sets enriched for disease heritability by leveraging gene co-regulation in TWAS data, finding immune-related associations in Alzheimer's disease.

The paper develops a method to identify population-specific causal effect sizes in functionally important regions using trans-ethnic genetic correlations.

This study identifies 13 genome-wide significant loci associated with SARS-CoV-2 infection or severe COVID-19 through a global meta-analysis of 49,562 patients.

PLEIO maps and interprets pleiotropic loci by jointly analyzing summary statistics of multiple traits, identifying 20 novel loci related to cardiovascular disease.

The paper develops ACLR to estimate genomic autocorrelation of causal SNP effects and finds significant negative autocorrelation at short distances in complex traits.

The paper estimates population-specific and shared causal SNPs from GWAS summary data across East Asian and European populations and finds shared SNPs are enriched in functional categories.

This paper introduces a method to accurately estimate SNP-heritability from large biobank data without assuming genetic architecture, showing existing methods can be off by up to 30%.

RAISS improves the imputation of genetic variants from GWAS summary statistics, achieving high accuracy for small effect size variants suitable for multi-trait analyses.

This paper introduces a probabilistic framework to fine-map transcriptome-wide association study signals, prioritizing causal genes while accounting for linkage disequilibrium and pleiotropy.

The paper quantifies the overlap between GWAS genes for 62 complex traits and Mendelian disorder genes, finding significant enrichment and elevated effect sizes for phenotypically-matched genes.

This paper introduces a Bayesian framework called UNITY to quantify genetic overlap between traits using GWAS summary statistics, showing it produces estimates consistent with known genetic relationships in height and BMI.

This paper introduces a Bayesian framework to identify regulatory effects at GWAS risk loci by integrating GWAS summary data with molecular QTL data.

This paper introduces a method to quantify local genetic correlations between complex traits using GWAS summary data, identifying genomic regions contributing to genetic correlations and inferring causality.

This study estimates local heritability from GWAS summary data for 30 traits, finding common SNPs contribute broadly, some traits have concentrated heritability, and some loci explain heritability across multiple traits.

The paper integrates gene expression and GWAS data to identify novel genes associated with obesity-related traits.