Pak Chung Sham

LRCQ estimates phenome-wide heritability enrichment of genomic loci using GWAS summary statistics, identifying enriched regions and their functional annotations.

LRCP identifies functionally related SNPs and genes by estimating phenome-wide genetic-effect correlations from GWAS summary statistics, improving accuracy over naive methods.

The paper introduces hEVO, a genomic foundation model trained on human haplotype sequences, achieving top performance in variant interpretation and functional annotation across multiple benchmarks.

The paper uses single-cell multiome data to identify QTLs regulating gene expression and chromatin accessibility during cellular senescence, revealing new causal variants and regulatory mechanisms.

This study conducts a GWAS and cross-ethnic meta-analysis of schizophrenia phenotypes in Hong Kong Chinese and European populations, identifying genetic variants and building predictive models for clinical outcomes.

MACHINE improves multi-ancestry fine-mapping by using a continuous prior and FDR control, identifying new causal variants in lipid traits and schizophrenia.

This study identifies genetic variants associated with antipsychotic-induced lipid and BMI changes in schizophrenia patients using GWAS, offering potential for personalized treatment strategies.

This study identifies common and rare variant risk loci for ADHD in a Hong Kong population, implicating cerebellar development and immune genes.

CausalCellInfer integrates single-cell RNA-seq with biobank-scale GWAS to identify cell-type-specific disease mechanisms, revealing known and novel biological signals in traits like Type 2 Diabetes and depression.

This study introduces a deep learning framework, FIND, to better classify genetic variants by their deleteriousness and trait-modulating effects, showing improved performance over existing methods.

This study links neurotransmitter pathway polygenic risk to specific symptom profiles in psychosis, identifying associations between glutamate and dopamine pathways with cognitive control and global functioning.

INSIDER is a statistical framework for analyzing bulk RNA expression data that models interactions between biological variables and decomposes variation into a shared latent space to improve interpretation and downstream analysis.

This paper introduces a Bayesian fine-mapping model, h2-D2, which outperforms existing methods in identifying causal variants and applies it to prostate cancer, discovering new variants and associated genes and pathways.

This study shows pathway-specific polygenic scores improve cross-ancestry prediction of psychosis risk and clinical outcomes compared to genome-wide polygenic scores.

The paper develops DeepAnchor, a deep learning model to characterize CTCF binding patterns and predict CTCF-anchored loops across human tissues, revealing sequence and chromatin features that influence loop formation and disease mechanisms.

The paper identifies causal regulatory elements in complex genetic loci using CRISPR perturbations and shows that many causal variants lack classical epigenetic markers.

This paper introduces a Bayesian network framework to estimate direct and indirect causal effects of genes on complex traits using GWAS data and gene expression, showing improved enrichment of known genes and pathways.

This paper introduces a new statistical method, RUNNER, to detect rare variants associated with complex diseases by comparing observed mutation burdens to predicted baseline values, showing improved power over existing methods.

This study uses multivariable Mendelian randomization to show that higher bone mineral density causally increases osteoarthritis risk independently of body mass index.

This paper uses Mendelian randomization to identify causal links between coagulation factors and COVID-19 severity, finding VWF and ADAMTS13 as significant factors.

This study identifies 38 novel SLE loci and shows genetic heterogeneity across ancestral groups, highlighting antibody production as a potential mechanism for disease disparities.

The paper extends a method to estimate parent-specific genetic effects on offspring traits, applying it to UK Biobank data to analyze birthweight and BMI.

DIPPER provides a spatiotemporal proteomics atlas of human intervertebral discs, revealing region-specific regulatory profiles and a hydration matrisome linked to MRI intensity.

LFMD improves low-frequency mutation detection in high-depth sequencing by combining duplex sequencing and bottleneck sequencing to enhance sensitivity and specificity.

The paper identifies noncoding genetic variants associated with Hirschsprung disease using a framework that captures multi-granular regulatory element perturbations.

This paper estimates indirect parental genetic effects on offspring phenotypes using virtual parental genotypes derived from sibling and half-sibling pairs, demonstrating the feasibility of this approach with GWAS data.

This paper identifies a common genetic factor across schizophrenia, bipolar disorder, and major depressive disorder using a trans-diagnostic, top-down approach, and links it to ZNF391 expression patterns in the brain that correlate with cognitive and neuroimaging traits.

This study identifies causal relationships between plasma proteins and psychiatric disorders using proteome-wide Mendelian randomization, revealing potential biomarkers and drug targets.

The paper uses whole exome sequencing to identify ciliary gene mutations in nonsyndromic biliary atresia, finding significant enrichment of deleterious variants in liver-expressed ciliary genes.

This study uses Mendelian randomization to find that triglycerides causally link to depression symptoms and self-harm, while HDL cholesterol shows weaker associations.