Wenmin Zhang

CaPri is a Bayesian Mendelian randomization framework that jointly estimates causal and pleiotropic effects, showing robust performance across varying pleiotropy proportions and real data applications.

This study benchmarks four Bayesian colocalization methods to validate MR-identified protein-trait associations for cardiometabolic traits, finding SharePro most frequently supports significant associations.

This paper develops a multi-ancestry polygenic risk score for BMI that predicts longitudinal weight change and shows strong associations across diverse genetic ancestries.

This study identifies 36 circulating proteins associated with a shared genetic liability for schizophrenia, depression, and bipolar disorder using genomic and proteomic data.

The paper examines colocalization and discordance between plasma and brain cis-pQTLs, finding that most colocalize but some show opposite genetic effects, with implications for interpreting protein-trait associations.

The paper maps adipokine-associated variants to effector genes across 1,669 traits, identifying TGFB2 and VEGFA as linked to insulin resistance and distinct disease risks.

This study investigates challenges in case-only analysis using polygenic risk scores for gene-environment interaction detection, showing false positives due to model misspecification and collider bias in large biobanks.

This study identifies OA-specific genetic effects and novel therapeutic targets by separating OA genetics from those shared with obesity using GWAS and proteomics data.

This paper introduces HLA-colocalization, a method for genetic colocalization at the HLA locus using HLA alleles, showing it identifies novel causal associations between viruses and autoimmune diseases.

MR Corge performs sensitivity analysis for Mendelian randomization by identifying core instruments to reduce horizontal pleiotropy bias and improve causal effect estimation.

This paper improves power to detect gene-environment interactions by adapting SharePro to account for genetic effect heterogeneity in fine-mapping.

RSparsePro improves fine-mapping accuracy in the presence of LD mismatch by using a probabilistic graphical model and variational inference, outperforming SuSiE in simulations and real applications.

This study uses proteome-phenome-wide Mendelian randomization across three ancestries to identify protein-phenotype associations and potential therapeutic targets.

The paper identifies variance quantitative trait loci (vQTLs) for vitamin D levels and characterizes gene-environment interactions involving modifiable factors and sex.

SharePro improves genetic colocalization analysis by handling multiple causal signals, showing increased power and efficiency in simulations and real data.

SparsePro improves fine-mapping of GWAS results by integrating summary statistics and functional annotations, achieving higher power and faster computation than existing methods.

This paper introduces Gene Score Regression (GSR), a method to partition gene-based variance of complex traits by accounting for gene-to-gene correlations, improving the accuracy of GWAS annotation.