Xianghong Hu

MRMU improves Mendelian randomization by accounting for measured covariates and unmeasured confounders, identifying robust risk factors for coronary artery disease and novel drug targets.

XMR improves causal inference in underrepresented populations by using cross-population GWAS summary statistics to increase instrumental variables and enhance statistical power.

This paper introduces IBSEP, a statistical framework that integrates bulk and scRNA-seq data to prioritize cell-type-specific eQTLs, showing improved performance over existing methods.

This study benchmarks 15 Mendelian Randomization methods using real genetic data to evaluate their performance in causal inference under various confounding scenarios.

XMAP improves fine-mapping by using cross-population GWAS data and accounting for confounding bias, enhancing statistical power and computational efficiency.

This paper introduces MR-APSS, a Mendelian Randomization method that addresses pleiotropy and sample structure to improve causal inference from GWAS summary statistics.

This paper introduces LOG-TRAM, a method to improve trans-ancestry association mapping by leveraging local genetic structure, enhancing power and reducing bias in under-represented populations.