Leqi Xu

The paper develops cell-type-specific polygenic risk scores for CAD using scRNA-seq data and identifies lipid interactions specific to adipocytes and other cell types.

EA-PheWAS improves gene-phenotype discovery by integrating phenotype embeddings with PheWAS, outperforming conventional methods in detecting associations.

The paper introduces CASE, a Bayesian framework for cell-type-specific and shared eQTL fine-mapping, showing improved performance over existing methods in simulations and real data.

This study identifies genetic loci influencing immune cell proportions and their associations with autoimmune diseases using GWAS and cWAS.

This paper introduces EEPRS, a framework that improves polygenic risk prediction by integrating electronic health records through phenotype embeddings, showing significant performance gains over traditional methods.

This paper investigates how APOEe4 influences molecular changes in Alzheimer's disease through proteomic, epigenetic, and genetic analyses of post-mortem brain samples, identifying key proteins and epigenetic markers associated with risk and resilience.

MIXPRS improves multi-population polygenic risk score prediction by integrating GWAS summary statistics without individual-level data, outperforming existing methods across multiple traits and populations.

JointPRS improves genetic risk prediction for non-European populations by integrating genetic correlation and multi-population data without individual-level tuning data.