Lei Hou

JointMR: A joint likelihood-based approach for causal effect estimation in overlapping Mendelian Randomization studies

JointMR integrates GWAS summary databases to estimate causal effects, providing unbiased and reliable results with higher power and better Type I error control than traditional methods.

TEMR: Trans-ethnic Mendelian Randomization Method using Large-scale GWAS Summary Datasets

TEMR improves causal effect estimation in non-European populations using trans-ethnic GWAS data, identifying 13 new causal relationships.

MRSL: A phenome-wide causal discovery algorithm based on GWAS summary data

MRSL uses GWAS summary data to discover causal relationships between biomarkers and diseases, achieving higher accuracy and speed than existing methods.

Consequence of adjustments for demographic or clinical covariates and a recommended solution in genome-wide association studies

This paper evaluates GWAS adjustment strategies and recommends adjusting for SNPs in linkage disequilibrium rather than demographic or clinical covariates.