Zhongshang Yuan

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

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

This paper introduces PMR-Egger, a probabilistic Mendelian randomization method to test and control horizontal pleiotropy in transcriptome-wide association studies, showing improved type I error control and power.

This study finds an inverse association between low birth weight and chronic kidney disease in observational studies but no causal link using Mendelian randomization.