Yu‐Jyun Huang

This study evaluates the impact of weak instrumental variables in two-sample Mendelian randomization, showing they can weaken effect estimates in small samples but are less harmful in large ones.

The paper introduces the expected polygenic risk score (ePRS) framework to address population stratification in polygenic risk score analysis by modeling ancestral makeup, showing it provides equitable risk quantification across diverse populations.

This paper develops a semi-empirical Bayesian framework to calibrate weak instrumental bias in sex-specific Mendelian randomization studies, revealing significant sex differences in causal effects of sleep phenotypes on cardiovascular diseases.

This study introduces a Bayesian Markov Random Field model to infer gene network edges, considering both existence and strength, and demonstrates its performance on glioblastoma data.

This study evaluates the impact of multivariate normality assumptions in gene-set analysis, finding that non-normality affects performance, especially in mixed tumor subtypes.