Haoran Xue

This paper introduces a multivariable Rao-Blackwellization method to correct Mendelian randomization biases from winner's curse, polygenicity, and sample structure, improving type-I error control and inference robustness.

The paper uses deep learning to extract neuroimaging features from MRI data for Alzheimer's disease GWAS, identifying associated SNPs linked to neurodegenerative disorders.

The paper introduces a causal direction inference method for GWAS summary data that handles correlated pleiotropy and identifies bi-directional causal relationships between traits.

This paper introduces new methods for inferring causal directions between traits using GWAS summary data, addressing limitations of Steiger's method by incorporating multiple SNPs and handling horizontal pleiotropy.

This paper evaluates the performance of standard TWAS versus 2SRI and error correction in TWAS, concluding that standard TWAS is effective for large samples and small effects.