Geyu Zhou

This paper improves genetic risk prediction across diseases by developing multi-trait polygenic scores that integrate GWAS data and show better performance than single-trait scores.

This study introduces PleioSDPR, a method that improves polygenic risk prediction by jointly modeling genetic effects across correlated traits, showing significant accuracy gains in real trait applications.

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.

This paper introduces a Bayesian method to correct attenuation bias in regression analysis using polygenic risk scores, showing improved coefficient estimation and credible interval coverage.

This paper improves the portability and fairness of polygenic scores by introducing Joint-Lassosum, which outperforms existing methods in diverse ancestries.

OTTERS is a TWAS framework that uses summary-level eQTL reference data to estimate eQTL weights and perform omnibus TWAS, showing improved power and applicability.

This paper introduces HapFM, a haplotype-based fine-mapping framework for plant GWAS, which improves mapping power and resolution compared to existing methods.

This paper introduces a Bayesian nonparametric method for predicting complex traits using summary statistics, showing it is adaptive, robust, and computationally efficient.

This paper introduces T-GEN, a method that improves gene-disease association identification by incorporating epigenetic annotations, resulting in more accurate and functionally relevant gene discoveries.