Shuanglin Zhang

This study constructs a bipartite Genotype and Phenotype Network to analyze genetic associations and improve phenome-wide association studies by identifying pleiotropic loci and controlling false discovery rates.

This paper introduces a Genotype and Phenotype Network (GPN) to improve multiple phenotype association studies by integrating genetic and phenotypic data, showing increased power in detecting pleiotropic loci.

The paper proposes gene selection methods that integrate genetic networks into case-control association studies, showing improved true positive rates for identifying rheumatoid arthritis-related genes.

This paper introduces HCLC-FC, a multivariate method for PheWAS that improves FDR control and identifies significant SNPs in large-scale EMR data.

This paper introduces ceCLC, a computationally efficient method for joint GWAS analysis of multiple phenotypes, which outperforms existing methods in controlling type I error and maintaining statistical power.

This paper introduces G-TOW, a statistical method for testing associations between optimally weighted combinations of common and rare variants and disease traits, showing improved power and accuracy over existing methods.

This paper develops new statistical methods to test gene-environment interactions for multiple traits in sequencing association studies, showing improved power over existing methods.

This paper introduces TOWmuT, a statistical method for testing associations between multiple traits and a weighted combination of genetic variants, showing it is more powerful than existing methods.