Qiuying Sha

Constructing genotype and phenotype network helps reveal disease heritability and phenome-wide association studies

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.

A novel method for multiple phenotype association studies based on genotype and phenotype network

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.

Gene selection by incorporating genetic networks into case-control association studies

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.

HCLC-FC: a novel statistical method for phenome-wide association studies

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

A computationally efficient clustering linear combination approach to jointly analyze multiple phenotypes for GWAS

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.

Testing gene-environment interactions for rare and/or common variants in sequencing association studies

This study introduces two new statistical methods to test gene-environment interactions for rare and common variants in sequencing association studies, showing improved power compared to existing methods.

A General Statistic to Test an Optimally WeightedCombination of Common and/or Rare Variants

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.

A gene based approach to test genetic association based on an Optimally Weighted Combination of Multiple Traits

This paper introduces a statistical method to test genetic association by combining multiple traits and variants within a gene, showing it has correct type I error rates and good power.

Test Gene-Environment Interactions for Multiple Traits in Sequencing Association Studies

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

Testing an Optimally Weighted Combination of Common and/or Rare Variants with Multiple Traits

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.