Jianjun Zhang

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.

Powerful statistical method to detect disease associated genes using publicly available GWAS summary data

The paper introduces a statistical method to detect disease-associated genes using GWAS summary data, identifying novel genes related to beta-cell function and lipid traits.

An Optimally Weighted Combination Method to DetectNovel Disease Associated Genes Using Publicly Available GWAS Summary Data

The paper introduces an optimal weighted combination test for detecting novel disease-associated genes using GWAS summary data and shows it outperforms existing methods in simulations and real data.

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.