Linxi Liu

This paper introduces a framework for identifying disease-associated features tailored to individual profiles using localized test statistics, applied to single-cell RNA sequencing data for Alzheimer's disease.

This paper introduces a method to identify causal variants from genome-wide summary statistics, outperforming existing methods and discovering additional loci for complex traits.

This paper introduces GhostKnockoff, a method to improve causal variant identification in GWAS by increasing statistical power and reducing proxy variants.

This paper introduces a gene-based testing framework that improves power and reduces false positives by integrating chromatin interactions and knockoff genotypes, showing better performance in GWAS and whole-genome sequencing data.

This paper introduces a statistical method using knockoff statistics to identify causal loci in whole-genome sequencing data, showing increased discovery potential in Alzheimer's and COPD studies.