Qiongshi Lu

This study benchmarks methods for estimating genetic correlation between complex traits using GWAS summary statistics, highlighting challenges like LD dependency and sample overlap.

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.

ORIGAMI simulates offspring genomes from parental data to improve polygenic transmission disequilibrium analysis, revealing significant within-family risk variation for complex traits.

This study identifies genetic variants and pathways associated with cognitive resilience to Alzheimer's disease, finding a novel locus linked to bile acid metabolism and methylation changes.

This study links a transdiagnostic polygenic score for psychopathology to specific depression trajectories from adolescence to adulthood, highlighting the role of negative emotionality in mediating these associations.

This paper investigates gene-environment interactions affecting vitamin D levels and optimal intake across different ethnic groups.

The paper develops a polygenic risk score integrating AD biomarkers and shows it better predicts cognitive decline than a clinical AD-based PRS.

This paper introduces PUMAS, a method to fine-tune polygenic risk scores using GWAS summary statistics, improving predictive performance and statistical power in downstream analyses.

The paper develops ancestry-specific polygenic scores for 25(OH)D levels, showing they predict blood concentrations and inform vitamin D supplementation needs.

The paper integrates genomics, metabolomics, and Alzheimer’s risk factors from 1,111 participants to identify gene-metabolite-AD risk factor relationships and metabolites explaining tau variance.

This study identifies sex-specific genetic variants associated with facial attractiveness using a genome-wide association approach in 3,928 samples.

The paper introduces a statistical framework for cross-tissue transcriptome-wide association analysis, improving gene-trait association detection and identifying novel genes related to late-onset Alzheimer's disease.

This paper introduces a method to estimate annotation-stratified genetic covariance between traits using GWAS summary statistics, identifying significant genetic covariances among 50 complex traits.

The paper introduces GenoSkyline-Plus, a framework for tissue-specific functional annotation of the human genome, showing its utility in identifying immune-related DNA elements linked to late-onset Alzheimer’s disease.

This paper introduces AnnoPred, a framework that uses functional annotations to improve genetic risk prediction for complex diseases, showing enhanced accuracy across multiple diseases.

This paper introduces GenoSkyline, a framework for tissue-specific functional annotations that improves GWAS signal prioritization and provides insights into complex traits.

This paper introduces GenoCanyon, a statistical framework that predicts functional non-coding regions in the human genome by integrating 22 annotations.

GenoWAP improves GWAS results by integrating functional annotations to prioritize risk loci and SNPs, showing stronger signals and better replication rates in Crohn's disease and schizophrenia.