Yuchang Wu

SARDINE uses EWAS summary statistics to estimate epigenetic clock-phenotype associations without individual-level data, revealing heterogeneity in biomarker associations with aging and diseases.

This study performs the first GWAS on a latent partner choice index using 206,617 individuals, identifying genetic correlates and solutions to reduce assortative mating biases in genetic studies.

This paper introduces a statistical framework for regularized ensemble polygenic risk prediction using GWAS summary statistics, showing improved performance across traits and populations.

This paper introduces a statistical framework to optimize and benchmark polygenic risk scores using GWAS summary statistics, showing improved performance over existing methods.

This paper shows sibling models fail to accurately uncover direct genetic effects and can introduce biases when genetic nurture effects are present.

This study identifies biases in proxy GWAS using parental Alzheimer's history, showing misleading genetic correlations and highlighting design issues in biobank cohorts.

The paper uses polygenic scores from sibling pairs in the UK Biobank to show compensatory processes in educational attainment, reducing genetic associations by 20% for higher-ranked siblings.

This study introduces PIGEON, a statistical framework for gene-environment interaction analysis using summary statistics, showing its superiority in multiple analytic settings.

This paper introduces QUAIL, a model for detecting genetic variants affecting trait variability, and finds 11 novel BMI vQTL with interactions with physical activity and sedentary behavior.

The paper uses GWAS to link infant mortality rates with genetic variants, finding genome-wide significant loci associated with mortality exposure and related traits.

This study introduces SUPERGNOVA, a framework for estimating local genetic correlations, and identifies 150 trait pairs with significant local genetic correlations, including autism spectrum disorder and cognitive performance.

This paper estimates direct and indirect genetic effects using summary statistics from multi-generational GWAS, showing distinct patterns for traits like educational attainment and autism spectrum disorder.

The paper identifies novel risk genes for autism spectrum disorder using transcriptome-wide transmission disequilibrium analysis across 7,805 ASD proband-parent trios.

The paper performs a metabolome-wide association study to identify CSF metabolite-phenotype associations in neurological and psychiatric disorders.

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

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.