Tony Chen

A Polygenic Score for Smoking Persistence Captures Differences in Cessation Age

This paper develops a polygenic score for smoking persistence and finds it is associated with earlier or later cessation ages across diverse populations.

SPLENDID incorporates continuous genetic ancestry in biobank-scale data to improve polygenic risk prediction across diverse populations

SPLENDID improves polygenic risk prediction across diverse populations by incorporating continuous genetic ancestry without requiring discrete ancestry labels.

STELLAR: A flexible ensemble learning framework integrating rare variants to enhance polygenic risk prediction

STELLAR improves polygenic risk prediction by integrating rare and common variants using ensemble learning, enhancing accuracy and refining risk stratification.

Rare coding and noncoding variants map 1,342 diseases and biomarkers in 490,549 whole genomes

The paper maps rare coding and noncoding variants to 1,342 diseases and biomarkers using whole genome sequencing data from 490,549 UK Biobank participants.

Evaluating Multi-Ancestry Genome-Wide Association Methods: Statistical Power, Population Structure, and Practical Implications

This study compares pooled analysis and meta-analysis for multi-ancestry GWAS, finding pooled analysis offers better statistical power while controlling population structure.

Genomic Insights for Personalized Care: Motivating At-Risk Individuals Toward Evidence-Based Health Practices

The paper develops polygenic risk scores to stratify lung cancer and smoking cessation risk, showing significant odds ratios in diverse populations and planning clinical trials to evaluate interventions.

Ensembled best subset selection using summary statistics for polygenic risk prediction

ALL-Sum improves polygenic risk prediction by combining efficient summary statistic-based regression with ensembling, outperforming existing methods in accuracy, speed, and memory usage.

A new method for multi-ancestry polygenic prediction improves performance across diverse populations

The paper introduces CT-SLEB, a method for multi-ancestry polygenic prediction that improves performance in non-European populations using GWAS summary statistics and simulation studies.