Haoyu Zhang

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

This paper identifies 116 risk loci for melanoma through GWAS, links susceptibility to immunity and Wnt signaling, and develops a PRS improving risk prediction.

This study analyzes breast cancer GWAS across multiple ancestries, finding similar heritability and genetic correlations, and identifies shared regulatory and cellular contexts.

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

This study integrates polygenic risk scores and proteomic risk scores to improve prediction of complex traits across diverse populations.

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

MetaSTAARlite is a tool for rare variant meta-analysis using summary statistics from biobank-scale whole-genome sequencing data, showing linear scaling and concordance with pooled analysis.

The paper presents an isoform-level lung cell atlas using single-cell long-read RNA-sequencing, revealing genetic effects on isoform regulation that improve cell-type classification and identify novel variants associated with lung cancer risk.

This paper improves type 2 diabetes polygenic risk scores by including rare and population-specific variants, showing better risk prediction in diverse populations.

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

This study identifies a unique mutational signature, SBS87, in testicular germ cell tumors (TGCTs) that resembles thiopurine-induced DNA damage and is associated with tumor latency and telomere elongation.

JointPRS improves genetic risk prediction for non-European populations by integrating genetic correlation and multi-population data without individual-level tuning data.

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.

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

This study uses population-scale whole genome sequencing to identify novel rare protein-coding variants associated with cardiometabolic traits, revealing new biological mechanisms and gene functions.

This paper introduces PROSPER, an ensemble penalized regression method for multi-ancestry polygenic risk prediction, showing improved performance across diverse populations.

MUSSEL improves polygenic risk prediction across populations by integrating GWAS summary statistics and using Bayesian hierarchical modeling with ensemble learning.

This paper introduces re-PLS Learning to predict multiple AD outcomes from brain cortical thickness, identifying joint predictive regions across cohorts.

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.

The paper identifies breast cancer risk variants associated with tumor subtypes and shows their differential effects on luminal and non-luminal subtypes.

This study assesses polygenicity and risk prediction using common variants across 14 cancers, finding varying numbers of susceptibility variants and risk score effectiveness.

This study identifies 32 new breast cancer susceptibility loci through GWAS, with associations varying by tumor subtype and genetic correlations among subtypes.

The paper introduces a mixed-model approach to test genetic associations with cancer risk, incorporating tumor characteristics, and shows it outperforms existing methods in identifying heterogeneous associations.

The paper develops a simplified power analysis framework for rare variant association studies and provides insights into genetic architecture and annotation quality.