Yi Ding

This study performs GWAS on 20 rare cancers using three cohorts, identifying nine novel susceptibility loci with replication across eight cancer types.

The paper integrates perturbational screens, eQTL, and GWAS data to identify mediating genes for complex traits, finding 546 significant genes enriched for immune response and cellular signaling.

This study finds that higher polygenic risk scores for breast cancer are associated with longer survival in patients, suggesting a link between genetic predisposition and less aggressive tumors.

ALADYNOULLI integrates longitudinal health records and genetic data to model disease progression, identifying interpretable disease signatures with cross-population consistency and improving clinical prediction.

The paper improves polygenic prediction accuracy by using diverse datasets, showing greater benefits for underrepresented populations.

The paper introduces context-aware prediction intervals for polygenic scores to improve calibration across diverse populations and traits.

This study develops multi-ancestry polygenic risk scores for venous thromboembolism using diverse GWAS data and shows improved predictive performance across European- and African-ancestry populations.

Admix-kit provides tools for genetic analysis of admixed populations, enabling simulation, association testing, and polygenic scoring.

This paper evaluates a European-derived polygenic score for tobacco use disorder in a diverse biobank, finding inconsistent performance across ancestries and associations with systemic health risks.

This paper quantifies how differences in allelic effect sizes across ancestry backgrounds impact GWAS power in admixed populations and finds modeling these differences does not significantly improve results.

This study evaluates polygenic risk score methods across diverse biobanks and ancestries, finding that European reference panels often yield better performance, while PRS-CS outperforms P+T for high-heritability traits.

The paper describes a biobank initiative that links genomic data with electronic health records to enable genome-wide and phenome-wide association studies across diverse populations.

This study develops snpSTARRseq, a high-throughput method to test non-coding variants from GWAS, showing 37% of prostate cancer risk loci alter enhancer activity.

This paper shows that meta-analysis fine-mapping is often miscalibrated due to cohort heterogeneity and introduces a QC method to identify suspicious loci.

Meta-analysis of harmonized GWAS from up to 18 biobanks (six ancestry groups) across 14 endpoints replicated 320 known loci and identified 188 apparently novel loci.

The paper evaluates polygenic score accuracy across genetic ancestry continua in diverse populations, finding significant variation in performance linked to genetic distance from training data.

The paper explores the relationship between genomic ancestry and health outcomes using a biobank linked to electronic health records, finding disease stratification across ancestry clusters.

The paper introduces CWAS, a method linking genetic variation to chromatin states, identifying novel prostate cancer risk loci and regulatory mechanisms not detectable by eQTLs.

The paper quantifies similarity in causal effects across local ancestries in admixed individuals and finds high genetic correlation (radmix=0.95) within individuals.

The paper partitions gene-level heritability by allele frequency to identify disease-relevant genes with heritability from rare variants, finding 370 gene-trait pairs not detected by existing methods.

The paper shows that individual PRS estimates have large uncertainty, impacting risk stratification, and introduces a method to quantify this uncertainty using Bayesian approaches.