Ying Wang

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

The paper uses admixture mapping in the All of Us Research Program to identify genetic ancestry associations with 22 traits, finding 71 novel loci where African ancestry explains phenotypic variation.

FELIX improves genetic association analysis by accounting for local ancestry in admixed populations, increasing significant loci and improving polygenic score prediction.

The paper performs large-scale GWAS and RVAS on 392,030 whole genomes from All of Us, identifying 49,863 associations and 193 novel pLoF gene-phenotype links through meta-analysis with UK Biobank.

This paper constructs phenome-derived polygenic scores and evaluates how they, along with social determinants of health, influence disease risk across different populations and diseases.

This paper improves genetic discovery and polygenic risk prediction for respiratory diseases by analyzing multiple traits and ancestries, identifying novel loci and developing a new polygenic risk score method.

This study analyzes 26 years of abstracts from two major human genetics conferences to identify geographic disparities in researcher representation and oral presentation success.

This study conducts GWAS in a large Korean cohort, identifying 301 novel loci and highlighting East Asian-specific genetic associations for 36 traits.

This study compares polygenic scores from genotyping arrays and whole-genome sequencing across traits and populations, finding that WGS generally improves prediction accuracy but with context-dependent advantages.

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

This paper introduces SBayesRC, a method that improves polygenic prediction of complex traits by integrating GWAS summary statistics with functional genomic annotations, showing significant accuracy gains across ancestries.

Cross-trait Elastic Net mixing of PGS Catalog scores (PRSmix+) lifted mean PRS accuracy 1.72-fold in Europeans and 1.42-fold in South Asians over the best single score.

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

Meta-analyzing per-ancestry GWAS of 7,266 UK Biobank traits across six genetic ancestry groups adds 14,676 loci missed by European-only analysis, including ancestry-enriched CAMK2D and G6PD signals.

This study identifies ancestry-specific and sex-specific genetic loci for primary open-angle glaucoma through a meta-analysis of 15 biobanks, highlighting shared biology with vascular mechanisms and cell proliferation.

The paper analyzes genetic, ancestral, and geospatial data from a biobank to explore their relationships with health outcomes in a historically diverse population.

This study shows that polygenic risk scores from multi-ancestry GWAS perform better in understudied populations than single-ancestry scores, except when the understudied group is small.

HAPNEST generates and evaluates large-scale synthetic genotype and phenotype datasets for polygenic risk score analysis.

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.

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.

This study investigates genetic correlations and causal relationships between multiple sclerosis and inflammatory bowel diseases, identifying shared SNPs and tissue-specific heritability patterns.

The paper compares ten polygenic score methods for psychiatric disorders across multiple cohorts, finding that some methods significantly improve prediction statistics over the baseline PC+T method.

The paper quantifies genetic heterogeneity between European and African populations for height and BMI, finding high shared genetic variation suggesting GWAS findings in Europeans are applicable to non-Europeans.

Candidate Explorer identifies causative mutations for immune phenotypes by integrating multiple data features into a score, improving mutation-phenotype association verification in mice.

This paper quantifies the accuracy loss of polygenic scores in non-European populations by modeling linkage disequilibrium, allele frequencies, and effect size correlations.