Ruth Johnson

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 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 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 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 uses HiChIP to link prostate cancer GWAS risk variants to target genes, finding new associations not detected by eQTL methods and validating them with CRISPRi.

Genome-wide association study and integrative analyses identify 27 genes in inflammation and coagulation pathways linked to COVID-19 hospitalization risk and symptomology.

The paper estimates population-specific and shared causal SNPs from GWAS summary data across East Asian and European populations and finds shared SNPs are enriched in functional categories.

This paper introduces BEAVR, a scalable method to estimate regional polygenicity using GWAS effect sizes and LD information, showing it provides unbiased estimates compared to existing methods.

This paper develops a machine learning model using EMR data to predict in-hospital mortality preoperatively, achieving higher accuracy than existing clinical scores.

This paper introduces a probabilistic framework to fine-map transcriptome-wide association study signals, prioritizing causal genes while accounting for linkage disequilibrium and pleiotropy.

This paper introduces a Bayesian framework called UNITY to quantify genetic overlap between traits using GWAS summary statistics, showing it produces estimates consistent with known genetic relationships in height and BMI.

This paper introduces fastPAINTOR, a method that improves fine-mapping accuracy at pleiotropic risk loci by integrating functional annotations and multi-trait data, reducing the number of SNPs needed for follow-up.