Kathryn S. Burch

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.

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 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 introduces SCORE, a scalable and accurate method for estimating genetic correlation in large datasets, achieving higher precision than existing methods.

The paper estimates the contribution of dominance effects to complex trait variation using biobank-scale data and finds that dominance heritability is small compared to additive heritability.

The paper proposes a two-step method to detect gene-environment interactions across multiple phenotypes, showing improved power in simulations and identifying significant signals in lipid data from UK Biobank.

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

The paper introduces an efficient algorithm for variance components analysis, enabling rapid estimation of hundreds of variance components across large genomic datasets and demonstrating its utility in heritability estimation and functional annotation partitioning.

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 introduces a method to accurately estimate SNP-heritability from large biobank data without assuming genetic architecture, showing existing methods can be off by up to 30%.

This study identifies 43 tissue-specific gene associations with Dupuytrens disease risk and finds significant genetic correlations with BMI, type II diabetes, and lipid traits.

This paper introduces a method to enhance GWAS power by incorporating functional annotations, resulting in increased detection of genome-wide significant loci.

The paper quantifies the overlap between GWAS genes for 62 complex traits and Mendelian disorder genes, finding significant enrichment and elevated effect sizes for phenotypically-matched genes.