James A. Perry

cellSTAAR integrates single-cell ATAC-seq with whole-genome sequencing to enhance power and interpretation of rare variant association tests in non-coding regions.

The paper analyzes exome array data from 9,721 ischemic stroke cases to identify coding variants associated with stroke, finding 15 significant variants, including 13 novel associations.

The paper performs whole genome sequence association analysis of fasting glucose and insulin levels in diverse cohorts, identifying 22 significant variants across 16 gene regions, including rare variants.

The paper estimates heritability for height and BMI using whole-genome sequence data and finds that rare variants contribute significantly to the missing heritability.

This paper identifies novel genetic loci for thoracic and abdominal aortic aneurysms using GWAS in the UK Biobank and finds distinct genetic architectures and shared pathophysiology with Marfan syndrome.

The paper identifies novel genetic determinants of lipidome variation in a founder population, highlighting rare alleles associated with specific lipid species and replicating known common loci.

OASIS enables real-time biological discovery from GWAS data by providing a web-based system with analysis tools and visualizations for Old Order Amish studies.

The paper identifies rare non-coding variants from whole genome sequencing in the TOPMed program, showing they contribute significantly to type 2 diabetes heritability.

The paper introduces a tool that automates sequential conditional analysis for genomic variant-phenotype associations, streamlining the process with minimal user input.

The paper analyzes de novo mutations in 1,465 genomes to show similar mutation rates across ancestries but reduced rates in the Amish population, with environmental factors possibly influencing mutation rates.

The paper identifies genetic variants affecting HbA1c levels in multi-ancestry cohorts, showing ancestry-specific impacts on diabetes diagnosis via HbA1c.

The paper identifies a missense variant in B4GALT1 associated with lower LDL-C and fibrinogen, supported by genetic, functional, and glycan profiling evidence.

The paper analyzes whole genome sequences from 16,324 individuals to identify genetic variants associated with blood lipid levels and develops a polygenic score for LDL-C.