Jeffrey R. O’Connell

The paper identifies 16 genetic loci interacting with sleep duration to influence glycemic traits in over 480,000 individuals, revealing distinct biological mechanisms for short and long sleep.

This study identifies age and survival-related transcriptomic signatures in longevity-enriched cohorts using RNAseq data from blood samples.

This study identifies twelve novel blood pressure loci and shows smoking modifies genetic effects on BP through immune-related pathways in over 1 million individuals.

This study identifies 22 novel gene-sleep duration interaction loci for blood pressure traits across 811,405 individuals, highlighting distinct genetic influences of short and long sleep on cardiovascular health.

This study uses whole genome sequencing to identify 18 novel genetic loci associated with plasma fibrinogen levels, including variants more common in African populations and linked to liver-related traits and disease.

The paper identifies 83 rare lncRNA variant sets associated with blood lipid levels using WGS data from the TOPMed program and validates some in UK Biobank data.

The paper develops PACER to infer clonal expansion rates from single timepoint data and identifies TCL1A activation as a mediator of clonal hematopoiesis fitness.

The paper introduces a framework for detecting noncoding rare variant associations using whole-genome sequencing data, applying it to lipid traits in the TOPMed program.

The paper performs whole genome sequence analysis of plasma lipid levels in 66,329 individuals, identifying new lipid-associated alleles, including rare non-coding variants.

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 evaluates 54 polygenic risk scores in long-lived individuals and their offspring, finding lower genetic risk for Alzheimer's, CAD, and lupus, but not for dementia in long-lived individuals.

The paper introduces BinomiRare, a test for rare variant association with binary outcomes in mixed models, showing it controls type 1 error in simulations and applies it to study genetic variants in disease traits.

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 method to detect short IBD segments using rare variant sharing, improving the identification of distant relatedness and older population divergence.

The paper addresses variance stratification in pooled whole genome sequencing data, showing how study-specific phenotypic variances affect analysis and introducing a method to improve statistical power and reduce false positives.

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 introduces a fully-adjusted two-stage method for rank normalization in genetic association studies to reduce Type I errors and improve power by adjusting for covariates in both stages.

The paper introduces SMMAT, a variant-set mixed model association test for continuous and binary traits in large-scale WGS studies, showing it controls type I error rates and applies to population structured and related samples.

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.