Nancy L. Heard‐Costa

TorchGWAS2 improves genome-wide association testing in related samples by using a deterministic variance-correction algorithm, achieving faster and more powerful results across large-scale omics data.

The paper analyzes expression and splicing quantitative trait loci (e/sQTL) across multiple tissues in the TOPMed dataset and finds that secondary e/sQTL signals are still discoverable, with many GWAS signals colocalizing with these e/sQTL.

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 integrates GWAS meta-analysis with human brain cell mapping to identify genetic loci and cell types involved in tinnitus, implicating the amygdala and midbrain.

The paper identifies genes associated with obesity risk loci by integrating genetic and transcriptomic data from blood and brain tissues.

The paper introduces MultiSTAAR, a statistical framework for multi-trait rare variant analysis in WGS studies, identifying new lipid trait associations in diverse populations.

The paper identifies genetic and phenotypic correlates of mosaic loss of chromosome Y in blood using whole genome sequencing data from the TOPMed program.

This study identifies gene-environment interactions between excessive daytime sleepiness and genetic variants linked to obstructive sleep apnea severity, revealing novel therapeutic targets.

The paper investigates the association of common and rare variants with Alzheimer's Disease in 13,371 diverse individuals using whole-genome sequencing, identifying significant genetic loci and population-specific associations.

This study uses whole genome sequence data from the ADSP to identify 17 significant variants in five genomic regions associated with Alzheimer's 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 identifies IL1RAP and AIM2 as key modifiers of CHIP-associated cardiovascular risk using UK Biobank data and CRISPR experiments in mice.

The paper identifies a gene-diet interaction modifying the association between macronutrient intake and glycemic traits, highlighting challenges in detecting such interactions due to measurement error and sample size.

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

This study identifies 35 genes associated with circulating lipid levels using rare coding variants from 170,000 exomes across multiple ancestries.

The paper estimates genetic and environmental correlations between complex phenotypes across different racial/ethnic groups and sexes using data from the TOPMed program.

The paper evaluates penetrance and variable expressivity of monogenic metabolic conditions using exome data from 77,184 individuals, finding that monogenic variants have larger effect sizes than polygenic scores but low penetrance.

The paper describes a system for harmonizing phenotype data across multiple studies in the TOPMed program, enabling reproducible and shared phenotype data for over 80 studies.

Over 400 million variants, 97% below 1% frequency, emerge from deep whole-genome sequencing of 53,831 diverse TOPMed participants; TOPMed haplotypes enable imputation down to ~0.01% frequency.

This study identifies DNA methylation sites associated with central adiposity traits in adults and adolescents using methylome-wide association analyses across multiple cohorts.

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.

This study identifies 15 common and 9 low-frequency protein-coding variants associated with body fat distribution, implicating novel genes related to lipid homeostasis and type 2 diabetes.

This study identifies rare genetic variants associated with type 2 diabetes using exome sequencing of 20,791 cases and 24,440 controls, and provides a Bayesian framework to improve variant prioritization.