Sheila M. Gaynor

This study identifies 207 height-associated genes using rare protein-altering variants from over 1.4 million individuals, showing large effect sizes and overlap with Mendelian height syndromes.

This study quantifies cancer risk and penetrance in CHEK2 heterozygotes using genomic data from two large biobanks, finding increased risks for several cancers and younger age of onset.

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 introduces SBAT, a method for joint testing of rare variant burden scores, showing it is well calibrated and outperforms existing tests in some scenarios.

The paper compares the effectiveness of whole genome sequencing, whole exome sequencing with imputation, and array genotyping in detecting genetic associations across 100 complex traits in the UK Biobank, finding that WES+IMP provides similar signal yield to WGS with greater efficiency.

The paper analyzes inflammation biomarkers using whole-genome sequencing from the TOPMed consortium, identifying both single-variant and rare variant associations with inflammatory traits.

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 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 association analysis of fasting glucose and insulin levels in diverse cohorts, identifying 22 significant variants across 16 gene regions, including rare variants.

The paper genotypes and sequences 140,000 adults from the Mexico City Prospective Study, revealing ancestry diversity and developing imputation resources for genetic studies.

The paper introduces a cloud-based workflow for rare variant analysis in whole genome sequencing studies, enabling scalable and reproducible analysis with functional annotations.

The paper introduces a method to improve eQTL inference in undersampled tissues by jointly modeling a target outcome and a correlated surrogate outcome.

The paper evaluates adjusted spectral clustering methods for detecting gene clusters in eQTL data, considering regression errors in network construction.

The paper assesses the reproducibility and functional importance of eQTL network nodes by analyzing degree centrality across 21 GTEx tissues.