Rachel S. Kelly

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.

This study uses polygenic risk scores and proteomic analysis to identify genetic and molecular associations with asthma in two family-based cohorts, highlighting IL1R1's role in asthma pathogenesis.

The paper analyzes circulating metabolites using genome-wide association data from the TOPMed program, identifying 1,778 loci and highlighting sex-specific genetic contributions.

The paper uses WGCNA to identify protein modules in 1-year-old children linked to respiratory diseases by age 6, showing associations with environmental and metabolomic factors.

This paper quantifies individual dataset contributions to prediction accuracy using cooperative learning in the CAMP study.

This paper introduces GENN, a method for multi-omic interactome analysis that improves prediction of biomedical outcomes by learning salient associations in a low-dimensional space.

This study investigates the metabolic role of vitamin D in children's neurodevelopment using a network approach, finding associations between vitamin D deficiency and changes in tryptophan, linoleic, and fatty acid metabolism linked to ASD-related phenotypes.

The paper develops OMICmAge, a multi-omics biomarker of biological age, and shows it outperforms existing markers in predicting chronic diseases and mortality.