Jessica Ann Lasky-Su

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 develops a reproducibility-based framework for creating DNA methylation algorithms on the MSA, showing that stable probe sets can support new biomarkers and transfer of existing ones with improved performance.

The paper identifies plasma metabolites associated with incident heart failure with reduced (HFrEF) and preserved ejection fraction (HFpEF) using metabolomics and machine learning, finding higher glutamine levels increase risk while omega-3 fatty acids decrease it.

The paper shows that blood DNA methylation can predict gene expression based on regulatory mode and enhancer architecture, with lineage genes being more predictable than stimulus-responsive genes.

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 identifies age-associated Th2-like CD8 memory T cells in human blood and links their accumulation to health conditions like asthma and diabetes.

This study identifies aging genes in human blood by integrating epigenetic and transcriptomic data, finding genes enriched for immune functions that better replicate across populations and link to aging outcomes.

The paper develops Biolearn, an open-source framework for curating and evaluating aging biomarkers across datasets, finding that harmonization and standardization improve their performance and generalizability.

TranslAGE provides a standardized framework for benchmarking epigenetic aging biomarkers by harmonizing datasets and precomputing scores across multiple performance domains.

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.

The paper introduces Binary Cellular Analysis to quantify growth and development by modeling cell division and proliferation, providing equations for fetal growth and body part formation.

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.

CpGPT is a foundation model for DNA methylation analysis that outperforms specialized models in aging-related tasks and can impute methylation profiles across diverse conditions.

This paper introduces Ageome, a framework to measure epigenetic age across molecular pathways in mice and humans, revealing pathway-specific aging patterns and heterogeneity in mortality prediction.

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.

This study performs a genome-wide association study using mass spectrometry proteomics to identify pQTLs, finding that epitope effects may affect some affinity proteomics results while confirming others.

The paper uses metabolomics and machine learning to identify metabolites associated with early childhood hs-CRP levels, finding 52 metabolites with potential bioactivity linked to systemic inflammation.

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

This paper uses DNA methylation data to analyze immune cell fractions across 25,629 blood samples, identifying associations with age, sex, health outcomes, and lifestyle factors.

The paper presents a comprehensive catalog of structural variants across 138,134 individuals, identifying novel variants and their associations with cardio-metabolic and hematologic traits.

The paper links growth and aging through a mathematical model showing both processes are tied to the reduction in the fraction of cells dividing, providing insights into aging mechanisms and potential applications in improving vaccine effectiveness in the elderly.

This study identifies eight novel genetic signals associated with asthma-COPD overlap through a genome-wide association analysis involving multiple cohorts.

The paper introduces two new growth processes, UNI-GROWTH and ALLO-GROWTH, and their mathematical equations to model cellular development across species.

This paper introduces RUTH, a new HWE test that accounts for population structure and genotype uncertainty, showing it reduces false positives compared to traditional methods.

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 59 sentinel variants and two novel genes associated with telomere length using whole genome sequences from 109,122 trans-ethnic individuals.

The paper introduces a framework for region-based association testing in family-based studies, showing improved performance over existing methods in simulations and a real dataset.

This study uses TOPMed whole genome sequences to improve imputation quality and detect rare variant associations in admixed African and Hispanic/Latino populations, identifying two novel associations with hematological traits.