Russell Paul Bowler

This paper introduces MT-LLE, a multi-task manifold learning method that improves disease modeling from longitudinal omics data by integrating clinical labels and temporal progression, achieving better clinical structure and progression forecasting than standard methods.

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.

This study investigates the association between mosaic loss of Y chromosome (mLOY) and lung function, emphysema, and epigenetic aging in men across multiple cohorts.

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 RAGE as a case study to demonstrate how structural biology and variant databases can inform pathogenicity prediction, highlighting challenges in interpreting variant effects.

This paper introduces SGTCCA-Net, a framework for multi-omics network inference that addresses limitations of existing methods by incorporating higher-order correlations and improving computational efficiency.

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 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 proteomic networks and genetic variants associated with smoking and COPD phenotypes using multi-ethnic proteomic data and network-based GWAS.

PathIntegrate integrates multi-omics data using pathway-based modeling to identify important pathways and molecules contributing to outcomes, demonstrated on COPD and COVID-19 datasets.

The paper identifies heterogeneity in acute exacerbations of COPD using wearable data and develops a deep learning framework with weak supervision and privacy protection for early detection.

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

This study shows that proteomic data can be linked to specific genomes using pQTL knowledge, highlighting privacy risks and the need for protections similar to genomic data.

The paper compares transcriptomic profiles of CF probands and their parents to healthy controls, revealing CFTR-related immune dysfunction in both homozygous and heterozygous carriers.

PaIRKAT improves testing power for pathway-phenotype associations by integrating graph structure into kernel regression, showing effectiveness in metabolomics data.

The paper reports whole-genome association analyses of sleep-disordered breathing traits in the NHLBI TOPMed program, identifying rare and common variant associations.

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.

The paper uses whole genome sequencing to identify genetic variants associated with plasma lipoprotein(a) levels and finds high heritability and inter-ethnic differences in Lp(a) risk.

The paper examines immune cell changes in COPD patients and finds associations between cell counts and lung function decline, suggesting immune response roles in disease progression.