Russell P. Tracy

The paper analyzes multi-omic, multi-tissue molecular responses to acute exercise in sedentary adults, identifying key pathways and exerkines involved in exercise effects.

The paper identifies genetic drivers of protein change over time using longitudinal proteomics data from MESA, UK Biobank, and CARDIA, finding that baseline adjustment improves replication but may introduce biases.

This study integrates CAD GWAS with transcriptomic data to identify genes and networks associated with CAD and subclinical atherosclerosis.

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 paper develops a machine learning framework to impute cross-platform proteomic data, improving replication of epidemiological findings and protein quality scoring.

This study identifies tissue-specific lncRNA responses to endurance exercise in rats, revealing sex differences and potential therapeutic implications.

cellSTAAR integrates single-cell ATAC-seq with whole-genome sequencing to enhance power and interpretation of rare variant association tests in non-coding regions.

This study identifies FNDC1 as a key regulator of vascular calcification through the FNDC1-NAMPT-NAD axis, showing its role in both microvascular and macrovascular diseases and its potential as a therapeutic target.

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

The paper conducts proteome-wide and transcriptome-wide association studies for blood lipids, finding high-level consistency between proteome and transcriptome-lipid associations across tissues.

The paper develops a method to quantify somatic mutation burden in whole-genome sequencing data without paired-tissue samples and identifies genetic and phenotypic correlates of clonal hematopoiesis without known driver mutations.

This study examines how endurance exercise training affects rat subcutaneous white adipose tissue, revealing sex-specific molecular responses and differences in adiposity and metabolism.

The paper maps interaction QTLs (iQTLs) for cell type, age, and other variables in blood multi-omic data to identify context-specific genetic regulatory effects.

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 impactful rare variants by integrating multi-omic data from MESA, showing that combining expression and protein data increases the detection of rare stop and frameshift variants.

This paper introduces MCFA, an unsupervised integration method for multi-omic data, and applies it to five omics types across 614 samples to infer shared and private factors.

The paper develops PACER to infer clonal expansion rates from single timepoint data and identifies TCL1A activation as a mediator of clonal hematopoiesis fitness.

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.

This paper improves multi-omics integration by enhancing orthogonality in canonical vectors and demonstrates transferability across cohorts for proteomics and methylomics data.

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.

This study profiles multi-omic changes in multiple tissues of rats during endurance exercise training, identifying molecular adaptations and sex-specific responses across time.

This study analyzes clonal hematopoiesis in over a million diverse individuals, identifying 72 genome-wide significant loci and linking CHIP to various phenotypes.

The paper tests for genetic interactions affecting causal variant effect sizes in gene expression and LDL-C across European and African American populations, finding significant evidence in gene expression but not LDL-C.

The paper uses bidirectional Mendelian randomization to show LTL lengthening increases CHIP risk, while CHIP accelerates LTL shortening, with modest mediation between CHIP and CAD via LTL.

The paper develops genetic predictors of plasma protein abundance in diverse populations and demonstrates their utility in proteome-wide association studies (PWAS) for complex traits.

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

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.

The paper identifies germline genetic variants associated with clonal hematopoiesis of indeterminate potential (CHIP) using whole genome sequences from the TOPMed program.