Thomas W. Blackwell

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 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.

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 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 connects genotype, phenotype, and drug response in dilated cardiomyopathy using molecular mechanism-based computational and experimental approaches for a troponin T mutation.

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 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.

The paper validates novel telomere length regulation genes POP5 and KBTBD6 identified through trans-ancestry GWAS using functional experiments in blood cells.

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 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 investigates how rare and common genetic variations contribute to QT interval duration, finding that both monogenic and polygenic factors influence QTc prolongation, with polygenic risk modulating the effect of rare variants.

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 introduces Giraffe, a pangenome mapper that improves genotyping of SNVs, indels, and SVs in 5,202 human genomes using the vg toolkit.

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 uses whole genome sequencing to identify genetic loci associated with platelet aggregation, including novel variants in RGS18, SVEP1, and PEAR1.

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.

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.

This study identifies rare genetic variants associated with type 2 diabetes using exome sequencing of 20,791 cases and 24,440 controls, and provides a Bayesian framework to improve variant prioritization.

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.