Joshua D. Smith

Himito is a graph-based toolkit for analyzing mitochondrial genomes using long reads, achieving superior performance in assembly and variant calling and revealing age-related patterns in the All of Us dataset.

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 benchmarks nine short-read sequencing chemistries across seven sequencers using donor-specific assemblies to measure sequencing errors against sample-matched references, revealing platform-specific error profiles and highlighting the importance of donor-specific references for unbiased comparison.

MitoScope is a long-read sequencing workflow for mitochondrial genome analysis that improves sensitivity and precision in variant detection and NUMT profiling.

The paper maps somatic mutation burden and functional impact across the cancer genome using telomere-to-telomere sequencing, revealing novel mutation hotspots and regulatory rewiring mechanisms.

The paper creates a long-read benchmark for genetic variants using a large pedigree, improving variant calling performance and introducing new truth sets for tandem repeats and structural variants.

The paper uses a four-generation family to systematically assess de novo mutations and recombination, finding high mutation rates and paternal bias, along with a detailed recombination map.

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 assesses pharmacogenomic variation and medication exposures in a diverse cohort using whole genome sequencing and EHR data, finding high prevalence of actionable variants and previously unreported alleles.

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.

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 integrates multi-omics data to analyze how sleep-disordered breathing traits affect gene expression, metabolites, and potential causal pathways across blood cell types.

The paper describes the process of obtaining an FDA IDE for returning genomic results via WGS in the All of Us Research Program and presents validation results.

The paper benchmarks RNA-seq association analyses for continuous exposures using DESeq2, edgeR, limma, and linear regression, finding linear regression methods are faster and better at controlling false detections.

The paper identifies genetic determinants of chemical resistance in yeast using in vitro evolution and whole genome sequencing, revealing key transcription factors involved in drug resistance.