Sarah Kim-Hellmuth

This study investigates how circadian immune regulation develops during postnatal life, finding that leukocyte rhythmicity and peripheral clocks mature progressively through development.

The paper presents a CITE-seq atlas of human neonatal immune development, identifying a 32-week gestational transition to interferon-primed immunity.

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 visualises molecular QTLs to better understand genetic effects on gene expression and identifies causal genes at GWAS loci.

The paper identifies 6,262 TF-eQTL interactions across 1,598 genes using GTEx data, linking genetic variants to transcription factor regulation and GWAS loci.

This study identifies a risk variant for autoimmune disease that acts as a trans-eQTL regulating IRF1 under immune stimulation, demonstrating its causal role and mechanism.

This study evaluates ten deconvolution tools on 4,000 clinical and synthetic samples, finding EPIC performs best on Framingham data and HPCA references are generally accurate.

This paper introduces Swarm Learning, a decentralized machine learning approach for disease classification using transcriptomic data, demonstrating its effectiveness in developing classifiers for COVID-19, tuberculosis, and leukemias while preserving privacy.

The paper uses GTEx data to analyze GWAS loci, showing dose-dependent RNA effects and proposing target genes for 47% of loci using combined methods.

The paper presents a large ASE resource from GTEx v8 with 431 million SNP-level and 153 million haplotype-level measurements, and extends phASER to estimate cis regulatory variant effect sizes.

The paper uses bulk RNA-seq data to estimate cell type abundances and identifies cell type interaction QTLs that provide finer resolution of tissue specificity than bulk cis-eQTLs.

The paper examines telomere length variation across 6,391 tissue samples from 952 individuals, showing tissue-specific differences, positive correlations between tissues, and associations with age and ancestry.

Integrating expression, allelic expression and splicing across tissues identifies rare variants with relevance to traits.

The paper quantifies protein levels across 32 human tissues and compares them to transcriptome data, revealing insights into post-transcriptional regulation and disease mechanisms.

The paper identifies distinct regulatory patterns and trait associations of lncRNA genes compared to protein-coding genes across human tissues.

GTEx v8 maps expression and splicing QTLs in 49 tissues from 838 donors: cis-eQTLs cover 94.7% of protein-coding genes, and cross-tissue QTL sharing mirrors cell-type composition.

The paper develops S-PrediXcan to compute gene expression associations from GWAS summary data and applies it to 44 GTEx tissues and 100+ phenotypes, finding tissue-specific effects and colocalized associations.

The paper identifies response eQTLs that modify genetic regulation during immune activation and shows their enrichment in autoimmune disease GWAS signals.