Genetic effects on gene expression often vary across cell types, motivating expression quantitative trait loci (eQTL) mapping in single-cell RNA sequencing data. Existing approaches generally aggregate expression across cells, use Gaussian models poorly suited to read counts or do not scale. Here we show that SAIGE-QTL...
Wei Zhou, Anna S. E. Cuomo, Lin-Feng Hu et al.· Nature Genetics· 0 citations
Understanding how genetic variation shapes gene regulation at single-cell resolution is crucial for elucidating complex disease mechanisms1, but has been limited by the lack of large-scale single-cell multi-omics data2-4. Here we present single-cell assay for transposase-accessible chromatin with sequencing of 3.5 mill...
A. Xue, Jia-Nan Fan, Oscar A. Dong et al.· Nature· 3 citations
Understanding the genetic basis of gene expression can shed light on the regulatory mechanisms underlying complex traits and diseases1,2. Single-cell-resolved measures of RNA levels and single-cell expression quantitative trait loci have revealed genetic regulation that drives cell states and types across diverse tissu...
Anna S. E. Cuomo, E. Spenceley, H. Tanudisastro et al.· Nature· 3 citations
This study establishes large-scale adipocyte cell villages as a powerful and generalizable framework to map the context-dependent regulatory architecture of cardiometabolic disease and provides a resource linking human genetic variation to adipocyte cellular programs.
Yi Huang, Joaquin Perez-Schindler, Sanchari Datta et al.· bioRxiv· 0 citations
The developed sc-pcQTL complements conventional single-gene eQTL analysis by identifying trait-relevant regulatory signals shared across neighboring genes and summarizes each cluster using principal components, and maps cis-pcQTLs.
Jun-Kai Zhang, Yi Huang, M. Claussnitzer et al.· bioRxiv· 0 citations
