Biology subjects

Qiu, W.-L.

Publications and source records attributed to Qiu, W.-L..

3 recordsLinked to original sources

bioRxiv2026

Metabolic disease-relevant stimuli unmask context-dependent genetic regulation of cardiometabolic loci in human adipocytes

Genome-wide association studies (GWAS) have identified thousands of loci associated with cardiometabolic disease, yet translating these associations into regulatory mechanisms, effector genes, and cellular programs remains a major challenge. A key limitation is that genetic effects are often highly context dependent, varying across cell states and environmental conditions that are difficult to model at scale. Here, we leverage CellGenBank, a population-scale biobank of primary human adipose-derived mesenchymal stem cells (AMSCs), to implement a multi-donor cell village in vitro system to map cardiometabolic disease genetic variation across adipocyte differentiation and metabolic stress conditions. We pooled AMSCs from 118 donors into multiplexed villages, differentiated them toward adipocytes, and profiled chromatin accessibility and gene expression using single-nucleus multiome sequencing under four disease-relevant conditions: basal, elevated free fatty acids, low glucose, and hypoxia. By combining with genetic demultiplexing, we quantify how regulatory element activity, gene expression, and higher-order cellular programs are modulated by both genotype and environmental context. Across conditions, we identify widespread context-specific cis-regulatory effects, including expression and chromatin accessibility quantitative trait loci that are masked in baseline states. Genetic effects frequently converge on coordinated transcriptional programs linked to lipid metabolism, insulin responsiveness, and stress adaptation, enabling the identification of cellular program QTLs that bridge variants, genes, and disease-relevant phenotypes. Integration with cardiometabolic GWAS reveals enhanced colocalization in condition- and state-resolved analyses, highlighting the importance of modeling environmental context to resolve disease mechanisms. Together, our 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.

genomics↗

bioRxiv2026

Mapping active cis-regulatory elements from transcription initiation events

Determining the activity of cis-regulatory elements (CREs) is essential for modeling gene regulation and interpreting genetic variation. Yet, current methods often lack the specificity to distinguish active regulation from permissive chromatin, the sensitivity to detect unstable enhancer RNAs, or the scalability required to profile limited input material and primary cells. Here, we introduce nucCAGE, a transcription start site (TSS) assay for profiling nuclear, capped RNAs, and PRIME, a computational framework for identifying active CREs from TSS data. Together, these methods increase sensitivity to low-abundance RNAs and enable robust detection of active regulatory elements across diverse contexts. Across multiple orthogonal functional and genetic benchmarks, including fine-mapped eQTLs, ClinVar variants, GWAS loci, and CRISPRi-tested elements, nucCAGE-derived PRIME predictions achieve superior recall compared to state-of-the-art methods while maintaining strong enrichment for phenotype-associated variation. Applying PRIME to the FANTOM5 dataset yields a comprehensive, cell-type-resolved atlas of active CREs that recapitulates known tissue-trait relationships. We demonstrate how this atlas can be used to nominate causal noncoding variants, linking immune-cell enhancer regulation of SMAD3 to asthma and NCOR2 to premature separation of placenta. Together, nucCAGE and PRIME provide a framework for high-sensitivity genome-wide discovery of active CREs and a resource for variant-to-function studies.

genomics↗

bioRxiv2024

Mapping enhancer-gene regulatory interactions from single-cell data

Mapping enhancers and their target genes in specific cell types is crucial for understanding gene regulation and human disease genetics. However, accurately predicting enhancer-gene regulatory interactions from single-cell datasets has been challenging. Here, we introduce a new family of classification models, scE2G, to predict enhancer-gene regulation. These models use features from single-cell ATAC-seq or multiomic RNA and ATAC-seq data and are trained on a CRISPR perturbation dataset including >10,000 evaluated element-gene pairs. We benchmark scE2G models against CRISPR perturbations, fine-mapped eQTLs, and GWAS variant-gene associations and demonstrate state-of-the-art performance at prediction tasks across multiple cell types and categories of perturbations. We apply scE2G to build maps of enhancer-gene regulatory interactions in heterogeneous tissues and interpret noncoding variants associated with complex traits, nominating regulatory interactions linking INPP4B and IL15 to lymphocyte counts. The scE2G models will enable accurate mapping of enhancer-gene regulatory interactions across thousands of diverse human cell types.

genetics↗