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Biomedical subjects

Jiangyuan Liu

Publications and source records attributed to Jiangyuan Liu.

2 recordsLinked to original sources

Interrogation of functional variants in COPD GWAS loci by massively parallel reporter assays.

RATIONALE: Genome-wide association study (GWAS) loci often contain many linked variants, making it difficult to determine which variant is functionally relevant. Massively parallel reporter assays (MPRA) allow experimental testing of candidate variants to identify those with regulatory activity. Prior chronic obstructive pulmonary disease (COPD) MPRA studies have largely focused on individual loci, whereas broader multi-locus, multi-cell-type interrogation remains limited. OBJECTIVES: We aim to identify functional variants in five COPD GWAS loci across three lung-relevant cell types. METHODS: We screened 1120 variants using MPRA in epithelial (16HBE), fibroblast (MRC5), and endothelial (HUVEC) cells followed by reporter assay validation. Public Hi-C, ChIP-seq and ATAC-seq datasets were analyzed to evaluate chromatin context near candidate variants. We further performed CRISPR interference (CRISPRi) targeting variant-containing regions and measured gene expression by RT-qPCR in primary normal human bronchial epithelial (NHBE) cells using two gRNAs per variant. Co-immunoprecipitation was performed to test interaction between selected candidate genes. MEASUREMENTS AND MAIN RESULTS: In MPRA, we identified 25 variants with allele-specific effects (∼2% of tested variants). Enrichment of H3K27Ac and open chromatin near rs35421223 was detected in 16HBE cells. CRISPRi identified two SNP-gene pairs, RUVBL1 and RAB7A regulated by rs35421223 in both the 16HBE cell line and primary NHBE cells. We detected interaction between RUVBL1 and the known COPD gene product FAM13A. CONCLUSIONS: Screening COPD loci across three cell types identified functional regulatory variants and linked them to candidate target genes for future mechanistic studies.

Journal Article

UnionLoops: a workflow for calling chromatin loops across related Hi-C datasets with improved specificity, precision, and sensitivity.

Chromatin loop calling from chromatin interaction data often exhibits substantial variability across related samples. We present UnionLoops, a computational workflow for chromatin loop calling across multiple related samples. UnionLoops integrates information across datasets to determine positions and dataset-specificity of looping interactions. It constructs a unified candidate loop set, applies consistent filtering and aggregation, and evaluates loop support across samples. We demonstrate that UnionLoops increases sensitivity for detecting shared chromatin loops, reduces spurious sample-specific calls, and improves concordance with independent genomic features, including CTCF and cohesin occupancy. UnionLoops enables improved biological interpretation of chromatin loop organization and dynamics across related conditions.

Chromatin