Browsing by Author "Yang, Tie-Lin"

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    High-throughput functional dissection of noncoding SNPs with biased allelic enhancer activity for insulin resistance-relevant phenotypes
    (Elsevier, 2023) Duan, Yuan-Yuan; Chen, Xiao-Feng; Zhu, Ren-Jie; Jia, Ying-Ying; Huang, Xiao-Ting; Zhang, Meng; Yang, Ning; Dong, Shan-Shan; Zeng, Mengqi; Feng, Zhihui; Zhu, Dong-Li; Wu, Hao; Jiang, Feng; Shi, Wei; Hu, Wei-Xin; Ke, Xin; Chen, Hao; Liu, Yunlong; Jing, Rui-Hua; Guo, Yan; Li, Meng; Yang, Tie-Lin; Medical and Molecular Genetics, School of Medicine
    Most of the single-nucleotide polymorphisms (SNPs) associated with insulin resistance (IR)-relevant phenotypes by genome-wide association studies (GWASs) are located in noncoding regions, complicating their functional interpretation. Here, we utilized an adapted STARR-seq to evaluate the regulatory activities of 5,987 noncoding SNPs associated with IR-relevant phenotypes. We identified 876 SNPs with biased allelic enhancer activity effects (baaSNPs) across 133 loci in three IR-relevant cell lines (HepG2, preadipocyte, and A673), which showed pervasive cell specificity and significant enrichment for cell-specific open chromatin regions or enhancer-indicative markers (H3K4me1, H3K27ac). Further functional characterization suggested several transcription factors (TFs) with preferential allelic binding to baaSNPs. We also incorporated multi-omics data to prioritize 102 candidate regulatory target genes for baaSNPs and revealed prevalent long-range regulatory effects and cell-specific IR-relevant biological functional enrichment on them. Specifically, we experimentally verified the distal regulatory mechanism at IRS1 locus, in which rs952227-A reinforces IRS1 expression by long-range chromatin interaction and preferential binding to the transcription factor HOXC6 to augment the enhancer activity. Finally, based on our STARR-seq screening data, we predicted the enhancer activity of 227,343 noncoding SNPs associated with IR-relevant phenotypes (fasting insulin adjusted for BMI, HDL cholesterol, and triglycerides) from the largest available GWAS summary statistics. We further provided an open resource (http://www.bigc.online/fnSNP-IR) for better understanding genetic regulatory mechanisms of IR-relevant phenotypes.