High-throughput functional dissection of noncoding SNPs with biased allelic enhancer activity for insulin resistance-relevant phenotypes

增强子 生物 单核苷酸多态性 遗传学 全基因组关联研究 染色质 表型 基因座(遗传学) 转录因子 基因 等位基因 基因型
作者
Yuanyuan Duan,Xiaofeng Chen,Zhu Rui,Yingying Jia,Xiaoting Huang,Meng Zhang,Ning Yang,Shan‐Shan Dong,Mengqi Zeng,Zhihui Feng,Dong‐Li Zhu,Hao Wu,Feng Jiang,Wei Shi,Wei‐Xin Hu,Xin Ke,Hao Chen,Yunlong Liu,Ruihua Jing,Yan Guo,Meng Li,Tie‐Lin Yang
出处
期刊:American Journal of Human Genetics [Elsevier BV]
卷期号:110 (8): 1266-1288 被引量:5
标识
DOI:10.1016/j.ajhg.2023.07.002
摘要

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.
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