全基因组关联研究
生物
遗传学
连锁不平衡
表观遗传学
遗传关联
计算生物学
功能基因组学
基因座(遗传学)
等位基因
单倍型
单核苷酸多态性
基因组
基因组学
基因
DNA甲基化
基因型
基因表达
作者
Erping Long,Harsh Patel,Alyxandra Golden,Monica Antony,Jinhu Yin,Karen Funderburk,James Feng,Lei Song,Jason W. Hoskins,Laufey T. Amundadottir,Rayjean J. Hung,Christopher I. Amos,Jianxin Shi,Nathaniel Rothman,Qing Lan,Jiyeon Choi
标识
DOI:10.1016/j.ajhg.2024.05.021
摘要
Genome-wide association studies (GWASs) have identified numerous lung cancer risk-associated loci. However, decoding molecular mechanisms of these associations is challenging since most of these genetic variants are non-protein-coding with unknown function. Here, we implemented massively parallel reporter assays (MPRAs) to simultaneously measure the allelic transcriptional activity of risk-associated variants. We tested 2,245 variants at 42 loci from 3 recent GWASs in East Asian and European populations in the context of two major lung cancer histological types and exposure to benzo(a)pyrene. This MPRA approach identified one or more variants (median 11 variants) with significant effects on transcriptional activity at 88% of GWAS loci. Multimodal integration of lung-specific epigenomic data demonstrated that 63% of the loci harbored multiple potentially functional variants in linkage disequilibrium. While 22% of the significant variants showed allelic effects in both A549 (adenocarcinoma) and H520 (squamous cell carcinoma) cell lines, a subset of the functional variants displayed a significant cell-type interaction. Transcription factor analyses nominated potential regulators of the functional variants, including those with cell-type-specific expression and those predicted to bind multiple potentially functional variants across the GWAS loci. Linking functional variants to target genes based on four complementary approaches identified candidate susceptibility genes, including those affecting lung cancer cell growth. CRISPR interference of the top functional variant at 20q13.33 validated variant-to-gene connections, including RTEL1, SOX18, and ARFRP1. Our data provide a comprehensive functional analysis of lung cancer GWAS loci and help elucidate the molecular basis of heterogeneity and polygenicity underlying lung cancer susceptibility.
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