Massively parallel variant-to-function mapping determines functional regulatory variants of non-small cell lung cancer

作者：Congcong Chen, Yang Li, Yayun Gu, Qiqi Zhai, Songwei Guo, Jun Xiang, Yuan Xie, Mingxing An, Chenmeijie Li, Na Qin, Yanan Shi, Yang Liu, Jun Zhou, Xianfeng Xu, Ziye Xu, Kai Wang, Meng Zhu, Yue Jiang, Yuanlin He, Jing Xu, Rong Yin, Liang Chen, Lin Xu, Juncheng Dai, Guangfu Jin, Zhibin Hu, Cheng Wang, Hongxia Ma, Hongbing Shen · 发表于：Nature Communications · 年份：2025 · DOI：10.1038/s41467-025-56725-w · 被引用次数：5 · 研究领域：RNA modifications and cancer、Cancer-related molecular mechanisms research、Genetic Associations and Epidemiology

Genome-wide association studies have identified thousands of genetic variants associated with non-small cell lung cancer (NSCLC), however, it is still challenging to determine the causal variants and to improve disease risk prediction. Here, we applied massively parallel reporter assays to perform NSCLC variant-to-function mapping at scale. A total of 1249 candidate variants were evaluated, and 30 potential causal variants within 12 loci were identified. Accordingly, we proposed three genetic architectures underlying NSCLC susceptibility: multiple causal variants in a single haplotype block (e.g. 4q22.1), multiple causal variants in multiple haplotype blocks (e.g. 5p15.33), and a single causal variant (e.g. 20q11.23). We developed a modified polygenic risk score using the potential causal variants from Chinese populations, improving the performance of risk prediction in 450,821 Europeans from the UK Biobank. Our findings not only augment the understanding of the genetic architecture underlying NSCLC susceptibility but also provide strategy to advance NSCLC risk stratification. Determining the causal variants at GWAS loci is crucial for understanding genetic disease mechanisms. Here, the authors apply MPRA to perform non-small cell lung cancer (NSCLC) variant-to-function mapping at scale and propose distinct genetic architectures underlying NSCLC susceptibility.