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

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.

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Publication Details

Journal
Nature Communications
Published
2025-02-06
DOI
https://doi.org/10.1038/s41467-025-56725-w
Citations
1
Primary Topic
RNA modifications and cancer
Type
article
Field-Weighted Citation Impact
0.81

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article

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

Hongbing Shen, Jun Zhou, Hongxia Ma, Rong Yin et al.
1 citations
Nature Communications
RNA modifications and cancer
0.81
article

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

Hongbing Shen, Jun Zhou, Hongxia Ma, Rong Yin, Mingxing An, Yuan Xie, Yuanlin He, Guangfu Jin, Juncheng Dai, Jing Xu, Cheng Wang, Qiang-Lan Zhai, Chenmeijie Li, Na Qin, Yang Li, Congcong Chen, Zhibin Hu, Songlin Guo, Liang Chen, Yang Liu, Kai You Wang, Xianfeng Xu, Jun Xiang, Meng Zhu, Lin Xu, Yue Jiang, Yayun Gu, Yanan Shi, Ziye Xu
article en
1 citations

Abstract

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.

Nature CommunicationsVol. 16(1)
Chinese Academy of Medical Sciences & Peking Union Medical College (CN), Nanjing Traditional Chinese Medicine Hospital (CN), Suzhou Municipal Hospital (CN), Jiangsu Cancer Hospital (CN), Changzhou No.2 People's Hospital (CN), Jiangsu Province Hospital (CN), Nanjing Medical University (CN)
National Natural Science Foundation of China, Government of Jiangsu Province, Chinese Academy of Medical Sciences
Good health and well-being
Openalex Percentile: Top 31%
RNA modifications and cancer
0.81
DOIOpen Access PDFOpenAlex
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