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Meta-Analysis
. 2023 May 26;14(1):3043.
doi: 10.1038/s41467-023-38196-z.

Genome-wide association study of lung adenocarcinoma in East Asia and comparison with a European population

Jianxin Shi #  1 Kouya Shiraishi #  2 Jiyeon Choi #  3 Keitaro Matsuo #  4 Tzu-Yu Chen #  5 Juncheng Dai #  6   7 Rayjean J Hung #  8 Kexin Chen #  9 Xiao-Ou Shu #  10 Young Tae Kim  11 Maria Teresa Landi  3 Dongxin Lin  12 Wei Zheng  10 Zhihua Yin  13 Baosen Zhou  14 Bao Song  15 Jiucun Wang  16   17 Wei Jie Seow  3   18   19 Lei Song  3 I-Shou Chang  20 Wei Hu  3 Li-Hsin Chien  5 Qiuyin Cai  10 Yun-Chul Hong  21 Hee Nam Kim  22 Yi-Long Wu  23 Maria Pik Wong  24 Brian Douglas Richardson  3   25 Karen M Funderburk  3 Shilan Li  3   26 Tongwu Zhang  3 Charles Breeze  3 Zhaoming Wang  27 Batel Blechter  3 Bryan A Bassig  3 Jin Hee Kim  28 Demetrius Albanes  3 Jason Y Y Wong  3 Min-Ho Shin  22 Lap Ping Chung  24 Yang Yang  29 She-Juan An  23 Hong Zheng  9 Yasushi Yatabe  30 Xu-Chao Zhang  23 Young-Chul Kim  31   32 Neil E Caporaso  3 Jiang Chang  33 James Chung Man Ho  34 Michiaki Kubo  35 Yataro Daigo  36   37 Minsun Song  38 Yukihide Momozawa  35 Yoichiro Kamatani  39 Masashi Kobayashi  40 Kenichi Okubo  40 Takayuki Honda  41 Dean H Hosgood  42 Hideo Kunitoh  43 Harsh Patel  3 Shun-Ichi Watanabe  44 Yohei Miyagi  45 Haruhiko Nakayama  46 Shingo Matsumoto  47 Hidehito Horinouchi  44 Masahiro Tsuboi  48 Ryuji Hamamoto  49 Koichi Goto  47 Yuichiro Ohe  44 Atsushi Takahashi  39 Akiteru Goto  50 Yoshihiro Minamiya  51 Megumi Hara  52 Yuichiro Nishida  52 Kenji Takeuchi  53 Kenji Wakai  53 Koichi Matsuda  54 Yoshinori Murakami  55 Kimihiro Shimizu  56 Hiroyuki Suzuki  57 Motonobu Saito  58 Yoichi Ohtaki  59 Kazumi Tanaka  59 Tangchun Wu  60 Fusheng Wei  61 Hongji Dai  9 Mitchell J Machiela  3 Jian Su  23 Yeul Hong Kim  62 In-Jae Oh  31   32 Victor Ho Fun Lee  63 Gee-Chen Chang  64   65   66   67 Ying-Huang Tsai  68   69 Kuan-Yu Chen  70 Ming-Shyan Huang  71 Wu-Chou Su  72 Yuh-Min Chen  73 Adeline Seow  18 Jae Yong Park  74 Sun-Seog Kweon  22   75 Kun-Chieh Chen  65 Yu-Tang Gao  76 Biyun Qian  9 Chen Wu  12 Daru Lu  16   17 Jianjun Liu  77   78 Ann G Schwartz  79 Richard Houlston  80 Margaret R Spitz  81 Ivan P Gorlov  81 Xifeng Wu  82 Ping Yang  83 Stephen Lam  84 Adonina Tardon  85 Chu Chen  86 Stig E Bojesen  87   88 Mattias Johansson  89 Angela Risch  90   91   92 Heike Bickeböller  93 Bu-Tian Ji  3 H-Erich Wichmann  94   95   96 David C Christiani  97 Gadi Rennert  98 Susanne Arnold  99 Paul Brennan  89 James McKay  89 John K Field  100 Sanjay S Shete  101 Loic Le Marchand  102 Geoffrey Liu  103 Angeline Andrew  104 Lambertus A Kiemeney  105 Shan Zienolddiny-Narui  106 Kjell Grankvist  107 Mikael Johansson  108 Angela Cox  109 Fiona Taylor  109 Jian-Min Yuan  110 Philip Lazarus  111 Matthew B Schabath  112 Melinda C Aldrich  113 Hyo-Sung Jeon  114 Shih Sheng Jiang  20 Jae Sook Sung  62 Chung-Hsing Chen  20 Chin-Fu Hsiao  5 Yoo Jin Jung  115 Huan Guo  116 Zhibin Hu  6 Laurie Burdett  3   117 Meredith Yeager  3   117 Amy Hutchinson  3   117 Belynda Hicks  3   117 Jia Liu  3   117 Bin Zhu  3   117 Sonja I Berndt  3 Wei Wu  13 Junwen Wang  118   119 Yuqing Li  120 Jin Eun Choi  114 Kyong Hwa Park  62 Sook Whan Sung  121 Li Liu  122 Chang Hyun Kang  115 Wen-Chang Wang  123 Jun Xu  124 Peng Guan  13   125 Wen Tan  12 Chong-Jen Yu  126 Gong Yang  10 Alan Dart Loon Sihoe  127 Ying Chen  18 Yi Young Choi  114 Jun Suk Kim  128 Ho-Il Yoon  129 In Kyu Park  115 Ping Xu  130 Qincheng He  13 Chih-Liang Wang  131 Hsiao-Han Hung  20 Roel C H Vermeulen  132 Iona Cheng  133 Junjie Wu  16   17 Wei-Yen Lim  18 Fang-Yu Tsai  20 John K C Chan  134 Jihua Li  135 Hongyan Chen  16   17 Hsien-Chih Lin  5 Li Jin  16   17 Jie Liu  15 Norie Sawada  136 Taiki Yamaji  137 Kathleen Wyatt  3   117 Shengchao A Li  3   117 Hongxia Ma  6   7 Meng Zhu  6   7 Zhehai Wang  15 Sensen Cheng  15 Xuelian Li  13   125 Yangwu Ren  13   125 Ann Chao  138 Motoki Iwasaki  136   137 Junjie Zhu  29 Gening Jiang  29 Ke Fei  29 Guoping Wu  61 Chih-Yi Chen  139   140 Chien-Jen Chen  141 Pan-Chyr Yang  142 Jinming Yu  15 Victoria L Stevens  143 Joseph F Fraumeni Jr  3 Nilanjan Chatterjee  3   144   145 Olga Y Gorlova  81   146 Chao Agnes Hsiung  5 Christopher I Amos  81   146 Hongbing Shen  6   7 Stephen J Chanock  3 Nathaniel Rothman  3 Takashi Kohno  2 Qing Lan  147
Affiliations
Meta-Analysis

Genome-wide association study of lung adenocarcinoma in East Asia and comparison with a European population

Jianxin Shi et al. Nat Commun. .

Abstract

Lung adenocarcinoma is the most common type of lung cancer. Known risk variants explain only a small fraction of lung adenocarcinoma heritability. Here, we conducted a two-stage genome-wide association study of lung adenocarcinoma of East Asian ancestry (21,658 cases and 150,676 controls; 54.5% never-smokers) and identified 12 novel susceptibility variants, bringing the total number to 28 at 25 independent loci. Transcriptome-wide association analyses together with colocalization studies using a Taiwanese lung expression quantitative trait loci dataset (n = 115) identified novel candidate genes, including FADS1 at 11q12 and ELF5 at 11p13. In a multi-ancestry meta-analysis of East Asian and European studies, four loci were identified at 2p11, 4q32, 16q23, and 18q12. At the same time, most of our findings in East Asian populations showed no evidence of association in European populations. In our studies drawn from East Asian populations, a polygenic risk score based on the 25 loci had a stronger association in never-smokers vs. individuals with a history of smoking (Pinteraction = 0.0058). These findings provide new insights into the etiology of lung adenocarcinoma in individuals from East Asian populations, which could be important in developing translational applications.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1. Manhattan plot for GWAS meta-analysis of lung adenocarcinoma in East Asians.
The x-axis represents chromosomal location, and the y-axis represents -log10(p-value). All p values were two-sided and not adjusted for multiple testing. The red horizontal line denotes the p value threshold for declaring genome-wide significance at 5×108. For each box, red text represents a novel variant (12 novel variants, including the lead variants from 10 novel loci, rs12664490 by conditional analysis at 6p21.1, a locus previously reported in East Asians, and rs71467682 at 15q21.2, a locus preciously reported in Europeans); black text represents a previously reported association (16 variants in total, including three independently associated variants in 5p15.33 locus). For each locus, a green circle represents the top p value from the discovery samples, a red diamond represents the p value combining the discovery and the replication data, a black square represents the p value combining our discovery data and Chinese samples in Dai et al. (for three variants identified in a cross-ancestry analysis of East Asians and Europeans in Dai et al., see Supplementary Table 3). In summary, 28 variants at 25 loci achieved genome-wide significance, including 16 previously reported variants and 12 novel variants.
Fig. 2
Fig. 2. Colocalization of lung adenocarcinoma GWAS signal from the new locus on Chr11 with FADS1 eQTL signal.
Colocalization analysis was performed using HyPrColoc with summary statistics from Taiwanese lung eQTL data (for FADS1 gene, A) and those of EA GWAS discovery set (B). LD R2 (1000 Genomes, EA) of each SNP with the GWAS lead SNP, rs174559 (red circle), is color-coded as shown in the top band. Colocalization posterior probability (PP) is shown next to the candidate SNP, rs174559. Note that the p value of rs174559 in GWAS was based on the discovery data and did not include the Japanese replication data. All eQTL p values were two-sided and not adjusted for multiple testing.
Fig. 3
Fig. 3. Comparing odds ratios (ORs) of lung adenocarcinoma susceptibility variants between East Asian (EA) and European (EUR) populations.
Here, the effect allele was defined as the minor allele in EA. Each error bar represents the 95% confidence interval of the OR (the center). A Susceptibility variants previously discovered (at genome-wide significance) in both EA and EUR populations. B Variants previously identified by multiple-ancestry meta-analysis of Chinese and EUR populations; C Variants were identified by multiple-ancestry meta-analysis combining EA samples in our study and EUR samples in ILCCO. D Variants identified only in EA populations. E Novel variants identified in the current study; F Variants identified only in EUR populations. Variants are labeled with *, **, *** and **** corresponding to 0.01 ≤ phet < 0.05, 0.001 ≤ phet < 0.01, 0.0001 ≤ phet < 0.001 and phet < 0.0001, respectively; here, phet (t-statistic, two-sided) is the p value for testing the heterogeneity of effect sizes between EA and EUR populations. Sample sizes for EUR populations in all panels: 11,273 cases and 55,483 controls. Sample sizes for EA populations: 11,753 cases and 30,562 controls for (A, B, C, D, and F); 21,658 cases and 150,676 controls for (E).
Fig. 4
Fig. 4. A polygenic risk score (PRS) is more strongly associated with risk of lung adenocarcinoma in never-smokers than in individuals with a history of smoking (P = 0.0058).
The PRS was defined based on 25 independent variants that achieved genome-wide significance in EA with weights derived from the meta-analysis of the current study (Supplementary Table 4). The odds ratios (ORs) and the standard errors of the 12 novel variants were based on 21,658 cases and 150,676 controls. The ORs and the standard errors of the other 13 variants were based on 11,753 cases and 30,562 controls. The figure shows the ORs and their 95% confidence intervals comparing each quintile group to the middle quintile for individuals with a history of smoking (blue) and never-smokers (red).
Fig. 5
Fig. 5. The expected area under the receiver operating characteristic curve (AUC) of a polygenic risk score (PRS) built based on a GWAS of specified sample sizes for lung adenocarcinoma in never-smoking East Asians.
For “1 million controls”, the x-coordinate represents the number of cases, assuming the study has 1 million controls. For “Equal number of cases and controls”, the x-coordinate represents the numbers of cases, assuming the same number of cases and controls.

References

    1. Bray F, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 2018;68:394–424. doi: 10.3322/caac.21492. - DOI - PubMed
    1. Cheng TY, et al. The International Epidemiology of Lung Cancer: Latest Trends, Disparities, and Tumor Characteristics. J. Thorac. Oncol. 2016;11:1653–1671. doi: 10.1016/j.jtho.2016.05.021. - DOI - PMC - PubMed
    1. Barta JA, Powell CA, Wisnivesky JP. Global Epidemiology of Lung Cancer. Ann. Glob. Health. 2019;85:8. doi: 10.5334/aogh.2419. - DOI - PMC - PubMed
    1. Cao M, Chen W. Epidemiology of lung cancer in China. Thorac. Cancer. 2019;10:3–7. doi: 10.1111/1759-7714.12916. - DOI - PMC - PubMed
    1. Kinoshita FL, Ito Y, Nakayama T. Trends in Lung Cancer Incidence Rates by Histological Type in 1975-2008: A Population-Based Study in Osaka, Japan. J. Epidemiol. 2016;26:579–586. doi: 10.2188/jea.JE20150257. - DOI - PMC - PubMed

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