Abstract 3396: Multiethnic genome-wide meta-analysis of 34,329 cases and 35,732 controls identifies cross-ancestry loci for lung cancer susceptibility
Bibliographic record
Abstract
Abstract Genome-wide association studies (GWAS) have revealed susceptible genetic risk factors for lung cancer, highlighting the role of smoking, family history, and DNA damage repair genes in disease etiology. Many studies have focused on European populations; however, lung cancer is a leading cause of cancer incidence and mortality around the world. Previous GWAS analyses have been focusing on a single population-based analyses to exclude the confounding effects such as the presence of systematic allele frequency differences between populations. Another efficient tool for GWAS of complex genetic diseases and traits is meta-analysis providing a practical strategy for detecting genetic variants with modest effect sizes. This study aimed to identify novel genetic susceptibility loci in a large, multiethnic GWAS of lung cancer. The HRC imputation of lung GWAS was carried out in the Sanger Imputation Server. The imputed GWAS with 34,429 cases and 35,732 controls from OncoArray lung cancer GWAS data were used in the study. We applied Fastpop to infer the ancestry membership in three intercontinental populations. We conducted genome-wide association meta-analyses (METAL) in up to 70,161 individuals of European (26,683 cases/25,278 controls), African (1,987 cases/3,779 controls), or Asian (7,062 cases/5,372 controls) ancestry using HRC imputed lung cancer data. The novel variants in or near DCBLD1 on 6q22.1 (OR=0.93,P=2.11 × 10−10), IRF4 on 6p25.3 (OR=1.11,P=3.96 × 10−8), PPIL6 on 6q21 (OR=1.10,P=4.41 × 10−9), ROS1 on 6p22.1 (OR=0.94,P=2.49 × 10−9) for overall lung cancer, ACTR2 on 2p14 OR=0.89,P=2.96 × 10−9), ATM on 11q22.3 (OR=3.61,P=8.88 × 10−10), PSMA4 on 15q25.1 (OR=0.88,P=1.07 × 10−12) for lung adenocarcinoma, ABCF1 (OR=1.35,P=4.54 × 10−12), C2 (OR=1.37,P=6.65 × 10−13), CFB (OR=1.37,P=5.44 × 10−13), CYP21A2 (OR=1.35,P=1.71 × 10−10), VWA7 (OR=1.36,P=1.3 × 10−12) on 6p21.33, HCG9 on 6p22.1 (OR=1.30,P=2.34 × 10−10), IREB2 on 15q25.1 (OR=1.20,P=9.25 × 10−19), TTC28 (OR=0.29,P=2.84 × 10−11), ZNRF3 (OR=0.37,P=3.55 × 10−10) on 22q12.1 for lung squamous cell carcinoma, and ZC3H15 on 2q32.1 (OR=2.73,P=2.62 × 10−8), NECTIN1 on 11q23.3 (OR=8.96,P=3.27 × 10−8) for lung small cell carcinoma were identified at a genome-wide level of significance in lung cancer. Our large, multiethnic GWAS meta-analysis of lung cancer has identified several novel genetic associations. Further work is required to elucidate the biological mechanisms underlying these associations. Our results suggest that multiethnic meta-analysis of larger lung cancer datasets may yield additional genetic risk loci of moderate effect size. Citation Format: Jinyoung Byun, Xiangjun Xiao, Younghun Han, Yafang Li, Ryan Sun, Xihao Li, Hufeng Zhou, Xihong Lin, James McKay, Rayjean Hung, Christopher Amos, INTEGRAL Consortium. Multiethnic genome-wide meta-analysis of 34,329 cases and 35,732 controls identifies cross-ancestry loci for lung cancer susceptibility [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3396.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.014 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".