Abstract 942: Imputation from The 1000 Genomes Project identifies rare large effect variants of BRCA2-K3326X and CHEK2-I157T as risk factors for lung cancer; a study from the TRICL consortium
Bibliographic record
Abstract
Abstract We conducted imputation to The 1000 Genomes Project of genome-wide association studies of lung cancer in populations of European ancestry, with 11,348 cases and 15,861 controls from four large studies, including subjects from 13 countries. As a follow-up, we conducted in-silico replication in two studies of 2,303 cases and 27,350 controls and directly genotyped an additional 7,943 cases and 10,945 controls from 14 countries. Data were imputed for all scans for over 10 million SNPs using data from The 1000 Genomes Project (Phase 1 integrated release 3, March 2012) as reference, using IMPUTE2, MaCH or minimac software. Tests of association between imputed SNPs and lung cancer were performed under a probabilistic dosage model in SNPTEST, ProbABEL, MaCH2dat or glm function in R. The fidelity of imputation as assessed by the correlation between imputed and directly typed SNPs was examined in a subset of samples from the four studies used for discovery and showed squared correlation coefficients ranging from 0.74 for the rare CHEK2 variant to 1.00 for the more common TP63 variant. The association between each SNP and lung cancer risk was assessed by the Cochran-Armitage trend test. Principle components generated using common SNPs were used to account for the possibility of inflation. Odds ratios (ORs) and associated 95% confidence intervals (CIs) were calculated by unconditional logistic regression. Meta-analysis was conducted using an inverse-variance approach. Cochran's Q-statistic to test for heterogeneity and the I2 statistic to quantify the proportion of the total variation due to heterogeneity were calculated. We identified large-effect genome-wide associations for squamous lung cancer with the rare variants of BRCA2-K3326X (rs11571833; odds ratio [OR]=2.47, P=4.74×10−20) and of CHEK2-I157T (rs17879961; OR=0.38 P=1.27×10−13). We also showed an association between common variation at 3q28 (TP63; rs13314271; OR=1.13, P=7.22×10−10) and lung adenocarcinoma previously only reported in Asians. There was no association between these loci and smoking quantity as measured by number of cigarettes smoked per day, using smoking information on 43,693 Icelandic subjects. These findings provide further evidence for inherited genetic susceptibility to lung cancer and its biological basis. Additionally, our analysis demonstrates that imputation can identify rare disease-causing variants having substantive effects on cancer risk from pre-existing GWAS data. Citation Format: Maria Teresa Landi, Yufei Wang, James D. Mckay, Thorunn Rafnar, Zhaoming Wang, Maria Timofeeva, Peter Broderick, Kari Stefansson, Angela Risch, Stephen J. Chanock, David C. Christiani, Rayjean J. Hung, Paul Brennan, Richard S. Houlston, Christopher I. Amos. Imputation from The 1000 Genomes Project identifies rare large effect variants of BRCA2-K3326X and CHEK2-I157T as risk factors for lung cancer; a study from the TRICL consortium. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 942. doi:10.1158/1538-7445.AM2014-942
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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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".