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
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,013 | 0,026 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,002 | 0,005 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,002 | 0,000 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».