| 1. |
- Brenner, Darren R, et al.
(författare)
-
Identification of lung cancer histology-specific variants applying Bayesian framework variant prioritization approaches within the TRICL and ILCCO consortia
- 2015
-
Ingår i: Carcinogenesis. - : Oxford University Press. - 0143-3334 .- 1460-2180. ; 36:11, s. 1314-1326
-
Tidskriftsartikel (refereegranskat)abstract
- Large-scale genome-wide association studies (GWAS) have likely uncovered all common variants at the GWAS significance level. Additional variants within the suggestive range (0.0001> P > 5×10−8) are, however, still of interest for identifying causal associations. This analysis aimed to apply novel variant prioritization approaches to identify additional lung cancer variants that may not reach the GWAS level. Effects were combined across studies with a total of 33456 controls and 6756 adenocarcinoma (AC; 13 studies), 5061 squamous cell carcinoma (SCC; 12 studies) and 2216 small cell lung cancer cases (9 studies). Based on prior information such as variant physical properties and functional significance, we applied stratified false discovery rates, hierarchical modeling and Bayesian false discovery probabilities for variant prioritization. We conducted a fine mapping analysis as validation of our methods by examining top-ranking novel variants in six independent populations with a total of 3128 cases and 2966 controls. Three novel loci in the suggestive range were identified based on our Bayesian framework analyses: KCNIP4 at 4p15.2 (rs6448050, P = 4.6×10−7) and MTMR2 at 11q21 (rs10501831, P = 3.1×10−6) with SCC, as well as GAREM at 18q12.1 (rs11662168, P = 3.4×10−7) with AC. Use of our prioritization methods validated two of the top three loci associated with SCC (P = 1.05×10−4 for KCNIP4, represented by rs9799795) and AC (P = 2.16×10−4 for GAREM, represented by rs3786309) in the independent fine mapping populations. This study highlights the utility of using prior functional data for sequence variants in prioritization analyses to search for robust signals in the suggestive range.
|
|
| 2. |
- Li, Yafang, et al.
(författare)
-
Genome-wide interaction study of smoking behavior and non-small cell lung cancer risk in Caucasian population
- 2018
-
Ingår i: Carcinogenesis. - : Oxford University Press (OUP). - 0143-3334 .- 1460-2180. ; 39:3, s. 336-346
-
Tidskriftsartikel (refereegranskat)abstract
- Non-small cell lung cancer is the most common type of lung cancer. Both environmental and genetic risk factors contribute to lung carcinogenesis. We conducted a genome-wide interaction analysis between single nucleotide polymorphisms (SNPs) and smoking status (never- versus ever-smokers) in a European-descent population. We adopted a two-step analysis strategy in the discovery stage: we first conducted a case-only interaction analysis to assess the relationship between SNPs and smoking behavior using 13 336 non-small cell lung cancer cases. Candidate SNPs with P-value <0.001 were further analyzed using a standard case-control interaction analysis including 13 970 controls. The significant SNPs with P-value <3.5 × 10-5 (correcting for multiple tests) from the case-control analysis in the discovery stage were further validated using an independent replication dataset comprising 5377 controls and 3054 non-small cell lung cancer cases. We further stratified the analysis by histological subtypes. Two novel SNPs, rs6441286 and rs17723637, were identified for overall lung cancer risk. The interaction odds ratio and meta-analysis P-value for these two SNPs were 1.24 with 6.96 × 10-7 and 1.37 with 3.49 × 10-7, respectively. In addition, interaction of smoking with rs4751674 was identified in squamous cell lung carcinoma with an odds ratio of 0.58 and P-value of 8.12 × 10-7. This study is by far the largest genome-wide SNP-smoking interaction analysis reported for lung cancer. The three identified novel SNPs provide potential candidate biomarkers for lung cancer risk screening and intervention. The results from our study reinforce that gene-smoking interactions play important roles in the etiology of lung cancer and account for part of the missing heritability of this disease.
|
|
