Identification of lung cancer histology-specific variants applying Bayesian framework variant prioritization approaches within the TRICL and ILCCO consortia
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 x 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 33 456 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 x 10(-7)) and MTMR2 at 11q21 (rs10501831, P = 3.1 x 10(-6)) with SCC, as well as GAREM at 18q12.1 (rs11662168, P = 3.4 x 10(-7)) with AC. Use of our prioritization methods validated two of the top three loci associated with SCC (P = 1.05 x 10(-4) for KCNIP4, represented by rs9799795) and AC (P = 2.16 x 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.