A New Efficient Method to Detect Genetic Interactions for Lung Cancer GWAS CURRENT STATUS: REVIEW

Genome-wide association studies (GWAS) have proven successful in predicting genetic risk of disease using single-locus models; however, identifying single nucleotide polymorphism (SNP) interactions at the genome-wide scale is limited due to computational and statistical challenges. We address the computational burden encountered when detecting SNP interactions for survival analysis, such as age of disease-onset. To confront this problem, we developed a novel algorithm, called the Efficient Survival Multifactor Dimensionality Reduction (ES-MDR) method, which uses Martingale Residuals as the outcome parameter to estimate survival outcomes, and implemented the Quantitative Multifactor Dimensionality Reduction method to identify significant interactions associated with age of disease-onset. To demonstrate efficacy, we evaluated this method on two simulation sets to estimate the type I error and power. Simulations show that ES-MDR identifies interactions using less computational workload and allows for adjustment of covariates. We applied ES-MDR on the OncoArray-TRICL Consortium data with 14,935 cases and 12,787 controls for lung cancer (SNPs = 108,254) to search over all two-way interactions to identify genetic interactions associated with lung cancer age-of-onset. We tested the best model in an independent data set from the OncoArray-TRICL data. Our experiment on the OncoArray-TRICL data identified many one-way and two-way models with a single-base deletion in the noncoding region of BRCA1 (HR = 1.24, P = 3.15 x 10-15), as the top marker to predict age of lung cancer onset. From the results of our extensive simulations and analysis of a large GWAS study, we demonstrate that our method is an efficient algorithm that identifies genetic interactions to include in our models to predict survival outcomes. the intergenic regions of UTP23 , RAD21 (rs10105870), and the deletion in the noncoding region of BRCA1 (rs749410065) (NC_000017.10:g.41196821delT nomenclature) display a clear separation of curves between the high and low risk groups. This demonstrates the efficacy of ES-MDR using Martingale Residuals to differentiate high risk and low risk groups based on genotype variation when evaluating lung cancer age-of-onset. We continue our analysis with a comparison of smoking only and smoking plus SNP models to determine the best performance in predicting lung cancer onset at different ages. We use a common graphical plot called the area under the receiver operating characteristic (ROC) curve, also known as AUC, to measure the performance of our models to discriminate the best parameters at predicting lung cancer onset at different ages based on accuracy. In Figure 3, the x-axis corresponds to the age of lung cancer onset, starting from 15 to greater than 80 years, and the y-axis indicates the AUC, ranging from 0.4 to 1. We examined the predictive performance of 6 different models with various tuning parameters identified from Cox Lasso regression, such as smoking only and smoking plus 2 SNPs, 4 SNPs, 13 SNPs, 19 SNPs, 29 SNPs, and 183 SNPs. This figure shows the averages of the estimated AUCs over the Oncoarray-TRICL data using the predictive scores from the independent left-out test set. The plot displays good predictive performances of models generated using ES-MDR. The AUC for models with more SNPs lies between 0.6 and 0.7 and continues to increase at later ages of onset. There is a noticeable decrease in AUC for ages 40 and below. This could be due to the limited number of lung cancer cases identified for individuals below the age of 40, which indicates that the models may not be appropriate to predict lung cancer diagnoses at 40 years and younger. The