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 addressed 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 used 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 data sets to estimate the type I error rate and power. Simulations showed that ES-MDR identified interactions using less computational workload and allowed 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 × 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 demonstrated that our method is an efficient algorithm that identified genetic interactions to include in our models to predict survival outcomes.

中文翻译：

---

检测肺癌遗传相互作用的新有效方法 GWAS

全基因组关联研究（GWAS）已被证明可以成功地使用单基因座模型预测疾病的遗传风险；然而，由于计算和统计方面的挑战，在全基因组范围内识别单核苷酸多态性（SNP）相互作用受到限制。我们解决了检测 SNP 相互作用以进行生存分析时遇到的计算负担，例如发病年龄。为了解决这个问题，我们开发了一种新颖的算法，称为高效生存多因素降维（ES-MDR）方法，该方法使用鞅残差作为结果参数来估计生存结果，并实现定量多因素降维方法来识别显着的相互作用与发病年龄有关。为了证明有效性，我们在两个模拟数据集上评估了该方法，以估计 I 类错误率和功效。模拟表明，ES-MDR 使用较少的计算工作量识别相互作用，并允许调整协变量。我们对 OncoArray-TRICL 联盟数据（包含 14,935 个肺癌病例和 12,787 个对照）（SNP = 108,254）应用 ES-MDR 来搜索所有双向相互作用，以确定与肺癌发病年龄相关的遗传相互作用。我们在 OncoArray-TRICL 数据的独立数据集中测试了最佳模型。我们对 OncoArray-TRICL 数据的实验确定了许多单向和双向模型，这些模型在 BRCA1 非编码区域中存在单碱基缺失（HR 1.24，P = 3.15 × 10–15），作为预测年龄的首要标记肺癌的发病。根据我们对大型 GWAS 研究的广泛模拟和分析的结果，我们证明了我们的方法是一种有效的算法，可以识别遗传相互作用并将其纳入我们的模型中以预测生存结果。