A new efficient method to detect genetic interactions for lung cancer GWAS
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A new efficient method to detect genetic interactions for lung cancer GWAS Jennifer Luyapan1,2, Xuemei Ji2, Siting Li1,2, Xiangjun Xiao3, Dakai Zhu2,3, Eric J. Duell4, David C. Christiani5,6, Matthew B. Schabath7, Susanne M. Arnold8, Shanbeh Zienolddiny9, Hans Brunnström10, Olle Melander11, Mark D. Thornquist12, Todd A. MacKenzie1,2, Christopher I. Amos1,2,3* and Jiang Gui1,2*
Abstract Background: 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 genomewide 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. Methods: 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. Results: Our experiment on the OncoArray-TRICL data identified many one-way and two-way models with a singlebase 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. Conclusions: 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. Keywords: Genetic interactions, Machine learning, Genome-wide association study, Lung cancer
*Correspondence: [email protected]; [email protected] 1 Quantitative Biomedical Science Program, Geisel School of Medicine, Dartmouth College, Hanover, NH 03755, USA Full list of author information is available at the end of the article
Background A fundamental aim of studying human genetics is to predict disease risk from genomic data. Genome-wide association studies (GWAS) that used single-locus models by testing each single nucleotide polymorphism (SNP) for association with a phenotype, proved to be instrumental in identifying thousands of genetic variants associated with human traits and disorders [1–4]. However,
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