Vit. artikkel


  • 2020

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 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. 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...

Luyapan, Jennifer; Ji, Xuemei; Li, Siting; Xiao, Xiangjun; Zhu, Dakai; Duell, Eric J.; Christiani, David C.; Schabath, Matthew B.; Arnold, Susanne M.; Zienolddiny, Shanbeh; Brunnström, Hans; Melander, Olle; Thornquist, Mark D.; MacKenzie, Todd A.; Amos, Christopher I.; Gui, Jiang
BMC Medical Genomics 13:162
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