BMC Medical Genomics (Oct 2020)

A new efficient method to detect genetic interactions for lung cancer GWAS

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

DOI
https://doi.org/10.1186/s12920-020-00807-9
Journal volume & issue
Vol. 13, no. 1
pp. 1 – 15

Abstract

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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 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 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. 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.

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