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Olle Melander

Principal investigator

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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

Summary, in English

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.


  • LUCC - Lund University Cancer Centre
  • Improved diagnostics and prognostics of lung cancer and metastases to the lungs
  • Cardiovascular Research - Hypertension
  • EpiHealth: Epidemiology for Health
  • EXODIAB: Excellence in Diabetes Research in Sweden

Publishing year





BMC Medical Genomics





Document type

Journal article


BioMed Central (BMC)


  • Medical Genetics
  • Cancer and Oncology


  • Genetic interactions
  • Genome-wide association study
  • Lung cancer
  • Machine learning



Research group

  • Improved diagnostics and prognostics of lung cancer and metastases to the lungs
  • Cardiovascular Research - Hypertension


  • ISSN: 1755-8794