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

Luyapan, Jennifer ; Ji, Xuemei ; Li, Siting ; Xiao, Xiangjun ; Zhu, Dakai ; Duell, Eric J. ; Christiani, David C. ; Schabath, Matthew B. ; Arnold, Susanne M. and Zienolddiny, Shanbeh , et al. (2020) In BMC Medical Genomics 13(1).
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... (More)

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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organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Genetic interactions, Genome-wide association study, Lung cancer, Machine learning
in
BMC Medical Genomics
volume
13
issue
1
article number
162
publisher
BioMed Central (BMC)
external identifiers
  • pmid:33126877
  • scopus:85094635728
ISSN
1755-8794
DOI
10.1186/s12920-020-00807-9
language
English
LU publication?
yes
id
7300bc69-a7cd-49ac-a59b-1830a7e668a4
date added to LUP
2020-11-13 09:19:36
date last changed
2024-04-03 17:19:15
@article{7300bc69-a7cd-49ac-a59b-1830a7e668a4,
  abstract     = {{<p>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<sup>–15</sup>), 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.</p>}},
  author       = {{Luyapan, Jennifer and Ji, Xuemei and Li, Siting and Xiao, Xiangjun and Zhu, Dakai and Duell, Eric J. and Christiani, David C. and Schabath, Matthew B. and Arnold, Susanne M. and Zienolddiny, Shanbeh and Brunnström, Hans and Melander, Olle and Thornquist, Mark D. and MacKenzie, Todd A. and Amos, Christopher I. and Gui, Jiang}},
  issn         = {{1755-8794}},
  keywords     = {{Genetic interactions; Genome-wide association study; Lung cancer; Machine learning}},
  language     = {{eng}},
  number       = {{1}},
  publisher    = {{BioMed Central (BMC)}},
  series       = {{BMC Medical Genomics}},
  title        = {{A new efficient method to detect genetic interactions for lung cancer GWAS}},
  url          = {{http://dx.doi.org/10.1186/s12920-020-00807-9}},
  doi          = {{10.1186/s12920-020-00807-9}},
  volume       = {{13}},
  year         = {{2020}},
}