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A new efficient met...
A new efficient method to detect genetic interactions for lung cancer GWAS
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- Luyapan, Jennifer (författare)
- Dartmouth College
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- Ji, Xuemei (författare)
- Dartmouth College
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- Li, Siting (författare)
- Dartmouth College
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- Xiao, Xiangjun (författare)
- Baylor College of Medicine
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- Zhu, Dakai (författare)
- Dartmouth College,Baylor College of Medicine
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- Duell, Eric J. (författare)
- Catalan Institute of Oncology
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- Christiani, David C. (författare)
- Harvard University,Massachusetts General Hospital
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- Schabath, Matthew B. (författare)
- H. Lee Moffitt Cancer Center & Research Institute
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- Arnold, Susanne M. (författare)
- University of Kentucky
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- Zienolddiny, Shanbeh (författare)
- National Institute of Occupational Health, Norway
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- Brunnström, Hans (författare)
- Lund University,Lunds universitet,Förbättrad diagnostik och prognostik vid lungcancer och metastaser till lunga,Forskargrupper vid Lunds universitet,LUCC: Lunds universitets cancercentrum,Övriga starka forskningsmiljöer,Improved diagnostics and prognostics of lung cancer and metastases to the lungs,Lund University Research Groups,LUCC: Lund University Cancer Centre,Other Strong Research Environments
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- Melander, Olle (författare)
- Lund University,Lunds universitet,Kardiovaskulär forskning - hypertoni,Forskargrupper vid Lunds universitet,Cardiovascular Research - Hypertension,Lund University Research Groups
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- Thornquist, Mark D. (författare)
- Fred Hutchinson Cancer Research Center
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- MacKenzie, Todd A. (författare)
- Dartmouth College
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- Amos, Christopher I. (författare)
- Baylor College of Medicine,Dartmouth College
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- Gui, Jiang (författare)
- Dartmouth College
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(creator_code:org_t)
- 2020-10-30
- 2020
- Engelska.
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Ingår i: BMC Medical Genomics. - : Springer Science and Business Media LLC. - 1755-8794. ; 13:1
- Relaterad länk:
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http://dx.doi.org/10... (free)
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https://bmcmedgenomi...
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https://lup.lub.lu.s...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Medicinska och farmaceutiska grundvetenskaper -- Medicinsk genetik (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Basic Medicine -- Medical Genetics (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Cancer och onkologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Cancer and Oncology (hsv//eng)
Nyckelord
- Genetic interactions
- Genome-wide association study
- Lung cancer
- Machine learning
Publikations- och innehållstyp
- art (ämneskategori)
- ref (ämneskategori)
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Till lärosätets databas
- Av författaren/redakt...
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Luyapan, Jennife ...
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Ji, Xuemei
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Li, Siting
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Xiao, Xiangjun
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Zhu, Dakai
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Duell, Eric J.
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visa fler...
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Christiani, Davi ...
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Schabath, Matthe ...
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Arnold, Susanne ...
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Zienolddiny, Sha ...
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Brunnström, Hans
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Melander, Olle
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Thornquist, Mark ...
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MacKenzie, Todd ...
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Amos, Christophe ...
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Gui, Jiang
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- Om ämnet
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- MEDICIN OCH HÄLSOVETENSKAP
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MEDICIN OCH HÄLS ...
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och Medicinska och f ...
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och Medicinsk geneti ...
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- MEDICIN OCH HÄLSOVETENSKAP
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MEDICIN OCH HÄLS ...
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och Klinisk medicin
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och Cancer och onkol ...
- Artiklar i publikationen
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BMC Medical Geno ...
- Av lärosätet
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Lunds universitet