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  • Luyapan, JenniferDartmouth College (författare)

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

  • Artikel/kapitelEngelska2020

Förlag, utgivningsår, omfång ...

  • 2020-10-30
  • Springer Science and Business Media LLC,2020

Nummerbeteckningar

  • LIBRIS-ID:oai:lup.lub.lu.se:7300bc69-a7cd-49ac-a59b-1830a7e668a4
  • https://lup.lub.lu.se/record/7300bc69-a7cd-49ac-a59b-1830a7e668a4URI
  • https://doi.org/10.1186/s12920-020-00807-9DOI

Kompletterande språkuppgifter

  • Språk:engelska
  • Sammanfattning på:engelska

Ingår i deldatabas

Klassifikation

  • Ämneskategori:art swepub-publicationtype
  • Ämneskategori:ref swepub-contenttype

Anmärkningar

  • 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 och genrebeteckningar

Biuppslag (personer, institutioner, konferenser, titlar ...)

  • Ji, XuemeiDartmouth College (författare)
  • Li, SitingDartmouth College (författare)
  • Xiao, XiangjunBaylor College of Medicine (författare)
  • Zhu, DakaiBaylor College of Medicine,Dartmouth College (författare)
  • Duell, Eric J.Catalan Institute of Oncology (författare)
  • Christiani, David C.Massachusetts General Hospital,Harvard University (författare)
  • Schabath, Matthew B.H. Lee Moffitt Cancer Center & Research Institute (författare)
  • Arnold, Susanne M.University of Kentucky (författare)
  • Zienolddiny, ShanbehNational Institute of Occupational Health, Norway (författare)
  • Brunnström, HansLund 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(Swepub:lu)med-hsb (författare)
  • Melander, OlleLund University,Lunds universitet,Kardiovaskulär forskning - hypertoni,Forskargrupper vid Lunds universitet,Cardiovascular Research - Hypertension,Lund University Research Groups(Swepub:lu)endo-ome (författare)
  • Thornquist, Mark D.Fred Hutchinson Cancer Research Center (författare)
  • MacKenzie, Todd A.Dartmouth College (författare)
  • Amos, Christopher I.Dartmouth College,Baylor College of Medicine (författare)
  • Gui, JiangDartmouth College (författare)
  • Dartmouth CollegeBaylor College of Medicine (creator_code:org_t)

Sammanhörande titlar

  • Ingår i:BMC Medical Genomics: Springer Science and Business Media LLC13:11755-8794

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