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BOOST: A Fast Approach to Detecting Gene-Gene Interactions in Genome-wide Case-Control Studies

Gene-gene interactions have long been recognized to be fundamentally important for understanding genetic causes of complex disease traits. At present, identifying gene-gene interactions from genome-wide case-control studies is computationally and methodologically challenging. In this paper, we intro... Full description

Journal Title: American journal of human genetics 2010-09-10, Vol.87 (3), p.325-340
Main Author: Wan, Xiang
Other Authors: Yang, Can , Yang, Qiang , Xue, Hong , Fan, Xiaodan , Tang, Nelson L.S , Yu, Weichuan
Format: Electronic Article Electronic Article
Language: English
Subjects:
Quelle: Alma/SFX Local Collection
Publisher: Cambridge, MA: Elsevier Inc
ID: ISSN: 0002-9297
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recordid: cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_2933337
title: BOOST: A Fast Approach to Detecting Gene-Gene Interactions in Genome-wide Case-Control Studies
format: Article
creator:
  • Wan, Xiang
  • Yang, Can
  • Yang, Qiang
  • Xue, Hong
  • Fan, Xiaodan
  • Tang, Nelson L.S
  • Yu, Weichuan
subjects:
  • Algebra, Boolean
  • Algorithms
  • Arthritis, Rheumatoid - genetics
  • Article
  • Biological and medical sciences
  • Case-Control Studies
  • Chromosomes, Human, Pair 6 - genetics
  • Computational Biology - methods
  • Computational Engineering
  • Computer Science
  • Computer Simulation
  • Diabetes
  • Diabetes Mellitus, Type 1 - genetics
  • Epistasis, Genetic
  • Finance
  • Fundamental and applied biological sciences. Psychology
  • General aspects. Genetic counseling
  • Genes
  • Genetic aspects
  • Genetic disorders
  • Genetic Heterogeneity
  • Genetic Loci - genetics
  • Genetic research
  • Genetics
  • Genetics of eukaryotes. Biological and molecular evolution
  • Genetics(clinical)
  • Genome-Wide Association Study - methods
  • Genomics
  • Genotype
  • Humans
  • Linear Models
  • Major histocompatibility complex
  • Medical genetics
  • Medical sciences
  • Models, Genetic
  • Molecular and cellular biology
  • Polymorphism, Single Nucleotide - genetics
  • Quantitative Biology
  • Quantitative Methods
  • Science
  • Software
  • Technology application
  • Usage
ispartof: American journal of human genetics, 2010-09-10, Vol.87 (3), p.325-340
description: Gene-gene interactions have long been recognized to be fundamentally important for understanding genetic causes of complex disease traits. At present, identifying gene-gene interactions from genome-wide case-control studies is computationally and methodologically challenging. In this paper, we introduce a simple but powerful method, named “BOolean Operation-based Screening and Testing” (BOOST). For the discovery of unknown gene-gene interactions that underlie complex diseases, BOOST allows examination of all pairwise interactions in genome-wide case-control studies in a remarkably fast manner. We have carried out interaction analyses on seven data sets from the Wellcome Trust Case Control Consortium (WTCCC). Each analysis took less than 60 hr to completely evaluate all pairs of roughly 360,000 SNPs on a standard 3.0 GHz desktop with 4G memory running the Windows XP system. The interaction patterns identified from the type 1 diabetes data set display significant difference from those identified from the rheumatoid arthritis data set, although both data sets share a very similar hit region in the WTCCC report. BOOST has also identified some disease-associated interactions between genes in the major histocompatibility complex region in the type 1 diabetes data set. We believe that our method can serve as a computationally and statistically useful tool in the coming era of large-scale interaction mapping in genome-wide case-control studies.
language: eng
source: Alma/SFX Local Collection
identifier: ISSN: 0002-9297
fulltext: fulltext
issn:
  • 0002-9297
  • 1537-6605
url: Link


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descriptionGene-gene interactions have long been recognized to be fundamentally important for understanding genetic causes of complex disease traits. At present, identifying gene-gene interactions from genome-wide case-control studies is computationally and methodologically challenging. In this paper, we introduce a simple but powerful method, named “BOolean Operation-based Screening and Testing” (BOOST). For the discovery of unknown gene-gene interactions that underlie complex diseases, BOOST allows examination of all pairwise interactions in genome-wide case-control studies in a remarkably fast manner. We have carried out interaction analyses on seven data sets from the Wellcome Trust Case Control Consortium (WTCCC). Each analysis took less than 60 hr to completely evaluate all pairs of roughly 360,000 SNPs on a standard 3.0 GHz desktop with 4G memory running the Windows XP system. The interaction patterns identified from the type 1 diabetes data set display significant difference from those identified from the rheumatoid arthritis data set, although both data sets share a very similar hit region in the WTCCC report. BOOST has also identified some disease-associated interactions between genes in the major histocompatibility complex region in the type 1 diabetes data set. We believe that our method can serve as a computationally and statistically useful tool in the coming era of large-scale interaction mapping in genome-wide case-control studies.
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subjectAlgebra, Boolean ; Algorithms ; Arthritis, Rheumatoid - genetics ; Article ; Biological and medical sciences ; Case-Control Studies ; Chromosomes, Human, Pair 6 - genetics ; Computational Biology - methods ; Computational Engineering ; Computer Science ; Computer Simulation ; Diabetes ; Diabetes Mellitus, Type 1 - genetics ; Epistasis, Genetic ; Finance ; Fundamental and applied biological sciences. Psychology ; General aspects. Genetic counseling ; Genes ; Genetic aspects ; Genetic disorders ; Genetic Heterogeneity ; Genetic Loci - genetics ; Genetic research ; Genetics ; Genetics of eukaryotes. Biological and molecular evolution ; Genetics(clinical) ; Genome-Wide Association Study - methods ; Genomics ; Genotype ; Humans ; Linear Models ; Major histocompatibility complex ; Medical genetics ; Medical sciences ; Models, Genetic ; Molecular and cellular biology ; Polymorphism, Single Nucleotide - genetics ; Quantitative Biology ; Quantitative Methods ; Science ; Software ; Technology application ; Usage
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descriptionGene-gene interactions have long been recognized to be fundamentally important for understanding genetic causes of complex disease traits. At present, identifying gene-gene interactions from genome-wide case-control studies is computationally and methodologically challenging. In this paper, we introduce a simple but powerful method, named “BOolean Operation-based Screening and Testing” (BOOST). For the discovery of unknown gene-gene interactions that underlie complex diseases, BOOST allows examination of all pairwise interactions in genome-wide case-control studies in a remarkably fast manner. We have carried out interaction analyses on seven data sets from the Wellcome Trust Case Control Consortium (WTCCC). Each analysis took less than 60 hr to completely evaluate all pairs of roughly 360,000 SNPs on a standard 3.0 GHz desktop with 4G memory running the Windows XP system. The interaction patterns identified from the type 1 diabetes data set display significant difference from those identified from the rheumatoid arthritis data set, although both data sets share a very similar hit region in the WTCCC report. BOOST has also identified some disease-associated interactions between genes in the major histocompatibility complex region in the type 1 diabetes data set. We believe that our method can serve as a computationally and statistically useful tool in the coming era of large-scale interaction mapping in genome-wide case-control studies.
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abstractGene-gene interactions have long been recognized to be fundamentally important for understanding genetic causes of complex disease traits. At present, identifying gene-gene interactions from genome-wide case-control studies is computationally and methodologically challenging. In this paper, we introduce a simple but powerful method, named “BOolean Operation-based Screening and Testing” (BOOST). For the discovery of unknown gene-gene interactions that underlie complex diseases, BOOST allows examination of all pairwise interactions in genome-wide case-control studies in a remarkably fast manner. We have carried out interaction analyses on seven data sets from the Wellcome Trust Case Control Consortium (WTCCC). Each analysis took less than 60 hr to completely evaluate all pairs of roughly 360,000 SNPs on a standard 3.0 GHz desktop with 4G memory running the Windows XP system. The interaction patterns identified from the type 1 diabetes data set display significant difference from those identified from the rheumatoid arthritis data set, although both data sets share a very similar hit region in the WTCCC report. BOOST has also identified some disease-associated interactions between genes in the major histocompatibility complex region in the type 1 diabetes data set. We believe that our method can serve as a computationally and statistically useful tool in the coming era of large-scale interaction mapping in genome-wide case-control studies.
copCambridge, MA
pubElsevier Inc
pmid20817139
doi10.1016/j.ajhg.2010.07.021
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