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GLIDE: GPU-based linear regression for detection of epistasis.
Kam-Thong, Tony; Azencott, Chloé-Agathe; Cayton, Lawrence; Pütz, Benno; Altmann, André; Karbalai, Nazanin; Sämann, Philipp G; Schölkopf, Bernhard; Müller-Myhsok, Bertram; Borgwardt, Karsten M.
Afiliação
  • Kam-Thong T; Machine Learning and Computational Biology Research Group, Max Planck Institutes Tübingen, Tübingen, Germany.
Hum Hered ; 73(4): 220-36, 2012.
Article em En | MEDLINE | ID: mdl-22965145
ABSTRACT
Due to recent advances in genotyping technologies, mapping phenotypes to single loci in the genome has become a standard technique in statistical genetics. However, one-locus mapping fails to explain much of the phenotypic variance in complex traits. Here, we present GLIDE, which maps phenotypes to pairs of genetic loci and systematically searches for the epistatic interactions expected to reveal part of this missing heritability. GLIDE makes use of the computational power of consumer-grade graphics cards to detect such interactions via linear regression. This enabled us to conduct a systematic two-locus mapping study on seven disease data sets from the Wellcome Trust Case Control Consortium and on in-house hippocampal volume data in 6 h per data set, while current single CPU-based approaches require more than a year's time to complete the same task.
Assuntos
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Base de dados: MEDLINE Assunto principal: Mapeamento Cromossômico / Biologia Computacional / Predisposição Genética para Doença / Epistasia Genética Idioma: En Ano de publicação: 2012 Tipo de documento: Article
Buscar no Google
Base de dados: MEDLINE Assunto principal: Mapeamento Cromossômico / Biologia Computacional / Predisposição Genética para Doença / Epistasia Genética Idioma: En Ano de publicação: 2012 Tipo de documento: Article