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, KM

doi:10.1159/000341885

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GLIDE: GPU-Based Linear Regression for Detection of Epistasis

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Borgwardt, KM
Dept. Empirical Inference, Max Planck Institute for Intelligent System, Max Planck Society;

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https://github.com/BorgwardtLab/Epistasis-GLIDE
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引用

Kam-Thong, T., Azencott, C.-A., Cayton, L., Pütz, B., Altmann, A., Karbalai, N., Sämann, P. G., Schölkopf, B., Müller-Myhsok, B., & Borgwardt, K. (2012). GLIDE: GPU-Based Linear Regression for Detection of Epistasis. Human Heredity, 73(4), 220-236. doi:10.1159/000341885.

引用: https://hdl.handle.net/21.11116/0000-000C-F36C-9

要旨

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.