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
Machine Learning and Computational Biology Research Group, Max Planck Institutes Tübingen, Tübingen, Germany.
Hum Hered. 2012;73(4):220-36. doi: 10.1159/000341885. Epub 2012 Sep 4.
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.
由于基因分型技术的最新进展，将表型映射到基因组中的单个位点已成为统计遗传学中的一项标准技术。然而，单基因座映射无法解释复杂性状中大部分的表型变异。在此，我们提出了GLIDE，它将表型映射到基因座对，并系统地搜索预期能揭示部分这种缺失遗传力的上位性相互作用。GLIDE利用消费级图形卡的计算能力，通过线性回归检测此类相互作用。这使我们能够对来自威康信托病例对照协会的七个疾病数据集以及内部海马体体积数据进行系统的双基因座映射研究，每个数据集只需6小时，而当前基于单个CPU的方法完成相同任务需要一年多的时间。
