Statistical Genetics, Max Planck Institute of Psychiatry, Munich, Germany.
Bioinformatics. 2011 Jul 1;27(13):i214-21. doi: 10.1093/bioinformatics/btr218.
In recent years, numerous genome-wide association studies have been conducted to identify genetic makeup that explains phenotypic differences observed in human population. Analytical tests on single loci are readily available and embedded in common genome analysis software toolset. The search for significant epistasis (gene-gene interactions) still poses as a computational challenge for modern day computing systems, due to the large number of hypotheses that have to be tested.
In this article, we present an approach to epistasis detection by exhaustive testing of all possible SNP pairs. The search strategy based on the Hilbert-Schmidt Independence Criterion can help delineate various forms of statistical dependence between the genetic markers and the phenotype. The actual implementation of this search is done on the highly parallelized architecture available on graphics processing units rendering the completion of the full search feasible within a day.
The program is available at http://www.mpipsykl.mpg.de/epigpuhsic/.
近年来，已经进行了许多全基因组关联研究，以确定解释人类群体中观察到的表型差异的遗传构成。单基因座的分析测试易于获得，并嵌入常见的基因组分析软件工具集中。由于必须测试大量的假设，因此寻找显著的上位性（基因-基因相互作用）仍然对现代计算系统构成计算挑战。
在本文中，我们提出了一种通过对所有可能的 SNP 对进行穷举测试来检测上位性的方法。基于 Hilbert-Schmidt 独立性准则的搜索策略有助于描绘遗传标记与表型之间的各种形式的统计相关性。该搜索的实际实现是在图形处理单元上的高度并行化架构上完成的，这使得在一天内完成完整搜索成为可能。
该程序可在 http://www.mpipsykl.mpg.de/epigpuhsic/ 获得。
