MLIP: using multiple processors to compute the posterior probability of linkage.
MLIP: using multiple processors to compute the posterior probability of linkage.
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DOI：
10.1186/1471-2105-9-s6-s2
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发表时间：
2008-05-28
影响因子：
3
通讯作者：
Vieland VJ
中科院分区：
文献类型：
--
作者：
Govil M;Segre AM;Vieland VJ
通过遗传连锁分析定位复杂性状可能涉及探索一个巨大的多维参数空间。后验连锁概率（PPL）是一类用于人类复杂性状遗传作图的统计量，旨在以数学上严格的方式对由多维参数空间表示的性状模型复杂性进行建模。然而，该方法需要计算没有函数形式的积分，使得计算困难，从而进一步测试，开发和应用。本文介绍了MLIP，多处理器两点遗传连锁分析系统，支持统计计算，如PPL，基于隐含在连锁可能性的完整参数空间。我们在这里解决的基本问题是使用额外的处理器是否有效地减少了PPL计算的总计算时间。我们使用各种各样的数据--模拟的和真实的的--来探索这个问题“我们能离得多近？“线性加速。我们的研究的实证结果表明，MLIP显着加快两点对数似然比计算模型参数的网格空间。所观察到的程序性能取决于数据的特征，包括所探索的参数网格空间的粒度以及谱系大小和结构。在进一步优化性能的同时，该程序的当前版本已经可以用于有效地计算PPL。由于MLIP，全多维基因组扫描现在可以在我们的中心定期完成，运行时间为几天，而不是几个月或几年。
Localization of complex traits by genetic linkage analysis may involve exploration of a vast multidimensional parameter space. The posterior probability of linkage (PPL), a class of statistics for complex trait genetic mapping in humans, is designed to model the trait model complexity represented by the multidimensional parameter space in a mathematically rigorous fashion. However, the method requires the evaluation of integrals with no functional form, making it difficult to compute, and thus further test, develop and apply. This paper describes MLIP, a multiprocessor two-point genetic linkage analysis system that supports statistical calculations, such as the PPL, based on the full parameter space implicit in the linkage likelihood. The fundamental question we address here is whether the use of additional processors effectively reduces total computation time for a PPL calculation. We use a variety of data – both simulated and real – to explore the question "how close can we get?" to linear speedup. Empirical results of our study show that MLIP does significantly speed up two-point log-likelihood ratio calculations over a grid space of model parameters. Observed performance of the program is dependent on characteristics of the data including granularity of the parameter grid space being explored and pedigree size and structure. While work continues to further optimize performance, the current version of the program can already be used to efficiently compute the PPL. Thanks to MLIP, full multidimensional genome scans are now routinely being completed at our centers with runtimes on the order of days, not months or years.