MLIP: using multiple processors to compute the posterior probability of linkage

Background: 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. Results: 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

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Manika Govil, Alberto Maria Segre, Veronica J. Vieland
• Keywords: BMCBI 2008, Genetic Linkage Analysis, Multidimensional Parameter Space, Parameter Space