Abstract
BackgroundLocalization 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.ResultsThe 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.ConclusionObserved 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.
Highlights
Localization of complex traits by genetic linkage analysis may involve exploration of a vast multidimensional parameter space
The simplest characterization of MLIP is as a two-layer model: an inner layer that is used to compute each individual LOD score, and an outer layer that oversees the systematic exploration of the multidimensional parameter space by dividing it into chunks, managing the distribution of these chunks to other processors, collecting the results, and subsequently writing them to disk
Since it is proportional to the posterior density of θ, the BR is multiplied across the datasets to sequentially update the posterior probability of linkage (PPL) over multiple sets of data and the PPL is recomputed using Eq 2 [12]
Summary
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. 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. MLIP is designed to facilitate genetic linkage analysis (gene mapping) by allowing the coverage of a complex, multidimensional parameter space using partitioning and parallelization of the computation. One research area of interest for geneticists is locating on the genome the gene(s) responsible for inherited traits This localization is facilitated by linkage analysis, a statistical genetic method. The model-based linkage statistic is the LOD score, defined as the log ratio of the likelihood of the observed data, divided by the likelihood assuming "no linkage." This statistic utilizes genetic map, pedigree, and trait model information (e.g., the population frequency of the trait, and the penetrance or probability that an individual with a given genetic combination at the trait locus manifests the trait) to obtain an estimate of the recombination fraction θ, the traditional measure of genetic distance between two genomic locations
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