Using Parallel Computing and Grid Systems for Genetic Mapping of Multifactorial Traits

We present a flexible parallel implementation of the exhaustive grid search algorithm for multidimensional QTL mapping problems. A generic, parallel algorithm is presented and a two-level scheme is introduced for partitioning the work corresponding to the independent computational tasks in the algorithm. At the outer level, a static blockcyclic partitioning is used, and at the inner level a dynamic pool-of-tasks model is used. The implementation of the parallelism at the outer level is performed using scripts, while MPI is used at the inner level. By comparing to results from the SweGrid system to those obtained using a shared memory server, we show that this type of application is highly suitable for execution in a grid framework. key words: QTL analysis, grid computing 1 Genetic Mapping of Quantitative Traits Many important traits in animals and plants are quantitative in nature. Examples include body weight and growth rate, susceptibility to infections and other diseases, and agricultural crop yield. Hence, understanding the genetic factors behind quantitative traits is of great importance. In a longer perspective such findings can be used for example in selective breeding programs and in drug development. The regions in the genome affecting a quantitative trait can be found by analysis of the genetic composition of individuals in experimental populations. The genetic regions are also known as Quantitative Trait Loci (QTL), and the procedure of finding these is called QTL mapping. A review of QTL mapping methods is given in [13]. In QTL mapping, a statistical model for how the genotypes of the individuals in the population affect the trait is exploited. The data for the model is produced by experiments where the genotypes are determined at a set of marker loci in the genome. This data is input to a QTL mapping computer code, where the computation of the model fit and the search for the most probable positions of