User friendly cluster computing for QTL analysis

Quantative trait likelyhood analysis (QTL) is a powerful tool used by life science researchers to unravel the relationship between phenotype and genotype. Modern genetic and genomic technologies provide researchers with unprecedented amounts of raw and processed data. QTL analysis combines experimental breeding techniques with the analysis of classical quantative traits (e.g. bodyweight, height). Using molecular markers and statistical techniques these classical traits are mapped back to locations on the genome, and provide interesting targets for further wetlab studies. Genetical genomics studies have mapped gene expression (eQTL), protein abundance (pQTL) and metabolite abundance (mQTL) to genetic variation using genome-wide linkage (GWL) and genome-wide association (GWA) experiments on various traits. These quantative traits include microarray, mass spectrometry and nuclear magnetic resonance (NMR) and in a wide range of eukaryotes, including human, yeast, mouse, rat , C. elegans and A. thaliana. Software (such as R/qtl, mapQTL or QTLcartographer) is available for QTL analysis on classical traits. Issues that arise: These software packages are ment to be used by a small community of experts and take considerable effort to be mastered by a novice user (1). With the advent of -omics traits and high throughput SNP marker analysis, datasets grow larger in size and demand more computation time (2) and data management skills (3). To address these three issues, userfriendly software was written around an existing data management framework (XGAP) to distribute calculation of QTL profiles across a cluster of computers. This dramatically decreases total calculation time for a given set, or increases the number of traits that can be analysed in a given timeframe.The XGAP (eXtensible Genotype and Phenotype) datamodel was used to store all experimental data. The use of XGAP gives researchers the power of enforced data consistency, data exchange interfaces for popular applications, customizable user interfaces, etcetera - but ensures maximum exchangability of biological data between other XGAP database systems. Finally, an easy-to-use graphical interface was designed to facilitate adding and analyzing of datasets in the XGAP cluster computation environment. Researchers can perform QTL analysis on the cluster using only two simple steps.