Animal-breeding advice from the grid

A new grid-based platform designed in Scotland helps veterinary scientists and farmers to figure out the vast number of genes that can sometimes contribute to just a single, valuable physical trait. It’s already been used to identify previously elusive traits in horses, fish, chickens, and crocodiles.

Most traits in plants and animals, especially the ones measured over a continuous range such as height, weight or propensity to a disease, depend on many, many genes and often environmental factors as well. These regions of the genome that have an effect on a given physical trait are called ‘quantitative trait loci’, or QTL. A knowledge of these genome regions is crucial for our understanding of variation between individuals and how traits are passed on from generation to generation.

Crocodile leather: Saltwater crocodiles in Australia are farmed for their skin, which is turned into luxury leather goods, and the number of scale rows can affect the quality of these goods. Veterinary scientists from the University of Sydney in Australia found the quantitative trait loci responsible for the number of scale rows using GridQTL. Image courtesy of Sias van Schalkwyk.

 Sara Knott from the University of Edinburgh and her team developed a grid-based platform, called GridQTL, that helps to identify these loci. “Knowledge of quantitative trait loci has many applications in the pharmaceutical industry and in risk management as well,” Knott said. Understanding quantitative trait loci helps to predict the risk related to diseases with an underlying genetics basis, for example.

Quantitative trait loci are also important to the way we interact with domesticated plants and animals. Careful genome analysis helps breeders to select the right parents for a next generation with improved physical traits. With insights from quantitative trait loci, it is possible to breed better animals and plants through husbandry practices alone.

The technique has been used to study cattle, sheep, obesity in pigs, or disease resistance in poultry. Recent applications have moved to other fields, from farmed fish to Australian crocodiles (see image).

GridQTL builds on the experience of QTLExpress, a software developed by Knott’s team at Edinburgh as a tool to analyze datasets. QTLExpress proved to be a success and increasing usage meant that the software required more computing power, and they needed it to be more reliable too.

The platform is used worldwide by about 300 active users and at least 30 papers have been published since January 2009 on data analyzed with this software. GridQTL allows users to input their data into an intuitive web-based graphical interface implemented with the GridSphere portal project. The portal hosts JSR 168 compliant Java portlets especially designed for job submission, querying, file management, data manipulation and other services. The computing jobs are submitted to the UK’s National Grid Service with the Globus Toolkit. On a single core computer, the task would take 30 hours to complete on single core computers, using grid computing means that job done in 12 minutes.

“It would be very difficult to run this kind of software without grid computing,” said Knott. “We have an uncertain user base and the grid provides the flexibility to sustain an adequate speed of analysis, regardless of online users.”

“It would also be very difficult to maintain the hardware ourselves.”

Knott is confident that GridQTL is not a headache for the user. “You don’t need to understand the grid, or know about programming to use GridQTL,” she says. Scientists simply add their data and the software provides the statistical analysis ready to be interpreted.