Name: John Allen
Institution: University of Edinburgh
Research: GridQTL – Computational Genetics via the Grid
An organism’s physical characteristics (phenotype) can be influenced by their genotype (DNA make up) and also by environmental factors. Some phenotypes can be simply influenced by a single location on one chromosome of their genotype and nothing else, for example Huntingdon’s disease and the most common form of Cystic Fibrosis. More complicated phenotypes are a result of many genes acting in symphony together with environmental factors and as a result are much harder to model.
QTL (Quantitative Trait Loci) mapping is one technique used to model these more complicated phenotypes or traits. QTLs can be locations of genes or regions on the chromosome that regulate the expression of genes (e.g. if a gene is switched on or off) and are used to determine the origin of many complex traits including product yield in crops and risk factors for disease in animal and human populations.
Sara’s team have produced in-house algorithms to map QTLs using genetic and phenotypic data resulting from inheritance studies of a range of organisms. The algorithms are accessed via a web based portal called GridQTL which provides a user friendly environment to run and manage QTL studies. The portal harnesses Grid technologies (such as Globus toolkit) to deal with the increased computational demands caused by available genetic data expanding from Mbytes to GBytes and beyond.
The team’s use of the NGS has greatly increased the productivity of their users (currently around 400) in the QTL community. One example of this is a GridQTL user at the University of Missouri Columbia. They ran a series of studies on carcass, post-natal growth and reproductive traits in commercial Angus cattle and found a speed up of from 20 people-weeks, using their old single server system, to 3 people-weeks to capture and analyse the data with GridQTL.
Since GridQTL was released to the QTL community in the summer of 2006, there have been nearly 400 individual users performing over 75,000 analyses in their QTL studies. This equates to 5 cpu years of computation time on NGS and local servers and they currently consume over 2000 cpu hours on the Grid each month. GridQTL is used in every continent of the world (though not quite Antarctica and its penguins yet!) by around 50 users a month. A map detailing the location of their users and their output of work is available from their website.
There are many other research studies currently being carried out by GridQTL users including:
- Milk production in sheep.
- Growth in young cattle.
- Fatness in pigs.
- Harvest traits in salmon.
- Domesticity studies of foxes.
- Obesity in mice
- Growth in broiler chickens.
- Wood quality of eucalyptus trees.
- Scale quality in saltwater crocodiles.
- Airway obstruction in sport horses.
With the number of users in the QTL community growing and the ever increasing volume of genotype data available, the use of the NGS to provide free distributed computing that could be easily accessed was critical to the successful performance of their QTL portal.
PI - Dr. Glenn Flux, Dr. Mike Partridge and Dr. Susan Buckley
Funding body - BBSRC(BEP2, BBS/B/1695X) (2005 – 2010); BBSRC GRANT APPLICATION REFERENCE NO: BB/G022658/1 (GridQTL+ - 2010-2013)
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