Q U - C I M:
Breeding Real Wheat from Virtual Wheat
High points in wheat breeding: Breeders cross different wheats and select among their progeny to generate improved wheats that have inherited all the good traits of both parents. As per the basic laws of inheritance, the highest peak in this graphic "adaptation landscape" represents the progeny that has all desired traits from both parents, the next highest represents the one that has fewer of these traits, and so on down to the lower peaks. The QU-CIM simulation module will help breeders reach the highest peak more efficiently.
In the works at CIMMYT and at Australia's University of Queensland is a computer tool so sophisticated that it can help wheat breeders make some of the toughest decisions they face when developing a variety.
QU-CIM, a computer tool designed specifically for simulating CIMMYT's wheat breeding program, "can help us work better, faster, and more economically," points out Maarten van Ginkel, who breeds bread wheat for irrigated and high rainfall environments and leads the QU-CIM effort on the CIMMYT side. "It can save labor, land, and money." When finished, it will be applicable to other crops and other plant breeding programs, including those in developing countries.
CIMMYT's bread wheat breeding program was chosen for the QU-CIM project because, according to Ian De Lacy, a biometrician and expert on database management, "the program has 53 years of accumulated breeding data and is one of the most important and successful plant breeding programs in the world." De Lacy is one of the researchers fine-tuning QU-CIM to respond to real-life breeding situations.
Australia's Grains Research and Development Corporation (GRDC) funds the work on QU-CIM, which is based on QU-GENE, a simulation platform developed at the University of Queensland. It can integrate enormous amounts of genetics-based data from widely different sources, process them in many ways, and produce alternative theoretical (but realistic) scenarios that breeders can draw on to make a decision.
Choices that Make
or Break a Breeding Program
Jiangkang Wang, a postdoctoral fellow at CIMMYT, feeds the system the information it needs to simulate the breeding program. "My biggest challenge is to describe the field-based breeding process in a genetic language the computer can understand," says Wang. A first experiment is underway in which QU-CIM compares two selection schemes applied by CIMMYT wheat breeders to achieve the same objective. The program will indicate which strategy works best depending on the breeding materials and goals that are fed into it.
The laws of genetics put forth by Mendel more than 130 years ago underpin the simulation module, which also contains genetic equations developed over the past century. To work, the simulator draws upon data from many sources, including CIMMYT's International Wheat Information System (IWIS) and geographic information systems. QU-CIM will also link to the Agricultural Production System Simulator (APSIM), a collection of biological, physical, system control, and other modules that interact to simulate the operation of a farming system. These links will endow the simulation module with knowledge of the genetic and other relationships affecting wheat, plus wheat's performance in real farming situations.
One of the module's strengths is that it accommodates the combined effects of different genes that affect the same trait at the same time, which is often the case. "Breeders know that the effect of putting genes together is not simple, like 1 + 1 = 2. There's a synergy at work here that sometimes causes 1 + 1 to equal much more than 2, and sometimes less," explains van Ginkel. Positive synergy can produce huge genetic gains, but apart from relying on experience and intuition, breeders have to conduct tedious, large-scale genetic studies on a few lines at a time to predict how and when this synergy might happen. With QU-CIM they can quickly discover how to achieve the synergistic effects they seek.
QU-CIM can also indicate when it is cost-effective and/or efficient to use a specific technology at a specific stage in the breeding process. For example, using molecular markers to identify plants with valuable traits early in the breeding process might seem appropriate, but at that stage the number of plants to be tested is still very great, as is the cost of testing. It might make more sense to apply the technology at a later stage, when the population of experimental plants has been pared down to a more economical number. But by that time the gene of interest may have been bred out of the population, or nearly so, which is also undesirable. What is a breeder to do? Apply the module to see how the two scenarios play out, and then make a more informed decision.
Simulating Environments
and Environmental Variability
QU-CIM does not give breeders just one set of growing conditions in which to run tests, but generates different versions of an artificial environment to simulate conditions in different years and run, say, 100 breeding cycles to see what the outcome would be. Why is this useful?
Consider following example. In North Africa four out of five years are dry. Farmers sow their wheat, and if they see the year will be very dry, they will not let the crop grow to harvest because the grain yield will be very low; instead they allow their livestock to graze on it. For that they need a wheat variety that produces lots of stems and leaves and appeals to the animals. But the variety also has to produce a lot of grain (and not fall over under the added weight), since farmers want to reap an abundant harvest one year out of five, when rainfall is adequate. In wetter years, more disease is present in the fields, so the variety has to be disease resistant. In this complex scenario, the simulation module would aid in setting breeding priorities by running many breeding cycles while weighing the importance of different traits depending on the variations in the environment where the variety will be grown.
Bringing Down
Breeding Costs
QU-CIM could bring down breeding costs by reducing the number of crosses breeders make to reach a particular goal, identifying the best breeding method to use, or determining the most cost-effective, efficient time to use it. It would also compare the cost of the input to the cost of the corresponding output to determine whether applying a given technology makes sense. With QU-CIM, wheat breeders will more easily and economically help countries meet their farmers' needs.
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For more information: Maarten van Ginkel (m.van-ginkel@cgiar.org) Jiangkang Wang (j.k.wang@cgiar.org) |
August, 2004