Germplasm collections can be very large, and that can put off potential users. What breeder really wants to screen thousands of accessions, when only a dozen might end up being useful? It’s not surprising, therefore, that people have looked for short-cuts. One approach is to make a “core collection.” You use the available data on the collection to select a sub-set which you hope will contain most of the original genetic diversity in a fraction (20%, say) of the total number of accessions. And then you evaluate that subset, rather than the whole collection, and use the results to delve back into the remaining 80% of the material, with hopefully a better chance of finding what you’re looking for.
That’s been done for lots of large collections now, with a certain amount of success in increasing their use — and usefulness. But breeders are not really satisfied. They want to shorten the odds even more. And the application of Geographic Information Systems (GIS) technology in something called the Focused Identification of Germplasm Strategy (FIGS) provides a potentially effective way of doing just that.
Jeremy described recently over at Bioversity how FIGS was used to increase the chances of finding a needle in a haystack by “start[ing] with a smaller haystack.” The haystack was 16,000 wheat accessions. The needle was resistance to powdery mildew.
It works like this: take 400 genebank samples known to have some resistance to powdery mildew and use the geographical location where they evolved and were collected to determine the environmental profile that can be associated with resistance. Then apply that profile to a further 16,089 samples with location data, using the profile as a template to identify those that were found in places that share the conditions associated with resistance. The result is a group of 1320 wheat varieties, mostly from Turkey, Iran and Afghanistan. This much more manageable subset was screened by growing them with diverse strains of powdery mildew. About 16% of the samples (211 of 1320) showed some resistance.
These varieties then moved to the next phase, molecular screening for the presence of different alleles of the Pm3 gene. More than half (111 of the 211) had Pm3 resistance, some in previously unknown forms. In the end the group isolated and identified 7 new functional alleles of the Pm3 gene. It took scientists 100 years to find the first 7 Pm3 alleles. FIGS doubled the number in a fraction of the time.
Very good. But is it always going to work? Another recent paper — in fact, a series of papers — counsels caution.
Researchers at USDA-ARS in Madison, Wisconsin and at the International Potato Centre (CIP), who collectively sit on the largest collection of wild potatoes in the world, have been looking for some time at how geography can help them better use their very diverse 2,500 or so accessions of about 190 species.
In contrast to the wheat powdery mildew example, previous work with these wild potatoes has found only weak associations between climatic variables and things like resistance to frost and to a couple of different fungal diseases. The latest paper looks at Colorado potato beetle resistance.1 It again finds little predictive power in environmental variables:
Resistance is not concentrated enough to provide guidance regarding geographic localities likely to contain a high proportion of populations containing Colorado potato beetle resistance.
Which species an accession belonged to was a much better clue to finding resistance to the pest than any combination of the 38 temperature, rainfall, altitude and latitude variables used in the analysis.
Now, one can argue about the ecological validity of these climatic variables. Or about whether some other factor — soils, say — would have fared better. Or about the completeness and representativeness of the geographic coverage of the collection. Or about whether the results would have been different if the technique had been applied to each species individually, rather than to the genepool as a whole. But this series of papers does suggest that, important as it undoubtedly often is, the use of location data may not be the universal panacea that some of us were hoping for.
Looks like we’ll need a diversity of strategies to find those needles.
- Jansky, S. H., Simon, R. & Spooner, D. M. (2009) A Test of Taxonomic Predictivity: Resistance to the Colorado Potato Beetle in Wild Relatives of Cultivated Potato. Journal of Economic Entomology, Volume 102(1):422-431. [↩]