Agricultural Research Service (ARS) scientists have led the way in finding a new technique to obtain genetic markers to sort out corn rootworm (Diabrotica virgifera) populations. This method is faster and cheaper than existing techniques, and it can be used to characterize genetic variation in any animal species.
Entomologists Tom W. Sappington and Kyung Seok Kim work at the ARS Corn Insects and Crop Genetics Research Unit in Ames, Iowa. For this research, they partnered with ARS North Central Agricultural Research Laboratory entomologist B. Wade French in Brookings, S.D., and University of Illinois colleagues Susan T. Ratcliffe and Lei Liu.
Researchers often use sections of DNA called short sequence repeats (SSRs) to study the interactions between different populations of the same species. Although they are superior genetic markers, SSRs are typically identified by random, expensive and time-consuming searches through the total DNA extracted from an individual organism.
However, SSRs are also found in sections of DNA called expressed sequence tags (ESTs). Species-specific EST databases support genetic studies, including the identification of SSRs, in a range of plants and animals.
The ARS team set out to resolve whether they could use SSRs obtained from existing corn rootworm EST databases—not SSRs identified from individual DNA—to study the genetic relationships of rootworm populations.
The researchers developed two sets of SSRs. One set contained 17 SSRs developed using DNA from individual corn rootworms. The other set of 17 SSRs was created from an existing database of 6,397 corn rootworm ESTs.
The scientists pitted the two sets in a head-to-head competition characterizing five western corn rootworm populations from around the United States and a Mexican corn rootworm population from Texas. They found that both sets could be used to assign individuals to the correct populations with up to 80 percent accuracy.
These findings suggest that researchers studying population genetics can save time and money by using ESTs in existing databases to identify SSRs, instead of developing new SSRs from scratch.
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