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REVIEW & INTERPRETATION

Mining and Harnessing Natural Variation: A Little MAGIC

^a Dep. of Botany and Plant Pathology, Purdue Univ., West Lafayette, IN 47907
^b USDA-ARS, Dep. of Plant Pathology, North Carolina State Univ., Raleigh, NC 27695
^c Dep. of Agronomy, Purdue Univ., West Lafayette, IN 47907

^* Corresponding author (gjohal{at}purdue.edu).

The success of a breeding program depends on having adequate diversity in the germplasm. However, as advanced breeding stocks and materials are generated, one casualty is the diversity itself. As a result, breeding programs in many crop species have reached a point of diminishing returns and it is feared that unless new diversity is infused into the breeding germplasm, we face catastrophic reductions in crop productivity if the climate turns adverse. Although some scientists favor transgenic approaches, a "back to nature" approach to genetic diversity may prove faster and more effective. Wild and exotic relatives of crop plants hold a wealth of alleles that, if we can find them, can help break yield barriers and enhance tolerance to stresses. Many approaches, based largely on quantitative trait loci genetics, have been proposed and used for this purpose, but most are either highly laborious or discover relevant variation inefficiently. Here, we propose a gene-centered approach, dubbed MAGIC (mutant-assisted gene identification and characterization), that uses Mendelian mutants or other genetic variants in a trait of interest as reporters to identify novel genes and variants for that trait. MAGIC is similar to enhancer–suppressor screens, but rather than relying on variation created in the laboratory, it reveals variation created and refined by nature over millions of years of evolution. This approach could be an effective tool for exploring novel variation and a valuable means to harness natural diversity and define genetic networks.

Abbreviations: CGV, cryptic genetic variation • HR, hypersensitive response • MAGIC, mutant-assisted gene identification and characterization • NAM, nested association mapping • QTL, quantitative trait locus • RIL, recombinant inbred line • WT, wild-type