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(Doebley et al., 1990; Doebley and Stec, 1991) mapped differences in plant architecture and plant yield between maize and its wild ancestor, teosinte. Subsequent mapping and mutation analyses led to the isolation of major genes that govern phenotypic differences between maize and teosinte, including teosinte branched1 (tb1), a gene controlling lateral branching (Doebley et al., 1995), and teosinte glume architecture (tga), which contributes to differences in inflorescence architecture (Wang et al., 2005).

These successes highlight the value of the QTL approach, but the method is not without its limitations. It can, for example, be difficult to develop mapping populations for perennial, inbreeding, and vegetatively propagated crops. Thus, some of the 15 crops in Table 11.1, such as bananas and palm trees, are intractable for study by QTL approaches. It is also important to remember that the results of QTL analysis often depend on the environment (Paterson et al., 1988) as well as the parental lines used in the cross (Doebley and Stec, 1991; Li et al., 2006a). Caution is therefore warranted in interpreting the generality of QTLs, especially in cases of multiple domestication or local adaptation. There are also numerous statistical issues, the most important of which is the limited power to accurately estimate the number and size of QTLs, an observation that has become known as the Beavis effect (Beavis, 1994, 1998). Although this limitation has not proven problematic for cloning genes of large phenotypic effect, statistical power poses a major concern for more classically quantitative traits like size, weight, or yield that are likely to be determined by a larger number of QTLs of smaller phenotypic effect, and statistical concerns become even more problematic for the estimation of complex phenomena such as epistasis (Carlborg and Haley, 2004).

QTL studies have provided and will continue to provide considerable utility for identifying genes and genomic regions that contribute to phenotypes of interest. Moreover, the rate at which such genes are identified will continue to increase as genomic data become available for more species; this increase is already evident in the 2006 publication year, which witnessed an explosion of the isolation of genes contributing to major phenotypic differences between domesticates and their wild ancestors. Although not solely attributable to QTL approaches, genes isolated in 2006 included two rice shattering genes (Konishi et al., 2006; Li et al., 2006b), a rice kernel color gene (Sweeney et al., 2006), a wheat shattering gene (Simons et al., 2006), and a wheat senescence gene affecting nutritional content (Uauy et al., 2006). Even so, only a handful of genes have been isolated by these approaches (Doebley et al., 2006), and the total output has been surprisingly small given both the large amount of money and human capital invested in QTL studies and the economic and societal importance of a relatively small number of plants (Table 11.1). Furthermore, the genes isolated to date are genes of very large effect, i.e., the “low hanging fruit”



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