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tial to assay genetic polymorphism in a number of randomly selected reference genes and compare them to the candidate gene. However, this is often difficult to do well, requiring many randomly sampled genes (see below) and computationally intensive simulation methods to estimate the underlying demographic model.

FROM THE BOTTOM UP: MOLECULAR POPULATION GENETICS

In marked contrast to the top-down or phenotype-first approaches already discussed, bottom-up approaches start by identifying genes with the signature of adaptation using population genetics and then make use of a broad array of genetic tools to identify the phenotypes to which these genes contribute. Bottom-up approaches are relatively new, and many of the methodologies are still being developed, but we believe that they have the potential to revolutionize crop genetics. Here we briefly introduce some of the methods and outline the challenges involved in identifying candidate genes using population genetics.

Fitting a Demographic Model

Ideally, bottom-up approaches begin by assaying genetic diversity in hundreds of loci, preferably from a sample of ≈100 individuals representing both the domesticate and its wild ancestor. Given sequence polymorphism data, several factors will affect the ability to detect the signal of adaptation, including the strength and history of selection, rates of mutation and recombination, and the demographic history of the population (Wright and Gaut, 2005). As mentioned above, demographic considerations are particularly important for crop plants, likely invalidating standard population genetic tests designed to detect the signal of selection. The standard tests typically assume that populations evolve according to the idealized Wright–Fisher model, with panmictic populations of constant population size. When these assumptions are inaccurate, as they certainly are for most domesticated species (Fig. 11.2), tests to detect selection can be wildly inaccurate. For example, computer simulations show that Tajima’s D, a commonly used test statistic for selection, identifies up to 25% of loci as selected after a change in population size due to a bottleneck, even when there has been no selection (Wright and Gaut, 2005). Another recent method incorrectly infers selection up to 90% of the time when Wright–Fisher assumptions do not hold (Jensen et al., 2005). Thus, departures from standard assumptions dramatically decrease the reliability of tests for selection and can distort the signature of selection beyond recognition (Slatkin and Wiehe, 1998; Przeworski et al., 2005). Clearly, one should view with skepticism studies of domesticated crops



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