(Doebley et al., 2006). Substantially more effort will likely be required to identify and clone genes of smaller effect.
In the hope of overcoming some of the limitations of QTL analysis, plant researchers have moved toward LD mapping as an additional means to identify genomic regions that contribute to phenotypes. In practice, LD mapping can be separated into two types, each focusing on a different level of genetic analysis. The first, like most QTL approaches, aims to identify genome-wide variation that associates with phenotypic variation. This requires measures of genetic variability in markers representing most of the genome and tests of phenotype–genotype association for each marker. The second type of association analysis attempts to pinpoint the causative genetic mutation(s) that effect phenotype; these latter studies typically focus on variation in one or few candidate genes rather than whole genomes.
The primary advantage of LD mapping is that it can rely on population samples; there is no need for crosses and the production of large numbers of progeny. This is an obvious benefit for the study of bananas, palms, or other long-lived perennial species (Table 11.1) and in general allows studies to proceed more rapidly. In addition, the population sample may contain many more informative meioses (i.e., all those that have occurred in the evolutionary history of the sample) than a traditional QTL mapping population. As a result, the phenotype of interest may be associated with a much smaller chromosomal segment than in a QTL population, in theory providing greater mapping resolution.
Like QTL methods, however, there are several features of experimental design that need to be carefully considered when undertaking LD mapping. First, distinguishing true associations from statistical noise requires large sample sizes, both for statistical power and to correct for multiple tests (Long and Langley, 1999; Macdonald and Long, 2004). Even with large sample sizes, researchers may have to assume that the effects of individual mutations are additive; testing for epistatic interactions between hundreds of markers further exacerbates the problem of multiple tests (Macdonald et al., 2005). One way to reduce this problem is to test for associations between phenotypes and haplotypes (or “haplotype blocks”) rather than individual markers (Clark, 2004). But unless haplotypes can be inferred experimentally (Morrell et al., 2006), as in selfing taxa such as barley and rice (Table 11.1), the necessary computational inference of haplotypes can prove an impediment to this approach.
Another design challenge is sample origin. Geographic structure or other departures from panmixis can result in spurious associations in