Academic journal article Genetics

Marker-Based Quantitative Genetics in the Wild?: The Heritability and Genetic Correlation of Chemical Defenses in Eucalyptus

Academic journal article Genetics

Marker-Based Quantitative Genetics in the Wild?: The Heritability and Genetic Correlation of Chemical Defenses in Eucalyptus

Article excerpt

ABSTRACT

Marker-based methods for estimating heritability and genetic correlation in the wild have attracted interest because traditional methods may be impractical or introduce bias via G × E effects, mating system variation, and sampling effects. However, they have not been widely used, especially in plants. A regression-based approach, which uses a continuous measure of genetic relatedness, promises to be particularly appropriate for use in plants with mixed-mating systems and overlapping generations. Using this method, we found significant narrow-sense heritability of foliar defense chemicals in a natural population of Eucalyptus melliodora. We also demonstrated a genetic basis for the phenotypic correlation underlying an ecological example of conditioned flavor aversion involving different biosynthetic pathways. Our results revealed that heritability estimates depend on the spatial scale of the analysis in a way that offers insight into the distribution of genetic and environmental variance. This study is the first to successfully use a marker-based method to measure quantitative genetic parameters in a tree. We suggest that this method will prove to be a useful tool in other studies and offer some recommendations for future applications of the method.

MARKER-based methods for estimating quantitative genetic parameters in wild populations have attracted interest for two principal reasons: (1) experimental methods may be impractical or unfeasible in some systems; and (2) traditional methods may introduce bias through genotype-environment interactions, mating system variation, or sampling effects. The genetic basis of quantitative trait variation is usually studied using known pedigrees and controlled environments, most often for the purpose of plant or animal breeding (LYNCH and WALSH 1998). However, when the aim is instead to measure parameters pertaining to evolution in wild organisms, it becomes desirable to describe genetic variation in quantitative traits in natural populations. While traditional methods are good for making predictions about potential breeding gains in domesticated organisms, the results may not accurately describe the organism in its natural state.

These problems are particularly relevant for longlived trees and for those with mixed-mating systems and restricted dispersal. Adult traits may take years to develop, so that common-garden trials are time-consuming and expensive. Estimates of genetic parameters from even well-designed common-garden or laboratory experiments may not accurately reflect the parameters affecting trait evolution in the field. Environmental factors, such as stress and competition, can change the observed genetic effects in experiments, compared with natural conditions, because genotype X environment interactions can affect additive genetic variances and covariances (DONOHUE et al. 2000; MUTIKAINEN et al. 2000; ORIANS et al 2003; OSIER and LINDROTH 2004; SGRO and HOFFMANN 2004). Statistical models for openpollinated trials often assume a common pollen pool and a constant degree of relatedness within families, which may be incorrect for species with correlated paternity or local spatial genetic structure, for example. Furthermore, collecting available seed may produce a biased sample in species with variable reproduction, particularly where flowering time is under genetic control.

Two strategies have been used for marker-based estimation of variance components in natural populations: the application of traditional mixed models to sibships reconstructed from marker data (LYNCH and WALSH 1998) and of a regression-based method that does not assume predefined classes of relationship (RiTLAND 1996b). Ritland's method uses a modified linear regression of phenotypic similarity on relatedness to estimate heritability. Whereas standard least-squares regression requires the predictor variable (in this case relatedness) to be known without error, this obstacle can be overcome by estimating its "actual" variance, as distinct from its statistical variance (RiTLAND 1996b). …

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