Predicting response to selection on a quantitative trait: a comparison between models for mixed-mating populations.

Department of Ecology and Evolutionary Biology, University of Kansas, Lawrence, KS, 66045, USA.
Journal of Theoretical Biology (Impact Factor: 2.35). 12/2000; 207(1):37-56. DOI: 10.1006/jtbi.2000.2154
Source: PubMed

ABSTRACT Two different theoretical frameworks have been developed to predict response to selection in a mixed mating population (in which reproduction occurs by a mixture of outcrossing and self-fertilization). The genotypic covariance model (GCM) and the structured linear model (SLM) rely on the same assumptions regarding quantitative trait inheritance, but use different genetic summary statistics. Here, we demonstrate the algebraic relationships between the various genetic metrics used in each theory. This is accomplished by reformulating the GCM in terms of the Wright-Kempthorne equation. We use stochastic simulations to investigate the relative accuracy of each theory for a range of selfing rates. The SLM is generally more accurate than the GCM, the most pronounced differences emerging in simulations with inbreeding depression for fitness. In fact, with strong inbreeding depression and high selfing rates, evolution can occur opposite the direction predicted by the GCM. The simulations also indicate that direct application of random mating models to partially selfing populations can produce very inaccurate predictions if quantitative trait loci exhibit dominance.

  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The additive genetic variance, V(A), is frequently used as a measure of evolutionary potential in natural plant populations. Many plants inbreed to some extent; a notable observation given that random mating is essential to the model that predicts evolutionary change from V(A). With inbreeding, V(A) is not the only relevant component of genetic variation. Several nonadditive components emerge from the combined effects of inbreeding and genetic dominance. An important empirical question is whether these components are quantitatively significant. We use maximum likelihood estimation to extract estimates for V(A) and the nonadditive 'inbreeding components' from an experimental study of the wildflower Mimulus guttatus. The inbreeding components contribute significantly to four of five floral traits, including several measures of flower size and stigma-anther separation. These results indicate that inbreeding will substantially alter the evolutionary response to natural selection on floral characters.
    Heredity 02/2003; 90(1):77-83. · 4.11 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Estimating the genetic variance available for traits informs us about a population's ability to evolve in response to novel selective challenges. In selfing species, theory predicts a loss of genetic diversity that could lead to an evolutionary dead-end, but empirical support remains scarce. Genetic variability in a trait is estimated by correlating the phenotypic resemblance with the proportion of the genome that two relatives share identical by descent ('realized relatedness'). The latter is traditionally predicted from pedigrees (Φ A : expected value) but can also be estimated using molecular markers (average number of alleles shared). Nevertheless, evolutionary biologists, unlike animal breeders, remain cautious about using marker-based relatedness coefficients to study complex phenotypic traits in populations. In this paper, we review published results comparing five different pedigree-free methods and use simulations to test individual-based models (hereafter called animal models) using marker-based relatedness coefficients, with a special focus on the influence of mating systems. Our literature review confirms that Ritland's regression method is unreliable, but suggests that animal models with marker-based estimates of relatedness and genomic selection are promising and that more testing is required. Our simulations show that using molecular markers instead of pedigrees in animal models seriously worsens the estimation of heritability in outcrossing populations, unless a very large number of loci is available. In selfing populations the results are less biased. More generally, populations with high identity disequilibrium (consanguineous or bottlenecked populations) could be propitious for using marker-based animal models, but are also more likely to deviate from the standard assumptions of quantitative genetics models (non-additive variance).
    PLoS ONE 01/2013; 8(6):e66983. · 3.53 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: We used 15 microsatellite markers to estimate the selfing rate (s), outcrossing rate (t(O) ) and hybridization between partially sympatric ecomorphs (t(H) ) of the coral Favia fragum. Genotyping of progeny arrays revealed complete self-fertilization in the Tall ecomorph and low outcrossing (t(O)  + t(H)  < 1%) in the Short ecomorph. Further, all larvae could be assigned with high probability to the same population as their parental dam, indicating no hybridization between ecomorphs (t(H)  = 0). Despite low ecological estimates of outcrossing, Q values from highly structured adult populations indicated that 9% of the adult samples were the products of outcrossing, and an additional 11% were hybrids. Reproductive isolation appears to have a strong geographical component, as we did not detect hybrids at a second site where the two ecomorphs were distributed in complete microallopatry. Adult estimates of gene flow within ecomorphs may be positively biased by ecomorph-specific patterns of inbreeding depression, but cryptic gene flow between ecomorphs is most likely explained by undetected outcrossing and the fact that hybrid lineages persist after repeated generations of self-fertilization. Our microsatellite data show that phenotypic differences between ecomorphs are maintained in sympatry despite evidence for hybridization.
    Molecular Ecology 02/2011; 20(4):812-28. · 6.28 Impact Factor