Quantitative Genetics of Continuous Reaction Norms: Thermal Sensitivity of Caterpillar Growth Rates

Kingsolver, Joel G.; Ragland, Gregory J.; Shlichta, J. Gwen

doi:10.1554/03-557

Cited by 52 publications

(88 citation statements)

References 45 publications

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“…With measurements on 638 females from 111 sires, we were still able to gain statistical support for only 64% of the estimated additive genetic variance in preference functions. In addition, our approach requires the use of more individuals than does measuring focal females multiple times, and this may be a limiting factor for some species (Kingsolver et al 2004). …”

Section: Discussionmentioning

confidence: 99%

“…First, evolutionary studies have usually been concerned with modeling a number of phenotypic (response) traits that vary with a single independent variable, such as age (Wilson et al 2005), or an environmental variable, such as temperature (Kingsolver et al 2004). In contrast, our female preference functions are characterized by a single response variable (the choice that a female made resulting in male mating success) and multiple independent variables (the nine male traits under sexual selection).…”

Section: Methodsmentioning

confidence: 99%

“…Second, random-coefficient models offer the opportunity to fit more complex functions to the data than simple linear relationships can, a feature that is often exploited in genetic studies (e.g., Kingsolver et al 2004;Wilson et al 2005). In such analyses, a limit on the complexity of the genetic covariance function that can be applied is determined by the number of character states of the independent variable (e.g., number of age classes or number of temperatures).…”

Section: Methodsmentioning

confidence: 99%

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Genetic Analysis of Female Preference Functions as Function‐Valued Traits

McGuigan

Homrigh

Blows

2008

The American Naturalist

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JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.. abstract: The genetic analysis of female preferences has been seen as a particularly challenging empirical endeavor because of difficulties in generating suitable preference metrics in experiments large enough to adequately characterize variation. In this article, we take an alternative approach, treating female preference as a function-valued trait and exploiting random-coefficient models to characterize the genetic basis of female preference without measuring preference functions in each individual. Applying this approach to Drosophila bunnanda, in which females assess males through a multivariate contact pheromone system, we gain three valuable insights into the genetic basis of female preference functions. First, most genetic variation was attributable to one eigenfunction, suggesting shared genetic control of preferences for nine male pheromones. Second, genetic variance in female preference functions was not associated with genetic variance in the pheromones, implying that genetic variation in female preference did not maintain genetic variation in male traits. Finally, breeding values for female preference functions were skewed away from the direction of selection on the male traits, suggesting directional selection on female preferences. The genetic analysis of female preference functions as function-valued traits offers a robust statistical framework for investigations of female preference, in addition to alleviating some experimental difficulties associated with estimating variation in preference functions.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Genetic Analysis of Female Preference Functions as Function‐Valued Traits

McGuigan

Homrigh

Blows

2008

The American Naturalist

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“…Random regressions have been applied to genetic analysis of growth curves, lactation curves and reaction norms in farm animal breeding (Henderson, 1982 ;Schaeffer, 2004), and also more recently in evolutionary genetics (e.g. Kingsolver et al, 2004). When combined with pedigree information, random regression models allow the estimation of genetic variances for the regression coefficients and of genetic correlations with other traits.…”

Section: Introductionmentioning

confidence: 99%

Genetic analysis of tolerance to infections using random regressions: a simulation study

Kause

2011

Genet. Res.

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SummaryTolerance to infections is the ability of a host to limit the impact of a given pathogen burden on host performance. This simulation study demonstrated the merit of using random regressions to estimate unbiased genetic variances for tolerance slope and its genetic correlations with other traits, which could not be obtained using the previously implemented statistical methods. Genetic variance in tolerance was estimated as genetic variance in regression slopes of host performance along an increasing pathogen burden level. Random regressions combined with covariance functions allowed genetic variance for host performance to be estimated at any point along the pathogen burden trajectory, providing a novel means to analyse infection-induced changes in genetic variation of host performance. Yet, the results implied that decreasing family size as well as a non-zero environmental or genetic correlation between initial host performance before infection and pathogen burden led to biased estimates for tolerance genetic variance. In both cases, genetic correlation between tolerance slope and host performance in a pathogen-free environment became artificially negative, implying a genetic trade-off when it did not exist. Moreover, recording a normally distributed pathogen burden as a threshold trait is not a realistic way of obtaining unbiased estimates for tolerance genetic variance. The results show that random regressions are suitable for the genetic analysis of tolerance, given suitable data structure collected either under field or experimental conditions.

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“…Thermal trade-offs have been investigated by comparing the performance of strains across different temperatures. Kingsolver et al (2004) considered growth rates of Pieris rapae L (Lepidoptera: Pieridae) caterpillars across a range of temperatures from 8°C to 40°C and found that growth at 35°C (when it was at a maximum) was negatively correlated genetically with growth at 40°C when there was a sharp decline, suggesting a trade-off between optimal and high temperature performance. Thermal selection experiments in D. melanogaster suggest that heat and cold resistance traits are largely genetically independent but that increased resistance may be accompanied by decreased performance in some life history traits (Mori & Kimura 2008).…”

Section: Quantitative Genetic Testsmentioning

confidence: 99%

A genetic perspective on insect climate specialists

Hoffmann¹

2010

Australian Journal of Entomology

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Many insects are climate specialists -restricted to a narrow range of latitudes, humidity conditions and/or altitudes. Yet there are also numerous climate generalists, whose distribution might span from the tropics to temperate areas. Comparisons of related insect species normally indicate that the resistance of species to climatic extremes and reproductive output under different climatic conditions match expectations based on their distributions. Yet these patterns do not ultimately explain why climate specialists and generalists have evolved over time. Three evolutionary hypotheses that are invoked to explain climate specialisation are (1) constraints arising from antagonistic pleiotropy (costs); (2) DNA decay due to mutational processes; and (3) the difficulty of adapting due to the requirement of multidimensional changes or because of gene flow. Here I outline these hypotheses, and consider predictions and supporting evidence from several sources including polymorphism studies, quantitative genetic studies, species comparisons and genomic comparisons. All three explanations are likely to contribute to climate specialisation and the DNA decay/multidimensional adaptation hypotheses deserve more consideration.

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Quantitative Genetics of Continuous Reaction Norms: Thermal Sensitivity of Caterpillar Growth Rates

Cited by 52 publications

References 45 publications

Genetic Analysis of Female Preference Functions as Function‐Valued Traits

Genetic Analysis of Female Preference Functions as Function‐Valued Traits

Genetic analysis of tolerance to infections using random regressions: a simulation study

A genetic perspective on insect climate specialists

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