Dataset: Results using simulated data used to conduct power analyses

Final no updates expectedDOI: 10.26008/1912/bco-dmo.877456.1Version 1 (2022-10-14)Dataset Type:model results

Principal Investigator: Katie Lotterhos (Northeastern University)

Co-Principal Investigator: Geoffrey C. Trussell (Northeastern University)

Contact: Molly Albecker (Northeastern University)

BCO-DMO Data Manager: Taylor Heyl (Woods Hole Oceanographic Institution)

BCO-DMO Data Manager: Shannon Rauch (Woods Hole Oceanographic Institution)


Project: RCN: Evolution in Changing Seas (RCN ECS)


Abstract

Spatial covariance between genotypic and environmental influences on phenotypes (CovGE) can result in the nonrandom distribution of genotypes across environmental gradients and is a potentially important factor driving local adaptation. However, a framework to quantify the magnitude and significance of CovGE has been lacking. We develop a novel quantitative/analytical approach to estimate and test the significance of CovGE from reciprocal transplant or common garden experiments, which we validat...

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To validate the and G×E& sampling estimates, we created simulations that mimicked experimental data, and provided an array of scenarios to understand how effect size, presence of GxE, total sample size, experimental design, and variability were affected, as well as the ability to detect and measure these patterns. We simulated datasets with total sample sizes (number of environments x number of genotypes x sample size) between 32 and 500 individuals.

For reciprocal transplant data, we simulated genotypic effects that increased linearly at rate y along an environmental variable (e) for genotypes equally spaced from environment j = [1, 2, ... nenv]. We generated unitless phenotypic data based on an equation in the Supplemental File "Equation for power output results" (power_analyses_pasted_graphic.pdf).
           
In this equation, the phenotype of individual k from genotype i in environment j is given by the genotypic effect, the reaction norm (where ej is the value of the environment and beta is the slope of the reaction norm), an interaction term for genotype i in environment j that describes the deviation of the reaction norm from linearity, and error.

Interaction terms were drawn from a normal distribution with mean of zero and variance equal to the number of genotypes. Random error was added by sampling from a normal distribution with a mean of zero and standard deviation of either 0.5 (low residual variation) or 1 (high residual variation). Scenarios with no random error (= 0) were used to assess population parameters.

For common garden designs, we adjusted this approach to model designs in which different numbers of genotypes were reared in two common environments. We generated a single phenotypic reaction (see supplemental docs) norm for each group of genotypes (i.e., genotypes native to the same environment) based on the first terms of Eqn. 4. Then we generated reaction norm data for individual genotypes by adding the interaction term and error to the overall reaction norms.

See Related Dataset Albecker et al. (2022) for model code.


Related Datasets

IsRelatedTo

Dataset: Metadata from meta-analysis on CovGE in phenotypic results
Albecker, M., Trussell, G., Lotterhos, K. (2022) Metadata for studies from meta-analysis investigating covariance between genetic and environmental (CovGE) effects in phenotypic results. Biological and Chemical Oceanography Data Management Office (BCO-DMO). (Version 1) Version Date 2022-08-09 doi:10.26008/1912/bco-dmo.877414.1
IsRelatedTo

Dataset: Results of meta-analysis on CovGE in phenotypic results
Albecker, M., Trussell, G., Lotterhos, K. (2022) Results from a meta-analysis investigating covariance between genetic and environmental (CovGE) effects in phenotypic results in published literature. Biological and Chemical Oceanography Data Management Office (BCO-DMO). (Version 1) Version Date 2022-10-14 doi:10.26008/1912/bco-dmo.877425.1
Software

Dataset: https://doi.org/10.5281/zenodo.6470547
Albecker, M. A., Casalott, &amp; Lotterhos, K. (2022). <i>RCN-ECS/CnGV: Archived CGV data and code - April 2022</i> (Version 1.0) [Computer software]. Zenodo. https://doi.org/10.5281/ZENODO.6470547

Related Publications

Results

Albecker, M. A., Trussell, G. C., &amp; Lotterhos, K. E. (2022). A novel analytical framework to quantify co‐gradient and countergradient variation. Ecology Letters, 25(6), 1521–1533. Portico. https://doi.org/10.1111/ele.14020