A Java Implementation of a Novel Quantitative Genetic Framework for the Evolution of Developmental Interactions
Document Type
Oral Presentation
Campus where you would like to present
SURC Ballroom C/D
Start Date
15-5-2014
End Date
15-5-2014
Keywords
Software, Evolution, Quantitative genetics
Abstract
Quantitative genetics is the study of complex biological traits, or traits controlled by more than one gene. Traditional quantitative genetic models use the (co)variances of traits to predict evolution in response to selection. However, traits often result from nonlinear interactions between developmental factors. Such interactions can produce large and rapid changes to trait (co)variances. Because of this, traditional models may not accurately predict evolutionary dynamics. The goal of this project is to determine the extent to which the developmental architecture of traits affects the evolutionary response of a given species. This may be achieved through the use of an updated mathematical framework that explicitly incorporates nonlinear interactions between developmental factors underlying one or more traits. As a first step, with the Java programming language we have developed a traditional model and a second, more advanced, model that allows a user to test hypotheses about how the developmental interactions among two traits affect their (co)variances and subsequent evolutionary trajectories. Additionally, our code and model framework are easily amenable to generating plots and graphs of trait relationships. With this software, users will be able to assess the accuracy of the updated model in comparison to the traditional framework. Future versions of our software will be available online as a user-friendly web tool, which will provide options to custom supply model parameters and equations of trait relationships.
For this presentation, Elizabeth Brooks received a College of the Sciences Best Poster Presentation Award for 2014.
Recommended Citation
Brooks, Elizabeth, "A Java Implementation of a Novel Quantitative Genetic Framework for the Evolution of Developmental Interactions" (2014). Symposium Of University Research and Creative Expression (SOURCE). 13.
https://digitalcommons.cwu.edu/source/2014/posters/13
Poster Number
10
Additional Mentoring Department
Biological Sciences
Additional Mentoring Department
Computer Science
A Java Implementation of a Novel Quantitative Genetic Framework for the Evolution of Developmental Interactions
SURC Ballroom C/D
Quantitative genetics is the study of complex biological traits, or traits controlled by more than one gene. Traditional quantitative genetic models use the (co)variances of traits to predict evolution in response to selection. However, traits often result from nonlinear interactions between developmental factors. Such interactions can produce large and rapid changes to trait (co)variances. Because of this, traditional models may not accurately predict evolutionary dynamics. The goal of this project is to determine the extent to which the developmental architecture of traits affects the evolutionary response of a given species. This may be achieved through the use of an updated mathematical framework that explicitly incorporates nonlinear interactions between developmental factors underlying one or more traits. As a first step, with the Java programming language we have developed a traditional model and a second, more advanced, model that allows a user to test hypotheses about how the developmental interactions among two traits affect their (co)variances and subsequent evolutionary trajectories. Additionally, our code and model framework are easily amenable to generating plots and graphs of trait relationships. With this software, users will be able to assess the accuracy of the updated model in comparison to the traditional framework. Future versions of our software will be available online as a user-friendly web tool, which will provide options to custom supply model parameters and equations of trait relationships.
For this presentation, Elizabeth Brooks received a College of the Sciences Best Poster Presentation Award for 2014.
Faculty Mentor(s)
Scoville, Alison; Jagodzinski, Filip