Presentation Detail

Quantitative Genetics

Hether, Tyler [1], Hohenlohe, Paul A. [2].

Genetic regulatory network motifs constrain adaptation through curvature in the landscape of mutational variation.

Systems biology has accumulated a wealth of understanding about the structure and functional relationships of genetic regulatory networks, leading to a more complete picture of the complex genotype-phenotype relationship. However, models of multivariate phenotypic evolution based on quantitative genetics have largely not incorporated a network-based understanding of genetic variation. Here we address this gap with a set of models of two-locus, two-phenotype genetic regulatory network motifs, covering a full range of regulatory interactions. We find that the pleiotropy and epistasis inherent in genetic networks result in different patterns of mutational variance and covariance at the phenotypic level, not only across network motifs but also across phenotypic space within single motifs. This has the effect of stretching and bending phenotypic space with respect to evolvability, analogous to the curvature of space-time under general relativity, and similar mathematical tools may apply in both cases. We explore the consequences of curvature in mutational variation by examining a negative-feedback network motif under divergent selection with gene flow. Adaptation is constrained by mutational (co)variance that differs across phenotypic space, and standing genetic variance within populations represents a tenuous balance among selection, migration, drift, and mutational (co)variance.

1 - University of Idaho, Bioinformatics and Computational Biology, Life Sciences South 441D, Moscow, Idaho, 83844-3051, United States
2 - University of Idaho, Biological Sciences, Life Sciences South 441D, University of Idaho, Moscow, Idaho, 83844-3051, United States

Keywords:
Adaptation
G matrix
M matrix
Adaptive divergence
Networks.

Presentation Type: Regular Oral Presentation
Session: 131
Location: Cotton D/Snowbird Center
Date: Tuesday, June 25th, 2013
Time: 11:45 AM
Number: 131006
Abstract ID:1042
Candidate for Awards:W.D. Hamilton Award for Outstanding Student Presentation