Evolution of Genetic Potential

Organisms employ a multitude of strategies to cope with the dynamical environments in which they live. Homeostasis and physiological plasticity buffer changes within the lifetime of an organism, while stochastic developmental programs and hypermutability track changes on longer timescales. An alternative long-term mechanism is “genetic potential”—a heightened sensitivity to the effects of mutation that facilitates rapid evolution to novel states. Using a transparent mathematical model, we illustrate the concept of genetic potential and show that as environmental variability decreases, the evolving population reaches three distinct steady state conditions: (1) organismal flexibility, (2) genetic potential, and (3) genetic robustness. As a specific example of this concept we examine fluctuating selection for hydrophobicity in a single amino acid. We see the same three stages, suggesting that environmental fluctuations can produce allele distributions that are distinct not only from those found under constant conditions, but also from the transient allele distributions that arise under isolated selective sweeps

Foraging environment determines the genetic architecture and evolutionary potential of trophic morphology in cichlid fishes

Phenotypic plasticity allows organisms to change their phenotype in response to shifts in the environment. While a central topic in current discussions of evolutionary potential, a comprehensive understanding of the genetic underpinnings of plasticity is lacking in systems undergoing adaptive diversification. Here, we investigate the genetic basis of phenotypic plasticity in a textbook adaptive radiation, Lake Malawi cichlid fishes. Specifically, we crossed two divergent species to generate an F3 hybrid mapping population. At early juvenile stages, hybrid families were split and reared in alternate foraging environments that mimicked benthic/scraping or limnetic/sucking modes of feeding. These alternate treatments produced a variation in morphology that was broadly similar to the major axis of divergence among Malawi cichlids, providing support for the flexible stem theory of adaptive radiation. Next, we found that the genetic architecture of several morphological traits was highly sensitive to the environment. In particular, of 22 significant quantitative trait loci (QTL), only one was shared between the environments. In addition, we identified QTL acting across environments with alternate alleles being differentially sensitive to the environment. Thus, our data suggest that while plasticity is largely determined by loci specific to a given environment, it may also be influenced by loci operating across environments. Finally, our mapping data provide evidence for the evolution of plasticity via genetic assimilation at an important regulatory locus, ptch1. In all, our data address long-standing discussions about the genetic basis and evolution of plasticity. They also underscore the importance of the environment in affecting developmental outcomes, genetic architectures, morphological diversity and evolutionary potential

Improved real-space genetic algorithm for crystal structure and polymorph prediction

Existing genetic algorithms for crystal structure and polymorph prediction can suffer from stagnation during evolution, with a consequent loss of efficiency and accuracy. An improved genetic algorithm is introduced herein which penalizes similar structures and so enhances structural diversity in the population at each generation. This is shown to improve the quality of results found for the theoretical prediction of simple model crystal structures. In particular, this method is demonstrated to find three new zero-temperature phases of the Dzugutov potential that have not been previously reported

Killing them softly:managing pathogen polymorphism and virulence in spatially variable environments

Understanding why pathogen populations are genetically variable is vital because genetic variation fuels evolution, which often hampers disease control efforts. Here I argue that classical models of evolution in spatially variable environments – specifically, models of hard and soft selection – provide a useful framework to understand the maintenance of pathogen polymorphism and the evolution of virulence. First, the similarities between models of hard and soft selection and pathogen life cycles are described, highlighting how the type and timing of pathogen control measures impose density regulation that may affect both the level of pathogen polymorphism and virulence. The article concludes with an outline of potential lines of future theoretical and experimental work

Understanding Evolutionary Potential in Virtual CPU Instruction Set Architectures

We investigate fundamental decisions in the design of instruction set architectures for linear genetic programs that are used as both model systems in evolutionary biology and underlying solution representations in evolutionary computation. We subjected digital organisms with each tested architecture to seven different computational environments designed to present a range of evolutionary challenges. Our goal was to engineer a general purpose architecture that would be effective under a broad range of evolutionary conditions. We evaluated six different types of architectural features for the virtual CPUs: (1) genetic flexibility: we allowed digital organisms to more precisely modify the function of genetic instructions, (2) memory: we provided an increased number of registers in the virtual CPUs, (3) decoupled sensors and actuators: we separated input and output operations to enable greater control over data flow. We also tested a variety of methods to regulate expression: (4) explicit labels that allow programs to dynamically refer to specific genome positions, (5) position-relative search instructions, and (6) multiple new flow control instructions, including conditionals and jumps. Each of these features also adds complication to the instruction set and risks slowing evolution due to epistatic interactions. Two features (multiple argument specification and separated I/O) demonstrated substantial improvements int the majority of test environments. Some of the remaining tested modifications were detrimental, thought most exhibit no systematic effects on evolutionary potential, highlighting the robustness of digital evolution. Combined, these observations enhance our understanding of how instruction architecture impacts evolutionary potential, enabling the creation of architectures that support more rapid evolution of complex solutions to a broad range of challenges

Evolutionary processes and its environmental correlates in the cranial morphology of western chipmunks (Tamias).

The importance of the environment in shaping phenotypic evolution lies at the core of evolutionary biology. Chipmunks of the genus Tamias (subgenus Neotamias) are part of a very recent radiation, occupying a wide range of environments with marked niche partitioning among species. One open question is if and how those differences in environments affected phenotypic evolution in this lineage. Herein we examine the relative importance of genetic drift versus natural selection in the origin of cranial diversity exhibited by clade members. We also explore the degree to which variation in potential selective agents (environmental variables) are correlated with the patterns of morphological variation presented. We found that genetic drift cannot explain morphological diversification in the group, thus supporting the potential role of natural selection as the predominant evolutionary force during Neotamias cranial diversification, although the strength of selection varied greatly among species. This morphological diversification, in turn, was correlated with environmental conditions, suggesting a possible causal relationship. These results underscore that extant Neotamias represent a radiation in which aspects of the environment might have acted as the selective force driving species' divergence

Conserved but flexible modularity in the zebrafish skull: implications for craniofacial evolvability

Morphological variation is the outward manifestation of development and provides fodder for adaptive evolution. Because of this contingency, evolution is often thought to be biased by developmental processes and functional interactions among structures, which are statistically detectable through forms of covariance among traits. This can take the form of substructures of integrated traits, termed modules, which together comprise patterns of variational modularity. While modularity is essential to an understanding of evolutionary potential, biologists currently have little understanding of its genetic basis and its temporal dynamics over generations. To address these open questions, we compared patterns of craniofacial modularity among laboratory strains, defined mutant lines and a wild population of zebrafish ( ). Our findings suggest that relatively simple genetic changes can have profound effects on covariance, without greatly affecting craniofacial shape. Moreover, we show that instead of completely deconstructing the covariance structure among sets of traits, mutations cause shifts among seemingly latent patterns of modularity suggesting that the skull may be predisposed towards a limited number of phenotypes. This new insight may serve to greatly increase the evolvability of a population by providing a range of 'preset' patterns of modularity that can appear readily and allow for rapid evolution

Quantitative Genetic Effects of Bottlenecks: Experimental Evidence from a Wild Plant Species, Nigella degenii

Understanding the genetic consequences of changes in population size is fundamental in a variety of contexts, such as adaptation and conservation biology. In the study presented here, we have performed a replicated experiment with the plant Nigella degenii to explore the quantitative genetic effects of a single-founder bottleneck. In agreement with additive theory, the bottleneck reduced the mean (co)variance within lines and caused stochastic, line-specific changes in the genetic (co)variance structure. However, a significant portion of the (co)variance structure was conserved, and 2 characters—leaf and flower (sepal) size—turned out to be positively correlated in all data sets, indicating a potential for correlated evolution in these characters, even after a severe bottleneck. The hierarchical partitioning of genetic variance for flower size was in good agreement with predictions from additive theory, whereas the remaining characters showed an excess of within-line variance and a deficiency of among-line variance. The latter discrepancies were most likely a result of selection, given the small proportion of lines (23%) that remained viable until the end of the experiment. Our results suggest that bottlenecked populations of N. degenii generally have a lower adaptive potential than the ancestral population but also highlight the idiosyncratic nature of bottleneck effects