Statistical method for testing the neutral mutation hypothesis by DNA polymorphism.

So far in this course we have dealt entirely with the evolution of characters that are controlled by simple Mendelian inheritance at a single locus. There are notes on the course website about gametic disequilibrium and how allele frequencies change at two loci simultaneously, but we didn’t discuss them. In every example we’ve considered we’ve imagined that we could understand something about evolution by examining the evolution of a single gene. That’s the domain of classical population genetics. For the next few weeks we’re going to be exploring a field that’s actually older than classical population genetics, although the approach we’ll be taking to it involves the use of population genetic machinery. If you know a little about the history of evolutionary biology, you may know that after the rediscovery of Mendel’s work in 1900 there was a heated debate between the “biometricians” (e.g., Galton and Pearson) and the “Mendelians” (e.g., de Vries, Correns, Bateson, and Morgan). Biometricians asserted that the really important variation in evolution didn’t follow Mendelian rules. Height, weight, skin color, and similar traits seemed to

Human biochemical genetics

Evolution of Protein Molecules

Genetics and analysis of quantitative traits

Single nucleotide polymorphism (SNP) discovery and genotyping are essential to genetic mapping. There remains a need for a simple, inexpensive platform that allows high-density SNP discovery and genotyping in large populations. Here we describe the sequencing of restriction-site associated DNA (RAD) tags, which identified more than 13,000 SNPs, and mapped three traits in two model organisms, using less than half the capacity of one Illumina sequencing run. We demonstrated that different marker densities can be attained by choice of restriction enzyme. Furthermore, we developed a barcoding system for sample multiplexing and fine mapped the genetic basis of lateral plate armor loss in threespine stickleback by identifying recombinant breakpoints in F2 individuals. Barcoding also facilitated mapping of a second trait, a reduction of pelvic structure, by in silico re-sorting of individuals. To further demonstrate the ease of the RAD sequencing approach we identified polymorphic markers and mapped an induced mutation in Neurospora crassa. Sequencing of RAD markers is an integrated platform for SNP discovery and genotyping. This approach should be widely applicable to genetic mapping in a variety of organisms.

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Rapid SNP Discovery and Genetic Mapping Using Sequenced RAD Markers

Epistasis, or interactions between genes, has long been recognized as fundamentally important to understanding the structure and function of genetic pathways and the evolutionary dynamics of complex genetic systems. With the advent of high-throughput functional genomics and the emergence of systems approaches to biology, as well as a new-found ability to pursue the genetic basis of evolution down to specific molecular changes, there is a renewed appreciation both for the importance of studying gene interactions and for addressing these questions in a unified, quantitative manner.

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Epistasis--the essential role of gene interactions in the structure and evolution of genetic systems.

Although pairwise interactions have always had a key role in ecology and evolutionary biology, the recent increase in the amount and availability of biological data has placed a new focus on the complex networks embedded in biological systems. The increased availability of computational tools to store and retrieve biological data has facilitated wide access to these data, not just by biologists but also by specialists from the social sciences, computer science, physics and mathematics. This fusion of interests has led to a burst of research on the properties and consequences of network structure in biological systems. Although traditional measures of network structure and function have started us off on the right foot, an important next step is to create biologically realistic models of network formation, evolution, and function. Here, we review recent applications of network thinking to the evolution of networks at the gene and protein level and to the dynamics and stability of communities. These studies have provided new insights into the organization and function of biological systems by applying existing techniques of network analysis. The current challenge is to recognize the commonalities in evolutionary and ecological applications of network thinking to create a predictive science of biological networks.

http://www.eebweb.arizona.edu/Faculty/Dornhaus/courses/materials/papers/Proulx%20Promislow%20Phillips%20networks%20ecol%20evol.pdf

Network thinking in ecology and evolution.

Recent developments in quantitative-genetic theory have shown that natural selection can be viewed as the multivariate relationship between fitness and phenotype. This relationship can be described by a multidimensional surface depicting fitness as a function of phenotypic traits. We examine the connection between this surface and the coefficients of phenotypic selection that can be estimated by multiple regression and show how the interpretation of multivariate selection can be facilitated through the use of the method of canonical analysis. The results from this analysis can be used to visualize the surface implied by a set of selection coefficients. Such a visualization provides a compact summary of selection coefficients, can aid in the comparison of selection surfaces, and can help generate testable hypotheses as to the adaptive significance of the traits under study. Further, we discuss traditional definitions of directional, stabilizing, and disruptive selection and conclude that selection may be more usefully classified into two general modes, directional and nonlinear selection, with stabilizing and disruptive selection as special cases of nonlinear selection.

Visualizing multivariate selection.

Quantitative genetics provides one of the most promising frameworks with which to unify the fields of macroevolution and microevolution. The genetic variance–covariance matrix ( G ) is crucial to quantitative genetic predictions about macroevolution. In spite of years of study, we still know little about how G evolves. Recent studies have been applying an increasingly phylogenetic perspective and more sophisticated statistical techniques to address G matrix evolution. We propose that a new field, comparative quantitative genetics, has emerged. Here we summarize what is known about several key questions in the field and compare the strengths and weaknesses of the many statistical and conceptual approaches now being employed. Past studies have made it clear that the key question is no longer whether G evolves but rather how fast and in what manner. We highlight the most promising future directions for this emerging field.

/pdf/comparative-quantitative-genetics-evolution-of-the-g-matrix-2qm09two25.pdf

Comparative quantitative genetics : evolution of the G matrix

ABSTRACr The importance of genetic interactions in the evolutionary process has been debated for more than half a century. Genetic interactions such as underdominance and epistasis (the interaction among genetic loci in their effects on phenotypes or fitness) can play a special role in the evolutionary process because they can create multiple fitness optima (adaptive peaks) separated by fitness minima (adaptive valleys). The valleys prevent deterministic evolution from one peak to another. We review the evidence that genetic interaction is a common phenomenon in natural populations. Some studies give strong circumstantial evidence for multiple fitness peaks, although the mapping of epistatic interactions onto fitness surfaces remains incompletely explored, and absolute proof that multiple peaks exist can be shown to be empirically im

Patrick C. Phillips

Papers

Epistasis--the essential role of gene interactions in the structure and evolution of genetic systems.

Network thinking in ecology and evolution.

Visualizing multivariate selection.

Comparative quantitative genetics : evolution of the G matrix

Multiple fitness peaks and epistasis