Search Results

Natural selection is not the only evolutionary “force” that can alter frequencies of genetic variants; random genetic drift (a sampling process) persistently changes the gene pool of populations, especially in small populations. Moreover, at the molecular level, negative selection is more prevalent than positive selection as deleterious mutations occur much more than do advantageous mutations. Motoo Kimura, a Japanese evolutionary geneticist, expanded on Darwinian evolution and formulated what is now called the neutral theory of molecular evolution. According to Kimura, patterns of molecular evolution are determined primarily by mutation, genetic drift, and negative selection. This chapter discusses the development of the neutral theory, and explains how it lays the foundation to produce tests used to detect positive selection and balancing selection. It also contains a discussion of hypothesis testing.

Molecular evolution is the study of the process of evolution at the level of DNA, RNA, and proteins, in which the neutral or nearly neutral evolution model has provided the theoretical basis. Yet, the role of positive selection at the molecular level remains a controversial issue. Recent advances in genomics, including whole-genome sequencing, high-throughput protein characterization, and bioinformatics have led to a dramatic increase in studies in comparative and evolutionary genomics. This chapter introduces some widely-used methods in genomic analysis. These include distance method, parsimony methods, maximum-likelihood methods, Bayesian methods, and ancestral sequence inference.

As evolutionary biologists have always been concerned with the genetic basis for the emergence of complex phenotypes, advances in genomics and systems biology are facilitating a paradigm shift of molecular evolutionary biology toward a better understanding of the relationship of genotypes and phenotypes. From an evolutionary perspective, the central question is whether natural selection is a necessary and/or sufficient force to explain the emergence of genomic and cellular features that underlie the building of complex organisms. Lynch has criticized the adaptive hypothesis for the origins of organismal complexity, claiming that nothing in evolution makes sense in light of population genetics that takes the effects of mutation, genetic drift, and natural selection into account. The importance of mutation types and genetic drifts on the phenotype evolution has also been emphasized by Nei and his associates. One plausible approach to resolving these fundamental issues is to model the features of biological complexity as parameters instead of emerged properties, under the principle of population genetics and molecular evolution. This chapter discusses some recent results in this trend.

This chapter explores the fundamental distinction between natural selection and genetic drift, starting from a consideration of how they are measured, and suggests that it may not be as fundamental as Wright suggested. It begins by examining the distinction between natural selection and genetic drift at the most basic, physical level—as part of the standardized variance in rate of increase observed in populations. The chapter then provides a brief historical overview of the origin of the concept of genetic drift in the work of Fisher and Wright. Finally, it examines some of the complexities of molecular evolution, such as hitchhiking, meiotic drive, variable selection, mutational bias, recombinational bias, jumping genes, and horizontal gene transfer.

Kevin J. Gaston defines biodiversity and lays out obstacles to its better understanding in this chapter. Biodiversity is the variety of life in all of its many manifestations. This variety can usefully be thought of in terms of three hierarchical sets of elements, which capture different facets: genetic diversity, organismal diversity, and ecological diversity. There is by definition no single measure of biodiversity, although two different kinds of measures (number and heterogeneity) can be distinguished. Pragmatically, and rather restrictively, biodiversity tends in the main to be measured in terms of a number measures of organismal diversity, and especially species richness. Biodiversity has been present for much of the history of the Earth, but the levels have changed dramatically and have proven challenging to document reliably. Biodiversity is variably distributed across the Earth, although some marked spatial gradients seem common to numerous higher taxonomic groups. The obstacles to an improved understanding of biodiversity are: (i) its sheer magnitude and complexity; (ii) the biases of the fossil record and the apparent variability in rates of molecular evolution; (iii) the relative paucity of quantitative sampling over much of the planet; and (iv) that levels and patterns of biodiversity are being profoundly altered by human activities.

Positive natural selection, though rare in comparison with negative selection, is the main evolutionary force responsible for adaptive evolutionary change. Using the neutral theory to generate null hypotheses, evolutionary geneticists have developed tests for detecting positive selection. Several of these tests make use of DNA sequence data sets that contain information on both variation existing within a species (polymorphism) and differences accumulated between species (divergence). This chapter focuses on the McDonald-Krietman test, a powerful but relatively simple test of detecting positive selection. Also discussed is how inferences about the action of selection can be made through the examination of linkage disequilibrium, patterns of correlations of genetic variants at different (but linked) sites. The chapter concludes with a discussion of the legacy of Kimura and his neutral theory of molecular evolution.

High-dimensional adaptive landscapes facilitate the origin of evolutionary adaptations and innovations, qualitatively new and beneficial phenotypes. This chapter discusses evidence for this assertion from three classes of systems important for evolutionary innovation, metabolic networks, gene regulation circuits, as well as protein and RNA macromolecules. In all three classes of systems, vast genotype networks — connected sets of genotypes with the same phenotype — exist and extend far through genotype space. These networks are essential for the ability of biological systems to explore many novel phenotypes, and they are a consequence of the high dimensionality of genotype spaces.

This chapter discusses basic techniques of computer simulation. Topics covered include random number generator, generation of continuous random variables, generation of discrete random variables, and simulating molecular evolution. Exercises are provided at the end of the chapter.

The significance of studying birds and their pathogens goes far beyond the applied conservation or epidemiological implications of their interactions. Evidence suggests that avian host–pathogen systems can be used to test fundamental theoretical predictions about adaptive evolution and coevolution in natural populations. This chapter highlights recent advances in the field of bird–pathogen evolution and coevolution, how these advances have come about, and future directions of research. Further, it shows that, while there is a growing body of work that provides support for both avian host and pathogen evolution, evidence for their antagonistic coevolution, the process of adaptation and counter-adaptation in response to the reciprocal selection pressures that they impose on each other, remains rare. Rigorously demonstrating the processes of evolution and coevolution is complex in natural populations and doing so necessarily requires borrowing methodological approaches from a range of disciplines to fully characterize phenotypic change, its genetic and mechanistic basis, as well as its adaptive benefits. Overcoming the challenge of such a task will, however, generate important insights into a range of processes, from disease transmission dynamics and pathogenesis to the maintenance of biodiversity.

This chapter characterizes the consequences of a shift of focus from individual genes to gene networks. It describes some of the opportunities and challenges that the genomic era brings to evolutionary biology, and some of the ways current research into genome evolution is extending the Modern Synthesis. The chapter also discusses three extensions to the Modern Synthesis that are emerging out of the tumult and excitement of evolutionary genomics. It suggests that applying the traditional approaches of population genetics, evolutionary genetics, and molecular evolution to genomic data sets presents nontrivial challenges.

This chapter provides a brief introduction to the theory and computation of Bayesian statistics and its applications to molecular evolution. It uses simple examples, such as distance estimation under the JC69 model, to introduce the general principles. It discusses the application of Bayesian inference to reconstruction of phylogenetic trees and to population genetics analysis under the coalescent. Exercises are provided at the end of the chapter.

This chapter focuses on the use of codon models for ancestral sequence reconstruction, and for use in clade models of functional divergence. It first reviews codon-based ancestral reconstruction methods, followed by an example of their use in inferring synonymous evolution in mammalian rhodopsins. It then discusses clade models of molecular evolution, followed by a description of a recently proposed clade model likelihood test of divergence and its application to teleost short-wavelength visual pigments. The promise of both approaches lies in the possibility of generating specific hypotheses of molecular function, which can be then interpreted in the context of data on molecular structure and function, particularly for genes for which a variety of biochemical assays and other functional data exist.

Scientists have access to artifacts of evolutionary history, but they have limited ability to infer the exact events that produced today’s living world. An intriguing question to arise from this limitation is whether the evolutionary paths of organisms are dominated by controlled processes, or whether they are inherently random, subject to different outcomes if repeated. Two experimental approaches, ancestral sequence reconstruction and experimental evolution, can be used to recapitulate ancient adaptive pathways and provide insights into the mutational steps that constitute an organism’s genetic heritage. Ancestral sequence reconstruction follows a backwards-from-present-day strategy in which ancestral forms of a gene or protein are reconstructed and studied mechanistically. Experimental evolution, by contrast, follows a forward-from-present-day strategy in which microbial populations are evolved in the laboratory under defined conditions. Here I describe a novel hybrid of these two methods, in which synthetic components constructed from inferred ancestral gene or protein sequences are placed into the genomes of modern organisms that are then experimentally evolved. Through this system, we aim to establish the comparative study of ancient phenotypes as a novel, statistically rigorous methodology with which to explore the impacts of biophysics and chance in evolution within the scope of the Extended Synthesis.

How does inanimate matter become transformed into animate matter? Living systems evolve by replication and selection at the molecular level and this chapter considers how to establish a synthetic, minimal system that can support molecular evolution and thus life. Molecular evolution cannot be explained by starting with high concentrations of activated chemicals that react toward their chemical equilibrium; persistent non-equilibria are required to maintain continuous reactivity and we especially consider thermal gradients as an early driving force for Darwinian molecular evolution. The temperature difference across water-filled compartments implements a laminar fluid convection with periodic temperature oscillations that allow for the melting and replication of DNA. Simultaneously, dissolved molecules are moved along the thermal gradient by an effect called thermophoresis. The combined result is an efficient molecule trap that exponentially favors long over short DNA and thus maintains complexity. Future experiments will reveal how thermal gradients could actively drive the Darwinian process of replication and selection.

This chapter introduces computer simulation and in particular simulation of the molecular evolutionary process. It covers the generation of random numbers as well as other discrete and continuous random variables. The chapter then discusses the simulation of the Poisson process, the variable-rate Poisson process, and discrete-time and continuous-time Markov chains. Different strategies for simulating sequence alignments through molecular evolution are then discussed.

When Perutz and Kendrew embarked on their determination of the structures of haemoglobin and myoglobin, most scientists felt that they would never succeed. These molecules contain approximately thousands of non-hydrogen atoms, whereas those molecules that had yielded to X-ray analysis previously contained fewer than a hundred non-hydrogen atoms. For real progress to be made in solving the structures of the giant proteins, a fundamentally new approach had to be evolved, which inter alia required massive computer power to handle the data contained in hundreds of thousands of X-ray diffraction patterns, and new experimental equipment like ultra-stable X-ray sources were required to record the diffraction data. The first successes were registered by Kendrew, who was able to reveal, in unprecedented detail, the atomically resolved structure of myoglobin with its haem group (containing a central iron atom) and all the details of the amino acid residues that constituted the backbone chain of the protein. Likewise, haemoglobin revealed its secrets. This also led to the discovery of sickle-cell anaemia, the first ever recorded molecular disease. It also shed new light on the pathology of anomalous haemoglobins in human populations.