Evolution and Genetic Dynamics

MEGA6: Molecular Evolutionary Genetics Analysis Version 6.0

The Molecular Evolutionary Genetics Analysis (MEGA) software has matured to contain a large collection of methods and tools of computational molecular evolution. Here, we describe new additions that make MEGA a more comprehensive tool for building timetrees of species, pathogens, and gene families using rapid relaxed-clock methods. Methods for estimating divergence times and confidence intervals are implemented to use probability densities for calibration constraints for node-dating and sequence sampling dates for tip-dating analyses. They are supported by new options for tagging sequences with spatiotemporal sampling information, an expanded interactive Node Calibrations Editor, and an extended Tree Explorer to display timetrees. Also added is a Bayesian method for estimating neutral evolutionary probabilities of alleles in a species using multispecies sequence alignments and a machine learning method to test for the autocorrelation of evolutionary rates in phylogenies. The computer memory requirements for the maximum likelihood analysis are reduced significantly through reprogramming, and the graphical user interface has been made more responsive and interactive for very big data sets. These enhancements will improve the user experience, quality of results, and the pace of biological discovery. Natively compiled graphical user interface and command-line versions of MEGA11 are available for Microsoft Windows, Linux, and macOS from www.megasoftware.net.

CONFIDENCE LIMITS ON PHYLOGENIES: AN APPROACH USING THE BOOTSTRAP

The recently-developed statistical method known as the "bootstrap" can be used to place confidence intervals on phylogenies. It involves resampling points from one's own data, with replacement, to create a series of bootstrap samples of the same size as the original data. Each of these is analyzed, and the variation among the resulting estimates taken to indicate the size of the error involved in making estimates from the original data. In the case of phylogenies, it is argued that the proper method of resampling is to keep all of the original species while sampling characters with replacement, under the assumption that the characters have been independently drawn by the systematist and have evolved independently. Majority-rule consensus trees can be used to construct a phylogeny showing all of the inferred monophyletic groups that occurred in a majority of the bootstrap samples. If a group shows up 95% of the time or more, the evidence for it is taken to be statistically significant. Existing computer programs can be used to analyze different bootstrap samples by using weights on the characters, the weight of a character being how many times it was drawn in bootstrap sampling. When all characters are perfectly compatible, as envisioned by Hennig, bootstrap sampling becomes unnecessary; the bootstrap method would show significant evidence for a group if it is defined by three or more characters.

MEGA5: Molecular Evolutionary Genetics Analysis Using Maximum Likelihood, Evolutionary Distance, and Maximum Parsimony Methods

Comparative analysis of molecular sequence data is essential for reconstructing the evolutionary histories of species and inferring the nature and extent of selective forces shaping the evolution of genes and species. Here, we announce the release of Molecular Evolutionary Genetics Analysis version 5 (MEGA5), which is a user-friendly software for mining online databases, building sequence alignments and phylogenetic trees, and using methods of evolutionary bioinformatics in basic biology, biomedicine, and evolution. The newest addition in MEGA5 is a collection of maximum likelihood (ML) analyses for inferring evolutionary trees, selecting best-fit substitution models (nucleotide or amino acid), inferring ancestral states and sequences (along with probabilities), and estimating evolutionary rates site-by-site. In computer simulation analyses, ML tree inference algorithms in MEGA5 compared favorably with other software packages in terms of computational efficiency and the accuracy of the estimates of phylogenetic trees, substitution parameters, and rate variation among sites. The MEGA user interface has now been enhanced to be activity driven to make it easier for the use of both beginners and experienced scientists. This version of MEGA is intended for the Windows platform, and it has been configured for effective use on Mac OS X and Linux desktops. It is available free of charge from http://www.megasoftware.net.

MEGA X: Molecular Evolutionary Genetics Analysis across Computing Platforms

The Molecular Evolutionary Genetics Analysis (Mega) software implements many analytical methods and tools for phylogenomics and phylomedicine. Here, we report a transformation of Mega to enable cross-platform use on Microsoft Windows and Linux operating systems. Mega X does not require virtualization or emulation software and provides a uniform user experience across platforms. Mega X has additionally been upgraded to use multiple computing cores for many molecular evolutionary analyses. Mega X is available in two interfaces (graphical and command line) and can be downloaded from www.megasoftware.net free of charge.

MODELTEST: testing the model of DNA substitution.

SUMMARY: The program MODELTEST uses log likelihood scores to establish the model of DNA evolution that best fits the data. AVAILABILITY: The MODELTEST package, including the source code and some documentation is available at http://bioag.byu. edu/zoology/crandall_lab/modeltest.html.

Mathematical model for studying genetic variation in terms of restriction endonucleases.

A mathematical model for the evolutionary change of restriction sites in mitochondrial DNA is developed. Formulas based on this model are presented for estimating the number of nucleotide substitutions between two populations or species. To express the degree of polymorphism in a population at the nucleotide level, a measure called "nucleotide diversity" is proposed.

Simple mathematical models with very complicated dynamics

The Mathematics of Infectious Diseases

Abstract. Many models for the spread of infectious diseases in populations have been analyzed mathematically and applied to specific diseases. Threshold theorems involving the basic reproduction number R0, the contact number σ, and the replacement number R are reviewed for the classic SIR epidemic and endemic models. Similar results with new expressions for R0 are obtained for MSEIR and SEIR endemic models with either continuous age or age groups. Values of R0 and σ are estimated for various diseases including measles in Niger and pertussis in the United States. Previous models with age structure, heterogeneity, and spatial structure are surveyed.

Population biology of infectious diseases: Part I

Statistical physics of human cooperation

Applying evolutionary biology to address global challenges

BACKGROUND Differences among species in their ability to adapt to environmental change threaten biodiversity, human health, food security, and natural resource availability. Pathogens, pests, and cancers often quickly evolve resistance to control measures, whereas crops, livestock, wild species, and human beings often do not adapt fast enough to cope with climate change, habitat loss, toxicants, and lifestyle change. To address these challenges, practices based on evolutionary biology can promote sustainable outcomes via strategic manipulation of genetic, developmental, and environmental factors. Successful strategies effectively slow unwanted evolution and reduce fitness in costly species or improve performance of valued organisms by reducing phenotype-environment mismatch or increasing group productivity. Tactics of applied evolutionary biology range broadly, from common policies that promote public health or preserve habitat for threatened species—but are easily overlooked as having an evolutionary rationale, to the engineering of new genomes. ADVANCES The scope and development of current tactics vary widely. In particular, genetic engineering attracts much attention (and controversy) but now is used mainly for traits under simple genetic control. Human gene therapy, which mainly involves more complex controls, has yet to be applied successfully at large scales. In contrast, other methods to alter complex traits are improving. These include artificial selection for drought- and flood-tolerant crops through bioinformatics and application of “life course” approaches in medicine to reduce human metabolic disorders. Successful control of unwanted evolution depends on governance initiatives that address challenges arising from both natural and social factors. Principal among these challenges are (i) global transfer of genes and selection agents; (ii) interlinked evolution across traditional sectors of society (environment, food, and health); and (iii) conflicts between individual and group incentives that threaten regulation of antibiotic use and crop refuges. Evolutionarily informed practices are a newer prospect in some fields and require more systematic research, as well as ethical consideration—for example, in attempts to protect wild species through assisted migration, in the choice of source populations for restoration, or in genetic engineering. OUTLOOK A more unified platform will better convey the value of evolutionary methods to the public, scientists, and decision-makers. For researchers and practitioners, applications may be expanded to other disciplines, such as in the transfer of refuge strategies that slow resistance evolution in agriculture to slow unwanted evolution elsewhere (for example, cancer resistance or harvest-induced evolution). For policy-makers, adoption of practices that minimize unwanted evolution and reduce phenotype-environment mismatch in valued species is likely essential to achieve the forthcoming Sustainable Development Goals and the 2020 Aichi Biodiversity Targets.

The integrative biology of genetic dominance

Dominance is a basic property of inheritance systems describing the link between a diploid genotype at a single locus and the resulting phenotype. Models for the evolution of dominance have long been framed as an opposition between the irreconcilable views of Fisher in 1928 supporting the role of largely elusive dominance modifiers and Wright in 1929, who viewed dominance as an emerging property of the structure of enzymatic pathways. Recent theoretical and empirical advances however suggest that these opposing views can be reconciled, notably using models investigating the regulation of gene expression and developmental processes. In this more comprehensive framework, phenotypic dominance emerges from departures from linearity between any levels of integration in the genotype-to-phenotype map. Here, we review how these different models illuminate the emergence and evolution of dominance. We then detail recent empirical studies shedding new light on the diversity of molecular and physiological mechanisms underlying dominance and its evolution. By reconciling population genetics and functional biology, we hope our review will facilitate cross-talk among research fields in the integrative study of dominance evolution.