A new measure for end of life planning, preparation, and preferences in Huntington disease: HDQLIFE end of life planning.

BACKGROUND: Huntington disease is a fatal inherited neurodegenerative disease. Because the end result of Huntington disease is death due to Huntington disease-related causes, there is a need for better understanding and caring for individuals at their end of life. AIM: The purpose of this study was to develop a new measure to evaluate end of life planning. DESIGN: We conducted qualitative focus groups, solicited expert input, and completed a literature review to develop a 16-item measure to evaluate important aspects of end of life planning for Huntington disease. Item response theory and differential item functioning analyses were utilized to examine the psychometric properties of items; exploratory factor analysis was used to establish meaningful subscales. PARTICIPANTS: Participants included 508 individuals with pre-manifest or manifest Huntington disease. RESULTS: Item response theory supported the retention of all 16 items on the huntington disease quality of life ("HDQLIFE") end of life planning measure. Exploratory factor analysis supported a four-factor structure: legal planning, financial planning, preferences for hospice care, and preferences for conditions (locations, surroundings, etc.) at the time of death. Although a handful of items exhibited some evidence of differential item functioning, these items were retained due to their relevant clinical content. The final 16-item scale includes an overall total score and four subscale scores that reflect the different end of life planning constructs. CONCLUSIONS: The 16-item HDQLIFE end of life planning measure demonstrates adequate psychometric properties; it may be a useful tool for clinicians to clarify patients' preferences about end of life care.

Generative models versus underlying symmetries to explain biological pattern.

Mathematical models play an increasingly important role in the interpretation of biological experiments. Studies often present a model that generates the observations, connecting hypothesized process to an observed pattern. Such generative models confirm the plausibility of an explanation and make testable hypotheses for further experiments. However, studies rarely consider the broad family of alternative models that match the same observed pattern. The symmetries that define the broad class of matching models are in fact the only aspects of information truly revealed by observed pattern. Commonly observed patterns derive from simple underlying symmetries. This article illustrates the problem by showing the symmetry associated with the observed rate of increase in fitness in a constant environment. That underlying symmetry reveals how each particular generative model defines a single example within the broad class of matching models. Further progress on the relation between pattern and process requires deeper consideration of the underlying symmetries.

Natural selection. VII. History and interpretation of kin selection theory.

Kin selection theory is a kind of causal analysis. The initial form of kin selection ascribed cause to costs, benefits and genetic relatedness. The theory then slowly developed a deeper and more sophisticated approach to partitioning the causes of social evolution. Controversy followed because causal analysis inevitably attracts opposing views. It is always possible to separate total effects into different component causes. Alternative causal schemes emphasize different aspects of a problem, reflecting the distinct goals, interests and biases of different perspectives. For example, group selection is a particular causal scheme with certain advantages and significant limitations. Ultimately, to use kin selection theory to analyse natural patterns and to understand the history of debates over different approaches, one must follow the underlying history of causal analysis. This article describes the history of kin selection theory, with emphasis on how the causal perspective improved through the study of key patterns of natural history, such as dispersal and sex ratio, and through a unified approach to demographic and social processes. Independent historical developments in the multivariate analysis of quantitative traits merged with the causal analysis of social evolution by kin selection.

Natural selection. VI. Partitioning the information in fitness and characters by path analysis.

Three steps aid in the analysis of selection. First, describe phenotypes by their component causes. Components include genes, maternal effects, symbionts and any other predictors of phenotype that are of interest. Second, describe fitness by its component causes, such as an individual's phenotype, its neighbours' phenotypes, resource availability and so on. Third, put the predictors of phenotype and fitness into an exact equation for evolutionary change, providing a complete expression of selection and other evolutionary processes. The complete expression separates the distinct causal roles of the various hypothesized components of phenotypes and fitness. Traditionally, those components are given by the covariance, variance and regression terms of evolutionary models. I show how to interpret those statistical expressions with respect to information theory. The resulting interpretation allows one to read the fundamental equations of selection and evolution as sentences that express how various causes lead to the accumulation of information by selection and the decay of information by other evolutionary processes. The interpretation in terms of information leads to a deeper understanding of selection and heritability, and a clearer sense of how to formulate causal hypotheses about evolutionary process. Kin selection appears as a particular type of causal analysis that partitions social effects into meaningful components.

Natural selection. V. How to read the fundamental equations of evolutionary change in terms of information theory.

The equations of evolutionary change by natural selection are commonly expressed in statistical terms. Fisher's fundamental theorem emphasizes the variance in fitness. Quantitative genetics expresses selection with covariances and regressions. Population genetic equations depend on genetic variances. How can we read those statistical expressions with respect to the meaning of natural selection? One possibility is to relate the statistical expressions to the amount of information that populations accumulate by selection. However, the connection between selection and information theory has never been compelling. Here, I show the correct relations between statistical expressions for selection and information theory expressions for selection. Those relations link selection to the fundamental concepts of entropy and information in the theories of physics, statistics and communication. We can now read the equations of selection in terms of their natural meaning. Selection causes populations to accumulate information about the environment.

Natural selection. IV. The Price equation.

The Price equation partitions total evolutionary change into two components. The first component provides an abstract expression of natural selection. The second component subsumes all other evolutionary processes, including changes during transmission. The natural selection component is often used in applications. Those applications attract widespread interest for their simplicity of expression and ease of interpretation. Those same applications attract widespread criticism by dropping the second component of evolutionary change and by leaving unspecified the detailed assumptions needed for a complete study of dynamics. Controversies over approximation and dynamics have nothing to do with the Price equation itself, which is simply a mathematical equivalence relation for total evolutionary change expressed in an alternative form. Disagreements about approach have to do with the tension between the relative valuation of abstract versus concrete analyses. The Price equation's greatest value has been on the abstract side, particularly the invariance relations that illuminate the understanding of natural selection. Those abstract insights lay the foundation for applications in terms of kin selection, information theory interpretations of natural selection and partitions of causes by path analysis. I discuss recent critiques of the Price equation by Nowak and van Veelen.

Natural selection. III. Selection versus transmission and the levels of selection.

George Williams defined an evolutionary unit as hereditary information for which the selection bias between competing units dominates the informational decay caused by imperfect transmission. In this article, I extend Williams' approach to show that the ratio of selection bias to transmission bias provides a unifying framework for diverse biological problems. Specific examples include Haldane and Lande's mutation-selection balance, Eigen's error threshold and quasispecies, Van Valen's clade selection, Price's multilevel formulation of group selection, Szathmáry and Demeter's evolutionary origin of primitive cells, Levin and Bull's short-sighted evolution of HIV virulence, Frank's timescale analysis of microbial metabolism and Maynard Smith and Szathmáry's major transitions in evolution. The insights from these diverse applications lead to a deeper understanding of kin selection, group selection, multilevel evolutionary analysis and the philosophical problems of evolutionary units and individuality.

Natural selection. II. Developmental variability and evolutionary rate.

In classical evolutionary theory, genetic variation provides the source of heritable phenotypic variation on which natural selection acts. Against this classical view, several theories have emphasized that developmental variability and learning enhance nonheritable phenotypic variation, which in turn can accelerate evolutionary response. In this paper, I show how developmental variability alters evolutionary dynamics by smoothing the landscape that relates genotype to fitness. In a fitness landscape with multiple peaks and valleys, developmental variability can smooth the landscape to provide a directly increasing path of fitness to the highest peak. Developmental variability also allows initial survival of a genotype in response to novel or extreme environmental challenge, providing an opportunity for subsequent adaptation. This initial survival advantage arises from the way in which developmental variability smooths and broadens the fitness landscape. Ultimately, the synergism between developmental processes and genetic variation sets evolutionary rate.

Natural selection. I. Variable environments and uncertain returns on investment.

Many studies have analysed how variability in reproductive success affects fitness. However, each study tends to focus on a particular problem, leaving unclear the overall structure of variability in populations. This fractured conceptual framework often causes particular applications to be incomplete or improperly analysed. In this article, I present a concise introduction to the two key aspects of the theory. First, all measures of fitness ultimately arise from the relative comparison of the reproductive success of individuals or genotypes with the average reproductive success in the population. That relative measure creates a diminishing relation between reproductive success and fitness. Diminishing returns reduce fitness in proportion to variability in reproductive success. The relative measurement of success also induces a frequency dependence that favours rare types. Second, variability in populations has a hierarchical structure. Variable success in different traits of an individual affects that individual's variation in reproduction. Correlation between different individuals' reproduction affects variation in the aggregate success of particular alleles across the population. One must consider the hierarchical structure of variability in relation to different consequences of temporal, spatial and developmental variability. Although a complete analysis of variability has many separate parts, this simple framework allows one to see the structure of the whole and to place particular problems in their proper relation to the general theory. The biological understanding of relative success and the hierarchical structure of variability in populations may also contribute to a deeper economic theory of returns under uncertainty.

A simple derivation and classification of common probability distributions based on information symmetry and measurement scale.

Commonly observed patterns typically follow a few distinct families of probability distributions. Over one hundred years ago, Karl Pearson provided a systematic derivation and classification of the common continuous distributions. His approach was phenomenological: a differential equation that generated common distributions without any underlying conceptual basis for why common distributions have particular forms and what explains the familial relations. Pearson's system and its descendants remain the most popular systematic classification of probability distributions. Here, we unify the disparate forms of common distributions into a single system based on two meaningful and justifiable propositions. First, distributions follow maximum entropy subject to constraints, where maximum entropy is equivalent to minimum information. Second, different problems associate magnitude to information in different ways, an association we describe in terms of the relation between information invariance and measurement scale. Our framework relates the different continuous probability distributions through the variations in measurement scale that change each family of maximum entropy distributions into a distinct family. From our framework, future work in biology can consider the genesis of common patterns in a new and more general way. Particular biological processes set the relation between the information in observations and magnitude, the basis for information invariance, symmetry and measurement scale. The measurement scale, in turn, determines the most likely probability distributions and observed patterns associated with particular processes. This view presents a fundamentally derived alternative to the largely unproductive debates about neutrality in ecology and evolution.

Measurement scale in maximum entropy models of species abundance.

The consistency of the species abundance distribution across diverse communities has attracted widespread attention. In this paper, I argue that the consistency of pattern arises because diverse ecological mechanisms share a common symmetry with regard to measurement scale. By symmetry, I mean that different ecological processes preserve the same measure of information and lose all other information in the aggregation of various perturbations. I frame these explanations of symmetry, measurement, and aggregation in terms of a recently developed extension to the theory of maximum entropy. I show that the natural measurement scale for the species abundance distribution is log-linear: the information in observations at small population sizes scales logarithmically and, as population size increases, the scaling of information grades from logarithmic to linear. Such log-linear scaling leads naturally to a gamma distribution for species abundance, which matches well with the observed patterns. Much of the variation between samples can be explained by the magnitude at which the measurement scale grades from logarithmic to linear. This measurement approach can be applied to the similar problem of allelic diversity in population genetics and to a wide variety of other patterns in biology.

The trade-off between rate and yield in the design of microbial metabolism.

Extra energy devoted to resource acquisition speeds metabolic rate, but reduces the net yield of energy. In direct competition, microbial strains with high rates of resource acquisition often outcompete strains with slower resource acquisition but higher yield, reducing the net output of the group. Here, I use mathematical models to analyse the genetic and demographic factors that tip the balance toward either rate or yield. My models clarify the widely discussed roles of kin selection and the spatial structure of populations. I also emphasize the strong effect of two previously ignored factors: demographic aspects of colony survival and reproduction strongly shape the design of metabolic rate and efficiency, and competitive mutants within long-lived colonies favour rate over yield, degrading the efficiency of the population.

Demography and the tragedy of the commons.

Individual success in group-structured populations has two components. First, an individual gains by outcompeting its neighbours for local resources. Second, an individual's share of group success must be weighted by the total productivity of the group. The essence of sociality arises from the tension between selfish gains against neighbours and the associated loss that selfishness imposes by degrading the efficiency of the group. Without some force to modulate selfishness, the natural tendencies of self interest typically degrade group performance to the detriment of all. This is the tragedy of the commons. Kin selection provides the most widely discussed way in which the tragedy is overcome in biology. Kin selection arises from behavioural associations within groups caused either by genetical kinship or by other processes that correlate the behaviours of group members. Here, I emphasize demography as a second factor that may also modulate the tragedy of the commons and favour cooperative integration of groups. Each act of selfishness or cooperation in a group often influences group survival and fecundity over many subsequent generations. For example, a cooperative act early in the growth cycle of a colony may enhance the future size and survival of the colony. This time-dependent benefit can greatly increase the degree of cooperation favoured by natural selection, providing another way in which to overcome the tragedy of the commons and enhance the integration of group behaviour. I conclude that analyses of sociality must account for both the behavioural associations of kin selection theory and the demographic consequences of life history theory.

The common patterns of nature.

We typically observe large-scale outcomes that arise from the interactions of many hidden, small-scale processes. Examples include age of disease onset, rates of amino acid substitutions and composition of ecological communities. The macroscopic patterns in each problem often vary around a characteristic shape that can be generated by neutral processes. A neutral generative model assumes that each microscopic process follows unbiased or random stochastic fluctuations: random connections of network nodes; amino acid substitutions with no effect on fitness; species that arise or disappear from communities randomly. These neutral generative models often match common patterns of nature. In this paper, I present the theoretical background by which we can understand why these neutral generative models are so successful. I show where the classic patterns come from, such as the Poisson pattern, the normal or Gaussian pattern and many others. Each classic pattern was often discovered by a simple neutral generative model. The neutral patterns share a special characteristic: they describe the patterns of nature that follow from simple constraints on information. For example, any aggregation of processes that preserves information only about the mean and variance attracts to the Gaussian pattern; any aggregation that preserves information only about the mean attracts to the exponential pattern; any aggregation that preserves information only about the geometric mean attracts to the power law pattern. I present a simple and consistent informational framework of the common patterns of nature based on the method of maximum entropy. This framework shows that each neutral generative model is a special case that helps to discover a particular set of informational constraints; those informational constraints define a much wider domain of non-neutral generative processes that attract to the same neutral pattern.

Natural selection maximizes Fisher information.

In biology, information flows from the environment to the genome by the process of natural selection. However, it has not been clear precisely what sort of information metric properly describes natural selection. Here, I show that Fisher information arises as the intrinsic metric of natural selection and evolutionary dynamics. Maximizing the amount of Fisher information about the environment captured by the population leads to Fisher's fundamental theorem of natural selection, the most profound statement about how natural selection influences evolutionary dynamics. I also show a relation between Fisher information and Shannon information (entropy) that may help to unify the correspondence between information and dynamics. Finally, I discuss possible connections between the fundamental role of Fisher information in statistics, biology and other fields of science.

Mechanisms of pathogenesis and the evolution of parasite virulence.

When studying how much a parasite harms its host, evolutionary biologists turn to the evolutionary theory of virulence. That theory has been successful in predicting how parasite virulence evolves in response to changes in epidemiological conditions of parasite transmission or to perturbations induced by drug treatments. The evolutionary theory of virulence is, however, nearly silent about the expected differences in virulence between different species of parasite. Why, for example, is anthrax so virulent, whereas closely related bacterial species cause little harm? The evolutionary theory might address such comparisons by analysing differences in tradeoffs between parasite fitness components: transmission as a measure of parasite fecundity, clearance as a measure of parasite lifespan and virulence as another measure that delimits parasite survival within a host. However, even crude quantitative estimates of such tradeoffs remain beyond reach in all but the most controlled of experimental conditions. Here, we argue that the great recent advances in the molecular study of pathogenesis provide a way forward. In light of those mechanistic studies, we analyse the relative sensitivity of tradeoffs between components of parasite fitness. We argue that pathogenic mechanisms that manipulate host immunity or escape from host defences have particularly high sensitivity to parasite fitness and thus dominate as causes of parasite virulence. The high sensitivity of immunomodulation and immune escape arise because those mechanisms affect parasite survival within the host, the most sensitive of fitness components. In our view, relating the sensitivity of pathogenic mechanisms to fitness components will provide a way to build a much richer and more general theory of parasite virulence.

Genetic variation of polygenic characters and the evolution of genetic degeneracy.

The classical model of mutation-selection balance for quantitative characters sums the effects of individual sites to determine overall character value. I develop an alternative version of this classical model in which character value depends on the averaging of the effects of the individual sites. In this new averaging model, the equilibrium patterns of variance in allelic effects and character values change with the number of sites that affect a character in a different way from the classical model of summing effects. Besides changing the patterns of variance, the averaging model favours the addition of loci to the control of character values, perhaps explaining in part the recent observation of widespread genetic degeneracy.

Programmed cell death and hybrid incompatibility.

We propose a new theory to explain developmental aberrations in plant hybrids. In our theory, hybrid incompatibilities arise from imbalances in the mechanisms that cause male sterility in hermaphroditic plants. Mitochondria often cause male sterility by killing the tapetal tissue that nurtures pollen mother cells. Recent evidence suggests that mitochondria destroy the tapetum by triggering standard pathways of programmed cell death. Some nuclear genotypes repress mitochondrial male sterility and restore pollen fertility. Normal regulation of tapetal development therefore arises from a delicate balance between the disruptive effects of mitochondria and the defensive countermeasures of the nuclear genes. In hybrids, incompatibilities between male-sterile mitochondria and nuclear restorers may frequently upset the regulatory control of programmed cell death, causing tapetal abnormalities and male sterility. We propose that hybrid misregulation of programmed cell death may also spill over into other tissues, explaining various developmental aberrations observed in hybrids.

Gene discovery and gene function assignment in filamentous fungi.

Filamentous fungi are a large group of diverse and economically important microorganisms. Large-scale gene disruption strategies developed in budding yeast are not applicable to these organisms because of their larger genomes and lower rate of targeted integration (TI) during transformation. We developed transposon-arrayed gene knockouts (TAGKO) to discover genes and simultaneously create gene disruption cassettes for subsequent transformation and mutant analysis. Transposons carrying a bacterial and fungal drug resistance marker are used to mutagenize individual cosmids or entire libraries in vitro. Cosmids are annotated by DNA sequence analysis at the transposon insertion sites, and cosmid inserts are liberated to direct insertional mutagenesis events in the genome. Based on saturation analysis of a cosmid insert and insertions in a fungal cosmid library, we show that TAGKO can be used to rapidly identify and mutate genes. We further show that insertions can create alterations in gene expression, and we have used this approach to investigate an amino acid oxidation pathway in two important fungal phytopathogens.

Recent advances in large-scale transposon mutagenesis.

Transposons were identified as mobile genetic elements over fifty years ago and subsequently became powerful tools for molecular-genetic studies. Recently, transposon-mutagenesis strategies have been developed to identify essential and pathogenicity-related genes in pathogenic microorganisms. Also, a number of in vitro transposition systems have been used to facilitate genome sequence analysis. Finally, transposon mutagenesis of yeast and complex eukaryotes has provided valuable functional genomic information to complement genome-sequencing projects.