Why genetic investigation of psychiatric disorders is so difficult

Genetic investigations of psychiatric disease have historically relied on subjectively assessed disease diagnoses to define phenotypes. Recent developments in several areas have provided various new approaches to behavioral disorder phenotyping that promise to advance our understanding of the genetic and environmental etiologies of these traits. Such developments include re-evaluation of the boundaries between different psychiatric categories, implementation of quantitative neurobiological assessments that may serve as endophenotypes, generation of increasingly sophisticated animal behavioral models, and investigation of explicit environmental covariates. At the same time, movement toward large-scale, collaborative efforts is increasing the effectiveness of traditional genetic mapping approaches.     

Phenoscope: an automated large‐scale phenotyping platform offering high spatial homogeneity

Increased phenotyping accuracy and throughput are necessary to improve our understanding of quantitative variation and to be able to deconstruct complex traits such as those involved in growth responses to the environment. Still, only a few facilities are known to handle individual plants of small stature for non‐destructive, real‐time phenotype acquisition from plants grown in precisely adjusted and variable experimental conditions. Here, we describe Phenoscope, a high‐throughput phenotyping platform that has the unique feature of continuously rotating 735 individual pots over a table. It automatically adjusts watering and is equipped with a zenithal imaging system to monitor rosette size and expansion rate during the vegetative stage, with automatic image analysis allowing manual correction. When applied to Arabidopsis thaliana, we show that rotating the pots strongly reduced micro‐environmental disparity: heterogeneity in evaporation was cut by a factor of 2.5 and the number of replicates needed to detect a specific mild genotypic effect was reduced by a factor of 3. In addition, by controlling a large proportion of the micro‐environmental variance, other tangible sources of variance become noticeable. Overall, Phenoscope makes it possible to perform large‐scale experiments that would not be possible or reproducible by hand. When applied to a typical quantitative trait loci (QTL) mapping experiment, we show that mapping power is more limited by genetic complexity than phenotyping accuracy. This will help to draw a more general picture as to how genetic diversity shapes phenotypic variation.     

Behavioral phenotyping of rodents

Established methods for analyzing behavioral traits in mutant lines of mice allow researchers to understand the outcomes of genetic manipulations in the nervous system. A rigorous six-tiered behavioral phenotyping strategy is described. Recommendations are offered for the design of mouse behavioral testing suites in animal housing facilities.     

Behavioral phenotyping of transgenic and knockout mice: practical concerns and potential pitfalls

New technologies in molecular genetics have dramatically increased the number of targeted gene mutations available to the biomedical research community. Many mutant mouse lines have been generated to provide animal models for human genetic disorders, offering insights into anatomical, neurochemical, and behavioral effects of aberrant gene expression. A variety of assays have been developed to identify and characterize phenotypic changes. In the behavioral domain, our phenotyping strategy involves a comprehensive standardized methodological approach that assesses general health, reflexes, sensory abilities, and motor functions. This assessment is followed by a series of complementary tasks in the specific behavioral domain(s) hypothesized to reveal the function(s) of the gene. Our multitiered approach minimizes intersubject variability by standardizing the experimental history for all animals, improves interlaboratory reliability by providing a clearly defined experimental protocol, and minimizes artifactual interpretations of behavioral data by careful preliminary assessments of basic behaviors, followed by multiple tests within the behavioral domain of interest. Despite meticulous attention to experimental protocol, attention to environmental factors is essential. Differences in noise, light, home cage environment, handling, and diet can dramatically alter behavior. Baseline differences in the behaviors of inbred strains used to generate targeted mutant mouse lines can directly influence the behavioral phenotype of the mutant line. Strategies aimed at minimizing environmental variability and contributions of background genes will enhance the robustness of mouse behavioral phenotyping assays.     

Functional genomics of neural and behavioral plasticity

How does the environment, particularly the social environment, influence brain and behavior and what are the underlying physiologic, molecular, and genetic mechanisms? Adaptations of brain and behavior to changes in the social or physical environment are common in the animal world, either as short-term (i.e., modulatory) or as long-term modifications (e.g., via gene expression changes) in behavioral or physiologic properties. The study of the mechanisms and constraints underlying these dynamic changes requires model systems that offer plastic phenotypes as well as a sufficient level of quantifiable behavioral complexity while being accessible at the physiological and molecular level. In this article, I explore how the new field of functional genomics can contribute to an understanding of the complex relationship between genome and environment that results in highly plastic phenotypes. This approach will lead to the discovery of genes under environmental control and provide the basis for the study of the interrelationship between an individual's gene expression profile and its social phenotype in a given environmental context.     

Gene-environment interactions differentially affect mouse strain behavioral parameters

Systematic phenotyping of mouse strains and mutants generated through genome-wide mutagenesis programs promises to deliver a wealth of functional genetic information. To this end, the appropriation of a standard series of phenotyping protocols is desirable to produce data sets that are consistent within and across laboratories and across time. Standard phenotyping protocols such as EMPReSS (European Mouse Phenotyping Resource for Standardised Screens) provide a series of protocols aimed at phenotyping multiple body systems that could realistically be adopted and/or reproduced in any laboratory. This includes a series of neurologic and behavioral screens, bearing in mind that this class of phenotype is well represented in targeted mutants and mutagenesis screens. Having cross-validated screening batteries in a number of laboratories and in a number of commonly used inbred strains, our group was interested in establishing whether subtle changes in cage environment could affect behavioral test outcome. Aside from unavoidable quantitative differences in test outcome, we identified significant and distinct genotype-environment-test interactions. For example, specific strain order in open-field center entries and total distance traveled can be reversed depending on the form of enrichment used, while prepulse inhibition of the acoustic startle response is, even quantitatively, unaffected by the enrichment condition. Our findings argue that unless systematically recorded, behavioral studies conducted under subtle variations in cage environment may lead to data misinterpretation, although this could be limited to particular behaviors. Further investigations into the extent and limits of genetic and environmental variables are critical for the realization of both behavioral and functional genomics endpoints.     

Immunity in a variable world

Immune function is likely to be a critical determinant of an organism's fitness, yet most natural animal and plant populations exhibit tremendous genetic variation for immune traits. Accumulating evidence suggests that environmental heterogeneity may retard the long-term efficiency of natural selection and even maintain polymorphism, provided alternative host genotypes are favoured under different environmental conditions. 'Environment' in this context refers to abiotic factors such as ambient temperature or availability of nutrient resources, genetic diversity of pathogens or competing physiological demands on the host. These factors are generally controlled in laboratory experiments measuring immune performance, but variation in them is likely to be very important in the evolution of resistance to infection. Here, we review some of the literature emphasizing the complexity of natural selection on immunity. Our aim is to describe how environmental and genetic heterogeneities, often excluded from experimentation as 'noise', may determine the evolutionary potential of populations or the potential for interacting species to coevolve.     

Describing and understanding behavioral responses to multiple stressors and multiple stimuli

Understanding the effects of environmental change on natural ecosystems is a major challenge, particularly when multiple stressors interact to produce unexpected “ecological surprises” in the form of complex, nonadditive effects that can amplify or reduce their individual effects. Animals often respond behaviorally to environmental change, and multiple stressors can have both population‐level and community‐level effects. However, the individual, not combined, effects of stressors on animal behavior are commonly studied. There is a need to understand how animals respond to the more complex combinations of stressors that occur in nature, which requires a systematic and rigorous approach to quantify the various potential behavioral responses to the independent and interactive effects of stressors. We illustrate a robust, systematic approach for understanding behavioral responses to multiple stressors based on integrating schemes used to quantitatively classify interactions in multiple‐stressor research and to qualitatively view interactions between multiple stimuli in behavioral experiments. We introduce and unify the two frameworks, highlighting their conceptual and methodological similarities, and use four case studies to demonstrate how this unification could improve our interpretation of interactions in behavioral experiments and guide efforts to manage the effects of multiple stressors. Our unified approach: (1) provides behavioral ecologists with a more rigorous and systematic way to quantify how animals respond to interactions between multiple stimuli, an important theoretical advance, (2) helps us better understand how animals behave when they encounter multiple, potentially interacting stressors, and (3) contributes more generally to the understanding of “ecological surprises” in multiple stressors research.     

The fallacy of behavioral phenotyping without standardisation

Behavioral phenotyping of mutant mice is a new and challenging task for the behavioral neuroscientist. Therefore, standardisation of the experimental conditions is required to permit comparisons between the results of experiments within and between laboratories. Once mutation-induced behavioral changes have been identified, phenotyping of mouse mutants should be performed along a systematic trajectory, which allows for an in-depth characterisation of the mutant under investigation.     

Advantages and limitations of nonhuman primates as animal models in genetic research on complex diseases

Abstract: The genetic similarity between humans and nonhuman primates makes nonhuman primates uniquely suited as models for genetic research on complex physiological and behavioral phenotypes. By comparison with human subjects, nonhuman primates, like other animal models, have several advantages for these types of studies: 1) constant environmental conditions can be maintained over long periods of time, greatly increasing the power to detect genetic effects; 2) different environmental conditions can be imposed sequentially on individuals to characterize genotype-environment interactions; 3) complex pedigrees that are much more powerful for genetic analysis than typically available human pedigrees can be generated; 4) genetic hypotheses can be tested prospectively by selective matings; and 5) essential invasive and terminal experiments can be conducted. Limitations of genetic research with nonhuman primates include cost and availability. However, the ability to manipulate both genetic and environmental factors in captive primate populations indicates the promise of genetic research with these important animal models for illuminating complex disease processes. The utility of nonhuman primates for biomedical research on human health problems is illustrated by examples concerning the use of baboons in studies of osteoporosis, alcohol metabolism, and lipoproteins.     

An assessment of Gallistel's (2012) rationalistic account of extinction phenomena

Gallistel (2012) asserts that animals use rationalistic reasoning (i.e., information theory and Bayesian inference) to make decisions that underlie select extinction phenomena. Rational processes are presumed to lead to evolutionarily optimal behavior. Thus, Gallistel's model is a type of optimality theory. But optimality theory is only a theory, a theory about an ideal organism, and its predictions frequently deviate appreciably from observed behavior of animals in the laboratory and the real world. That is, behavior of animals is often far from optimal, as is evident in many behavioral phenomena. Hence, appeals to optimality theory to explain, rather than illuminate, actual behavior are misguided.     

Genetics of smoking and depression

Smoking and depression are significant public health problems with multiple etiological dimensions and outcomes. Although each condition is important by itself, they are important because they often potentiate each other. Consequently, it is also essential to understand the nature their relationship. This representative review focuses on the genetic etiology of the relationship in the context of reviewing first the epidemiology of depression and smoking, and then by exploring behavioral and molecular genetic studies, and other psychiatric and medical comorbidities. At this point, epidemiological evidence for a relationship between depression and smoking/nicotine dependence is compelling. Although behavioral genetic results differ somewhat by gender and in accordance with specific definitions of depression and smoking variables, recent studies show converging evidence for common genetic factors underlying the relationship, often in addition to non-shared environmental factors. The search for underlying genes and genetic mechanisms is at an early stage, but shows promising candidate genes and genetic approaches for future studies.     

Statistical mechanics of animal movement: Animals's decision-making can result in superdiffusive spread

Peculiarities of individual animal movement and dispersal have been a major focus of recent research as they are thought to hold the key to the understanding of many phenomena in spatial ecology. Superdiffusive spread and long-distance dispersal have been observed in different species but the underlying biological mechanisms often remain obscure. In particular, the effect of relevant animal behavior has been largely unaddressed. In this paper, we show that a superdiffusive spread can arise naturally as a result of animal behavioral response to small-scale environmental stochasticity. Surprisingly, the emerging fast spread does not require the standard assumption about the fat tail of the dispersal kernel.     

How does variation in the environment and individual cognition explain the existence of consistent behavioral differences?

According to recent studies on animal personalities, the level of behavioral plasticity, which can be viewed as the slope of the behavioral reaction norm, varies among individuals, populations, and species. Still, it is conceptually unclear how the interaction between environmental variation and variation in animal cognition affect the evolution of behavioral plasticity and expression of animal personalities. Here, we (1) use literature to review how environmental variation and individual variation in cognition explain population and individual level expression of behavioral plasticity and (2) draw together empirically yet nontested, conceptual framework to clarify how these factors affect the evolution and expression of individually consistent behavior in nature. The framework is based on simple principles: first, information acquisition requires cognition that is inherently costly to build and maintain. Second, individual differences in animal cognition affect the differences in behavioral flexibility, i.e. the variance around the mean of the behavioral reaction norm, which defines plasticity. Third, along the lines of the evolution of cognition, we predict that environments with moderate variation favor behavioral flexibility. This occurs since in those environments costs of cognition are covered by being able to recognize and use information effectively. Similarly, nonflexible, stereotypic behaviors may be favored in environments that are either invariable or highly variable, since in those environments cognition does not give any benefits to cover the costs or cognition is not able to keep up with environmental change, respectively. If behavioral plasticity develops in response to increasing environmental variability, plasticity should dominate in environments that are moderately variable, and expression of animal personalities and behavioral syndromes may differ between environments. We give suggestions how to test our hypothesis and propose improvements to current behavioral testing protocols in the field of animal personality.     

Next-generation phenotyping: requirements and strategies for enhancing our understanding of genotype–phenotype relationships and its relevance to crop improvement

More accurate and precise phenotyping strategies are necessary to empower high-resolution linkage mapping and genome-wide association studies and for training genomic selection models in plant improvement. Within this framework, the objective of modern phenotyping is to increase the accuracy, precision and throughput of phenotypic estimation at all levels of biological organization while reducing costs and minimizing labor through automation, remote sensing, improved data integration and experimental design. Much like the efforts to optimize genotyping during the 1980s and 1990s, designing effective phenotyping initiatives today requires multi-faceted collaborations between biologists, computer scientists, statisticians and engineers. Robust phenotyping systems are needed to characterize the full suite of genetic factors that contribute to quantitative phenotypic variation across cells, organs and tissues, developmental stages, years, environments, species and research programs. Next-generation phenotyping generates significantly more data than previously and requires novel data management, access and storage systems, increased use of ontologies to facilitate data integration, and new statistical tools for enhancing experimental design and extracting biologically meaningful signal from environmental and experimental noise. To ensure relevance, the implementation of efficient and informative phenotyping experiments also requires familiarity with diverse germplasm resources, population structures, and target populations of environments. Today, phenotyping is quickly emerging as the major operational bottleneck limiting the power of genetic analysis and genomic prediction. The challenge for the next generation of quantitative geneticists and plant breeders is not only to understand the genetic basis of complex trait variation, but also to use that knowledge to efficiently synthesize twenty-first century crop varieties.     

Single-Kernel Ionomic Profiles Are Highly Heritable Indicators of Genetic and Environmental Influences on Elemental Accumulation in Maize Grain (Zea mays)

The ionome, or elemental profile, of a maize kernel can be viewed in at least two distinct ways. First, the collection of elements within the kernel are food and feed for people and animals. Second, the ionome of the kernel represents a developmental end point that can summarize the life history of a plant, combining genetic programs and environmental interactions. We assert that single-kernel-based phenotyping of the ionome is an effective method of analysis, as it represents a reasonable compromise between precision, efficiency, and power. Here, we evaluate potential pitfalls of this sampling strategy using several field-grown maize sample sets. We demonstrate that there is enough genetically determined diversity in accumulation of many of the elements assayed to overcome potential artifacts. Further, we demonstrate that environmental signals are detectable through their influence on the kernel ionome. We conclude that using single kernels as the sampling unit is a valid approach for understanding genetic and environmental effects on the maize kernel ionome.     

Model-Independent Phenotyping of C. elegans Locomotion Using Scale-Invariant Feature Transform

To uncover the genetic basis of behavioral traits in the model organism C. elegans, a common strategy is to study locomotion defects in mutants. Despite efforts to introduce (semi-)automated phenotyping strategies, current methods overwhelmingly depend on worm-specific features that must be hand-crafted and as such are not generalizable for phenotyping motility in other animal models. Hence, there is an ongoing need for robust algorithms that can automatically analyze and classify motility phenotypes quantitatively. To this end, we have developed a fully-automated approach to characterize C. elegans’ phenotypes that does not require the definition of nematode-specific features. Rather, we make use of the popular computer vision Scale-Invariant Feature Transform (SIFT) from which we construct histograms of commonly-observed SIFT features to represent nematode motility. We first evaluated our method on a synthetic dataset simulating a range of nematode crawling gaits. Next, we evaluated our algorithm on two distinct datasets of crawling C. elegans with mutants affecting neuromuscular structure and function. Not only is our algorithm able to detect differences between strains, results capture similarities in locomotory phenotypes that lead to clustering that is consistent with expectations based on genetic relationships. Our proposed approach generalizes directly and should be applicable to other animal models. Such applicability holds promise for computational ethology as more groups collect high-resolution image data of animal behavior.     

Consistent behavioral phenotype differences between inbred mouse strains in the IntelliCage

The between‐laboratory effects on behavioral phenotypes and spatial learning performance of three strains of laboratory mice known for divergent behavioral phenotypes were evaluated in a fully balanced and synchronized study using a completely automated behavioral phenotyping device (IntelliCage). Activity pattern and spatial conditioning performance differed consistently between strains, i.e. exhibited no interaction with the between‐laboratory factor, whereas the gross laboratory effect showed up significantly in the majority of measures. It is argued that overall differences between laboratories may not realistically be preventable, as subtle differences in animal housing and treatment will not be controllable, in practice. However, consistency of strain (or treatment) effects appears to be far more important in behavioral and brain sciences than the absolute overall level of such measures. In this respect, basic behavioral and learning measures proved to be highly consistent in the IntelliCage, therefore providing a valid basis for meaningful research hypothesis testing. Also, potential heterogeneity of behavioral status because of environmental and social enrichment has no detectable negative effect on the consistency of strain effects. We suggest that the absence of human interference during behavioral testing is the most prominent advantage of the IntelliCage and suspect that this is likely responsible for the between‐laboratory consistency of findings, although we are aware that this ultimately needs direct testing.     

Decision accuracy in complex environments is often maximized by small group sizes

Individuals in groups, whether composed of humans or other animal species, often make important decisions collectively, including avoiding predators, selecting a direction in which to migrate and electing political leaders. Theoretical and empirical work suggests that collective decisions can be more accurate than individual decisions, a phenomenon known as the ‘wisdom of crowds’. In these previous studies, it has been assumed that individuals make independent estimates based on a single environmental cue. In the real world, however, most cues exhibit some spatial and temporal correlation, and consequently, the sensory information that near neighbours detect will also be, to some degree, correlated. Furthermore, it may be rare for an environment to contain only a single informative cue, with multiple cues being the norm. We demonstrate, using two simple models, that taking this natural complexity into account considerably alters the relationship between group size and decision-making accuracy. In only a minority of environments do we observe the typical wisdom of crowds phenomenon (whereby collective accuracy increases monotonically with group size). When the wisdom of crowds is not observed, we find that a finite, and often small, group size maximizes decision accuracy. We reveal that, counterintuitively, it is the noise inherent in these small groups that enhances their accuracy, allowing individuals in such groups to avoid the detrimental effects of correlated information while exploiting the benefits of collective decision-making. Our results demonstrate that the conventional view of the wisdom of crowds may not be informative in complex and realistic environments, and that being in small groups can maximize decision accuracy across many contexts.