Cortical Hierarchies Perform Bayesian Causal Inference in Multisensory Perception

To form a veridical percept of the environment, the brain needs to integrate sensory signals from a common source but segregate those from independent sources. Thus, perception inherently relies on solving the “causal inference problem.” Behaviorally, humans solve this problem optimally as predicted by Bayesian Causal Inference; yet, the underlying neural mechanisms are unexplored. Combining psychophysics, Bayesian modeling, functional magnetic resonance imaging (fMRI), and multivariate decoding in an audiovisual spatial localization task, we demonstrate that Bayesian Causal Inference is performed by a hierarchy of multisensory processes in the human brain. At the bottom of the hierarchy, in auditory and visual areas, location is represented on the basis that the two signals are generated by independent sources (= segregation). At the next stage, in posterior intraparietal sulcus, location is estimated under the assumption that the two signals are from a common source (= forced fusion). Only at the top of the hierarchy, in anterior intraparietal sulcus, the uncertainty about the causal structure of the world is taken into account and sensory signals are combined as predicted by Bayesian Causal Inference. Characterizing the computational operations of signal interactions reveals the hierarchical nature of multisensory perception in human neocortex. It unravels how the brain accomplishes Bayesian Causal Inference, a statistical computation fundamental for perception and cognition. Our results demonstrate how the brain combines information in the face of uncertainty about the underlying causal structure of the world.     

Is heritability explanatorily useful?

The paper addresses the question of whether heritability can be useful in establishing genetics as an explanation for an individual's display of some trait or behavior. After reviewing the fundamental philosophical challenge to heritability--that heritability is a population level (versus individual level) measure--an argument is presented for rethinking the role heritability occupies in both causal and explanatory claims. It is argued that heritability can be useful for genetically based explanations of individual traits, if (1) the conditions for proper genetic explanation are modestly reconceived, and (2) the measure is seen as an evidential tool which conforms to standard methods for determining causal factors.     

Epistemic causality and evidence-based medicine

Causal claims in biomedical contexts are ubiquitous albeit they are not always made explicit. This paper addresses the question of what causal claims mean in the context of disease. It is argued that in medical contexts causality ought to be interpreted according to the epistemic theory. The epistemic theory offers an alternative to traditional accounts that cash out causation either in terms of "difference-making" relations or in terms of mechanisms. According to the epistemic approach, causal claims tell us about which inferences (e.g., diagnoses and prognoses) are appropriate, rather than about the presence of some physical causal relation analogous to distance or gravitational attraction. It is shown that the epistemic theory has important consequences for medical practice, in particular with regard to evidence-based causal assessment.     

Productivity,relevance and natural selection

Recent papers by a number of philosophers have been concerned with the question of whether natural selection is a causal process, and if it is, whether the causes of selection are properties of individuals or properties of populations. I shall argue that much confusion in this debate arises because of a failure to distinguish between causal productivity and causal relevance. Causal productivity is a relation that holds between events connected via continuous causal processes, while causal relevance is a relationship that can hold between a variety of different kinds of facts and the events that counterfactually depend upon them. I shall argue that the productive character of natural selection derives from the aggregation of individual processes in which organisms live, reproduce and die. At the same time, a causal explanation of the distribution of traits will necessarily appeal both to causally relevant properties of individuals and to causally relevant properties that exist only at the level of the population.

Practical implications of modes of statistical inference for causal effects and the critical role of the assignment mechanism.

Causal inference in an important topic and one that is now attracting serious attention of statisticians. Although there exist recent discussions concerning the general definition of causal effects and a substantial literature on specific techniques for the analysis of data in randomized and nonrandomized studies, there has been relatively little discussion of modes of statistical inference for causal effects. This presentation briefly describes and contrasts four basic modes of statistical inference for causal effects, emphasizes the common underlying causal framework with a posited assignment mechanism, and describes practical implications in the context of an example involving the effects of switching from a name-band to a generic drug. A fundamental conclusion is that in such nonrandomized studies, sensitivity of inference to the assignment mechanism is the dominant issue, and it cannot be avoided by changing modes of inference, for instance, by changing from randomization-based to Bayesian methods.     

Population Level Causation and a Unified Theory of Natural Selection

Sober (1984) presents an account of selection motivated by the view that one property can causally explain the occurrence of another only if the first plays a unique role in the causal production of the second. Sober holds that a causal property will play such a unique role if it is a population level cause of its effect, and on this basis argues that there is selection for a trait T only if T is a population level cause of survival and reproductive success. Sterelny and Kitcher (1988) claim against Sober that some traits directly subject to selection will not satisfy the probabilistic condition on population level causation. In this paper I show that Sober has the resources to resist the Sterelny-Kitcher complaint, but I argue that not all traits that satisfy the probabilistic condition play the required unique role in the production of their effects.     

A Bayesian framework for inference of the genotype-phenotype map for segregating populations

Complex genetic interactions lie at the foundation of many diseases. Understanding the nature of these interactions is critical to developing rational intervention strategies. In mammalian systems hypothesis testing in vivo is expensive, time consuming, and often restricted to a few physiological endpoints. Thus, computational methods that generate causal hypotheses can help to prioritize targets for experimental intervention. We propose a Bayesian statistical method to infer networks of causal relationships among genotypes and phenotypes using expression quantitative trait loci (eQTL) data from genetically randomized populations. Causal relationships between network variables are described with hierarchical regression models. Prior distributions on the network structure enforce graph sparsity and have the potential to encode prior biological knowledge about the network. An efficient Monte Carlo method is used to search across the model space and sample highly probable networks. The result is an ensemble of networks that provide a measure of confidence in the estimated network topology. These networks can be used to make predictions of system-wide response to perturbations. We applied our method to kidney gene expression data from an MRL/MpJ × SM/J intercross population and predicted a previously uncharacterized feedback loop in the local renin-angiotensin system.     

A robust statistical method for association-based eQTL analysis

Background

It has been well established that theoretical kernel for recently surging genome-wide association study (GWAS) is statistical inference of linkage disequilibrium (LD) between a tested genetic marker and a putative locus affecting a disease trait. However, LD analysis is vulnerable to several confounding factors of which population stratification is the most prominent. Whilst many methods have been proposed to correct for the influence either through predicting the structure parameters or correcting inflation in the test statistic due to the stratification, these may not be feasible or may impose further statistical problems in practical implementation.

Methodology

We propose here a novel statistical method to control spurious LD in GWAS from population structure by incorporating a control marker into testing for significance of genetic association of a polymorphic marker with phenotypic variation of a complex trait. The method avoids the need of structure prediction which may be infeasible or inadequate in practice and accounts properly for a varying effect of population stratification on different regions of the genome under study. Utility and statistical properties of the new method were tested through an intensive computer simulation study and an association-based genome-wide mapping of expression quantitative trait loci in genetically divergent human populations.

Results/Conclusions

The analyses show that the new method confers an improved statistical power for detecting genuine genetic association in subpopulations and an effective control of spurious associations stemmed from population structure when compared with other two popularly implemented methods in the literature of GWAS.     

Causal Efficacy and the Analysis of Variance

The causal impacts of genes and environment on any one biological trait are inextricably entangled, and consequently it is widely accepted that it makes no sense in singleton cases to privilege either factor for particular credit. On the other hand, at a population level it may well be the case that one of the factors is responsible for more variation than the other. Standard methodological practice in biology uses the statistical technique of analysis of variance to measure this latter kind of causal efficacy. In this paper, I argue that: 1) analysis of variance is in fact badly suited to this role; and 2) a superior alternative definition is available that readily reconciles both the entangled-singleton and the population-variation senses of causal efficacy.     

Thermodynamic and metabolic effects on the scaling of production and population energy use

Ecosystem properties result in part from the characteristics of individual organisms. How these individual traits scale to impact ecosystem‐level processes is currently unclear. Because metabolism is a fundamental process underlying many individual‐ and population‐level variables, it provides a mechanism for linking individual characteristics with large‐scale processes. Here we use metabolism and ecosystem thermodynamics to scale from physiology to individual biomass production and population‐level energy use. Temperature‐corrected rates of individual‐level biomass production show the same body‐size dependence across a wide range of aerobic eukaryotes, from unicellular organisms to mammals and vascular plants. Population‐level energy use for both mammals and plants are strongly influenced by both metabolism and thermodynamic constraints on energy exchange between trophic levels. Our results show that because metabolism is a fundamental trait of organisms, it not only provides a link between individual‐ and ecosystem‐level processes, but can also highlight other important factors constraining ecological structure and dynamics.     

Metabolic Signatures of Adiposity in Young Adults: Mendelian Randomization Analysis and Effects of Weight Change

Background

Increased adiposity is linked with higher risk for cardiometabolic diseases. We aimed to determine to what extent elevated body mass index (BMI) within the normal weight range has causal effects on the detailed systemic metabolite profile in early adulthood.

Methods and Findings

We used Mendelian randomization to estimate causal effects of BMI on 82 metabolic measures in 12,664 adolescents and young adults from four population-based cohorts in Finland (mean age 26 y, range 16–39 y; 51% women; mean ± standard deviation BMI 24±4 kg/m²). Circulating metabolites were quantified by high-throughput nuclear magnetic resonance metabolomics and biochemical assays. In cross-sectional analyses, elevated BMI was adversely associated with cardiometabolic risk markers throughout the systemic metabolite profile, including lipoprotein subclasses, fatty acid composition, amino acids, inflammatory markers, and various hormones (p<0.0005 for 68 measures). Metabolite associations with BMI were generally stronger for men than for women (median 136%, interquartile range 125%–183%). A gene score for predisposition to elevated BMI, composed of 32 established genetic correlates, was used as the instrument to assess causality. Causal effects of elevated BMI closely matched observational estimates (correspondence 87%±3%; R ² = 0.89), suggesting causative influences of adiposity on the levels of numerous metabolites (p<0.0005 for 24 measures), including lipoprotein lipid subclasses and particle size, branched-chain and aromatic amino acids, and inflammation-related glycoprotein acetyls. Causal analyses of certain metabolites and potential sex differences warrant stronger statistical power. Metabolite changes associated with change in BMI during 6 y of follow-up were examined for 1,488 individuals. Change in BMI was accompanied by widespread metabolite changes, which had an association pattern similar to that of the cross-sectional observations, yet with greater metabolic effects (correspondence 160%±2%; R ² = 0.92).

Conclusions

Mendelian randomization indicates causal adverse effects of increased adiposity with multiple cardiometabolic risk markers across the metabolite profile in adolescents and young adults within the non-obese weight range. Consistent with the causal influences of adiposity, weight changes were paralleled by extensive metabolic changes, suggesting a broadly modifiable systemic metabolite profile in early adulthood. Please see later in the article for the Editors'' Summary     

Biopolymers and evolution

Biopolymers are usually studied being extracted from the whole system of a cell or of an organism. Some important features are lost during such a procedure. It is necessary to take into account the behavior of proteins and nucleic acids in metabolic networks and to investigate their evolution. The substitutions of amino-acids metabolic networks residues are biologically possible in the polypeptides and proteins if they do not influence their spatial structure and function. The correlations of the primary structure with these properties are degenerate. The protein can be treated as "an edited statistical copolymer" (Ptitsyn). In the process of "edition" an important role is played by the ions of transient metals. Nucleic acids possess similar properties. It can be shown that the deleterious mutations of proteins can be compensated by the changes of their amount, spatial and temporal characteristics of the synthesis. Not only the structure of the protein is important but also the exact answers of the questions: how much, when and where? The contemporary theory of evolution unites phylogeny and onthogeny. The directionality of evolution is determined both by natural selection and by the already existing structure of an organism. Hence many characters are not adaptive. This is valid also for the molecular level of the structure. Thus three independent groups of facts and suggestions are presented, which confirm the neutral theory of evolution (Kimura) and elucidate its physical meaning. The molecular evolution does not coincide with the biological evolution.     

What Can Causal Networks Tell Us about Metabolic Pathways?

Graphical models describe the linear correlation structure of data and have been used to establish causal relationships among phenotypes in genetic mapping populations. Data are typically collected at a single point in time. Biological processes on the other hand are often non-linear and display time varying dynamics. The extent to which graphical models can recapitulate the architecture of an underlying biological processes is not well understood. We consider metabolic networks with known stoichiometry to address the fundamental question: “What can causal networks tell us about metabolic pathways?”. Using data from an Arabidopsis BaySha population and simulated data from dynamic models of pathway motifs, we assess our ability to reconstruct metabolic pathways using graphical models. Our results highlight the necessity of non-genetic residual biological variation for reliable inference. Recovery of the ordering within a pathway is possible, but should not be expected. Causal inference is sensitive to subtle patterns in the correlation structure that may be driven by a variety of factors, which may not emphasize the substrate-product relationship. We illustrate the effects of metabolic pathway architecture, epistasis and stochastic variation on correlation structure and graphical model-derived networks. We conclude that graphical models should be interpreted cautiously, especially if the implied causal relationships are to be used in the design of intervention strategies.     

On the concept of individual in ecology and evolution

Part of the art of theory building is to construct effective basic concepts, with a large reach and yet powerful as tools for getting at conclusions. The most basic concept of population biology is that of individual. An appropriately reengineered form of this concept has become the basis for the theories of structured populations and adaptive dynamics. By appropriately delimiting individuals, followed by defining their states as well as their environment, it become possible to construct the general population equations that were introduced and studied by Odo Diekmann and his collaborators. In this essay I argue for taking the properties that led to these successes as the defining characteristics of the concept of individual, delegating the properties classically invoked by philosophers to the secondary role of possible empirical indicators for the presence of those characteristics. The essay starts with putting in place as rule for effective concept engineering that one should go for relations that can be used as basis for deductive structure building rather than for perceived ontological essence. By analysing how we want to use it in the mathematical arguments I then build up a concept of individual, first for use in population dynamical considerations and then for use in evolutionary ones. These two concepts do not coincide, and neither do they on all occasions agree with common intuition-based usage.     

Estimation of regional polygenicity from GWAS provides insights into the genetic architecture of complex traits

The number of variants that have a non-zero effect on a trait (i.e. polygenicity) is a fundamental parameter in the study of the genetic architecture of a complex trait. Although many previous studies have investigated polygenicity at a genome-wide scale, a detailed understanding of how polygenicity varies across genomic regions is currently lacking. In this work, we propose an accurate and scalable statistical framework to estimate regional polygenicity for a complex trait. We show that our approach yields approximately unbiased estimates of regional polygenicity in simulations across a wide-range of various genetic architectures. We then partition the polygenicity of anthropometric and blood pressure traits across 6-Mb genomic regions (N = 290K, UK Biobank) and observe that all analyzed traits are highly polygenic: over one-third of regions harbor at least one causal variant for each of the traits analyzed. Additionally, we observe wide variation in regional polygenicity: on average across all traits, 48.9% of regions contain at least 5 causal SNPs, 5.44% of regions contain at least 50 causal SNPs. Finally, we find that heritability is proportional to polygenicity at the regional level, which is consistent with the hypothesis that heritability enrichments are largely driven by the variation in the number of causal SNPs.     

The ontogeny of personality traits in the desert funnel‐web spider,Agelenopsis lisa (Araneae: Agelenidae)

Trait consistency over time is one of the cornerstones of animal personality. Behavioral syndromes are the result of correlations between behaviors. While repeatability in behavior is not a requirement for behavioral syndromes, the two concepts studied together provide a more comprehensive understanding of how behavior can change over ontogeny. The roles of ontogenetic processes in the emergence of personality and behavioral syndromes have received much individual attention. However, the characterization of both individual trait consistency and behavioral syndromes across both sexes, as in our study, has been relatively rare. Ontogeny refers to changes that occur from conception to maturation, and juveniles might be expected to undergo different selection pressures than sexually mature individuals and also will experience profound changes in hormones, morphology, and environment during this period. In this study, we test for behavioral trait consistency and behavioral syndromes across six time points during ontogenetic development in the desert funnel‐web spider (Agelenopsis lisa). Our results indicate behavioral traits generally lack consistency (repeatability) within life stages and across ontogeny. However, penultimate males and mature females do exhibit noticeable mean‐level changes, with greater aggressive responses toward prey, shorter latencies to explore their environment and in the exhibition of risk‐averse responses to predatory cues. These traits also show high repeatability. Some trait correlations do exist as well. In particular, a strong correlation between aggressiveness toward prey and exploration factors is observed in mature males. However, because correlations among these factors are unstable across ontogeny and vary in strength over time, we conclude that behavioral syndromes do not exist in this species. Nevertheless, our results indicate that increased consistency, increasing average trait values, and varying correlations between traits may coincide with developmentally important changes associated with sexual maturation, albeit at different time points in males and females. This period of the life cycle merits systematic examination across taxa.     

Fast identification of biological pathways associated with a quantitative trait using group lasso with overlaps

Where causal SNPs (single nucleotide polymorphisms) tend to accumulate within biological pathways, the incorporation of prior pathways information into a statistical model is expected to increase the power to detect true associations in a genetic association study. Most existing pathways-based methods rely on marginal SNP statistics and do not fully exploit the dependence patterns among SNPs within pathways.We use a sparse regression model, with SNPs grouped into pathways, to identify causal pathways associated with a quantitative trait. Notable features of our "pathways group lasso with adaptive weights" (P-GLAW) algorithm include the incorporation of all pathways in a single regression model, an adaptive pathway weighting procedure that accounts for factors biasing pathway selection, and the use of a bootstrap sampling procedure for the ranking of important pathways. P-GLAW takes account of the presence of overlapping pathways and uses a novel combination of techniques to optimise model estimation, making it fast to run, even on whole genome datasets.In a comparison study with an alternative pathways method based on univariate SNP statistics, our method demonstrates high sensitivity and specificity for the detection of important pathways, showing the greatest relative gains in performance where marginal SNP effect sizes are small.     

Contemporary changes in phenotypic variation,and the potential consequences for eco-evolutionary dynamics

Most studies assessing rates of phenotypic change focus on population mean trait values, whereas a largely overlooked additional component is changes in population trait variation. Theoretically, eco-evolutionary dynamics mediated by such changes in trait variation could be as important as those mediated by changes in trait means. To date, however, no study has comprehensively summarised how phenotypic variation is changing in contemporary populations. Here, we explore four questions using a large database: How do changes in trait variances compare to changes in trait means? Do different human disturbances have different effects on trait variance? Do different trait types have different effects on changes in trait variance? Do studies that established a genetic basis for trait change show different patterns from those that did not? We find that changes in variation are typically small; yet we also see some very large changes associated with particular disturbances or trait types. We close by interpreting and discussing the implications of our findings in the context of eco-evolutionary studies.     

A study of intragroup biological change induced by social group fission in Macaca mulatta using discrete cranial traits

Eight discrete cranial traits are used as biological indicators to investigate the effect of social group fission on intragroup genetic change leading to intergroup differentiation in Macaca mulatta. The timing of discrete cranial trait frequency change and group fission coincide, indicating a possible causal relationship between fission and genetic change. A significant change in the male mating population during and after fission is proposed as the mechanism causing intragroup genetic change, along with the effects of fluctuations in segregation ratios.     

Trait convergence and diversification arising from a complex evolutionary history in Hawaiian species of <Emphasis Type="Italic">Scaevola</Emphasis>

Species variation in functional traits may reflect diversification relating to convergence and/or divergence depending on environmental pressures and phylogenetic history. We tested trait-environment relationships and their basis in finer-scale evolutionary processes among nine extant Hawaiian species of Scaevola L. (Goodeniaceae), a taxon with a complex history of three independent colonizations by different phylogenetic lineages, parallel ecological specialization, and homoploid hybridization events in Hawai‘i. Using a wild population for each species, we evaluated traits related to plant function (morphology, leaf and wood anatomy, nutrient and carbon isotope composition). Hawaiian Scaevola species were distributed across coastal, dry forest and wet forest environments; multivariate environmental analysis using abiotic and biotic factors further showed that species from distantly related lineages inhabited similar environments. Many traits correlated with environment (based on the multivariate environmental analysis), considering both distantly related species and more closely related species. Scaevola species within shared habitats generally showed trait convergence across distantly related lineages, particularly among wet forest species. Furthermore, trait diversification through divergence was extensive among closely related Scaevola species that radiated into novel environments, especially in plant morphology and traits affecting water relations. Homoploid hybrid-origin species were “intermediate” compared to their ancestral parent species, and possessed trait combinations relevant for their current habitat. The diversity in functional traits reflected strong influences of both ecology and evolutionary history in native Hawaiian Scaevola species, and trait correspondence with environment was due to the combination of multiple processes within the taxon: trait pre-adaptation and filtering, evolutionary convergence, divergence, and hybridization.