The Relative Importance of Heredity and Environment in Determining the Piebald Pattern of Guinea-Pigs

Direct radiative effects of aerosols and clouds and their impacts on the ecosystem gross primary productivity in summer at typical sites in China

Aerosols and clouds indirectly influence the productivity of terrestrial ecosystems through their direct radiative effects (DRE). In this study, typical sites were selected in seven geographic regions of China, and four sky conditions were classified based on the aerosol optical depth (AOD) and cloud fraction (CF). The DREs of aerosols and clouds on the photosynthetically active radiation (PAR) and their impacts on the gross primary productivity (GPP) were analyzed. The results indicated that both aerosols and clouds imposed negative effects on the total PAR (PARtotal) and the direct PAR (PARdir) and a positive effect on the diffuse PAR fraction (PARkd). Aerosols promoted the diffuse PAR (PARdiff), whereas the effect of clouds on PARdiff shifted from positive to negative at high AOD levels. Aerosols alone increased the GPP by an average of 0.10 g C m-2 h-1 across all the sites, while the combined effect of aerosols and clouds caused an increase in the GPP of 0.14 g C m-2 h-1. The response of the GPP to temperature followed a quadratic function, with the optimum temperature varying between 22 °C and 31 °C across the different sites. As the vapor pressure difference (VPD) increased, the GPP decreased. Under diffuse radiation (PARkd≥0.8) conditions, photosynthesis showed a greater ability to withstand high temperatures and water stresses than under direct radiation (PARkd≤0.2) conditions. When the temperature and VPD remained constant, the GPP under diffuse radiation conditions was always greater than that under direct radiation conditions due to the diffuse fertilization effect (DFE). Path analysis revealed that GPP was co-regulated by multiple environmental factors (PARtotal, PARdiff, temperature, and VPD) through both direct and indirect pathways, with their relative importance varying across sites.

Animal genetics 100 years ago

One hundred years ago, the first book with the phrase "Animal Genetics" in its title was published. It was written by F.A.E. Crew, then Lecturer in Genetics and foundation Director of the Department of Research in Animal Breeding at the University of Edinburgh. The 352 pages of text provide a most interesting summary of the knowledge of animal genetics at that time. It is impressive to see the extent to which the understanding of genetics had developed in just a couple of decades since the rediscovery of Mendelism. There was, for example, recognition that genes are borne on chromosomes; that XX/XY sex determination provides a very satisfactory explanation for most of the relevant evidence; that sex-linked inheritance has a practical application; that variation in quantitative traits is determined by the combined action of many genes and many non-genetic factors; that inbreeding results in substantial decreases in fecundity and fertility due to homozygosity for undesirable alleles; that crossing between lines or breeds gives rise to hybrid vigour (heterosis); and that many disorders are inherited in a Mendelian fashion, and hence can be controlled by informed breeding. There is, however, no mention of Fisher's 1918 paper nor of Wright's recently published inbreeding coefficient and coefficient of relationship. Crew's book inspired the next generation of geneticists, such as Fred Hutt, who travelled from Canada to Edinburgh to do a PhD with Crew, and who later published his own very influential book with the same title, which was dedicated to Crew.

The causal arrows from genotype, environment, and management to plant phenotype are double headed

Cause-and-effect arrows are drawn from genotype (G), environment (E), and agronomic management (M) to the plant phenotype in crop stands in a useful but incomplete framework that informs research questions, experimental design, statistical analysis, data interpretation, modelling, and breeding and agronomic applications. Here we focus on the overlooked bidirectionality of these arrows. The phenotype-to-genotype arrow includes increased mutation rates in stressed phenotypes, relative to basal rates. From a developmental viewpoint, the phenotype modulates gene expression, returning multiple cellular phenotypes with a common genome. The phenotype-to-environment arrow is captured in the process of niche construction, which spans from persistent and global to transient and local. Research on crop rotations recognizes the influence of the phenotype on the environment but is divorced from niche construction theory. The phenotype-to-management arrow involves, for example, a diseased crop that may trigger fungicide treatment. Making explicit the bidirectionality of the arrows in the G×E×M framework contributes to narrowing the gap between data-driven technologies and integrative theory, and is an invitation to think cautiously of the internal teleonomy of plants in contrast to the view of the phenotype as the passive end of the arrows in the current framework.

Local adaptation and phenotypic plasticity drive leaf trait variation in the California endemic toyon (Heteromeles arbutifolia)

PREMISE

To survive climate change and habitat loss, plants must rely on phenotypic changes in response to the environment, local adaptation, or migration. Understanding the drivers of intraspecific variation is critical to anticipate how plant species will respond to climate change and to inform conservation decisions. Here we explored the extent of local adaptation and phenotypic plasticity in Heteromeles arbutifolia, toyon, a species endemic to the California Floristic Province.

METHODS

We collected leaves from 286 individuals across toyon's range and used seeds from 37 individuals to establish experimental gardens in the northern and southern parts of toyon's range. We measured leaf functional traits of the wild-collected leaves and functional and fitness traits of the offspring grown in the experimental gardens. We then investigated the relationships between traits and source environment.

RESULTS

Most traits we investigated responded plastically to the environment, and some traits in young seedlings were influenced by maternal effects. We found strong evidence that variation in leaf margins is a result of local adaptation to variation in temperature and temperature range. However, the source environment was not related to fitness traits or survival in the experimental gardens.

CONCLUSIONS

Our findings reiterate the adaptive role of toothed leaf margins in colder and more seasonally variable environments. Additionally, we provide evidence that fitness of toyon is not dependent on where they are sourced, and thus toyon can be sourced across its range for restoration purposes.

Nonshared environment: Real but random

Background

In the excitement about genomics, it is easy to lose sight of one of the most important findings from behavioural genetics: At least half of the variance of psychopathology is caused by environmental effects that are not shared by children growing up in the same family, which includes error of measurement. However, a 30-year search for the systematic causes of nonshared environment in a line-up of the usual suspects, especially parenting, has not identified the culprits.

Method

I briefly review this research, but primarily consider the conceptual framework of the search for 'missing' nonshared environmental effects.

Results

The search has focused on exogenous events like parenting, but nonshared environment might not be caused by anything we would call an event. Instead, it might reflect endogenous processes such as noisy biological systems (such as somatic mutations and epigenetics) or, at a psychological level, idiosyncratic subjective perceptions of past and present experiences, which could be called nonshared environmental experience to distinguish it from exogenous events. Although real, nonshared environment might be random in the philosophy of science sense of being unpredictable, even though it can have stable effects that predict subsequent behaviour.

Conclusion

I wade into the weeds of randomness and suggest that this so-called 'gloomy prospect' might not be so gloomy.

Linking ecosystems to public health based on combination of social and ecological systems

Promotion of public health is one of the most important benefits of ecosystems. Nevertheless, the relationship between ecosystems and social health' needs is not well understood. Therefore, a study was done to investigate the potential of natural (forests and rangelands) and artificial (urban parks and gardens) ecosystems in ensuring the five dimensions of public health (i.e. physical, mental, spiritual, social and environmental) in the social systems (urban and rural societies). Therefore, 47 health indicators were used in order to relate different ecosystems and social' needs to five dimensions of public health through questionnaire. The results indicated that natural ecosystems had the greatest potential in providing mental, spiritual and environmental health due to ecological characteristics of wilderness and aesthetic. The artificial ecosystems had the greatest potential in providing physical and social health due to their easy access. However, there was a match between social health' needs and ecosystem potential in the rural areas. The study highlighted the need for promotion of ecological indicators related to mental health in urban areas by enhancing silence and aesthetic in artificial ecosystems. Presented framework can provide comprehensive information on the weaknesses and strengths of different ecosystems to promote public health based on social needs and fixing the weaknesses of artificial ecosystems in urban areas.

How do stochastic processes and genetic threshold effects explain incomplete penetrance and inform causal disease mechanisms?

Incomplete penetrance is the rule rather than the exception in Mendelian disease. In syndromic monogenic disorders, phenotypic variability can be viewed as the combination of incomplete penetrance for each of multiple independent clinical features. Within genetically identical individuals, such as isogenic model organisms, stochastic variation at molecular and cellular levels is the primary cause of incomplete penetrance according to a genetic threshold model. By defining specific probability distributions of causal biological readouts and genetic liability values, stochasticity and incomplete penetrance provide information about threshold values in biological systems. Ascertainment of threshold values has been achieved by simultaneous scoring of relatively simple phenotypes and quantitation of molecular readouts at the level of single cells. However, this is much more challenging for complex morphological phenotypes using experimental and reductionist approaches alone, where cause and effect are separated temporally and across multiple biological modes and scales. Here I consider how causal inference, which integrates observational data with high confidence causal models, might be used to quantify the relative contribution of different sources of stochastic variation to phenotypic diversity. Collectively, these approaches could inform disease mechanisms, improve predictions of clinical outcomes and prioritize gene therapy targets across modes and scales of gene function. This article is part of a discussion meeting issue 'Causes and consequences of stochastic processes in development and disease'.

LTP2 hypomorphs show genotype-by-environment interaction in early seedling traits in Arabidopsis thaliana

Isogenic individuals can display seemingly stochastic phenotypic differences, limiting the accuracy of genotype-to-phenotype predictions. The extent of this phenotypic variation depends in part on genetic background, raising questions about the genes involved in controlling stochastic phenotypic variation. Focusing on early seedling traits in Arabidopsis thaliana, we found that hypomorphs of the cuticle-related gene LIPID TRANSFER PROTEIN 2 (LTP2) greatly increased variation in seedling phenotypes, including hypocotyl length, gravitropism and cuticle permeability. Many ltp2 hypocotyls were significantly shorter than wild-type hypocotyls while others resembled the wild-type. Differences in epidermal properties and gene expression between ltp2 seedlings with long and short hypocotyls suggest a loss of cuticle integrity as the primary determinant of the observed phenotypic variation. We identified environmental conditions that reveal or mask the increased variation in ltp2 hypomorphs and found that increased expression of its closest paralog LTP1 is necessary for ltp2 phenotypes. Our results illustrate how decreased expression of a single gene can generate starkly increased phenotypic variation in isogenic individuals in response to an environmental challenge.

Causal inference in drug discovery and development

To discover new drugs is to seek and to prove causality. As an emerging approach leveraging human knowledge and creativity, data, and machine intelligence, causal inference holds the promise of reducing cognitive bias and improving decision-making in drug discovery. Although it has been applied across the value chain, the concepts and practice of causal inference remain obscure to many practitioners. This article offers a nontechnical introduction to causal inference, reviews its recent applications, and discusses opportunities and challenges of adopting the causal language in drug discovery and development.

Psychosis spectrum features, neurocognition and functioning in a longitudinal study of youth with 22q11.2 deletion syndrome

BACKGROUND

Neuropsychiatric disorders are common in 22q11.2 Deletion Syndrome (22q11DS) with about 25% of affected individuals developing schizophrenia spectrum disorders by young adulthood. Longitudinal evaluation of psychosis spectrum features and neurocognition can establish developmental trajectories and impact on functional outcome.

METHODS

157 youth with 22q11DS were assessed longitudinally for psychopathology focusing on psychosis spectrum symptoms, neurocognitive performance and global functioning. We contrasted the pattern of positive and negative psychosis spectrum symptoms and neurocognitive performance differentiating those with more prominent Psychosis Spectrum symptoms (PS+) to those without prominent psychosis symptoms (PS-).

RESULTS

We identified differences in the trajectories of psychosis symptoms and neurocognitive performance between the groups. The PS+ group showed age associated increase in symptom severity, especially negative symptoms and general nonspecific symptoms. Correspondingly, their level of functioning was worse and deteriorated more steeply than the PS- group. Neurocognitive performance was generally comparable in PS+ and PS- groups and demonstrated a similar age-related trajectory. However, worsening executive functioning distinguished the PS+ group from PS- counterparts. Notably, of the three executive function measures examined, only working memory showed a significant difference between the groups in rate of change. Finally, structural equation modeling showed that neurocognitive decline drove the clinical change.

CONCLUSIONS

Youth with 22q11DS and more prominent psychosis features show worsening of symptoms and functional decline driven by neurocognitive decline, most related to executive functions and specifically working memory. The results underscore the importance of working memory in the developmental progression of psychosis.

LTP2 hypomorphs show genotype-by-environment interaction in early seedling traits in Arabidopsis thaliana

Isogenic individuals can display seemingly stochastic phenotypic differences, limiting the accuracy of genotype-to-phenotype predictions. The extent of this phenotypic variation depends in part on genetic background, raising questions about the genes involved in controlling stochastic phenotypic variation. Focusing on early seedling traits in Arabidopsis thaliana, we found that hypomorphs of the cuticle-related gene LTP2 greatly increased variation in seedling phenotypes, including hypocotyl length, gravitropism and cuticle permeability. Many ltp2 hypocotyls were significantly shorter than wild-type hypocotyls while others resembled the wild type. Differences in epidermal properties and gene expression between ltp2 seedlings with long and short hypocotyls suggest a loss of cuticle integrity as the primary determinant of the observed phenotypic variation. We identified environmental conditions that reveal or mask the increased variation in ltp2 hypomorphs, and found that increased expression of its closest paralog LTP1 is necessary for ltp2 phenotypes. Our results illustrate how decreased expression of a single gene can generate starkly increased phenotypic variation in isogenic individuals in response to an environmental challenge.

An evaluation of homeostatic plasticity for ecosystems using an analytical data science approach

The natural world is constantly changing, and planetary boundaries are issuing severe warnings about biodiversity and cycles of carbon, nitrogen, and phosphorus. In other views, social problems such as global warming and food shortages are spreading to various fields. These seemingly unrelated issues are closely related, but it can be said that understanding them in an integrated manner is still a step away. However, progress in analytical technologies has been recognized in various fields and, from a microscopic perspective, with the development of instruments including next-generation sequencers (NGS), nuclear magnetic resonance (NMR), gas chromatography-mass spectrometry (GC/MS), and liquid chromatography-mass spectrometry (LC/MS), various forms of molecular information such as genome data, microflora structure, metabolome, proteome, and lipidome can be obtained. The development of new technology has made it possible to obtain molecular information in a variety of forms. From a macroscopic perspective, the development of environmental analytical instruments and environmental measurement facilities such as satellites, drones, observation ships, and semiconductor censors has increased the data availability for various environmental factors. Based on these background, the role of computational science is to provide a mechanism for integrating and understanding these seemingly disparate data sets. This review describes machine learning and the need for structural equations and statistical causal inference of these data to solve these problems. In addition to introducing actual examples of how these technologies can be utilized, we will discuss how to use these technologies to implement environmentally friendly technologies in society.

Developmental noise is an overlooked contributor to innate variation in psychological traits

Stochastic developmental variation is an additional important source of variance - beyond genes and environment - that should be included in considering how our innate psychological predispositions may interact with environment and experience, in a culture-dependent manner, to ultimately shape patterns of human behaviour.

Independent phenotypic plasticity axes define distinct obesity sub-types

Studies in genetically 'identical' individuals indicate that as much as 50% of complex trait variation cannot be traced to genetics or to the environment. The mechanisms that generate this 'unexplained' phenotypic variation (UPV) remain largely unknown. Here, we identify neuronatin (NNAT) as a conserved factor that buffers against UPV. We find that Nnat deficiency in isogenic mice triggers the emergence of a bi-stable polyphenism, where littermates emerge into adulthood either 'normal' or 'overgrown'. Mechanistically, this is mediated by an insulin-dependent overgrowth that arises from histone deacetylase (HDAC)-dependent β-cell hyperproliferation. A multi-dimensional analysis of monozygotic twin discordance reveals the existence of two patterns of human UPV, one of which (Type B) phenocopies the NNAT-buffered polyphenism identified in mice. Specifically, Type-B monozygotic co-twins exhibit coordinated increases in fat and lean mass across the body; decreased NNAT expression; increased HDAC-responsive gene signatures; and clinical outcomes linked to insulinemia. Critically, the Type-B UPV signature stratifies both childhood and adult cohorts into four metabolic states, including two phenotypically and molecularly distinct types of obesity.

God playing dice, revisited: determinism and indeterminism in studies of stochastic phenotypic variation

Empirical studies of phenotypic variation show that genetic and environmental heterogeneity account for only part of it. Usually, the magnitude of the residual variation is comparable with that of the genetic component, while notably exceeding the magnitude of the environmental component. This can be interpreted in two ways. A deterministic interpretation associates it with artifacts such as measurement error and genetic and environmental heterogeneity that is unaccounted for. An indeterministic interpretation argues that it is random or stochastic phenotypic variation (SPV) resulting from developmental instability - a developing organism's inability to produce a consistent phenotype in a given environment. Classical example of debates between determinists and indeterminists took place about a century ago in quantum physics. In discussing Heidelberg's Uncertainty Principle, Einstein metaphorically expressed his deterministic position: 'God does not play dice with universe'. The indeterministic Uncertainty Principle, however, was eventually widely accepted. Currently, most biologists implicitly or explicitly support deterministic interpretations of phenotypic variation patterns. Here, a wide range of data on morphological traits (studied with analysis of fluctuating asymmetry) and non-morphological traits are analyzed to provide evidence that SPV is not an artifact, but a valid phenomenon. This evidence supports conclusions that observed associations between SPV and stress can be analyzed in the framework of dynamic energy budget theory, and are inextricably linked through energy homeostasis.

A network modeling approach provides insights into the environment-specific yield architecture of wheat

Wheat (Triticum aestivum) yield is impacted by a diversity of developmental processes which interact with the environment during plant growth. This complex genetic architecture complicates identifying quantitative trait loci (QTL) that can be used to improve yield. Trait data collected on individual processes or components of yield have simpler genetic bases and can be used to model how QTL generate yield variation. The objectives of this experiment were to identify QTL affecting spike yield, evaluate how their effects on spike yield proceed from effects on component phenotypes, and to understand how the genetic basis of spike yield variation changes between environments. A 358 F5:6 RIL population developed from the cross of LA-95135 and SS-MPV-57 was evaluated in two replications at five locations over the 2018 and 2019 seasons. The parents were two soft red winter wheat cultivars differing in flowering, plant height, and yield component characters. Data on yield components and plant growth were used to assemble a structural equation model (SEM) to characterize the relationships between QTL, yield components and overall spike yield. The effects of major QTL on spike yield varied by environment, and their effects on total spike yield were proportionally smaller than their effects on component traits. This typically resulted from contrasting effects on component traits, where an increase in traits associated with kernel number was generally associated with a decrease in traits related to kernel size. In all, the complete set of identified QTL was sufficient to explain most of the spike yield variation observed within each environment. Still, the relative importance of individual QTL varied dramatically. Path analysis based on coefficients estimated through SEM demonstrated that these variations in effects resulted from both different effects of QTL on phenotypes and environment-by-environment differences in the effects of phenotypes on one another, providing a conceptual model for yield genotype-by-environment interactions in wheat.

Research on the Graphical Model Structure Characteristic of Strong Exogeneity Based on Twin Network Method and Its Application in Causal Inference

Strong exogeneity is an important assumption in the study of causal inference, but it is difficult to identify according to its definition. The twin network method provides a graphical model tool for analyzing the variable relationship, involving the actual world and the hypothetical world, which facilitates the investigating of strong exogeneity. In this paper, the graphical model structure characteristic of strong exogeneity is investigated based on the twin network method. Compared with other derivation methods of graphical diagnosis, the method based on the twin network is more concise, clearer, and easier to understand. Under the condition of strong exogeneity, it is easy to estimate the probability of causation based on observational data. As an example, the application of graphical model structure characteristic of strong exogeneity in causal inference in the context of lung cancer simple sets (LUCAS) is illustrated.

Simulated nonlinear genetic and environmental dynamics of complex traits

Genetic studies of complex traits often show disparities in estimated heritability depending on the method used, whether by genomic associations or twin and family studies. We present a simulation of individual genomes with dynamic environmental conditions to consider how linear and nonlinear effects, gene-by-environment interactions, and gene-by-environment correlations may work together to govern the long-term development of complex traits and affect estimates of heritability from common methods. Our simulation studies demonstrate that the genetic effects estimated by genome wide association studies in unrelated individuals are inadequate to characterize gene-by-environment interaction, while including related individuals in genome-wide complex trait analysis (GCTA) allows gene-by-environment interactions to be recovered in the heritability. These theoretical findings provide an explanation for the "missing heritability" problem and bridge the conceptual gap between the most common findings of GCTA and twin studies. Future studies may use the simulation model to test hypotheses about phenotypic complexity either in an exploratory way or by replicating well-established observations of specific phenotypes.

On the Fourier transform of a quantitative trait: Implications for compressive sensing

This paper explores the genotype-phenotype relationship. It outlines conditions under which the dependence of a quantitative trait on the genome might be predictable, based on measurement of a limited subset of genotypes. It uses the theory of real-valued Boolean functions in a systematic way to translate trait data into the Fourier domain. Important trait features, such as the roughness of the trait landscape or the modularity of a trait have a simple Fourier interpretation. Roughness at a gene location corresponds to high sensitivity to mutation, while a modular organization of gene activity reduces such sensitivity. Traits where rugged loci are rare will naturally compress gene data in the Fourier domain, leading to a sparse representation of trait data, concentrated in identifiable, low-level coefficients. This Fourier representation of a trait organizes epistasis in a form which is isometric to the trait data. As Fourier matrices are known to be maximally incoherent with the standard basis, this permits employing compressive sensing techniques to work from data sets that are relatively small-sometimes even of polynomial size-compared to the exponentially large sets of possible genomes. This theory provides a theoretical underpinning for systematic use of Boolean function machinery to dissect the dependency of a trait on the genome and environment.

Causation, not collinearity: Identifying sources of bias when modelling the evolution of brain size and other allometric traits

Many biological traits covary with body size, resulting in an allometric relationship. Identifying the evolutionary drivers of these traits is complicated by possible relationships between a candidate selective agent and body size itself, motivating the widespread use of multiple regression analysis. However, the possibility that multiple regression may generate misleading estimates when predictor variables are correlated has recently received much attention. Here, we argue that a primary source of such bias is the failure to account for the complex causal structures underlying brains, bodies, and agents. When brains and bodies are expected to evolve in a correlated manner over and above the effects of specific agents of selection, neither simple nor multiple regression will identify the true causal effect of an agent on brain size. This problem results from the inclusion of a predictor variable in a regression analysis that is (in part) a consequence of the response variable. We demonstrate these biases with examples and derive estimators to identify causal relationships when traits evolve as a function of an existing allometry. Model mis‐specification relative to plausible causal structures, not collinearity, requires further consideration as an important source of bias in comparative analyses.

Mediation analysis methods used in observational research: a scoping review and recommendations

BACKGROUND

Mediation analysis methodology underwent many advancements throughout the years, with the most recent and important advancement being the development of causal mediation analysis based on the counterfactual framework. However, a previous review showed that for experimental studies the uptake of causal mediation analysis remains low. The aim of this paper is to review the methodological characteristics of mediation analyses performed in observational epidemiologic studies published between 2015 and 2019 and to provide recommendations for the application of mediation analysis in future studies.

METHODS

We searched the MEDLINE and EMBASE databases for observational epidemiologic studies published between 2015 and 2019 in which mediation analysis was applied as one of the primary analysis methods. Information was extracted on the characteristics of the mediation model and the applied mediation analysis method.

RESULTS

We included 174 studies, most of which applied traditional mediation analysis methods (n = 123, 70.7%). Causal mediation analysis was not often used to analyze more complicated mediation models, such as multiple mediator models. Most studies adjusted their analyses for measured confounders, but did not perform sensitivity analyses for unmeasured confounders and did not assess the presence of an exposure-mediator interaction.

CONCLUSIONS

To ensure a causal interpretation of the effect estimates in the mediation model, we recommend that researchers use causal mediation analysis and assess the plausibility of the causal assumptions. The uptake of causal mediation analysis can be enhanced through tutorial papers that demonstrate the application of causal mediation analysis, and through the development of software packages that facilitate the causal mediation analysis of relatively complicated mediation models.

Path Analysis to Assess Socio-Economic and Mitigation Measure Determinants for Daily Coronavirus Infections

(1) Background: With the rapid global spread of the coronavirus disease 2019 (COVID-19) and the relatively high daily cases recorded in a short time compared to other types of seasonal flu, the world remains under continuous threat unless we identify the key factors that contribute to these unexpected records. This identification is important for developing effective criteria and plans to reduce the spread of the COVID-19 pandemic and can guide national authorities to tighten or reduce mitigation measures, in addition to spreading awareness of the important factors that contribute to the propagation of the disease. (2) Methods: The data represents the daily infections (210 days) in four different countries (China, Italy, Iran, and Lebanon) taken approximately in the same duration, between January and March 2020. Path analysis was implemented on the data to detect the significant factors that affect the daily COVID-19 infections. (3) Results: The path coefficients show that quarantine commitment (β = −0.823) and full lockdown measures (β = −0.775) have the largest direct effect on COVID-19 daily infections. The results also show that more experience (β = −0.35), density in society (β = −0.288), medical resources (β = 0.136), and economic resources (β = 0.142) have indirect effects on daily COVID-19 infections. (4) Conclusions: The COVID-19 daily infections directly decrease with complete lockdown measures, quarantine commitment, wearing masks, and social distancing. COVID-19 daily cases are indirectly associated with population density, special events, previous experience, technology used, economic resources, and medical resources.

Nature, Nurture, and Noise: Developmental Instability, Fluctuating Asymmetry, and the Causes of Phenotypic Variation

Phenotypic variation arises from genetic and environmental variation, as well as random aspects of development. The genetic (nature) and environmental (nurture) components of this variation have been appreciated since at least 1900. The random developmental component (noise) has taken longer for quantitative geneticists to appreciate. Here, I sketch the historical development of the concepts of random developmental noise and developmental instability, and its quantification via fluctuating asymmetry. The unsung pioneers in this story are Hugo DeVries (fluctuating variation, 1909), C. H. Danforth (random variation between monozygotic twins, 1919), and Sewall Wright (random developmental variation in piebald guinea pigs, 1920). The first pioneering study of fluctuating asymmetry, by Sumner and Huestis in 1921, is seldom mentioned, possibly because it failed to connect the observed random asymmetry with random developmental variation. This early work was then synthesized by Boris Astaurov in 1930 and Wilhelm Ludwig in 1932, and then popularized by Drosophila geneticists beginning with Kenneth Mather in 1953. Population phenogeneticists are still trying to understand the origins and behavior of random developmental variation. Some of the developmental noise represents true stochastic behavior of molecules and cells, while some represents deterministic chaos, nonlinear feedback, and symmetry breaking.

Putting Psychology to the Test: Rethinking Model Evaluation Through Benchmarking and Prediction

Consensus on standards for evaluating models and theories is an integral part of every science. Nonetheless, in psychology, relatively little focus has been placed on defining reliable communal metrics to assess model performance. Evaluation practices are often idiosyncratic and are affected by a number of shortcomings (e.g., failure to assess models' ability to generalize to unseen data) that make it difficult to discriminate between good and bad models. Drawing inspiration from fields such as machine learning and statistical genetics, we argue in favor of introducing common benchmarks as a means of overcoming the lack of reliable model evaluation criteria currently observed in psychology. We discuss a number of principles benchmarks should satisfy to achieve maximal utility, identify concrete steps the community could take to promote the development of such benchmarks, and address a number of potential pitfalls and concerns that may arise in the course of implementation. We argue that reaching consensus on common evaluation benchmarks will foster cumulative progress in psychology and encourage researchers to place heavier emphasis on the practical utility of scientific models.

Identifying geographically differentiated features of Ethopian Nile tilapia (Oreochromis niloticus) morphology with machine learning

Visual characteristics are among the most important features for characterizing the phenotype of biological organisms. Color and geometric properties define population phenotype and allow assessing diversity and adaptation to environmental conditions. To analyze geometric properties classical morphometrics relies on biologically relevant landmarks which are manually assigned to digital images. Assigning landmarks is tedious and error prone. Predefined landmarks may in addition miss out on information which is not obvious to the human eye. The machine learning (ML) community has recently proposed new data analysis methods which by uncovering subtle features in images obtain excellent predictive accuracy. Scientific credibility demands however that results are interpretable and hence to mitigate the black-box nature of ML methods. To overcome the black-box nature of ML we apply complementary methods and investigate internal representations with saliency maps to reliably identify location specific characteristics in images of Nile tilapia populations. Analyzing fish images which were sampled from six Ethiopian lakes reveals that deep learning improves on a conventional morphometric analysis in predictive performance. A critical assessment of established saliency maps with a novel significance test reveals however that the improvement is aided by artifacts which have no biological interpretation. More interpretable results are obtained by a Bayesian approach which allows us to identify genuine Nile tilapia body features which differ in dependence of the animals habitat. We find that automatically inferred Nile tilapia body features corroborate and expand the results of a landmark based analysis that the anterior dorsum, the fish belly, the posterior dorsal region and the caudal fin show signs of adaptation to the fish habitat. We may thus conclude that Nile tilapia show habitat specific morphotypes and that a ML analysis allows inferring novel biological knowledge in a reproducible manner.

Multilevel Modeling in Classical Twin and Modern Molecular Behavior Genetics

For more than a decade, it has been known that many common behavior genetics models for a single phenotype can be estimated as multilevel models (e.g., van den Oord 2001; Guo and Wang 2002; McArdle and Prescott 2005; Rabe-Hesketh et al. 2007). This paper extends the current knowledge to (1) multiple phenotypes such that the method is completely general to the variance structure hypothesized, and (2) both higher and lower levels of nesting. The multi-phenotype method also allows extended relationships to be considered (see also, Bard et al. 2012; Hadfield and Nakagawa 2010). The extended relationship model can then be continuously expanded to merge with the case typically seen in the molecular genetics analyses of unrelated individuals (e.g., Yang et al. 2011). We use the multilevel form of behavior genetics models to fit a multivariate three level model that allows for (1) child level variation from unique environments and additive genetics, (2) family level variation from additive genetics and common environments, and (3) neighborhood level variation from broader geographic contexts. Finally, we provide R (R Development Core Team 2020) functions and code for multilevel specification of several common behavior genetics models using OpenMx (Neale et al. 2016).

Fluctuating Asymmetry and Developmental Instability, a Guide to Best Practice

Best practices in studies of developmental instability, as measured by fluctuating asymmetry, have developed over the past 60 years. Unfortunately, they are haphazardly applied in many of the papers submitted for review. Most often, research designs suffer from lack of randomization, inadequate replication, poor attention to size scaling, lack of attention to measurement error, and unrecognized mixtures of additive and multiplicative errors. Here, I summarize a set of best practices, especially in studies that examine the effects of environmental stress on fluctuating asymmetry.

Exploring the Link Between Additive Heritability and Prediction Accuracy From a Ridge Regression Perspective

Genome-Wide Association Studies (GWAS) explain only a small fraction of heritability for most complex human phenotypes. Genomic heritability estimates the variance explained by the SNPs on the whole genome using mixed models and accounts for the many small contributions of SNPs in the explanation of a phenotype. This paper approaches heritability from a machine learning perspective, and examines the close link between mixed models and ridge regression. Our contribution is two-fold. First, we propose estimating genomic heritability using a predictive approach via ridge regression and Generalized Cross Validation (GCV). We show that this is consistent with classical mixed model based estimation. Second, we derive simple formulae that express prediction accuracy as a function of the ratio n p , where n is the population size and p the total number of SNPs. These formulae clearly show that a high heritability does not imply an accurate prediction when p > n. Both the estimation of heritability via GCV and the prediction accuracy formulae are validated using simulated data and real data from UK Biobank.

Heritability curves: A local measure of heritability in family models

Classical heritability models for family data split the phenotype variance into genetic and environmental components. For instance, the ACE model in twin studies assumes the phenotype variance decomposes as a²  + c²  + e² , representing (additive) genetic effects, common (shared) environment, and residual environment, respectively. However, for some phenotypes it is biologically plausible that the genetic and environmental components may vary over the range of the phenotype. For instance, very large or small values of the phenotype may be caused by "sporadic" environmental factors, whereas the mid-range phenotype variation may be more under the control of common genetic factors. This article introduces a "local" measure of heritability, where the genetic and environmental components are allowed to depend on the value of the phenotype itself. Our starting point is a general formula for local correlation between two random variables. For estimation purposes, we use a multivariate Gaussian mixture, which is able to capture nonlinear dependence and respects certain distributional constraints. We derive an analytical expression for the associated correlation curve, and show how to decompose the correlation curve into genetic and environmental parts, for instance, a² (y) + c² (y) + e² (y) for the ACE model, where we estimate the components as functions of the phenotype y. Furthermore, our model allows switching, for instance, from the ACE model to the ADE model within the range of the same phenotype. When applied to birth weight (BW) data on Norwegian mother-father-child trios, we conclude from the model that low and high BW are less heritable traits than medium BW. We also demonstrate switching between the ACE and ADE model when studying body mass index in adult monozygotic and dizygotic twins.

'There and Back Again'-Forward Genetics and Reverse Phenotyping in Pulmonary Arterial Hypertension

Although the invention of right heart catheterisation in the 1950s enabled accurate clinical diagnosis of pulmonary arterial hypertension (PAH), it was not until 2000 when the landmark discovery of the causative role of bone morphogenetic protein receptor type II (BMPR2) mutations shed new light on the pathogenesis of PAH. Since then several genes have been discovered, which now account for around 25% of cases with the clinical diagnosis of idiopathic PAH. Despite the ongoing efforts, in the majority of patients the cause of the disease remains elusive, a phenomenon often referred to as "missing heritability". In this review, we discuss research approaches to uncover the genetic architecture of PAH starting with forward phenotyping, which in a research setting should focus on stable intermediate phenotypes, forward and reverse genetics, and finally reverse phenotyping. We then discuss potential sources of "missing heritability" and how functional genomics and multi-omics methods are employed to tackle this problem.

The Power to Detect Cultural Transmission in the Nuclear Twin Family Design With and Without Polygenic Risk Scores and in the Transmitted-Nontransmitted (Alleles) Design

We compare the power of two different approaches to detect passive genotype-environment (GE) covariance originating from cultural and genetic transmission operating simultaneously. In the traditional nuclear twin family (NTF) design, cultural transmission is estimated from the phenotypic covariance matrices of the mono- and dizygotic twins and their parents. Here, phenotyping is required in all family members. A more recent method is the transmitted-nontransmitted (T-NT) allele design, which exploits measured genetic variants in parents and offspring to test for effects of nontransmitted alleles from parents. This design requires two-generation genome-wide data and a powerful genome-wide association study (GWAS) for the phenotype in addition to phenotyping in offspring. We compared the power of both designs. Using exact data simulation, we demonstrate three points: how the power of the T-NT design depends on the predictive power of polygenic risk scores (PRSs); that when the NTF design can be applied, its power to detect cultural transmission and GE covariance is high relative to T-NT; and that, given effect sizes from contemporary GWAS, adding PRSs to the NTF design does not yield an appreciable increase in the power to detect cultural transmission. However, it may be difficult to collect phenotypes of parents and the possible importance of gene × age interaction, and secular generational effects can cause complications for many important phenotypes. The T-NT design avoids these complications.

Heritability Estimates of Antler and Body Traits in White-Tailed Deer (Odocoileus virginianus) From Genomic-Relatedness Matrices

Estimating heritability (h2) is required to predict the response to selection and is useful in species that are managed or farmed using trait information. Estimating h2 in free-ranging populations is challenging due to the need for pedigrees; genomic-relatedness matrices (GRMs) circumvent this need and can be implemented in nearly any system where phenotypic and genome-wide single-nucleotide polymorphism (SNP) data are available. We estimated the heritability of 5 body and 3 antler traits in a free-ranging population of white-tailed deer (Odocoileus virginianus) on Anticosti Island, Quebec, Canada. We generated classic and robust GRMs from >10,000 SNPs: hind foot length, dressed body mass, and peroneus muscle mass had high h2 values of 0.62, 0.44, and 0.55, respectively. Heritability in male-only antler features ranged from 0.07 to 0.33. We explored the influence of filtering by minor allele frequency and data completion on h2: GRMs derived from fewer SNPs had reduced h2 estimates and the relatedness coefficients significantly deviated from those generated with more SNPs. As a corollary, we discussed limitations to the application of GRMs in the wild, notably how skewed GRMs, specifically many unrelated individuals, can increase variance around h2 estimates. This is the first study to estimate h2 on a free-ranging population of white-tailed deer and should be informative for breeding designs and management as these traits could respond to selection.

Social and economic development and pregnancy mental health: secondary analyses of data from rural Vietnam

BACKGROUND

This study aimed to establish whether changes in the socioeconomic context were associated with changes in population-level antenatal mental health indicators in Vietnam.

METHODS

Social, economic and public policies introduced in Vietnam (1986-2010) were mapped. Secondary analyses of data from two cross-sectional community-based studies conducted in 2006 (n = 134) and 2010 (n = 419), involving women who were ≥ 28 weeks pregnant were completed. Data for these two studies had been collected in structured individual face-to-face interviews, and included indicators of antenatal mental health (mean Edinburgh Postnatal Depression Scale Vietnam-validation (EPDS-V) score), intimate partner relationships (Intimate Bonds Measure Vietnam-validation) and sociodemographic characteristics. Socioeconomic characteristics and mean EPDS-V scores in the two study years were compared and mediation analyses were used to establish whether indicators of social and economic development mediated differences in EPDS-V scores.

RESULTS

Major policy initiatives for poverty reduction, hunger eradication and making domestic violence a crime were implemented between 2006 and 2010. Characteristics and circumstances of pregnant women in Ha Nam improved significantly. Mean EPDS-V score was lower in 2010, indicating better population-level antenatal mental health. Household wealth and intimate partner controlling behaviours mediated the difference in EPDS-V scores between 2006 and 2010.

CONCLUSIONS

Changes in the socioeconomic and political context, particularly through policies to improve household wealth and reduce domestic violence, appear to influence women's lives and population-level antenatal mental health. Cross-sectoral policies that reduce social risk factors may be a powerful mechanism to improve antenatal mental health at a population level.

Correlation without a cause: an epidemiological odyssey

In the 1980s debate intensified over whether there was a protective effect of high-density lipoprotein cholesterol (HDL-C) or an adverse effect of triglycerides on coronary heart disease (CHD) risk. In a 1991 paper reprinted in the IJE we suggested that the high degree of correlation between the two, together with plausible levels of measurement error, made it unlikely that conventional epidemiological approaches could contribute to causal understanding. The consensus that HDL-C was protective, popularly reified in the notion of ‘good cholesterol’, strengthened over subsequent years. Reviewing the biostatistical and epidemiological literature from before and after 1991 we suggest that within the observational epidemiology pantheon only Mendelian randomization studies—that began to appear at the same time as the initial negative randomized controlled trials—made a meaningful contribution. It is sobering to realize that many issues that appear suitable targets for epidemiological investigation are simply refractory to conventional approaches. The discipline should surely revisit this and other high-profile cases of consequential epidemiological failure—such as that with respect to vitamin E supplementation and CHD risk—rather than pass them over in silence.

fMRI in Non-human Primate: A Review on Factors That Can Affect Interpretation and Dynamic Causal Modeling Application

Dynamic causal modeling (DCM)-a framework for inferring hidden neuronal states from brain activity measurements (e. g., fMRI) and their context-dependent modulation-was developed for human neuroimaging, and has not been optimized for non-human primate (NHP) studies, which are usually done under anesthesia. Animal neuroimaging studies offer the potential to improve effective connectivity modeling using DCM through combining functional imaging with invasive procedures such as in vivo optogenetic or electrical stimulation. Employing a Bayesian approach, model parameters are estimated based on prior knowledge of conditions that might be related to neural and BOLD dynamics (e.g., requires empirical knowledge about the range of plausible parameter values). As such, we address the following questions in this review: What factors need to be considered when applying DCM to NHP data? What differences in functional networks, cerebrovascular architecture and physiology exist between human and NHPs that are relevant for DCM application? How do anesthetics affect vascular physiology, BOLD contrast, and neural dynamics-particularly, effective communication within, and between networks? Considering the factors that are relevant for DCM application to NHP neuroimaging, we propose a strategy for modeling effective connectivity under anesthesia using an integrated physiologic-stochastic DCM (IPS-DCM).

Post-Modern Epidemiology: When Methods Meet Matter

In the last third of the 20th century, etiological epidemiology within academia in high-income countries shifted its primary concern from attempting to tackle the apparent epidemic of noncommunicable diseases to an increasing focus on developing statistical and causal inference methodologies. This move was mutually constitutive with the failure of applied epidemiology to make major progress, with many of the advances in understanding the causes of noncommunicable diseases coming from outside the discipline, while ironically revealing the infectious origins of several major conditions. Conversely, there were many examples of epidemiologic studies promoting ineffective interventions and little evident attempt to account for such failure. Major advances in concrete understanding of disease etiology have been driven by a willingness to learn about and incorporate into epidemiology developments in biology and cognate data science disciplines. If fundamental epidemiologic principles regarding the rooting of disease risk within populations are retained, recent methodological developments combined with increased biological understanding and data sciences capability should herald a fruitful post-Modern Epidemiology world.

Outrageous fortune or destiny? Family influences on status achievement in the early life course

Psychologists using quantitative studies of the trait intelligence have established with much confidence that the impact of genes on intelligence increases with age, while the environmental effect of the family of origin declines. We examined the conjecture that a similar trend of increasing effect of genes/declining family environmental effect characterizes other status-related outcomes when arranged in typical age-graded sequence over adolescence and early adulthood. We used DeFries-Fulker (1985) (DF) analysis with longitudinal data on 1,576 pairs of variously-related young adult siblings (MZ twins; DZ twins; full siblings; half siblings; cousins; and nonrelated siblings; mean age 28) to estimate univariate quantitative genetic decompositions for fifteen status-related outcomes roughly ordered along the early life course: Verbal IQ, High school GPA, College plans, High school graduation, Some college, College graduation, Graduate school, Educational attainment, Occupational education, Occupational wages, Personal earnings, Household income, Household assets, Home ownership, and Subjective social status, with and without covariate controls for Age, Female gender, and Race/ethnicity (black, Hispanic, other; reference white). Results for successive outcomes did not support the conjecture of increasing heritability with maturity. Rather, the impacts of both the genes and the family environment tended to decline over the life course, resulting in a downward trend in family influences from all sources. There was some evidence of a recrudescence in relative influence of the family environment for outcomes related to the household that are often shared with a spouse, such as home ownership, suggesting a role of assortative mating in status reproduction. Other findings and limitations of the study are discussed.

Variance Components Models for Analysis of Big Family Data of Health Outcomes in the Lifelines Cohort Study

Large multigenerational cohort studies offer powerful ways to study the hereditary effects on various health outcomes. However, accounting for complex kinship relations in big data structures can be methodologically challenging. The traditional kinship model is computationally infeasible when considering thousands of individuals. In this article, we propose a computationally efficient alternative that employs fractional relatedness of family members through a series of founding members. The primary goal of this study is to investigate whether the effect of determinants on health outcome variables differs with and without accounting for family structure. We compare a fixed-effects model without familial effects with several variance components models that account for heritability and shared environment structure. Our secondary goal is to apply the fractional relatedness model in a realistic setting. Lifelines is a three-generation cohort study investigating the biological, behavioral, and environmental determinants of healthy aging. We analyzed a sample of 89,353 participants from 32,452 reconstructed families. Our primary conclusion is that the effect of determinants on health outcome variables does not differ with and without accounting for family structure. However, accounting for family structure through fractional relatedness allows for estimating heritability in a computationally efficient way, showing some interesting differences between physical and mental quality of life heritability. We have shown through simulations that the proposed fractional relatedness model performs better than the standard kinship model, not only in terms of computational time and convenience of fitting using standard functions in R, but also in terms of bias of heritability estimates and coverage.

From R.A. Fisher's 1918 Paper to GWAS a Century Later

The genetics and evolution of complex traits, including quantitative traits and disease, have been hotly debated ever since Darwin. A century ago, a paper from R.A. Fisher reconciled Mendelian and biometrical genetics in a landmark contribution that is now accepted as the main foundation stone of the field of quantitative genetics. Here, we give our perspective on Fisher's 1918 paper in the context of how and why it is relevant in today's genome era. We mostly focus on human trait variation, in part because Fisher did so too, but the conclusions are general and extend to other natural populations, and to populations undergoing artificial selection.

Disentangling causal webs in the brain using functional magnetic resonance imaging: A review of current approaches

In the past two decades, functional Magnetic Resonance Imaging (fMRI) has been used to relate neuronal network activity to cognitive processing and behavior. Recently this approach has been augmented by algorithms that allow us to infer causal links between component populations of neuronal networks. Multiple inference procedures have been proposed to approach this research question but so far, each method has limitations when it comes to establishing whole-brain connectivity patterns. In this paper, we discuss eight ways to infer causality in fMRI research: Bayesian Nets, Dynamical Causal Modelling, Granger Causality, Likelihood Ratios, Linear Non-Gaussian Acyclic Models, Patel's Tau, Structural Equation Modelling, and Transfer Entropy. We finish with formulating some recommendations for the future directions in this area.

Benchmark validation of statistical models: Application to mediation analysis of imagery and memory

This article describes benchmark validation, an approach to validating a statistical model. According to benchmark validation, a valid model generates estimates and research conclusions consistent with a known substantive effect. Three types of benchmark validation-(a) benchmark value, (b) benchmark estimate, and (c) benchmark effect-are described and illustrated with examples. Benchmark validation methods are especially useful for statistical models with assumptions that are untestable or very difficult to test. Benchmark effect validation methods were applied to evaluate statistical mediation analysis in eight studies using the established effect that increasing mental imagery improves recall of words. Statistical mediation analysis led to conclusions about mediation that were consistent with established theory that increased imagery leads to increased word recall. Benchmark validation based on established substantive theory is discussed as a general way to investigate characteristics of statistical models and a complement to mathematical proof and statistical simulation. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Developmental cognitive neuroscience using latent change score models: A tutorial and applications

Assessing and analysing individual differences in change over time is of central scientific importance to developmental neuroscience. However, the literature is based largely on cross-sectional comparisons, which reflect a variety of influences and cannot directly represent change. We advocate using latent change score (LCS) models in longitudinal samples as a statistical framework to tease apart the complex processes underlying lifespan development in brain and behaviour using longitudinal data. LCS models provide a flexible framework that naturally accommodates key developmental questions as model parameters and can even be used, with some limitations, in cases with only two measurement occasions. We illustrate the use of LCS models with two empirical examples. In a lifespan cognitive training study (COGITO, N = 204 (N = 32 imaging) on two waves) we observe correlated change in brain and behaviour in the context of a high-intensity training intervention. In an adolescent development cohort (NSPN, N = 176, two waves) we find greater variability in cortical thinning in males than in females. To facilitate the adoption of LCS by the developmental community, we provide analysis code that can be adapted by other researchers and basic primers in two freely available SEM software packages (lavaan and Ωnyx).

A Psychological Approach to ‘Public Perception’ of Land-Use Planning: A Case Study of Jiangsu Province, China

Public perception and attitudes towards public affairs in the USA since the 1960s become a societal issue of growing importance in the field of planning. Good land-use planning should deliver a bright future vision in a way that unites and inspires groups to implement it. The introduction of public perception into planning helps to understand the process of how the public develop their awareness, value judgments, behavior and attitudes. In this research, we built the framework of public perception in land-use planning based on the affect, behavior, cognition (ABC) theory of consumer behavior. We gathered empirical data for Jiangsu province in China. We used structural equation modeling, a commonly used statistical analysis method for examining the structural relationship between multiple variables. We found that the public perception towards public affairs contributed to forming a multiple iterative interaction effect, which evolves a process from primary cognition to knowledge extraction, internalized absorption, emotional judgement and finally externalization into a certain attitudes and behaviors. On the cognitive level, our research result showed that public expectation and perceived quality have opposite effects on perceived difference and the public expectation is more influential. If the planning vision provides a clear and convincing picture of the future, and the information of planning is easy to understand, the public’s cognition and emotion can be well integrated. The core element of the emotional level is perceived value. The public is more concerned about a new planning project if it can add the value to the land, protect community environment, and improve the condition of low-income and minority populations. On the behavior level, public continuous behavior intentions could enhance perceived value, subjective norms and perceived availability. The research could further account for the root of public attitudes and behavior. This is crucial to China's land-use policy, and may well provide important lessons for other developing countries.

Epigenetic control of variation and stochasticity in metabolic disease

BACKGROUND

The alarming rise of obesity and its associated comorbidities represents a medical burden and a major global health and economic issue. Understanding etiological mechanisms underpinning susceptibility and therapeutic response is of primary importance. Obesity, diabetes, and metabolic diseases are complex trait disorders with only partial genetic heritability, indicating important roles for environmental programing and epigenetic effects.

SCOPE OF THE REVIEW

We will highlight some of the reasons for the scarce predictability of metabolic diseases. We will outline how genetic variants generate phenotypic variation in disease susceptibility across populations. We will then focus on recent conclusions about epigenetic mechanisms playing a fundamental role in increasing variability and subsequently disease triggering.

MAJOR CONCLUSIONS

Currently, we are unable to predict or mechanistically define how "missing heritability" drives disease. Unravelling this black box of regulatory processes will allow us to move towards a truly personalized and precision medicine.

Genetic insights into the morass of metastatic heterogeneity

Tumour heterogeneity poses a substantial problem for the clinical management of cancer. Somatic evolution of the cancer genome results in genetically distinct subclones in the primary tumour with different biological properties and therapeutic sensitivities. The problem of heterogeneity is compounded in metastatic disease owing to the complexity of the metastatic process and the multiple biological hurdles that the tumour cell must overcome to establish a clinically overt metastatic lesion. New advances in sequencing technology and clinical sample acquisition are providing insights into the phylogenetic relationship of metastases and primary tumours at the level of somatic tumour genetics while also illuminating fundamental mechanisms of the metastatic process. In addition to somatically acquired genetic heterogeneity in the tumour cells, inherited population-based genetic heterogeneity can profoundly modify metastatic biology and further complicate the development of effective, broadly applicable antimetastatic therapies. Here, we examine how genetic heterogeneity impacts metastatic disease and the implications of current knowledge for future research endeavours and therapeutic interventions.

Syndemics: A theory in search of data or data in search of a theory?

The concept of a syndemic was proposed more than two decades ago to explain how large-scale social forces might give rise to co-occurring epidemics that synergistically interact to undermine health in vulnerable populations. This conceptual instrument has the potential to help policymakers and program implementers in their endeavors to improve population health. Accordingly, it has become an increasingly popular heuristic for advocacy, most notably in the field of HIV treatment and prevention. However, most empirical studies purporting to validate the theory of syndemics actually do no such thing. Tomori et al. (2018) provide a novel case study from India illustrating how the dominant empirical approach fails to promote deeper understanding about how hazardous alcohol use, illicit drug use, depression, childhood sexual abuse, and intimate partner violence interact to worsen HIV risk among men who have sex with men. In this commentary, I relate the theory of syndemics to other established social science and public health theories of disease distribution, identify possible sources of conceptual and empirical confusion, and provide concrete suggestions for how to validate the theory using a mixed-methods approach. The hope is that more evidence can be mobilized -- whether informed by the theory of syndemics or not -- to improve health and psychosocial wellbeing among vulnerable populations worldwide.