A dimensional perspective on the genetics of obsessive-compulsive disorder

This review covers recent findings in the genomics of obsessive-compulsive disorder (OCD), obsessive-compulsive symptoms, and related traits from a dimensional perspective. We focus on discoveries stemming from technical and methodological advances of the past five years and present a synthesis of human genomics research on OCD. On balance, reviewed studies demonstrate that OCD is a dimensional trait with a highly polygenic architecture and genetic correlations to multiple, often comorbid psychiatric phenotypes. We discuss the phenotypic and genetic findings of these studies in the context of the dimensional framework, relying on a continuous phenotype definition, and contrast these observations with discoveries based on a categorical diagnostic framework, relying on a dichotomous case/control definition. Finally, we highlight gaps in knowledge and new directions for OCD genetics research.

Using major depression polygenic risk scores to explore the depressive symptom continuum

Background Major depression (MD) is often characterised as a categorical disorder; however, observational studies comparing sub-threshold and clinical depression suggest MD is continuous. Many of these studies do not explore the full continuum and are yet to consider genetics as a risk factor. This study sought to understand if polygenic risk for MD could provide insight into the continuous nature of depression. Methods Factor analysis on symptom-level data from the UK Biobank (N = 148 957) was used to derive continuous depression phenotypes which were tested for association with polygenic risk scores (PRS) for a categorical definition of MD (N = 119 692). Results Confirmatory factor analysis showed a five-factor hierarchical model, incorporating 15 of the original 18 items taken from the PHQ-9, GAD-7 and subjective well-being questionnaires, produced good fit to the observed covariance matrix (CFI = 0.992, TLI = 0.99, RMSEA = 0.038, SRMR = 0.031). MD PRS associated with each factor score (standardised β range: 0.057–0.064) and the association remained when the sample was stratified into case- and control-only subsets. The case-only subset had an increased association compared to controls for all factors, shown via a significant interaction between lifetime MD diagnosis and MD PRS (p value range: 2.23 × 10−3–3.94 × 10−7). Conclusions An association between MD PRS and a continuous phenotype of depressive symptoms in case- and control-only subsets provides support against a purely categorical phenotype; indicating further insights into MD can be obtained when this within-group variation is considered. The stronger association within cases suggests this variation may be of particular importance.

Boom-bust population dynamics increase diversity in evolving competitive communities

The processes and mechanisms underlying the origin and maintenance of biological diversity have long been of central importance in ecology and evolution. The competitive exclusion principle states that the number of coexisting species is limited by the number of resources, or by the species’ similarity in resource use. Natural systems such as the extreme diversity of unicellular life in the oceans provide counter examples. It is known that mathematical models incorporating population fluctuations can lead to violations of the exclusion principle. Here we use simple eco-evolutionary models to show that a certain type of population dynamics, boom-bust dynamics, can allow for the evolution of much larger amounts of diversity than would be expected with stable equilibrium dynamics. Boom-bust dynamics are characterized by long periods of almost exponential growth (boom) and a subsequent population crash due to competition (bust). When such ecological dynamics are incorporated into an evolutionary model that allows for adaptive diversification in continuous phenotype spaces, desynchronization of the boom-bust cycles of coexisting species can lead to the maintenance of high levels of diversity.

3MCor: An Integrative Web Server for Metabolome-Microbiome-Metadata Correlation Analysis

Background: Mounting evidences have shown that microbiome and metabolome are closely linked to human health and dual-omics studies expanded our knowledge and understanding of health and life. Here, we designed and developed a full-function and easy-to-use platform, 3MCor (http://3mcor.cn/), for metabolome and microbiome correlation analysis under the instruction of phenotype and with the consideration of confounders.Results: Many traditional and newly reported correlation analysis methods were integrated for intra- and inter-correlation analysis. Three inter-correlation pipelines are provided for global, hierarchical, and pairwise analysis. Especially, the incorporated network analysis function is conducive to a rapid identification of network clusters and key nodes from a complicated correlation network. Complete numerical results (csv files) and rich figures (pdf files) will be generated in minutes. To our knowledge, 3MCor is the first platform developed specifically for the correlation analysis of metabolome and microbiome. Its functions were compared with corresponding modules of existing omics data analysis platforms. Results from 2 real-world data sets, one from a public library with a continuous phenotype and one from our lab with a categorical phenotype, were used to demonstrate its simple and flexible operation, comprehensive outputs, and distinctive contribution to dual-omics studies. Conclusions: 3MCor is powerful with complementary pipelines and comprehensive considerations of phenotypes, confounders, and the interactions among omics features. In addition to the web server, the backend R script is available at https://github.com/chentianlu/3MCorServer.

Allostasis Machines: a model for understanding internal states and technological environments

In the present paper we will approach enactivism from the perspective of internal regulation: while the environment shapes the organism, it is also true that organisms have complex internal states with regulatory machinery with a set of continuous phenotype-environment interactions. The aim of the present paper is to provide a visual means to analyze these interactions in individuals and computational agents alike. An essential component of our approach is the representation of continuous internal states through the usage of the single continuous indicator we call an Allostasis Machine (AM). Consequently, we consider potential perturbation regimes for both naturalistic and virtual environments: within the naturalistic cases, it is possible to observe the effects of perturbations in isolation, or as overlapping, multiplicative events. In virtual cases, we can observe perturbations as the outcome of both realistic and fantastical environments. To conclude, we discuss how AMs can be utilized to improve our understanding of both the theoretical basis of embodied interaction and the dynamic regulation of complex psychophysiological states.

Boom-bust population dynamics can increase diversity in evolving competitive communities

The processes and mechanisms underlying the origin and maintenance of biological diversity have long been of central importance in ecology and evolution. The competitive exclusion principle states that the number of coexisting species is limited by the number of resources, or by the species’ similarity in resource use. Natural systems such as the extreme diversity of unicellular life in the oceans provide counter examples. It is known that mathematical models incorporating population fluctuations can lead to violations of the exclusion principle. Here we use simple eco-evolutionary models to show that a certain type of population dynamics, boom-bust dynamics, can allow for the evolution of much larger amounts of diversity than would be expected with stable equilibrium dynamics. Boom-bust dynamics are characterized by long periods of almost exponential growth (boom) and a subsequent population crash due to competition (bust). When such ecological dynamics are incorporated into an evolutionary model that allows for adaptive diversification in continuous phenotype spaces, desynchronization of the boom-bust cycles of coexisting species can lead to the maintenance of high levels of diversity.

Using Major Depression Polygenic Risk Scores to Explore the Depressive Symptom Continuum

BackgroundIt is not clear whether major depression (MD) is a categorical disorder or if depressive symptoms exist on a continuum based on severity. Observational studies comparing sub-threshold and clinical depression suggest MD is continuous, but many do not explore the full continuum and are yet to consider genetics as a risk factor. This study sought to understand if polygenic risk for MD could provide insight into the continuous nature of MD.MethodsFactor analysis on symptom-level data from the UK Biobank (N=148,957) was used to derive continuous depression phenotypes which were tested for association with polygenic risk scores for a categorical definition of MD (N=119,692).ResultsConfirmatory factor analysis showed a five-factor hierarchical model, incorporating 15 of the original 18 items, produced good fit to the observed covariance matrix (CFI = 0.992, TLI = 0.99, RMSEA = 0.038, SRMR = 0.031). MD PRS associated with each factor score (standardised ß range: 0.057 – 0.064) and the association remained when the sample was stratified into case- and control-only subsets. The case-only subset had an increased association compared to controls for all factors, shown via a significant interaction between lifetime MD diagnosis and MD PRS (p-value range: 2.28×10−3 - 4.56×10−7).ConclusionsAn association between MD PRS and a continuous phenotype of depressive symptoms in case- and control-only subsets provides support against a purely categorical phenotype; indicating further insights into MD can be obtained when this within-group variation is considered. The stronger association within cases suggests this variation may be of particular importance.

Marker-Trait Complete Analysis

1AbstractA recurring problem in genomics involves testing association of one or more traits of interest to multiple genomic features. Feature-trait squared correlations r2 are commonly-used statistics, sensitive to trend associations. It is often of interest to perform testing across collections {r2} over markers and/or traits using both maxima and sums. However, both trait-trait correlations and marker-marker correlations may be strong and must be considered. The primary tools for multiple testing suffer from various shortcomings, including p-value inaccuracies due to asymptotic methods that may not be applicable. Moreover, there is a lack of general tools for fast screening and follow-up of regions of interest.To address these difficulties, we propose the MTCA approach, for Marker-Trait Complete Analysis. MTCA encompasses a large number of existing approaches, and provides accurate p-values over markers and traits for maxima and sums of r2 statistics. MTCA uses the conditional inference implicit in permutation as a motivational frame-work, but provides an option for fast screening with two novel tools: (i) a multivariate-normal approximation for the max statistic, and (ii) the concept of eigenvalue-conditional moments for the sum statistic. We provide examples for gene-based association testing of a continuous phenotype and cis-eQTL analysis, but MTCA can be applied in a much wider variety of settings and platforms.