Major evolutionary transitions as Bayesian structure learning

A major evolutionary transition in individuality involves the formation of a cooperative group and the transformation of that group into an evolutionary entity. Human cooperation shares principles with those of multicellular organisms that have undergone transitions in individuality: division of labour, communication, and fitness interdependence. After the split from the last common ancestor of hominoids, early hominins adapted to an increasingly terrestrial niche for several million years. We posit that new challenges in this niche set in motion a positive feedback loop in selection pressure for cooperation that ratcheted coevolutionary changes in sociality, communication, brains, cognition, kin relations and technology, eventually resulting in egalitarian societies with suppressed competition and rapid cumulative culture. The increasing pace of information innovation and transmission became a key aspect of the evolutionary niche that enabled humans to become formidable cooperators with explosive population growth, the ability to cooperate and compete in groups of millions, and emergent social norms, e.g. private property. Despite considerable fitness interdependence, the rise of private property, in concert with population explosion and socioeconomic inequality, subverts potential transition of human groups into evolutionary entities due to resurgence of latent competition and conflict. This article is part of the theme issue 'Human socio-cultural evolution in light of evolutionary transitions'.

Evolutionary transitions in individuality are events in the history of life leading to the emergence of new levels of individuality. Recent studies have described an ecological scaffolding scenario of such transitions focused on the evolutionary consequences of an externally imposed renewing meta-population structure with limited dispersal. One difficulty for such a scenario has been explaining the stability of collective-level traits when scaffolding conditions no longer apply. Here, we show that the stability of scaffolded traits can rely on evolutionary hysteresis: even if the environment is reverted to an ancestral state, collectives do not return to ancestral phenotypes. We describe this phenomenon using a stochastic meta-population model and adaptive dynamics. Further, we show that ecological scaffolding may be limited to Goldilocks zones of the environment. We conjecture that Goldilocks zones—even if they might be rare—could act as initiators of evolutionary transitions and help to explain the near ubiquity of collective-level individuality.

The evolutionary transition to multicellularity probably began with the formation of simple undifferentiated cellular groups. Such groups evolve readily in diverse lineages of extant unicellular taxa, suggesting that there are few genetic barriers to this first key step. This may act as a double-edged sword: labile transitions between unicellular and multicellular states may facilitate the evolution of simple multicellularity, but reversion to a unicellular state may inhibit the evolution of increased complexity. In this paper, we examine how multicellular adaptations can act as evolutionary ‘ratchets’, limiting the potential for reversion to unicellularity. We consider a nascent multicellular lineage growing in an environment that varies between favouring multicellularity and favouring unicellularity. The first type of ratcheting mutations increase cell-level fitness in a multicellular context but are costly in a single-celled context, reducing the fitness of revertants. The second type of ratcheting mutations directly decrease the probability that a mutation will result in reversion (either as a pleiotropic consequence or via direct modification of switch rates). We show that both types of ratcheting mutations act to stabilize the multicellular state. We also identify synergistic effects between the two types of ratcheting mutations in which the presence of one creates the selective conditions favouring the other. Ratcheting mutations may play a key role in diverse evolutionary transitions in individuality, sustaining selection on the new higher-level organism by constraining evolutionary reversion.This article is part of the themed issue ‘The major synthetic evolutionary transitions’.

Generation time and fitness tradeoffs during the evolution of multicellularity

Review of: "Programmed Cell Death and the Origin of Wing Polyphenism in Ants: Implications for Major Evolutionary Transitions in Individuality"

Complexity is inherent in most biological phenomena, yet there is little effort to teach biological complexity per se in the classroom. Levels of organization and hierarchical complexity are familiar features of living systems and taken for granted in most instructional materials including the Next Generation Science Standards (NGSS) of the United States. However, the evolution of the hierarchical organization of life is not being taught because there has been no instructional framework for doing so until now. We seek to address this gap in instruction by translating recent research on evolutionary transitions in individuality (ETI theory) into an integrative, instructional framework for teaching the hierarchy of life that aligns with the three dimensions of the NGSS. ETI theory presents an evolutionary framework for teaching hierarchical complexity using the social principles of cooperation and conflict. These principles are intuitive for students because they are analogous to many of the social situations in their lives. By making use of the ETI framework, instructors can explicitly teach the evolution of the hierarchical organization of life, the organizing framework for all of biology.

Eukaryogenesis is widely viewed as an improbable evolutionary transition uniquely affecting the evolution of life on this planet. However, scientific and popular rhetoric extolling this event as a singularity lacks rigorous evidential and statistical support. Here, we question several of the usual claims about the specialness of eukaryogenesis, focusing on both eukaryogenesis as a process and its outcome, the eukaryotic cell. We argue in favor of four ideas. First, the criteria by which we judge eukaryogenesis to have required a genuinely unlikely series of events 2 billion years in the making are being eroded by discoveries that fill in the gaps of the prokaryote:eukaryote “discontinuity.” Second, eukaryogenesis confronts evolutionary theory in ways not different from other evolutionary transitions in individuality; parallel systems can be found at several hierarchical levels. Third, identifying which of several complex cellular features confer on eukaryotes a putative richer evolutionary potential remains an area of speculation: various keys to success have been proposed and rejected over the five-decade history of research in this area. Fourth, and perhaps most importantly, it is difficult and may be impossible to eliminate eukaryocentric bias from the measures by which eukaryotes as a whole are judged to have achieved greater success than prokaryotes as a whole. Overall, we question whether premises of existing theories about the uniqueness of eukaryogenesis and the greater evolutionary potential of eukaryotes have been objectively formulated and whether, despite widespread acceptance that eukaryogenesis was “special,” any such notion has more than rhetorical value.

When rejecting and accepting hypotheses, it is important to determine the risk of false positive conclusions. Hierarchical Bayesian models are frequently applied in cognitive science, psychology, cognitive neuroscience, and psychiatry to reduce the variability of low-level parameters through partial pooling, and to obtain more robust parameter estimates for complex models using limited data. This may lower the risk of false positive inferences from lower-level estimates. In contrast, higher-level (e.g. group-level) estimates may be more affected by modeling choices (e.g. prior distributions), population-level variances in effects, and inference methods, which may in turn affect false positive rates for group-level effects. Here we used simulation and computational modeling to systematically examine false positive rates and effects of specific modeling choices on inferences in a simple hierarchical Bayesian temporal discounting model. To this end, 600 data sets of a temporal discounting task with two conditions were simulated, drawing individual condition effect parameters from a null distribution with varying standard deviations. For each simulated data set, the group-level mean change in the discount rate was modeled with eight different prior variances of the condition effect. Results were subsequently evaluated using several commonly applied Bayesian inference methods: the Savage-Dickey Bayes factor (BF), the directional Bayes factor (dBF), P(effect > 0), and the highest density interval (HDI) against zero decision rule. Results confirm that inference methods that directly depend on the posterior distribution can be used regardless of prior variances, whereas the Savage-Dickey BF should only be used with sufficiently informative priors. Based on the simulation work, decision thresholds that ensure false positive rates below 5% are provided for each inference method. Results are discussed in the context of computational modeling approaches in cognitive (neuro-)science and psychology.

Tags are evolvable labels that provide genetic programs a flexible mechanism for specification. Tags are used to label and refer to programmatic elements, such as functions or jump targets. However, tags differ from traditional, more rigid methods for handling labeling because they allow for inexact references; that is, a referring tag need not exactly match its referent. Here, we explore how adjusting the threshold for how what qualifies as a match affects adaptive evolution. Further, we propose broadened applications of tags in the context of a genetic programming (GP) technique called SignalGP. SignalGP gives evolution direct access to the event-driven paradigm. Program modules in SignalGP are tagged and can be triggered by signals (with matching tags) from the environment, from other agents, or due to internal regulation. Specifically, we propose to extend this tag based system to: (1) provide more fine-grained control over module execution and regulation (e.g., promotion and repression) akin to natural gene regulatory networks, (2) employ a mosaic of GP representations within a single program, and (3) facilitate major evolutionary transitions in individuality (i.e., allow hierarchical program organization to evolve de novo).

There have been few investigations of cancer prognosis models based on Bayesian hierarchical models. In this study, we used a novel Bayesian method to screen mRNAs and estimate the effects of mRNAs on the prognosis of patients with lung adenocarcinoma. Based on the identified mRNAs, we can build a prognostic model combining mRNAs and clinical features, allowing us to explore new molecules with the potential to predict the prognosis of lung adenocarcinoma. The mRNA data (n = 594) and clinical data (n = 470) for lung adenocarcinoma were obtained from the TCGA database. Gene set enrichment analysis (GSEA), univariate Cox proportional hazards regression, and the Bayesian hierarchical Cox proportional hazards model were used to explore the mRNAs related to the prognosis of lung adenocarcinoma. Multivariate Cox proportional hazard regression was used to identify independent markers. The prediction performance of the prognostic model was evaluated not only by the internal cross-validation but also by the external validation based on the GEO dataset (n = 437). With the Bayesian hierarchical Cox proportional hazards model, a 14-gene signature that included CPS1, CTPS2, DARS2, IGFBP3, MCM5, MCM7, NME4, NT5E, PLK1, POLR3G, PTTG1, SERPINB5, TXNRD1, and TYMS was established to predict overall survival in lung adenocarcinoma. Multivariate analysis demonstrated that the 14-gene signature (HR 3.960, 95% CI 2.710–5.786), T classification (T1, reference; T3, HR 1.925, 95% CI 1.104–3.355) and N classification (N0, reference; N1, HR 2.212, 95% CI 1.520–3.220; N2, HR 2.260, 95% CI 1.499–3.409) were independent predictors. The C-index of the model was 0.733 and 0.735, respectively, after performing cross-validation and external validation, a nomogram was provided for better prediction in clinical application. Bayesian hierarchical Cox proportional hazards models can be used to integrate high-dimensional omics information into a prediction model for lung adenocarcinoma to improve the prognostic prediction and discover potential targets. This approach may be a powerful predictive tool for clinicians treating malignant tumours.

The effect of bottleneck size on evolution in nested Darwinian populations

Insect societies, i.e. the colonies of eusocial ants, bees, wasps and termites, have been likened to multicellular organisms for more than a century. This framework of superorganisms has to date largely been used as a mechanistic description of colony functioning, or as an example of an evolutionary transition in individuality. Here I take the superorganismal view a step further, and explore what can potentially be gained if we truly accept insect societies as organisms. I suggest ways to test evolutionary theories about organismal features originally derived for solitary organisms using traits of insect societies as analogies. I explore examples such as evolution of anisogamy, sex allocation and fertilization strategies and life histories, and point out promising directions for comparative work, and potential confounding factors in such analyses, derived from social insect studies.

Merging supermassive black hole binaries are expected as a consequence of galaxy mergers, yet the detailed evolution path and underlying merging mechanisms of these binaries are still subject to large theoretical uncertainties. In this work, we propose to combine the (future) gravitational wave measurements of supermassive black hole binary merger events with the galaxy merger rate distributions from galaxy surveys/cosmological simulations, to infer the delay time of binary mergers, as a function of binary mass. The delay time encodes key information about binary evolution, which can be used to test the predictions of various evolution models. With a Mock data set of supermassive black hole binary merger events, we discuss how to infer the distribution of delay time with hierarchical Bayesian inference and test evolution models with the Bayesian model selection method. The astrophysical model uncertainties are also considered in the hierarchical Bayesian inference and Bayesian model selection.

BackgroundThe ChIP-chip technology has been used in a wide range of biomedical studies, such as identification of human transcription factor binding sites, investigation of DNA methylation, and investigation of histone modifications in animals and plants. Various methods have been proposed in the literature for analyzing the ChIP-chip data, such as the sliding window methods, the hidden Markov model-based methods, and Bayesian methods. Although, due to the integrated consideration of uncertainty of the models and model parameters, Bayesian methods can potentially work better than the other two classes of methods, the existing Bayesian methods do not perform satisfactorily. They usually require multiple replicates or some extra experimental information to parametrize the model, and long CPU time due to involving of MCMC simulations.ResultsIn this paper, we propose a Bayesian latent model for the ChIP-chip data. The new model mainly differs from the existing Bayesian models, such as the joint deconvolution model, the hierarchical gamma mixture model, and the Bayesian hierarchical model, in two respects. Firstly, it works on the difference between the averaged treatment and control samples. This enables the use of a simple model for the data, which avoids the probe-specific effect and the sample (control/treatment) effect. As a consequence, this enables an efficient MCMC simulation of the posterior distribution of the model, and also makes the model more robust to the outliers. Secondly, it models the neighboring dependence of probes by introducing a latent indicator vector. A truncated Poisson prior distribution is assumed for the latent indicator variable, with the rationale being justified at length.ConclusionThe Bayesian latent method is successfully applied to real and ten simulated datasets, with comparisons with some of the existing Bayesian methods, hidden Markov model methods, and sliding window methods. The numerical results indicate that the Bayesian latent method can outperform other methods, especially when the data contain outliers.

Childhood acute lymphoblastic leukemia (ALL) is a condition that arises from complex etiologies. The absence of consistent environmental risk factors and the presence of modest familial associations suggest ALL is a complex trait with an underlying genetic component. The identification of genetic factors associated with disease is complicated by complex genetic covariance structures and multiple testing issues. Both issues can be resolved with appropriate Bayesian variable selection methods. The present study was undertaken to extend our hierarchical Bayesian model for case-parent triads to incorporate single nucleotide polymorphisms (SNPs) and incorporate the biological grouping of SNPs within genes. Based on previous evidence that genetic variation in the folate metabolic pathway influences ALL risk, we evaluated 128 tagging SNPs in 16 folate metabolic genes among 118 ALL case-parent triads recruited from the Texas Children’s Cancer Center (Houston, TX) between 2003 and 2010. We used stochastic search gene suggestion (SSGS) in hierarchical Bayesian models to evaluate the association between folate metabolic SNPs and ALL. Using Bayes factors among these variants in childhood ALL case-parent triads, two SNPs were identified with a Bayes factor greater than 1. There was evidence that the minor alleles of NOS3 rs3918186 (OR = 2.16; 95% CI: 1.51-3.15) and SLC19A1 rs1051266 (OR = 2.07; 95% CI: 1.25-3.46) were positively associated with childhood ALL. Our findings are suggestive of the role of inherited genetic variation in the folate metabolic pathway on childhood ALL risk, and they also suggest the utility of Bayesian variable selection methods in the context of case-parent triads for evaluating the role of SNPs on disease risk.