Discrete Traits Research Articles

A Phenotype-Genotype Codon Model for Detecting Adaptive Evolution.

A central objective in biology is to link adaptive evolution in a gene to structural and/or functional phenotypic novelties. Yet most analytic methods make inferences mainly from either phenotypic data or genetic data alone. A small number of models have been developed to infer correlations between the rate of molecular evolution and changes in a discrete or continuous life history trait. But such correlations are not necessarily evidence of adaptation. Here, we present a novel approach called the phenotype-genotype branch-site model (PG-BSM) designed to detect evidence of adaptive codon evolution associated with discrete-state phenotype evolution. An episode of adaptation is inferred under standard codon substitution models when there is evidence of positive selection in the form of an elevation in the nonsynonymous-to-synonymous rate ratio $\omega$ to a value $\omega > 1$. As it is becoming increasingly clear that $\omega > 1$ can occur without adaptation, the PG-BSM was formulated to infer an instance of adaptive evolution without appealing to evidence of positive selection. The null model makes use of a covarion-like component to account for general heterotachy (i.e., random changes in the evolutionary rate at a site over time). The alternative model employs samples of the phenotypic evolutionary history to test for phenomenological patterns of heterotachy consistent with specific mechanisms of molecular adaptation. These include 1) a persistent increase/decrease in $\omega$ at a site following a change in phenotype (the pattern) consistent with an increase/decrease in the functional importance of the site (the mechanism); and 2) a transient increase in $\omega$ at a site along a branch over which the phenotype changed (the pattern) consistent with a change in the site's optimal amino acid (the mechanism). Rejection of the null is followed by post hoc analyses to identify sites with strongest evidence for adaptation in association with changes in the phenotype as well as the most likely evolutionary history of the phenotype. Simulation studies based on a novel method for generating mechanistically realistic signatures of molecular adaptation show that the PG-BSM has good statistical properties. Analyses of real alignments show that site patterns identified post hoc are consistent with the specific mechanisms of adaptation included in the alternate model. Further simulation studies show that the covarion-like component of the PG-BSM plays a crucial role in mitigating recently discovered statistical pathologies associated with confounding by accounting for heterotachy-by-any-cause. [Adaptive evolution; branch-site model; confounding; mutation-selection; phenotype-genotype.].

Multi-model inference of non-random mating from an information theoretic approach

Non-random mating has a significant impact on the evolution of organisms. Here, I developed a modelling framework for discrete traits (with any number of phenotypes) to explore different models connecting the non-random mating causes (mate competition and/or mate choice) and their consequences (sexual selection and/or assortative mating).I derived the formulaefor the maximum likelihood estimates of each model and used information criteria to perform multi-model inference. Simulation results showed a good performance of both model selection and parameter estimation. The methodology was applied to ecotypes data of the marine gastropod Littorina saxatilis from Galicia (Spain), to show that the mating pattern is better described by models with two parameters that involve both mate choice and competition, generating positive assortative mating plus female sexual selection. As far as I know, this is the first standardized methodology for model selection and multi-model inference of mating parameters for discrete traits. The advantages of this framework include the ability of setting up models from which the parameters connect causes, as mate competition and mate choice, with their outcome in the form of data patterns of sexual selection and assortative mating. For some models, the parameters may have a double effect i.e. they produce sexual selection and assortative mating, while for others there are separated parameters for one kind of pattern or another. From an empirical point of view, it is much easier to study patterns than processes and, for this reason, the causal mechanisms of sexual selection are not so well known as the patterns they produce. The goal of the present work is to propose a new tool that helps to distinguish among different alternative processes behind the observed mating pattern.The full methodology was implemented in a software called InfoMating (available at http://acraaj.webs6.uvigo.es/InfoMating/Infomating.htm).

Reticulate Evolution Helps Explain Apparent Homoplasy in Floral Biology and Pollination in Baobabs (Adansonia; Bombacoideae; Malvaceae).

Baobabs (Adansonia) are a cohesive group of tropical trees with a disjunct distribution in Australia, Madagascar, and continental Africa, and diverse flowers associated with two pollination modes. We used custom-targeted sequence capture in conjunction with new and existing phylogenetic comparative methods to explore the evolution of floral traits and pollination systems while allowing for reticulate evolution. Our analyses suggest that relationships in Adansonia are confounded by reticulation, with network inference methods supporting at least one reticulation event. The best supported hypothesis involves introgression between Adansonia rubrostipa and core Longitubae, both of which are hawkmoth pollinated with yellow/red flowers, but there is also some support for introgression between the African lineage and Malagasy Brevitubae, which are both mammal-pollinated with white flowers. New comparative methods for phylogenetic networks were developed that allow maximum-likelihood inference of ancestral states and were applied to study the apparent homoplasy in floral biology and pollination mode seen in Adansonia. This analysis supports a role for introgressive hybridization in morphological evolution even in a clade with highly divergent and geographically widespread species. Our new comparative methods for discrete traits on species networks are implemented in the software PhyloNetworks. [Comparative methods; Hyb-Seq; introgression; network inference; population trees; reticulate evolution; species tree inference; targeted sequence capture.].

PARAMO: A Pipeline for Reconstructing Ancestral Anatomies Using Ontologies and Stochastic Mapping

AbstractComparative phylogenetics has been largely lacking a method for reconstructing the evolution of phenotypic entities that consist of ensembles of multiple discrete traits—entire organismal anatomies or organismal body regions. In this study, we provide a new approach named PARAMO (PhylogeneticAncestralReconstruction ofAnatomy byMappingOntologies) that appropriately models anatomical dependencies and uses ontology-informed amalgamation of stochastic maps to reconstruct phenotypic evolution at different levels of anatomical hierarchy including entire phenotypes. This approach provides new opportunities for tracking phenotypic radiations and evolution of organismal anatomies.

A Bayesian Approach for Inferring the Impact of a Discrete Character on Rates of Continuous-Character Evolution in the Presence of Background-Rate Variation.

Understanding how and why rates of character evolution vary across the Tree of Life is central to many evolutionary questions; for example, does the trophic apparatus (a set of continuous characters) evolve at a higher rate in fish lineages that dwell in reef versus nonreef habitats (a discrete character)? Existing approaches for inferring the relationship between a discrete character and rates of continuous-character evolution rely on comparing a null model (in which rates of continuous-character evolution are constant across lineages) to an alternative model (in which rates of continuous-character evolution depend on the state of the discrete character under consideration). However, these approaches are susceptible to a “straw-man” effect: the influence of the discrete character is inflated because the null model is extremely unrealistic. Here, we describe MuSSCRat, a Bayesian approach for inferring the impact of a discrete trait on rates of continuous-character evolution in the presence of alternative sources of rate variation (“background-rate variation”). We demonstrate by simulation that our method is able to reliably infer the degree of state-dependent rate variation, and show that ignoring background-rate variation leads to biased inferences regarding the degree of state-dependent rate variation in grunts (the fish group Haemulidae). [Bayesian phylogenetic comparative methods; continuous-character evolution; data augmentation; discrete-character evolution.]

A multivariate phylogenetic comparative method incorporating a flexible function between discrete and continuous traits

One major challenge of using the phylogenetic comparative method (PCM) is the analysis of the evolution of interrelated continuous and discrete traits in a single multivariate statistical framework. In addition, more intricate parameters such as branch-specific directional selection have rarely been integrated into such multivariate PCM frameworks. Here, originally motivated to analyze the complex evolutionary trajectories of group size (continuous variable) and social systems (discrete variable) in African subterranean rodents, we develop a flexible approach using approximate Bayesian computation (ABC). Specifically, our multivariate ABC-PCM method allows the user to flexibly model an underlying latent evolutionary function between continuous and discrete traits. The ABC-PCM also simultaneously incorporates complex evolutionary parameters such as branch-specific selection. This study highlights the flexibility of ABC-PCMs in analyzing the evolution of phenotypic traits interrelated in a complex manner.

Endostructural morphology in hominoid mandibular third premolars: Discrete traits at the enamel-dentine junction

The mandibular third premolar (P3) exhibits substantial differences in size and shape among hominoid taxa, and displays a number of discrete traits that have proven to be useful in studies of hominin taxonomy and phylogeny. Discrete traits at the enamel-dentine junction (EDJ) can be accurately assessed on moderately worn specimens, and often appear sharper than at the outer-enamel surface (OES). Here we use microtomography to image the P3 EDJ of a broad sample of extant apes, extinct hominins and modern humans (n = 100). We present typologies for three important premolar discrete traits at the EDJ (transverse crest, marginal ridge and buccal grooves), and score trait frequencies within our sample. We find that the transverse crest is variable in extant apes, while the majority of hominins display a transverse crest which runs directly between the two major premolar cusps. Some Neanderthals display a unique form in which the transverse crest fails to reach the protoconid. We find that mesial marginal ridge discontinuity is common in Australopithecus anamensis and Australopithecus afarensis while continuous marginal ridges largely characterize Australopithecus africanus and Paranthropus. Interrupted mesial and distal marginal ridges are again seen in Homo sapiens and Neanderthals. Premolar buccal grooves, previously identified at the OES as important for hominin systematics, are again found to show a number of taxon-specific patterns at the EDJ, including a clear difference between Australopithecus and Paranthropus specimens. However, their appearance may be dependent on the morphology of other parts of the crown such as the protoconid crest, and the presence of accessory dentine horns. Finally, we discuss rare variations in the form of dentine horns that underlie premolar cusps, and their potential homology to similar morphologies in other tooth positions.

Inferring host roles in bayesian phylodynamics of global avian influenza A virus H9N2

Role of avian hosts in shaping persistence, evolution, and dispersal of global low pathogenic avian influenza virus (LPAIV) H9N2 remains uncertain. Under Bayesian Markov Chain Monte Carlo framework, we used the discrete trait analysis (DTA) to reconstruct host and location switches in the evolutionary history of global H9N2 given hemagglutinin gene sequences from 18 countries/regions between 1976 and 2018. We employed generalized linear models (GLMs) to inform virus migration rates by empirical predictors. Global H9N2 isolates were mostly sampled from domestic Phasianidae in low- and middle-income countries with poor bio-security. Anatidae was inferred as the ancestral source from which the virus spread to domestic waterfowl, and later to domestic Phasianidae who have become the dominant host to sustain the virus, especially in Asia. Poultry trade was a well-supported driver to H9N2 spread across countries/regions. Strict bio-security and separation between wild and domestic poultry can be used to mitigate virus spread.

Cognitive adaptability and narrow personality traits: Proposing a person–entrepreneurship fit for established entrepreneurs in South Africa

Background: The person–entrepreneurship fit has been confirmed for new ventures, but has not been determined for established entrepreneurs. Personality traits and entrepreneurial cognitions are seen as important factors that lead to long-term performance, which is needed for established entrepreneurs to survive. Objectives: A model is proposed which suggests that a closer association between certain narrow personality traits and the cognitive adaptability dimensions indicates a closer person–entrepreneurship fit. Method: A quantitative approach was followed whereby 2650 established entrepreneurs in South Africa were surveyed, and a multiple regression analysis was conducted. Results: Most studies on personality are conducted using the five-factor model of personality traits, and not necessarily the narrow traits. In this regard, new narrow traits emerged within the five main personality traits. The findings increase the understanding of the cognitive adaptability, and how it relates to these discrete narrow personality traits. Conclusion: The findings of this study may inform the policymakers who are trying to encourage entrepreneurial activity, and the transition of nascent entrepreneurs to established entrepreneurs in the context of developing as well as developed markets. Educators could incorporate the cognitive adaptability dimensions in entrepreneurial training and support programmes, which could encourage nascent and start-up entrepreneurs to become established entrepreneurs.

Bayesian phylodynamics of avian influenza A virus H9N2 in Asia with time-dependent predictors of migration.

Model-based phylodynamic approaches recently employed generalized linear models (GLMs) to uncover potential predictors of viral spread. Very recently some of these models have allowed both the predictors and their coefficients to be time-dependent. However, these studies mainly focused on predictors that are assumed to be constant through time. Here we inferred the phylodynamics of avian influenza A virus H9N2 isolated in 12 Asian countries and regions under both discrete trait analysis (DTA) and structured coalescent (MASCOT) approaches. Using MASCOT we applied a new time-dependent GLM to uncover the underlying factors behind H9N2 spread. We curated a rich set of time-series predictors including annual international live poultry trade and national poultry production figures. This time-dependent phylodynamic prediction model was compared to commonly employed time-independent alternatives. Additionally the time-dependent MASCOT model allowed for the estimation of viral effective sub-population sizes and their changes through time, and these effective population dynamics within each country were predicted by a GLM. International annual poultry trade is a strongly supported predictor of virus migration rates. There was also strong support for geographic proximity as a predictor of migration rate in all GLMs investigated. In time-dependent MASCOT models, national poultry production was also identified as a predictor of virus genetic diversity through time and this signal was obvious in mainland China. Our application of a recently introduced time-dependent GLM predictors integrated rich time-series data in Bayesian phylodynamic prediction. We demonstrated the contribution of poultry trade and geographic proximity (potentially unheralded wild bird movements) to avian influenza spread in Asia. To gain a better understanding of the drivers of H9N2 spread, we suggest increased surveillance of the H9N2 virus in countries that are currently under-sampled as well as in wild bird populations in the most affected countries.

A66 Tracing the evolutionary history of an emerging Salmonella 4,[5],12:i:- clone in the United States

Salmonellosis is one of the leading causes of foodborne disease worldwide, with an estimated one million cases a year in the United States. Salmonella 4,[5],12: i:-, a monophasic variant of Salmonella typhimurium, is an emerging serovar that has been associated with multiple foodborne outbreaks throughout the world, mostly attributed to pig and pig products. Recently, we have demonstrated that two distinct groups of Salmonella 4,[5],12:i:- circulate in the USA and Europe, with the majority of isolates recovered during recent years belonging to an emerging multidrug-resistant clade (Elnekave et al. 2018). We applied Bayesian phylodynamic reconstruction to uncover the evolutionary history of this clade. We used a dataset of whole-genome sequences of 1446 4,[5],12:i:- isolates from different sources (livestock, human, food products, and others) from the USA (n = 752) and Europe (n = 694), collected between 2008 and 2017 and belonging to the Multilocus Subtype 34, which was predominant in the emerging clade (Elnekave et al. 2018). A subset (n = 110) of Salmonella 4,[5],12:i: isolates was then randomly selected after stratifying by location and year of isolation in order to achieve balanced sampling. Evidence of temporal signal was confirmed by looking at root-to-tip divergences using TempEst. Evolutionary hypotheses using strict and relaxed-clock models were tested using BEAST for a variety of demographic models and assuming a general time reversible substitution model. Model selection was performed by estimating Bayes Factors using path sampling and stepping-stone sampling. The selected model was then used for applying discrete trait models comparing different scenarios of transmission between locations (i.e. bidirectional symmetric/asymmetric or unidirectional). Our preliminary phylodynamic inference results indicate that the origin of this subtype was in Europe and dates back to 1990 (HPD 95%: 1984–2001). We report an exponential growth rate of 0.362 per year, which corresponds to a doubling time of 1.43 years. Our results suggest that this subtype was introduced to the US in the year 2000 (HPD 95%: 1994–2006). Phylodynamic analysis suggests that the recent increase in isolation of Salmonella 4,[5],12:i:- from different sources in the USA may be due to the exponential expansion of an emerging clone which originated in Europe and then expanded to the USA. The emergence and expansion of this serovar is of great public health importance due to the high prevalence of multidrug resistance traits found in USA isolates from this group and especially due to the presence of plasmid-mediated resistance genes for quinolones and extended spectrum cephalosporins, key antimicrobials used for the treatment of invasive Salmonella infections.

Investigation of the validity of two Bayesian ancestral state reconstruction models for estimating Salmonella transmission during outbreaks.

Ancestral state reconstruction models use genetic data to characterize a group of organisms’ common ancestor. These models have been applied to salmonellosis outbreaks to estimate the number of transmissions between different animal species that share similar geographical locations, with animal host as the state. However, as far as we are aware, no studies have validated these models for outbreak analysis. In this study, salmonellosis outbreaks were simulated using a stochastic Susceptible-Infected-Recovered model, and the host population and transmission parameters of these simulated outbreaks were estimated using Bayesian ancestral state reconstruction models (discrete trait analysis (DTA) and structured coalescent (SC)). These models were unable to accurately estimate the number of transmissions between the host populations or the amount of time spent in each host population. The DTA model was inaccurate because it assumed the number of isolates sampled from each host population was proportional to the number of individuals infected within each host population. The SC model was inaccurate possibly because it assumed that each host population's effective population size was constant over the course of the simulated outbreaks. This study highlights the need for phylodynamic models that can take into consideration factors that influence the characteristics and behavior of outbreaks, e.g. changing effective population sizes, variation in infectious periods, intra-population transmissions, and disproportionate sampling of infected individuals.

Inferring time-dependent migration and coalescence patterns from genetic sequence and predictor data in structured populations.

Population dynamics can be inferred from genetic sequence data by using phylodynamic methods. These methods typically quantify the dynamics in unstructured populations or assume migration rates and effective population sizes to be constant through time in structured populations. When considering rates to vary through time in structured populations, the number of parameters to infer increases rapidly and the available data might not be sufficient to inform these. Additionally, it is often of interest to know what predicts these parameters rather than knowing the parameters themselves. Here, we introduce a method to infer the predictors for time-varying migration rates and effective population sizes by using a generalized linear model (GLM) approach under the marginal approximation of the structured coalescent. Using simulations, we show that our approach is able to reliably infer the model parameters and its predictors from phylogenetic trees. Furthermore, when simulating trees under the structured coalescent, we show that our new approach outperforms the discrete trait GLM model. We then apply our framework to a previously described Ebola virus dataset, where we infer the parameters and its predictors from genome sequences while accounting for phylogenetic uncertainty. We infer weekly cases to be the strongest predictor for effective population size and geographic distance the strongest predictor for migration. This approach is implemented as part of the BEAST2 package MASCOT, which allows us to jointly infer population dynamics, i.e. the parameters and predictors, within structured populations, the phylogenetic tree, and evolutionary parameters.

Modularity, criticality, and evolvability of a developmental gene regulatory network.

The existence of discrete phenotypic traits suggests that the complex regulatory processes which produce them are functionally modular. These processes are usually represented by networks. Only modular networks can be partitioned into intelligible subcircuits able to evolve relatively independently. Traditionally, functional modularity is approximated by detection of modularity in network structure. However, the correlation between structure and function is loose. Many regulatory networks exhibit modular behaviour without structural modularity. Here we partition an experimentally tractable regulatory network-the gap gene system of dipteran insects-using an alternative approach. We show that this system, although not structurally modular, is composed of dynamical modules driving different aspects of whole-network behaviour. All these subcircuits share the same regulatory structure, but differ in components and sensitivity to regulatory interactions. Some subcircuits are in a state of criticality, while others are not, which explains the observed differential evolvability of the various expression features in the system.

Variation of 3D outer and inner crown morphology in modern human mandibular premolars.

ObjectivesThis study explores the outer and inner crown of lower third and fourth premolars (P3, P4) by analyzing the morphological variation among diverse modern human groups.Materials and MethodsWe studied three‐dimensional models of the outer enamel surface and the enamel–dentine junction (EDJ) from μCT datasets of 77 recent humans using both an assessment of seven nonmetric traits and a standard geometric morphometric (GM) analysis. For the latter, the dental crown was represented by four landmarks (dentine horns and fossae), 20 semilandmarks along the EDJ marginal ridge, and pseudolandmarks along the crown and cervical outlines.ResultsCertain discrete traits showed significantly different regional frequencies and sexual dimorphism. The GM analyses of both P3s and P4s showed extensive overlap in shape variation of the various populations (classification accuracy 15–69%). The first principal components explained about 40% of shape variance with a correlation between 0.59 and 0.87 of the features of P3s and P4s. Shape covariation between P3s and P4s expressed concordance of high and narrow or low and broad crowns.ConclusionsDue to marked intragroup and intergroup variation in GM analyses of lower premolars, discrete traits such as the number of lingual cusps and mesiolingual groove expression provide better geographic separation of modern human populations. The greater variability of the lingual region suggests a dominance of functional constraints over geographic provenience or sex. Additional information about functionally relevant aspects of the crown surface and odontogenetic data are needed to unravel the factors underlying dental morphology in modern humans.

Evolution of phytochemical diversity in Pilocarpus (Rutaceae)

The evolution of phytochemical diversity and biosynthetic pathways in plants can be evaluated from a phylogenetic and environmental perspective. Pilocarpus Vahl (Rutaceae), an economically important medicinal plant in the family Rutaceae, has a great diversity of imidazole alkaloids and coumarins. In this study, we used phylogenetic comparative methods to determine whether there is a phylogenetic signal for chemical traits across the genus Pilocarpus; this included ancestral reconstructions of continuous and discrete chemical traits. Bioclimatic variables found to be associated with the distribution of this genus were used to perform OLS regressions between chemical traits and bioclimatic variables. Next, these regression models were evaluated to test whether bioclimatic traits could significantly predict compound concentrations. Our study found that in terms of compound concentration, variation is most significantly associated with adaptive environmental convergence rather than phylogenetic relationships. The best predictive model of chemical traits was the OLS regression that modeled the relationship between coumarin and precipitation in the coldest quarter. However, we also found one chemical trait was dependent on phylogenetic history and bioclimatic factors. These findings emphasize that consideration of both environmental and phylogenetic factors is essential to tease out the intricate processes in the evolution of chemical diversity in plants. These methods can benefit fields such as conservation management, ecology, and evolutionary biology.

Rise of an empire: analyzing the high diversity of the earliest sauropodomorph dinosaurs through distinct hypotheses

ABSTRACT Several new early sauropodomorphs have been described from Carnian South American strata that include the oldest unambiguous dinosaurs worldwide. Hence, these findings suggest a high diversity in the origin of the group. Here we offer three hypotheses based in novel data that can explain this diversity at the first branches of the long evolutionary tree of the sauropodomorphs. The first hypothesis explains that the Triassic specimens excavated from Brazilian strata are not coeval owing to the lack of stratigraphic control. Fortunately, new studies are constraining the age of Triassic outcrops from Brazil. The second hypothesis suggests an overstated diversity in response to the unknown ontogenetic pathways adopted by early dinosaurs. This hypothesis still demands new findings in order to clarify these ontogenetic pathways. The third hypothesis supports a high diversity in the same time span in response to distinct ecological roles adopted by the early sauropodomorphs. Indeed, there are specimens with different tooth morphologies, indicating diverse feeding strategies. This hypothesis is supported in an ecomorphological analysis derived from discrete anatomical traits. Therefore, although facing many conflicting questions regarding alpha taxonomy of Carnian sauropodomorphs, new discoveries and alternative approaches are leading towards a better understanding of the early evolution of Sauropodomorpha.

Tracing the Impact of Public Health Interventions on HIV-1 Transmission in Portugal Using Molecular Epidemiology.

BackgroundEstimation of temporal changes in human immunodeficiency virus (HIV) transmission patterns can help to elucidate the impact of preventive strategies and public health policies.MethodsPortuguese HIV-1 subtype B and G pol genetic sequences were appended to global reference data sets to identify country-specific transmission clades. Bayesian birth-death models were used to estimate subtype-specific effective reproductive numbers (Re). Discrete trait analysis (DTA) was used to quantify mixing among transmission groups.ResultsWe identified 5 subtype B Portuguese clades (26–79 sequences) and a large monophyletic subtype G Portuguese clade (236 sequences). We estimated that major shifts in HIV-1 transmission occurred around 1999 (95% Bayesian credible interval [BCI], 1998–2000) and 2000 (95% BCI, 1998–2001) for subtypes B and G, respectively. For subtype B, Re dropped from 1.91 (95% BCI, 1.73–2.09) to 0.62 (95% BCI,.52–.72). For subtype G, Re decreased from 1.49 (95% BCI, 1.39–1.59) to 0.72 (95% BCI, .63–.8). The DTA suggests that people who inject drugs (PWID) and heterosexuals were the source of most (>80%) virus lineage transitions for subtypes G and B, respectively.ConclusionsThe estimated declines in Re coincide with the introduction of highly active antiretroviral therapy and the scale-up of harm reduction for PWID. Inferred transmission events across transmission groups emphasize the importance of prevention efforts for bridging populations.

Comparison of non-parametric methods in genomic evaluation of discrete traits

The prediction performance of SVM and Bayesian methods in genomic evaluation of discrete traits was compared in different scenarios of heritability level (0.1, 0.3, and 0.5), number of quantitative trait loci, QTL (60, 300 and 600) and reference population size (1000, 2000 and 5000 individuals). To do this, a genome comprised of 3 chromosomes was simulated on which 6000 bi-allelic SNP markers were distributed. The QTL effects were modeled with gamma distribution was used to model. Pearson's correlation between the true and predicted genomic breeding values was used as the measure of the predictive accuracy. Results showed the significant effect of heritability on prediction accuracy in a way that by increasing heritability, the accuracy of prediction increased significantly. However, the accuracy of prediction was not affected by the number of QTLs. In addition, by increasing the size of the reference population from 1000 to 5000 individuals, the accuracy of prediction increased. Among different kernel functions used to construct SVM, the radial function had higher predictive performance and therefore was used to analysis data. In all the scenarios studied, BayesA outperformed SVM, especially where the heritability level was low. Therefore, the BayesA was recommended for genomic selection of discrete traits. In addition, it was recommended that in different scenarios of genomic selection, different methods should be compared and the best method should be applied for genomic selection.

Trilobite evolutionary rates constrain the duration of the Cambrian explosion

Trilobites are often considered exemplary for understanding the Cambrian explosion of animal life, due to their unsurpassed diversity and abundance. These biomineralized arthropods appear abruptly in the fossil record with an established diversity, phylogenetic disparity, and provincialism at the beginning of Cambrian Series 2 (∼521 Ma), suggesting a protracted but cryptic earlier history that possibly extends into the Precambrian. However, recent analyses indicate elevated rates of phenotypic and genomic evolution for arthropods during the early Cambrian, thereby shortening the phylogenetic fuse. Furthermore, comparatively little research has been devoted to understanding the duration of the Cambrian explosion, after which normal Phanerozoic evolutionary rates were established. We test these hypotheses by applying Bayesian tip-dating methods to a comprehensive dataset of Cambrian trilobites. We show that trilobites have a Cambrian origin, as supported by the trace fossil record and molecular clocks. Surprisingly, they exhibit constant evolutionary rates across the entire Cambrian, for all aspects of the preserved phenotype: discrete, meristic, and continuous morphological traits. Our data therefore provide robust, quantitative evidence that by the time the typical Cambrian fossil record begins (∼521 Ma), the Cambrian explosion had already largely concluded. This suggests that a modern-style marine biosphere had rapidly emerged during the latest Ediacaran and earliest Cambrian (∼20 million years), followed by broad-scale evolutionary stasis throughout the remainder of the Cambrian.