Statistical and conceptual challenges in the comparative analysis of principal components
Josef C Uyeda, Daniel S. Caetano, Matthew W Pennell
Quantitative geneticists long ago recognized the value of studying evolution in a multivariate framework (Pearson, 1903). Due to linkage, pleiotropy, coordinated selection and mutational covariance, the evolutionary response in any phenotypic trait can only be properly understood in the context of other traits (Lande, 1979; Lynch and Walsh, 1998). This is of course also well?appreciated by comparative biologists. However, unlike in quantitative genetics, most of the statistical and conceptual tools for analyzing phylogenetic comparative data (recently reviewed in Pennell and Harmon, 2013) are designed for analyzing a single trait (but see, for example Revell and Harmon, 2008; Revell and Harrison, 2008; Hohenlohe and Arnold, 2008; Revell and Collar, 2009; Schmitz and Motani, 2011; Adams, 2014b). Indeed, even classical approaches for testing for correlated evolution between two traits (e.g., Felsenstein, 1985; Grafen, 1989; Harvey and Pagel, 1991) are not actually multivariate as each trait is assumed to have evolved under a process that is independent of the state of the other (Hansen and Orzack, 2005; Hansen and Bartoszek, 2012). As a result of these limitations, researchers with multivariate datasets are often faced with a choice: analyze each trait as if they were independent or else decompose the dataset into statistically independent set of traits, such that each set can be analyzed with the univariate methods.
Clades and clans: a comparison study of two evolutionary models
Sha Zhu, Cuong Than, Taoyang Wu
Subjects: Populations and Evolution (q-bio.PE)
The Yule-Harding-Kingman (YHK) model and the proportional to distinguishable arrangements (PDA) model are two binary tree generating models that are widely used in evolutionary biology. Understanding the distributions of clade sizes under these two models provides valuable insights into macro-evolutionary processes, and is important in hypothesis testing and Bayesian analyses in phylogenetics. Here we show that these distributions are log-convex, which implies that very large clades or very small clades are more likely to occur under these two models. Moreover, we prove that there exists a critical value $\kappa(n)$ for each $n\geqslant 4$ such that for a given clade with size $k$, the probability that this clade is contained in a random tree with $n$ leaves generated under the YHK model is higher than that under the PDA model if $1<k<\kappa(n)$, and lower if $\kappa(n)<k<n$. Finally, we extend our results to binary unrooted trees, and obtain similar results for the distributions of clan sizes.
On the number of ranked species trees producing anomalous ranked gene trees
Filippo Disanto, Noah A. Rosenberg
Subjects: Populations and Evolution (q-bio.PE)
Analysis of probability distributions conditional on species trees has demonstrated the existence of anomalous ranked gene trees (ARGTs), ranked gene trees that are more probable than the ranked gene tree that accords with the ranked species tree. Here, to improve the characterization of ARGTs, we study enumerative and probabilistic properties of two classes of ranked labeled species trees, focusing on the presence or avoidance of certain subtree patterns associated with the production of ARGTs. We provide exact enumerations and asymptotic estimates for cardinalities of these sets of trees, showing that as the number of species increases without bound, the fraction of all ranked labeled species trees that are ARGT-producing approaches 1. This result extends beyond earlier existence results to provide a probabilistic claim about the frequency of ARGTs.
Phylogenomic analyses of deep gastropod relationships reject Orthogastropoda
Felipe Zapata, Nerida G Wilson, Mark Howison, Sónia CS Andrade, Katharina M J?rger, Michael Schrödl, Freya E Goetz, Gonzalo Giribet, Casey W Dunn
Gastropods are a highly diverse clade of molluscs that includes many familiar animals, such as limpets, snails, slugs, and sea slugs. It is one of the most abundant groups of animals in the sea and the only molluscan lineage that has successfully colonised land. Yet the relationships among and within its constituent clades have remained in flux for over a century of morphological, anatomical and molecular study. Here we re-evaluate gastropod phylogenetic relationships by collecting new transcriptome data for 40 species and analysing them in combination with publicly available genomes and transcriptomes. Our datasets include all five main gastropod clades: Patellogastropoda, Vetigastropoda, Neritimorpha, Caenogastropoda and Heterobranchia. We use two different methods to assign orthology, subsample each of these matrices into three increasingly dense subsets, and analyse all six of these supermatrices with two different models of molecular evolution. All twelve analyses yield the same unrooted network connecting the five major gastropod lineages. This reduces deep gastropod phylogeny to three alternative rooting hypotheses. These results reject the prevalent hypothesis of gastropod phylogeny, Orthogastropoda. Our dated tree is congruent with a possible end-Permian recovery of some gastropod clades, namely Caenogastropoda and some Heterobranchia subclades.
V genes in primates from whole genome shotgun data
David N Olivieri, Francisco Gambon-Deza
The adaptive immune system uses V genes for antigen recognition. The evolutionary diversification and selection processes within and across species and orders are poorly understood. Here, we studied the amino acid (AA) sequences obtained of translated in-frame V exons of immunoglobulins (IG) and T cell receptors (TR) from 16 primate species whose genomes have been sequenced. Multi-species comparative analysis supports the hypothesis that V genes in the IG loci undergo birth/death processes, thereby permitting rapid adaptability over evolutionary time. We also show that multiple cladistic groupings exist in the TRA (35 clades) and TRB (25 clades) V gene loci and that each primate species typically contributes at least one V gene to each of these clade. The results demonstrate that IG V genes and TR V genes have quite different evolutionary pathways; multiple duplications can explain the IG loci results, while co-evolutionary pressures can explain the phylogenetic results, as seen in genes of the TR loci. We describe how each of the 35 V genes clades of the TRA locus and 25 clades of the TRB locus must have specific and necessary roles for the viability of the species.
Phylogenetics and the human microbiome
Frederick A Matsen IV
Comments: to appear in Systematic Biology
Subjects: Populations and Evolution (q-bio.PE); Genomics (q-bio.GN)
The human microbiome is the ensemble of genes in the microbes that live inside and on the surface of humans. Because microbial sequencing information is now much easier to come by than phenotypic information, there has been an explosion of sequencing and genetic analysis of microbiome samples. Much of the analytical work for these sequences involves phylogenetics, at least indirectly, but methodology has developed in a somewhat different direction than for other applications of phylogenetics. In this paper I review the field and its methods from the perspective of a phylogeneticist, as well as describing current challenges for phylogenetics coming from this type of work.
Bayesian Coalescent Epidemic Inference: Comparison of Stochastic and Deterministic SIR Population Dynamics
Alex Popinga, Tim Vaughan, Tanja Stadler, Alexei Drummond
Subjects: Populations and Evolution (q-bio.PE)
Estimation of epidemiological and population parameters from molecular sequence data has become central to the understanding of infectious disease dynamics. Various models have been proposed to infer details of the dynamics that describe epidemic progression. These include inference approaches derived from Kingmans coalescent as well as from birth death branching processes. The development of alternative approaches merits investigation of their characteristics and differences. Here we use recently described coalescent theory for epidemic dynamics to develop stochastic and deterministic coalescent SIR tree priors. We implement these in a Bayesian phylogenetic inference framework to permit joint estimation of SIR epidemic parameters and the sample genealogy. We assess the models performance and contrast results obtained with a recently published birth death sampling model for epidemic inference. Comparisons are made by analyzing sets of genealogies simulated under precisely known epidemiological parameters. We also compare results of analyses using published HIV1 sequence data obtained from known UK infection clusters. We show that the coalescent SIR model is effective at estimating epidemiological parameters from data with large fundamental reproductive number R0 and large population size S0. We find that the stochastic variant generally outperforms its deterministic counterpart. However, each of these Bayesian estimators are shown to have undesirable properties in certain circumstances, especially for epidemic outbreaks with R0 close to one or with small susceptible populations.
Are phylogenetic patterns the same in anthropology and biology?
The use of phylogenetic methods in anthropological fields such as archaeology, linguistics and stemmatology (involving what are often called ?culture data?) is based on an analogy between human cultural evolution and biological evolution. We need to understand this analogy thoroughly, including how well anthropology data fit the model of a phylogenetic tree, as used in biology. I provide a direct comparison of anthropology datasets with both phenotype and genotype datasets from biology. The anthropology datasets fit the tree model approximately as well as do the genotype data, which is detectably worse than the fit of the phenotype data. This is true for datasets with <500 parsimony-informative characters, as well as for larger datasets. This implies that cross-cultural (horizontal) processes have been important in the evolution of cultural artifacts, as well as branching historical (vertical) processes, and thus a phylogenetic network will be a more appropriate model than a phylogenetic tree.
Bayesian inference of sampled ancestor trees for epidemiology and fossil calibration
Alexandra Gavryushkina, David Welch, Tanja Stadler, Alexei Drummond
(Submitted on 18 Jun 2014)
Phylogenetic analyses which include fossils or molecular sequences that are sampled through time require models that allow one sample to be a direct ancestor of another sample. As previously available phylogenetic inference tools assume that all samples are tips, they do not allow for this possibility. We have developed and implemented a Bayesian Markov Chain Monte Carlo (MCMC) algorithm to infer what we call sampled ancestor trees, that is, trees in which sampled individuals can be direct ancestors of other sampled individuals. We use a family of birth-death models where individuals may remain in the tree process after the sampling, in particular we extend the birth-death skyline model [Stadler et al, 2013] to sampled ancestor trees. This method allows the detection of sampled ancestors as well as estimation of the probability that an individual will be removed from the process when it is sampled. We show that sampled ancestor birth-death models where all samples come from different time points are non-identifiable and thus require one parameter to be known in order to infer other parameters. We apply this method to epidemiological data, where the possibility of sampled ancestors enables us to identify individuals that infected other individuals after being sampled and to infer fundamental epidemiological parameters. We also apply the method to infer divergence times and diversification rates when fossils are included among the species samples, so that fossilisation events are modelled as a part of the tree branching process. Such modelling has many advantages as argued in literature. The sampler is available as an open-source BEAST2 package (this https URL ancestors/).
Assessing phenotypic correlation through the multivariate phylogenetic latent liability model
Gabriela B. Cybis, Janet S. Sinsheimer, Trevor Bedford, Alison E. Mather, Philippe Lemey, Marc A. Suchard
(Submitted on 15 Jun 2014)
Understanding which phenotypic traits are consistently correlated throughout evolution is a highly pertinent problem in modern evolutionary biology. Here, we propose a multivariate phylogenetic latent liability model for assessing the correlation between multiple types of data, while simultaneously controlling for their unknown shared evolutionary history informed through molecular sequences. The latent formulation enables us to consider in a single model combinations of continuous traits, discrete binary traits, and discrete traits with multiple ordered and unordered states. Previous approaches have entertained a single data type generally along a fixed history, precluding estimation of correlation between traits and ignoring uncertainty in the history. We implement our model in a Bayesian phylogenetic framework, and discuss inference techniques for hypothesis testing. Finally, we showcase the method through applications to columbine flower morphology, antibiotic resistance in Salmonella, and epitope evolution in influenza.