Cécile Ané

TESTING FOR DIFFERENT RATES OF CONTINUOUS TRAIT EVOLUTION USING LIKELIHOOD

Abstract Rates of phenotypic evolution have changed throughout the history of life, producing variation in levels of morphological, functional, and ecological diversity among groups. Testing for the presence of these rate shifts is a key component of evaluating hypotheses about what causes them. In this paper, general predictions regarding changes in phenotypic diversity as a function of evolutionary history and rates are developed, and tests are derived to evaluate rate changes. Simulations show that these tests are more powerful than existing tests using standardized contrasts. The new approaches are distributed in an application called Brownie and in r8s.

The iPlant Collaborative: Cyberinfrastructure for Plant Biology

The iPlant Collaborative (iPlant) is a United States National Science Foundation (NSF) funded project that aims to create an innovative, comprehensive, and foundational cyberinfrastructure in support of plant biology research (PSCIC, 2006). iPlant is developing cyberinfrastructure that uniquely enables scientists throughout the diverse fields that comprise plant biology to address Grand Challenges in new ways, to stimulate and facilitate cross-disciplinary research, to promote biology and computer science research interactions, and to train the next generation of scientists on the use of cyberinfrastructure in research and education. Meeting humanitys projected demands for agricultural and forest products and the expectation that natural ecosystems be managed sustainably will require synergies from the application of information technologies. The iPlant cyberinfrastructure design is based on an unprecedented period of research community input, and leverages developments in high-performance computing, data storage, and cyberinfrastructure for the physical sciences. iPlant is an open-source project with application programming interfaces that allow the community to extend the infrastructure to meet its needs. iPlant is sponsoring community-driven workshops addressing specific scientific questions via analysis tool integration and hypothesis testing. These workshops teach researchers how to add bioinformatics tools and/or datasets into the iPlant cyberinfrastructure enabling plant scientists to perform complex analyses on large datasets without the need to master the command-line or high-performance computational services.

BUCKy: Gene tree/species tree reconciliation with Bayesian concordance analysis

MOTIVATION BUCKy is a C++ program that implements Bayesian concordance analysis. The method uses a non-parametric clustering of genes with compatible trees, and reconstructs the primary concordance tree from clades supported by the largest proportions of genes. A population tree with branch lengths in coalescent units is estimated from quartet concordance factors. AVAILABILITY BUCKy is open source and distributed under the GNU general public license at www.stat.wisc.edu/∼ane/bucky/.

A Linear-Time Algorithm for Gaussian and Non-Gaussian Trait Evolution Models

We developed a linear-time algorithm applicable to a large class of trait evolution models, for efficient likelihood calculations and parameter inference on very large trees. Our algorithm solves the traditional computational burden associated with two key terms, namely the determinant of the phylogenetic covariance matrix V and quadratic products involving the inverse of V. Applications include Gaussian models such as Brownian motion-derived models like Pagels lambda, kappa, delta, and the early-burst model; Ornstein-Uhlenbeck models to account for natural selection with possibly varying selection parameters along the tree; as well as non-Gaussian models such as phylogenetic logistic regression, phylogenetic Poisson regression, and phylogenetic generalized linear mixed models. Outside of phylogenetic regression, our algorithm also applies to phylogenetic principal component analysis, phylogenetic discriminant analysis or phylogenetic prediction. The computational gain opens up new avenues for complex models or extensive resampling procedures on very large trees. We identify the class of models that our algorithm can handle as all models whose covariance matrix has a 3-point structure. We further show that this structure uniquely identifies a rooted tree whose branch lengths parametrize the trait covariance matrix, which acts as a similarity matrix. The new algorithm is implemented in the R package phylolm, including functions for phylogenetic linear regression and phylogenetic logistic regression.

The Role of Pollinator Shifts in the Floral Diversification of Iochroma (Solanaceae)

Abstract Differences in floral traits among plant species have often been attributed to adaptation to pollinators. We explored the importance of pollinator shifts in explaining floral divergence among 15 species of Iochroma. We examined four continuously varying floral traits: corolla length, nectar reward, display size, and flower color. Pollinator associations were characterized with a continuously varying measure of pollinator importance (the product of visitation and pollen deposition) for four groups of pollinators: hummingbirds, Hymenoptera, Lepidoptera, and Diptera. A phylogenetic generalized least squares approach was used to estimate correlations between pollinator groups and floral traits across a sample of Bayesian trees using different models of trait evolution. Multivariate analyses were also employed to identify suites of traits associated with each pollinator group. We found that nonphylogenetic models typically fit the data better than phylogenetic models (Brownian motion, Ornstein–Uhlenbeck), and thus results varied little across trees. Our results indicated that species with high nectar reward and large displays are significantly more likely to be pollinated by hummingbirds and less likely to be pollinated by all groups of insects. Corolla length and flower color did not show any consistently significant associations with pollinator groups. For these two traits, we discuss alternative evolutionary forces, including phylogenetic inertia and community-level factors.

Fine-Scale Phylogenetic Discordance across the House Mouse Genome

Population genetic theory predicts discordance in the true phylogeny of different genomic regions when studying recently diverged species. Despite this expectation, genome-wide discordance in young species groups has rarely been statistically quantified. The house mouse subspecies group provides a model system for examining phylogenetic discordance. House mouse subspecies are recently derived, suggesting that even if there has been a simple tree-like population history, gene trees could disagree with the population history due to incomplete lineage sorting. Subspecies of house mice also hybridize in nature, raising the possibility that recent introgression might lead to additional phylogenetic discordance. Single-locus approaches have revealed support for conflicting topologies, resulting in a subspecies tree often summarized as a polytomy. To analyze phylogenetic histories on a genomic scale, we applied a recently developed method, Bayesian concordance analysis, to dense SNP data from three closely related subspecies of house mice: Mus musculus musculus, M. m. castaneus, and M. m. domesticus. We documented substantial variation in phylogenetic history across the genome. Although each of the three possible topologies was strongly supported by a large number of loci, there was statistical evidence for a primary phylogenetic history in which M. m. musculus and M. m. castaneus are sister subspecies. These results underscore the importance of measuring phylogenetic discordance in other recently diverged groups using methods such as Bayesian concordance analysis, which are designed for this purpose.

Comparing Two Bayesian Methods for Gene Tree/Species Tree Reconstruction: Simulations with Incomplete Lineage Sorting and Horizontal Gene Transfer

With the increasing interest in recognizing the discordance between gene genealogies, various gene tree/species tree reconciliation methods have been developed. We present here the first attempt to assess and compare two such Bayesian methods, Bayesian estimation of species trees (BEST) and BUCKy (Bayesian untangling of concordance knots), in the presence of several known processes of gene tree discordance. DNA alignments were simulated under the influence of incomplete lineage sorting (ILS) and of horizontal gene transfer (HGT). BEST and BUCKy both account for uncertainty in gene tree estimation but differ substantially in their assumptions of what caused gene tree discordance. BEST estimates a species tree using the coalescent model, assuming that all gene tree discordance is due to ILS. BUCKy does not assume any specific biological process of gene tree discordance through the use of a nonparametric clustering of concordant genes. BUCKy estimates the concordance factor (CF) of a clade, which is defined as the proportion of genes that truly have the clade in their trees. The estimated concordance tree is then built from clades with the highest estimated CFs. Because of their different assumptions, it was expected that BEST would perform better in the presence of ILS and that BUCKy would perform better in the presence of HGT. As expected, the species tree was more accurately reconstructed by BUCKy in the presence of HGT, when the HGT events were unevenly placed across the species tree. BUCKy and BEST performed similarly in most other cases, including in the presence of strong ILS and of HGT events that were evenly placed across the tree. However, BUCKy was shown to underestimate the uncertainty in CF estimation, with short credibility intervals. Despite this, the discordance pattern estimated by BUCKy could be compared with the signature of ILS. The resulting test for the adequacy of the coalescent model proved to have low Type I error. It was powerful when HGT was the major source of discordance and when HGT events were unevenly placed across the species tree.

Do potatoes and tomatoes have a single evolutionary history, and what proportion of the genome supports this history?

BackgroundPhylogenies reconstructed with only one or a few independently inherited loci may be unresolved or incongruent due to taxon and gene sampling, horizontal gene transfer, or differential selection and lineage sorting at individual loci. In an effort to remedy this situation, we examined the utility of conserved orthologous set (COSII) nuclear loci to elucidate the phylogenetic relationships among 29 diploid Solanum species in the sister clades that include tomato and potato, and in Datura inoxia as a far outgroup. We screened 40 COSII markers with intron content over 60% that are mapped in different chromosomes; selected a subset of 19 by the presence of single band amplification of size mostly between 600 and 1200 bp; sequenced these 19 COSII markers, and performed phylogenetic analyses with individual and concatenated datasets. The present study attempts to provide a fully resolved phylogeny among the main clades in potato and tomato that can help to identify the appropriate markers for future studies using additional species.ResultsAmong potatoes, when total evidence is invoked, one single predominant history is highlighted with complete resolution within and among the three main clades. It also supports the hypothesis of the North and Central American B-genome origin of the tuber-bearing members of Solanum sect. Petota and shows a clear division between A genomes in clades 3 and 4, and B genomes in clade 1+2. On the other hand, when a prior agreement approach is invoked other potato evolutionary histories are revealed but with less support. These alternative histories could be explained by past hybridization, or fast rates of speciation. In the case of tomato, the analyses with all sequence data completely resolved 19 of 21 clades, for the first time revealed the monophyly of five clades, and gave further support for the recent segregation of new species from the former Solanum peruvianum. Concordance analyses revealed and summarized the extensive discordance among COSII markers. Some potential reasons for discordance could be methodological, to include systematic errors due to using a wrong model of sequence evolution, coupled with long branches, or mixtures of branch lengths within COSII, or undetected paralogy or alignment bias. Other reasons could be biological processes such as hybridization or lineage sorting.ConclusionThis study confirms and quantifies the utility of using DNA sequences from different parts of the genome in phylogenetic studies to avoid possible bias in the sampling. It shows that 11–18 loci are enough to get the dominant history in this group of Solanum, but more loci would be needed to discern the distribution of gene genealogies in more depth, and thus detect which mechanism most likely shaped the discordance.

Inferring Phylogenetic Networks with Maximum Pseudolikelihood under Incomplete Lineage Sorting

Phylogenetic networks are necessary to represent the tree of life expanded by edges to represent events such as horizontal gene transfers, hybridizations or gene flow. Not all species follow the paradigm of vertical inheritance of their genetic material. While a great deal of research has flourished into the inference of phylogenetic trees, statistical methods to infer phylogenetic networks are still limited and under development. The main disadvantage of existing methods is a lack of scalability. Here, we present a statistical method to infer phylogenetic networks from multi-locus genetic data in a pseudolikelihood framework. Our model accounts for incomplete lineage sorting through the coalescent model, and for horizontal inheritance of genes through reticulation nodes in the network. Computation of the pseudolikelihood is fast and simple, and it avoids the burdensome calculation of the full likelihood which can be intractable with many species. Moreover, estimation at the quartet-level has the added computational benefit that it is easily parallelizable. Simulation studies comparing our method to a full likelihood approach show that our pseudolikelihood approach is much faster without compromising accuracy. We applied our method to reconstruct the evolutionary relationships among swordtails and platyfishes (Xiphophorus: Poeciliidae), which is characterized by widespread hybridizations.

Bayesian species delimitation combining multiple genes and traits in a unified framework

Delimitation of species based exclusively on genetic data has been advocated despite a critical knowledge gap: how might such approaches fail because they rely on genetic data alone, and would their accuracy be improved by using multiple data types. We provide here the requisite framework for addressing these key questions. Because both phenotypic and molecular data can be analyzed in a common Bayesian framework with our program iBPP, we can compare the accuracy of delimited taxa based on genetic data alone versus when integrated with phenotypic data. We can also evaluate how the integration of phenotypic data might improve species delimitation when divergence occurs with gene flow and/or is selectively driven. These two realities of the speciation process are ignored by currently available genetic approaches. Our model accommodates phenotypic characters that exhibit different degrees of divergence, allowing for both neutral traits and traits under selection. We found a greater accuracy of estimated species boundaries with the integration of phenotypic and genetic data, with a strong beneficial influence of phenotypic data from traits under selection when the speciation process involves gene flow. Our results highlight the benefits of multiple data types, but also draws into question the rationale of species delimitation based exclusively on genetic data.