Dataset Information

Full Bayesian Comparative Phylogeography from Genomic Data.

ABSTRACT: A challenge to understanding biological diversification is accounting for community-scale processes that cause multiple, co-distributed lineages to co-speciate. Such processes predict non-independent, temporally clustered divergences across taxa. Approximate-likelihood Bayesian computation (ABC) approaches to inferring such patterns from comparative genetic data are very sensitive to prior assumptions and often biased toward estimating shared divergences. We introduce a full-likelihood Bayesian approach, ecoevolity, which takes full advantage of information in genomic data. By analytically integrating over gene trees, we are able to directly calculate the likelihood of the population history from genomic data, and efficiently sample the model-averaged posterior via Markov chain Monte Carlo algorithms. Using simulations, we find that the new method is much more accurate and precise at estimating the number and timing of divergence events across pairs of populations than existing approximate-likelihood methods. Our full Bayesian approach also requires several orders of magnitude less computational time than existing ABC approaches. We find that despite assuming unlinked characters (e.g., unlinked single-nucleotide polymorphisms), the new method performs better if this assumption is violated in order to retain the constant characters of whole linked loci. In fact, retaining constant characters allows the new method to robustly estimate the correct number of divergence events with high posterior probability in the face of character-acquisition biases, which commonly plague loci assembled from reduced-representation genomic libraries. We apply our method to genomic data from four pairs of insular populations of Gekko lizards from the Philippines that are not expected to have co-diverged. Despite all four pairs diverging very recently, our method strongly supports that they diverged independently, and these results are robust to very disparate prior assumptions.

SUBMITTER: Oaks JR

PROVIDER: S-EPMC6472446 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

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Full Bayesian Comparative Phylogeography from Genomic Data.

Oaks Jamie R JR

Systematic biology 20190501 3

A challenge to understanding biological diversification is accounting for community-scale processes that cause multiple, co-distributed lineages to co-speciate. Such processes predict non-independent, temporally clustered divergences across taxa. Approximate-likelihood Bayesian computation (ABC) approaches to inferring such patterns from comparative genetic data are very sensitive to prior assumptions and often biased toward estimating shared divergences. We introduce a full-likelihood Bayesian ...[more]

PMID: 30239868

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Project description:BackgroundRecent advances in genome technologies and the subsequent collection of genomic information at various molecular resolutions hold promise to accelerate the discovery of new therapeutic targets. A critical step in achieving these goals is to develop efficient clinical prediction models that integrate these diverse sources of high-throughput data. This step is challenging due to the presence of high-dimensionality and complex interactions in the data. For predicting relevant clinical outcomes, we propose a flexible statistical machine learning approach that acknowledges and models the interaction between platform-specific measurements through nonlinear kernel machines and borrows information within and between platforms through a hierarchical Bayesian framework. Our model has parameters with direct interpretations in terms of the effects of platforms and data interactions within and across platforms. The parameter estimation algorithm in our model uses a computationally efficient variational Bayes approach that scales well to large high-throughput datasets.ResultsWe apply our methods of integrating gene/mRNA expression and microRNA profiles for predicting patient survival times to The Cancer Genome Atlas (TCGA) based glioblastoma multiforme (GBM) dataset. In terms of prediction accuracy, we show that our non-linear and interaction-based integrative methods perform better than linear alternatives and non-integrative methods that do not account for interactions between the platforms. We also find several prognostic mRNAs and microRNAs that are related to tumor invasion and are known to drive tumor metastasis and severe inflammatory response in GBM. In addition, our analysis reveals several interesting mRNA and microRNA interactions that have known implications in the etiology of GBM.ConclusionsOur approach gains its flexibility and power by modeling the non-linear interaction structures between and within the platforms. Our framework is a useful tool for biomedical researchers, since clinical prediction using multi-platform genomic information is an important step towards personalized treatment of many cancers. We have a freely available software at: http://odin.mdacc.tmc.edu/~vbaladan.

Project description:The Mississippi Embayment is a prominent physiographic feature of eastern North America consisting of primarily lowland aquatic habitats and a fish fauna that is largely distinct from nearby highland regions. Numerous studies have demonstrated that both pre-Pleistocene and Pleistocene events have had a strong influence on the distributions and relationships of highland fishes in eastern North America. However, the extent to which these same events affected Embayment distributed taxa remains largely unexplored. The purpose of this study was to investigate the relative roles of pre-Pleistocene and Pleistocene events in shaping phylogeographic relationships of four stream dwelling fishes in the Mississippi Embayment. Molecular genetic analyses of the mitochondrial gene cytochrome b were performed for three ictalurid catfish species (Noturus miurus, n = 67; Noturus hildebrandi, n = 93, and Noturus phaeus, n = 44) and one minnow species (Cyprinella camura, n = 78), all distributed in tributary streams of the Mississippi Embayment. Phylogenetic relationships and divergence times among haplotypes for each species were estimated using maximum likelihood and Bayesian methods. Phylogenetic analyses recovered 6 major haplotype clades within N. miurus, 5 within N. hildbrandi, 8 within N. phaeus, and 8 within C. camura. All three Noturus species show a high degree of isolation by drainage, which is less evident in C. camura. A clade of haplotypes from tributaries in the southern portion of the Mississippi Embayment was consistently recovered in all four species. Divergence times among clades spanned the Pleistocene, Pliocene, and Miocene. Novel relationships presented here for C. camura and N. phaeus suggest the potential for cryptic species. Pre-Pleistocene and Pleistocene era sea level fluctuations coincide with some divergence events, but no single event explains any common divergence across all taxa. Like their highland relatives, a combination of both pre-Pleistocene and Pleistocene era events have driven divergences among Embayment lineages.

Dataset Information

Full Bayesian Comparative Phylogeography from Genomic Data.

Publications

Full Bayesian Comparative Phylogeography from Genomic Data.

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