Efficient Bayesian phylogenomic dating with new models of trait evolution and rich diversities of living and fossil species

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As species diverge, they accumulate nucleotide substitutions in their genomes at a rate approximately constant in time. Thus, substitutions serve as timepieces to infer species divergences. By incorporating information from the fossil record, the inferred speciation timings can be calibrated to geological time. This method, known as molecular-clock dating, has broad applications in evolutionary biology, such as studying the timing of spread of viral pandemics, ancient rates of diversification in animals and plants, the relationship of species evolution with past climate or extinction events, human evolution, or the origin of agriculture and animal domestication. Indeed, evolutionary timetrees provide much richer information about species histories than trees without temporal information, thus allowing the formulation and testing of hypotheses on evolutionary timescales.Currently, Bayesian methods are the-state-of-the-art in molecular-clock dating as they allow flexible modelling of evolutionary processes and integration of fossil uncertainties in the analysis. Progresses in Bayesian clock-dating include stochastic models of rate variation among lineages (so-called relaxed clock models), modelling of trait evolution in extant and extinct taxa, and development of "soft-bounds" and flexible fossil calibration densities. While these advances have made the Bayesian method attractive for clock-dating, Bayesian computation relies on MCMC sampling which requires computationally expensive stochastic simulation, precluding the Bayesian method for analysis of large-scale datasets. This is unfortunate since large scale molecular datasets are now commonplace: several high-throughput genome sequencing projects have now been announced or are in progress and we expect a flood of genome-scale data for several thousand species (e.g. the 10K animal genomes and the UK's 66K eukaryotic genomes projects). This deluge of genome data has been accompanied by an explosive increase in the number of morphological datasets based on a computational revolution in comparative anatomy - the widespread deployment of X-Ray Tomography and photogrammetry resulting in vast databases of trait data: MorphoBank and Phenome10K now store over 64,200 surface scans for over 7,000 species. Computational tools capable of exploiting these newly generated datasets are now urgently required. For example, with current methods, inference of a 66K-species timetree would require at least 55 years of computing time (extrapolating from some of our previous analyses). Evidently, the efficiency of analytic methods has not kept apace with the volume of data available and increasingly required to tackle large scale questions in evolutionary biology. In this project we will overcome two major challenges in Bayesian clock dating of species divergences: (i) the mixing and computational limitations of MCMC algorithms in analyses of large datasets, and (ii) the limitations of current trait models of evolution in timetree inference. We will design novel MCMC algorithms to improve the mixing efficiency making use of new ideas about MCMC algorithm design and improve the computational efficiency through code improvement and parallelization. We will incorporate advanced trait models to infer timetrees of extant and fossil species. In particular, we will adapt trait models to analyse large genomic trait datasets such as RNA-seq expression data. The newly developed algorithms will be implemented in our MCMCtree software, and applied to several large-scale empirical datasets with densely sampled extant and fossil species. The data analyses will provide important motivations for method development and serve to showcase our new software by addressing fundamental questions in evolutionary biology. Our proposal addresses the BBSRC's strategic priorities of "data driven biology" and "system approaches to the biosciences".