Bruce Rannala

The birth-death-shift (BDS) process is a stochastic-diversification model in which diversification rates may vary across both extant and extinct and unsampled lineages. We present a general approach for inferring branch-specific rates of lineage diversification using Bayesian statistics from phylogenies containing only living species. We validate our approach through computer simulations and test it on primate data, finding that estimates of branch-specific diversification rates are robust to the assumed prior distribution on the number of diversification-rate shifts. The implementation of the lineage-specific birth-death-shift model in RevBayes provides biologists with a statistically principled tool for studying lineage diversification patterns.

The multispecies coalescent model provides a framework for detecting gene flow using genomic data between species. This study uses simulation to examine false positive rates in a Bayesian test of gene flow, considering factors including recombination, natural selection, species divergence timing, and whether gene flow involves sister or nonsister lineages. The test has very low false positives in most scenarios. However, gene flow detection between sister lineages may be prone to high false positives in cases of very recent species divergence and very high recombination rate. The test demonstrates robustness to various selection types at low recombination rates, though prolonged balancing selection can produce false gene flow signals between sister lineages. Recent species divergence alone with no recombination does not cause false positives. Gene flow detection between nonsister lineages remains robust across all recombination and divergence levels.

A method is developed to jointly estimate the time and population of origin of a mutation as well as the ancestral and derived states in a structured population using population genomic data. The method is evaluated using simulations and applied to genome data from the 1000 Genomes Project, analyzing seven single nucleotide polymorphisms across six skin pigmentation genes in populations from Great Britain, China, and Kenya. Results demonstrate that mutation age and population of origin can be quite uncertain, even with long sequences or many samples, but this uncertainty is accurately captured using credible intervals and sets.

The birth-death process (BDP) is widely used in evolutionary biology as a model for generating phylogenetic trees of species. The generalized birth-death process (GBDP) allows rate variation over time, with speciation and extinction rates as arbitrary functions of time. We review the probability theory underpinning the GBDP as a model of cladogenesis and recent findings concerning its identifiability. The GBDP with arbitrary continuous rate functions has been shown to be non-identifiable from lineage-through-time data, meaning even with species phylogenies of infinite size the parameters cannot be estimated. However, a restricted class of birth-death processes with piecewise-constant rates has been shown to be identifiable. We illustrate these findings through examples and discuss implications for biologists interested in inferring the past tempo and mode of evolution using reconstructed phylogenetic trees.

Phylogenetic tree inference when recombination is present is examined. Two primary methodologies are compared: concatenation approaches treat all loci as sharing one gene tree, while species tree methods assume each locus has its own gene tree with no intralocus recombination. Three strategies for managing recombination effects are evaluated: assessing statistical robustness when recombination goes unaccounted for; identifying and eliminating recombinant regions; and employing methods that accommodate varying gene trees across sequences. Recombination is likely to be more detrimental for concatenation methods, having little impact on topology or divergence time estimates for species tree inference methods. Recombination detection may prove unnecessary for species tree analysis, and removing recombinant loci could introduce bias in parameter calculations.

Hybridization of individuals from genetically differentiated populations causes their genomes to become a mosaic of population ancestries. Widespread availability of genetic markers during the late twentieth century stimulated the development of statistical methods for inferring both recent hybrids and population admixture proportions in individuals. Newer methods focus on population ancestries of chromosomal segments in individuals (local ancestry inference or chromosome painting). This review covers established statistical methods for inferring recent hybrids (specifically F1, F2, and first-generation backcrosses), individual population admixture proportions, and population ancestry of chromosome segments in individuals, focusing on the biological assumptions underlying different methods.

Ancient DNA (aDNA) is increasingly being used to investigate questions such as the phylogenetic relationships and divergence times of extant and extinct species. If aDNA samples are sufficiently old, expected branch lengths (in units of nucleotide substitutions) are reduced relative to contemporary samples. This can be accounted for by incorporating sample ages into phylogenetic analyses. Existing methods that use tip (sample) dates infer gene trees rather than species trees, which can lead to incorrect or biased inferences of the species tree. Methods using a multispecies coalescent (MSC) model overcome these issues. We developed an MSC model with tip dates and implemented it in the program bpp. The method performed well for a range of biologically realistic scenarios, estimating calibrated divergence times and mutation rates precisely. Simulations suggest that estimation precision can be best improved by prioritizing sampling of many loci and more ancient samples. Incorrectly treating ancient samples as contemporary in analyzing simulated data, mimicking a common practice of empirical analyses, led to large systematic biases in model parameters, including divergence times. Two genomic datasets of mammoths and elephants were analyzed, demonstrating the method’s empirical utility.

As demonstrated by the SARS-CoV-2 pandemic, the emergence of novel viral strains with increased transmission rates poses a serious threat to global health. Statistical models of genome sequence evolution may provide a critical tool for early detection of these strains. Using a novel stochastic model that links transmission rates to the entire viral genome sequence, we study the utility of phylogenetic methods that use a phylogenetic tree relating viral samples versus count-based methods that use case counts of variants over time exclusively to detect increased transmission rates and identify candidate causative mutations. We find that phylogenies in particular can detect novel transmission-enhancing variants very soon after their origin and may facilitate the development of early detection systems for outbreak surveillance. Phylogenetic trees of viruses facilitate the early detection of highly transmissible variants of concern.