Biology subjects

Amaury Lambert

Publications and source records attributed to Amaury Lambert.

3 recordsLinked to original sources

bioRxiv2016

Accuracy of demographic inferences from Site Frequency Spectrum: The case of the Yoruba population

Some methods for demographic inference based on the observed genetic diversity of current populations rely on the use of summary statistics such as the Site Frequency Spectrum (SFS). Demographic models can be either model-constrained with numerous parameters such as growth rates, timing of demographic events and migration rates, or model-flexible, with an unbounded collection of piecewise constant sizes. It is still debated whether demographic histories can be accurately inferred based on the SFS. Here we illustrate this theoretical issue on an example of demographic inference for an African population. The SFS of the Yoruba population (data from the 1000 Genomes Project) is fit to a simple model of population growth described with a single parameter (e.g., founding time). We infer a time to the most recent common ancestor of 1.7 million years for this population. However, we show that the Yoruba SFS is not informative enough to discriminate between several different models of growth. We also show that for such simple demographies, the fit of one-parameter models outperforms the model-flexible method recently developed by Liu and Fu. The use of this method on simulated data suggests that it is biased by the noise intrinsically present in the data.

Evolutionary Biology↗

bioRxiv2016

The species problem from the modeler’s point of view

How to define a partition of individuals into species is a long-standing question called the species problem in systematics. Here, we focus on this problem in the thought experiment where individuals reproduce clonally and both the differentiation process and the population genealogies are explicitly known. We point out three desirable properties of species partitions: (A) Heterotypy between species, (B) Homotypy within species and (M) Monophyly of each species. We then ask: How and when is it possible to delineate species in a way satisfying these properties?\n\nWe point out that the three desirable properties cannot in general be satisfied simultaneously, but that any two of them can. We mathematically prove the existence of the finest partition satisfying (A) and (M) and the coarsest partition satisfying (B) and (M). For each of them, we propose a simple algorithm to build the associated phylogeny out of the genealogy.\n\nThe ways we propose to phrase the species problem shed new light on the interaction between the genealogical and phylogenetic scales in modeling work. The two definitions centered on the monophyly property can readily be used at a higher taxonomic level as well, e.g. to cluster species into monophyletic genera.

Evolutionary Biology↗

bioRxiv2016

A unifying comparative phylogenetic framework including traits coevolving across interacting lineages

Models of phenotypic evolution fit to phylogenetic comparative data are widely used to make inferences regarding the tempo and mode of trait evolution. A wide range of models is already available for this type of analysis, and the field is still under active development. One of the most needed developments concerns models that better account for the effect of within- and between-clade interspecific interactions on trait evolution, that can result from processes as diverse as competition, predation, parasitism, or mutualism. Here, we begin by developing a very general comparative phylogenetic framework for (multi)-trait evolution that can be applied to both ultrametric and non-ultrametric trees. This framework not only encapsulates all previous classical models of univariate and multivariate phenotypic evolution, but also paves the way for the consideration of a much broader series of models in which lineages co-evolve, meaning that trait changes in one lineage are influenced by the value of traits in other, interacting lineages. Next, we provide a standard way for deriving the probabilistic distribution of traits at tip branches under our framework. We show that a multivariate normal distribution remains the expected distribution for a broad class of models accounting for interspecific interactions. Our derivations allow us to fit various models efficiently, and in particular greatly reduce the computation time needed to fit the recently proposed phenotype matching model. Finally, we illustrate the utility of our framework by developing a toy model for mutualistic coevolution. Our framework should foster a new era in the study of coevolution from comparative data.

Evolutionary Biology↗