Populations and Evolution (q-bio.PE)

Abstract: Based on the normal distribution and its properties, i.e., average and variance, Fisher works have provided a conceptual framework to identify genotype-phenotype associations. While Fisher intuition has proved fruitful over the past century, the current demands for higher mapping precisions have led to the formulation of a new genotype-phenotype association method a.k.a. GIFT (Genomic Informational Field Theory). Not only is the method more powerful in extracting information from genotype and phenotype datasets, GIFT can also deal with any phenotype distribution density function. Here we apply GIFT to a hypothetical Cauchy-distributed phenotype. As opposed to the normal distribution that restricts fluctuations to a finite variance defined by the bulk of the distribution, Cauchy distribution embraces large phenotypic fluctuations and as a result, averages and variances from Cauchy-distributed phenotypes cannot be defined mathematically. While classic genotype-phenotype association methods (GWAS) are unable to function without proper average and variance, it is demonstrated here that GIFT can associate genotype to phenotype in this case. As phenotypic plasticity, i.e., phenotypic fluctuation, is central to surviving sudden environmental changes, by applying GIFT the unique characteristic of the genotype permitting evolution of biallelic organisms to take place is determined in this case.

Abstract: The introduction of the spatial Lambda-Fleming-Viot model (LV) in population genetics was mainly driven by the pioneering work of Alison Etheridge, in collaboration with Nick Barton and Amandine V\'eber about ten years ago (1,2). The LV model provides a sound mathematical framework for describing the evolution of a population of related individuals along a spatial continuum. It alleviates the "pain in the torus" issue with Wright and Mal\'ecot's isolation by distance model and is sampling consistent, making it a tool of choice for statistical inference. Yet, little is known about the potential connections between the LV and other stochastic processes generating trees and the spatial coordinates along the corresponding lineages. This work focuses on a version of the LV whereby lineages move infinitely rapidly over infinitely small distances. Using simulations, we show that the induced LV tree-generating process is well approximated by a birth-death model. Our results also indicate that Brownian motions modelling the movements of lineages along birth-death trees do not generally provide a good approximation of the LV due to habitat boundaries effects that play an increasingly important role in the long run. Finally, we describe efficient algorithms for fast simulation of the backward and forward in time versions of the LV model.

Abstract: Comparisons of single-cell RNA sequencing (scRNA-seq) data across species can reveal links between cellular gene expression and the evolution of cell functions, features, and phenotypes. These comparisons invoke evolutionary histories, as depicted with phylogenetic trees, that define relationships between species, genes, and cells. Here we illustrate a tree-based framework for comparing scRNA-seq data, and contrast this framework with existing methods. We describe how we can use trees to identify homologous and comparable groups of genes and cells, based on their predicted relationship to genes and cells present in the common ancestor. We advocate for mapping data to branches of phylogenetic trees to test hypotheses about the evolution of cellular gene expression. We describe the kinds of data that can be compared, and the types of questions that each comparison has the potential to address. Finally, we reconcile species phylogenies, gene phylogenies, cell phylogenies, and cell lineages as different representations of the same concept: the tree of cellular life. By integrating phylogenetic approaches into scRNA-seq analyses, we can overcome challenges for building informed comparisons across species, and robustly test hypotheses about gene and cell evolution.