Inferring Long-Term Effective Population Size with Mutation-Selection Models
| Autor: | Nicolas Lartillot, Vincent Lanore, Thibault Latrille |
|---|---|
| Přispěvatelé: | Bioinformatique, phylogénie et génomique évolutive (BPGE), Département PEGASE [LBBE] (PEGASE), Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS) |
| Rok vydání: | 2021 |
| Předmět: |
0106 biological sciences
population size Mutation rate mutation rate codon models Fitness landscape Population genetics mutation–selection models Biology [SDV.BID.SPT]Life Sciences [q-bio]/Biodiversity/Systematics Phylogenetics and taxonomy AcademicSubjects/SCI01180 010603 evolutionary biology 01 natural sciences Evolution Molecular 03 medical and health sciences Effective population size Genetic drift [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry Molecular Biology/Genomics [q-bio.GN] Genetics Methods Animals Selection Genetic Molecular Biology Ecology Evolution Behavior and Systematics Selection (genetic algorithm) Phylogeny 030304 developmental biology Mammals Population Density 0303 health sciences [STAT.AP]Statistics [stat]/Applications [stat.AP] population genetic Models Genetic Population size [SDV.BID.EVO]Life Sciences [q-bio]/Biodiversity/Populations and Evolution [q-bio.PE] AcademicSubjects/SCI01130 Bayes Theorem life-history traits Evolutionary biology Mutation (genetic algorithm) Mutation Epistasis phylogenetic [STAT.ME]Statistics [stat]/Methodology [stat.ME] |
| Zdroj: | Molecular Biology and Evolution Molecular Biology and Evolution, 2021, 38 (10), pp.4573-4587. ⟨10.1093/molbev/msab160⟩ Molecular Biology and Evolution, Oxford University Press (OUP), 2021, 38 (10), pp.4573-4587. ⟨10.1093/molbev/msab160⟩ |
| ISSN: | 1537-1719 0737-4038 |
| DOI: | 10.1093/molbev/msab160⟩ |
| Popis: | Mutation-selection phylogenetic codon models are grounded on population genetics first principles and represent a principled approach for investigating the intricate interplay between mutation, selection and drift. In their current form, mutation-selection codon models are entirely characterized by the collection of site-specific amino-acid fitness profiles. However, thus far, they have relied on the assumption of a constant genetic drift, translating into a unique effective population size (Ne) across the phylogeny, clearly an unreasonable hypothesis. This assumption can be alleviated by introducing variation inNebetween lineages. In addition toNe, the mutation rate (μ) is susceptible to vary between lineages, and both should co-vary with life-history traits (LHTs). This suggests that the model should more globally account for the joint evolutionary process followed by all of these lineage-specific variables (Ne,μ, and LHTs). In this direction, we introduce an extended mutation-selection model jointly reconstructing in a Bayesian Monte Carlo framework the fitness landscape across sites and long-term trends inNe,μand LHTs along the phylogeny, from an alignment of DNA coding sequences and a matrix of observed LHTs in extant species. The model was tested against simulated data and applied to empirical data in mammals, isopods and primates. The reconstructed history ofNein these groups appears to correlate with LHTs or ecological variables in a way that suggests that the reconstruction is reasonable, at least in its global trends. On the other hand, the range of variation in Ne inferred across species is surprisingly narrow. This last point suggests that some of the assumptions of the model, in particular concerning the assumed absence of epistatic interactions between sites, are potentially problematic. |
| Databáze: | OpenAIRE |
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