Estimation of contemporary effective population size in plant populations: Limitations of genomic datasets.

Autor: Gargiulo R; Royal Botanic Gardens, Kew Richmond UK., Decroocq V; INRAE Univ. Bordeaux, UMR 1332 BFP Villenave d'Ornon France., González-Martínez SC; INRAE Univ. Bordeaux Cestas France., Paz-Vinas I; Department of Biology Colorado State University Fort Collins Colorado USA.; CNRS, ENTPE, UMR5023 LEHNA Université Claude Bernard Lyon 1 Villeurbanne France., Aury JM; Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Univ Evry Université Paris-Saclay Evry France., Lesur Kupin I; INRAE Univ. Bordeaux Cestas France., Plomion C; INRAE Univ. Bordeaux Cestas France., Schmitt S; AMAP Univ. Montpellier, CIRAD, CNRS, INRAE, IRD Montpellier France., Scotti I; INRAE, URFM Avignon France., Heuertz M; INRAE Univ. Bordeaux Cestas France.
Jazyk: angličtina
Zdroj: Evolutionary applications [Evol Appl] 2024 May 03; Vol. 17 (5), pp. e13691. Date of Electronic Publication: 2024 May 03 (Print Publication: 2024).
DOI: 10.1111/eva.13691
Abstrakt: Effective population size ( N e ) is a pivotal evolutionary parameter with crucial implications in conservation practice and policy. Genetic methods to estimate N e have been preferred over demographic methods because they rely on genetic data rather than time-consuming ecological monitoring. Methods based on linkage disequilibrium (LD), in particular, have become popular in conservation as they require a single sampling and provide estimates that refer to recent generations. A software program based on the LD method, GONE, looks particularly promising to estimate contemporary and recent-historical N e (up to 200 generations in the past). Genomic datasets from non-model species, especially plants, may present some constraints to the use of GONE, as linkage maps and reference genomes are seldom available, and SNP genotyping is usually based on reduced-representation methods. In this study, we use empirical datasets from four plant species to explore the limitations of plant genomic datasets when estimating N e using the algorithm implemented in GONE, in addition to exploring some typical biological limitations that may affect N e estimation using the LD method, such as the occurrence of population structure. We show how accuracy and precision of N e estimates potentially change with the following factors: occurrence of missing data, limited number of SNPs/individuals sampled, and lack of information about the location of SNPs on chromosomes, with the latter producing a significant bias, previously unexplored with empirical data. We finally compare the N e estimates obtained with GONE for the last generations with the contemporary N e estimates obtained with the programs currentNe and NeEstimator.
Competing Interests: The authors have no conflict of interest to declare.
(© 2024 The Authors. Evolutionary Applications published by John Wiley & Sons Ltd.)
Databáze: MEDLINE
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