title: Genomic resources and their influence on the detection of the signal of positive selection in genome scans.
abstract: Genome scans represent powerful approaches to investigate the action of natural selection on the genetic variation of natural populations and to better understand local adaptation. This is very useful, for example, in the field of conservation biology and evolutionary biology. Thanks to Next Generation Sequencing, genomic resources are growing exponentially, improving genome scan analyses in non-model species. Thousands of SNPs called using Reduced Representation Sequencing are increasingly used in genome scans. Besides, genome sequences are also becoming increasingly available, allowing better processing of short-read data, offering physical localization of variants, and improving haplotype reconstruction and data imputation. Ultimately, genome sequences are also becoming the raw material for selection inferences. Here, we discuss how the increasing availability of such genomic resources, notably genome sequences, influences the detection of signals of selection. Mainly, increasing data density and having the information of physical linkage data expand genome scans by (i) improving the overall quality of the data, (ii) helping the reconstruction of demographic history for the population studied to decrease false-positive rates and (iii) improving the statistical power of methods to detect the signal of selection. Of particular importance, the availability of a high-quality reference genome can improve the detection of the signal of selection by (i) allowing matching the potential candidate loci to linked coding regions under selection, (ii) rapidly moving the investigation to the gene and function and (iii) ensuring that the highly variable regions of the genomes that include functional genes are also investigated. For all those reasons, using reference genomes in genome scan analyses is highly recommended.
authors.affiliation: CEFE UMR 5175, CNRS - Université de Montpellier - Université Paul-Valéry Montpellier -EPHE, laboratoire Biogéographie et écologie des vertébrés, 1919 route de Mende, 34293, Montpellier Cedex 5, France.
authors.forename: S
authors.initials: S
authors.lastname: Manel
authors.affiliation: CEFE UMR 5175, CNRS - Université de Montpellier - Université Paul-Valéry Montpellier -EPHE, laboratoire Biogéographie et écologie des vertébrés, 1919 route de Mende, 34293, Montpellier Cedex 5, France.
authors.forename: C
authors.initials: C
authors.lastname: Perrier
authors.affiliation: Aix Marseille Université, CNRS, IRD, Avignon Université, IMBE UMR 7263, Station Marine d'Endoume, 13007, Marseille, France.|Aix Marseille Université, CNRS, Centrale Marseille, I2M UMR 7373, Evolution Biologique Modélisation, 3 Place Victor Hugo, 13331 Marseille Cedex Case 19, France.
authors.forename: M
authors.initials: M
authors.lastname: Pratlong
authors.affiliation: Equipe ATIP, URMITE UM 63 CNRS 7278 IRD 198 Inserm U1095, IHU Méditerranée Infection, Aix-Marseille Université, 27 Boulevard Jean Moulin, 13385 Marseille Cedex 05, France.
authors.forename: L
authors.initials: L
authors.lastname: Abi-Rached
authors.affiliation: XEGEN SAS, 15 Rue de la République, 13420, Gemenos, France.
authors.forename: J
authors.initials: J
authors.lastname: Paganini
authors.affiliation: Aix Marseille Université, CNRS, Centrale Marseille, I2M UMR 7373, Evolution Biologique Modélisation, 3 Place Victor Hugo, 13331 Marseille Cedex Case 19, France.
authors.forename: P
authors.initials: P
authors.lastname: Pontarotti
authors.affiliation: Aix Marseille Université, CNRS, IRD, Avignon Université, IMBE UMR 7263, Station Marine d'Endoume, 13007, Marseille, France.
authors.forename: D
authors.initials: D
authors.lastname: Aurelle
mesh_terms: D000818:Animals; D005075:Biological Evolution; D014644:Genetic Variation; D005828:Genetics, Population; D023281:Genomics / Q000379:methods*; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods; D020641:Polymorphism, Single Nucleotide; D012641:Selection, Genetic; D017422:Sequence Analysis, DNA / Q000379:methods
keywords: conservation biology; landscape genetics; landscape genomics; local adaptation; natural population; population genomics