SECTION 4. Applications of genomics

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Boitard, Simon | Chikhi, Lounès | da Fonseca, Rute | Ginja, Catarina | Kantanen, Juha | Lenstra, Johannes, A | Orozco-Terwengel, Pablo | Pausch, Hubert

Edité par CCSD ; FAO -

International audience. This section will describe how genetic and genomic information can be used to describe patterns (such as genetic diversity, differentiation, classification, and genetic clines) or to infer historical processes (expansions, contractions, admixture and gene flow) affecting animal genetic diversity. In the first case, one can quantify how genetic diversity changes per individual, location or environment or by domestication. The second group of approaches aims to identify, quantify and determine the date of ancient demographic events, possibly including direct or indirect selection acting on specific genomic regions. The principles and limits of several methods that have become available in the last couple of decades are noted. This section provides the underlying genetic theory behind the most common analyses undertaken for genomic characterization, and describes the methods used. The seminal research associated with each approach is cited. This section is complemented by Appendix 9, which provides a summary of the main steps and analyses that are frequently undertaken in genomic characterization, and provides examples of the commonly used software for each step.Developments from the genomics era allow novel options to address previously unfathomable questions with increasingly sophisticated methods, including modelling. In some cases, the resulting patterns can only be interpreted by understanding the underlying models and processes. In addition, the analysis of patterns and processes should be integrated. For instance, most available approaches to identify selection typically make strong assumptions about the demographic history that generate “neutral” patterns, while methods reconstructing demographic events often ignore selection. Inferring selection and demography simultaneously, in order to explain spatial patterns, is one of the greatestchallenges of evolutionary genomics.

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