Genome-wide association studies (GWAS) have revolutionized the investigation of complex traits over the past decade and have unveiled numerous useful genotype-phenotype associations in plants. In this book, we describe the key concepts and methods underlying GWAS, including the genetic architecture underlying variation for phenotypic traits, the structure of genetic variation in plants, technologies for capturing genetic information, study designs, and the statistical models and bioinformatics tools used for data analysis. This book provides an extremely valuable resource for the plant research community by rendering GWAS analysis less challenging and more accessible to a broader group of users.We have divided the contents of the book into six main parts. In a first part (Part I; Chapters 1-5), we provide general overviews of the entire GWAS process as well as of the main components of GWAS (phenotypic data, genotypic data, association models, and interpretation of outputs). These first chapters are written in the form of review articles, providing an overall view of the current status of the science in these areas.In the rest of the volume (Chapters 6-20), each individual chapter focuses on a specific topic relevant to performing a GWAS or presents an example or case study for a specific context. In these chapters, the authors provide a hands-on, step-by-step guide on how to perform various analyses that contribute to a GWAS.In Part II, comprised of Chapters 6-8, we focus on the analysis of phenotypic data. Chapter 6 covers the analysis of multi-environment phenotypic data (typically from field trials) that must be integrated to provide a single phenotypic value for each trait and each accession in the association panel. In Chapter 7, the authors describe the analysis of data obtained from high-throughput phenotyping platforms. While some of these traits may be in common with those described in the previous chapter, most often the traits are measured in a controlled environment and compound traits (e.g., yield) are broken down into component traits (number of tillers, number of seed per head, size of seed). Finally, in Chapter 8, the special case of "omics"-derived traits is presented. In this case, the phenotypic datasets are typically very large. In a transcriptomic analysis, for example, one will seek to capture the expression of all genes of a crop in each accession under a particular set of conditions. Similarly, for traits derived from proteomic or metabolomic characterization of accessions, a vast number of traits need to be handled.Chapters 9-12 comprise Part III of the book and cover various topics related to the production and handling of genotypic data. The first of these chapters (Chapter 9) covers the most common case where genotypic data in the form of SNPs must be produced and curated to ensure an adequate coverage of the genome. In Chapter 10, we present the special case of structural variants (SVs), another type of variant that has started to be assessed when performing GWAS in plants but that p...