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Preparation and Curation of Multiyear, Multilocation, Multitrait Datasets

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Genome-Wide Association Studies

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2481))

Abstract

Genome-wide association studies (GWAS) are a powerful approach to dissect genotype-phenotype associations and identify causative regions. However, this power is highly influenced by the accuracy of the phenotypic data. To obtain accurate phenotypic values, the phenotyping should be achieved through multienvironment trials (METs). In order to avoid any technical errors, the required time needs to be spent on exploring, understanding, curating and adjusting the phenotypic data in each trial before combining them using an appropriate linear mixed model (LMM). The LMM is chosen to minimize as much as possible any effect that can lead to misestimation of the phenotypic values. The purpose of this chapter is to explain a series of important steps to explore and analyze data from METs used to characterize an association panel. Two datasets are used to illustrate two different scenarios.

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Author information

Authors and Affiliations

  1. Consortium de recherche sur la pomme de terre du Québec (CRPTQ), Québec, Canada

    Amina Abed

  2. International Center for Agricultural Research in the Dry Areas (ICARDA), Rabat, Morocco

    Zakaria Kehel

Authors
  1. Amina Abed
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  2. Zakaria Kehel
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Corresponding authors

Correspondence to Amina Abed or Zakaria Kehel .

Editor information

Editors and Affiliations

  1. Département de Phytologie, Université Laval, Quebec City, QC, Canada

    Davoud Torkamaneh

  2. Institut de biologie intégrative et des systems, Université Laval, Québec, QC, Canada

    François Belzile

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© 2022 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature

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Abed, A., Kehel, Z. (2022). Preparation and Curation of Multiyear, Multilocation, Multitrait Datasets. In: Torkamaneh, D., Belzile, F. (eds) Genome-Wide Association Studies. Methods in Molecular Biology, vol 2481. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2237-7_6

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  • DOI: https://doi.org/10.1007/978-1-0716-2237-7_6

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-2236-0

  • Online ISBN: 978-1-0716-2237-7

  • eBook Packages: Springer Protocols

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