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Genome-Wide Association Study Statistical Models: A Review

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Genome-Wide Association Studies

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

Abstract

Statistical models are at the core of the genome-wide association study (GWAS). In this chapter, we provide an overview of single- and multilocus statistical models, Bayesian, and machine learning approaches for association studies in plants. These models are discussed based on their basic methodology, cofactors adjustment accounted for, statistical power and computational efficiency. New statistical models and machine learning algorithms are both showing improved performance in detecting missed signals, rare mutations and prioritizing causal genetic variants; nevertheless, further optimization and validation studies are required to maximize the power of GWAS.

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Authors and Affiliations

  1. Department of Plant Agriculture, University of Guelph, Guelph, ON, Canada

    Mohsen Yoosefzadeh-Najafabadi & Milad Eskandari

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

    François Belzile & Davoud Torkamaneh

  3. Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Quebec City, QC, Canada

    François Belzile & Davoud Torkamaneh

Authors
  1. Mohsen Yoosefzadeh-Najafabadi
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  2. Milad Eskandari
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  3. François Belzile
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  4. Davoud Torkamaneh
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Corresponding author

Correspondence to Davoud Torkamaneh .

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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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Yoosefzadeh-Najafabadi, M., Eskandari, M., Belzile, F., Torkamaneh, D. (2022). Genome-Wide Association Study Statistical Models: A Review. 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_4

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  • DOI: https://doi.org/10.1007/978-1-0716-2237-7_4

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