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Data Integration, Imputation, and Meta-analysis for Genome-Wide Association Studies

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Genome-Wide Association Studies

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

Abstract

Growing genomic and phenotypic datasets require different groups around the world to collaborate and integrate these valuable resources to maximize their benefit and increase reference population sizes for genomic prediction and genome-wide association studies (GWAS). However, different studies use different genotyping techniques which requires a synchronizing step for the genotyped variants called “imputation” before combining them. Optimally, different GWAS datasets can be analysed within a meta-analysis, which recruits summary statistics instead of actual data. This chapter describes the general principles for genotypic imputation and meta-GWAS analysis with a description of study designs and command lines required for such analyses.

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Author information

Authors and Affiliations

  1. Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia

    Reem Joukhadar & Hans D. Daetwyler

  2. School of Applied Systems Biology, La Trobe University, Bundoora, VIC, Australia

    Hans D. Daetwyler

Authors
  1. Reem Joukhadar
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  2. Hans D. Daetwyler
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Corresponding author

Correspondence to Hans D. Daetwyler .

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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Joukhadar, R., Daetwyler, H.D. (2022). Data Integration, Imputation, and Meta-analysis for Genome-Wide Association Studies. 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_11

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  • DOI: https://doi.org/10.1007/978-1-0716-2237-7_11

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-2236-0

  • Online ISBN: 978-1-0716-2237-7

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