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Interpretation of Manhattan Plots and Other Outputs of 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

With increasing marker density, estimation of recombination rate between a marker and a causal mutation using linkage analysis becomes less important. Instead, linkage disequilibrium (LD) becomes the major indicator for gene mapping through genome-wide association studies (GWAS). In addition to the linkage between the marker and the causal mutation, many other factors may contribute to the LD, including population structure and cryptic relationships among individuals. As statistical methods and software evolve to improve statistical power and computing speed in GWAS, the corresponding outputs must also evolve to facilitate the interpretation of input data, the analytical process, and final association results. In this chapter, our descriptions focus on (1) considerations in creating a Manhattan plot displaying the strength of LD and locations of markers across a genome; (2) criteria for genome-wide significance threshold and the different appearance of Manhattan plots in single-locus and multiple-locus models; (3) exploration of population structure and kinship among individuals; (4) quantile–quantile (QQ) plot; (5) LD decay across the genome and LD between the associated markers and their neighbors; (6) exploration of individual and marker information on Manhattan and QQ plots via interactive visualization using HTML. The ultimate objective of this chapter is to help users to connect input data to GWAS outputs to balance power and false positives, and connect GWAS outputs to the selection of candidate genes using LD extent.

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Acknowledgments

This project was partially funded by the National Science Foundation of the United States (Award # DBI 1661348 and ISO 2029933), the United States Department of Agriculture - National Institute of Food and Agriculture (Hatch project 1014919, Award #s 2018-70005-28792, 2019-67013-29171, and 2020-67021-32460), the Washington Grain Commission, the United States (Endowment and Award #s 126593 and 134574), the Program of Chinese National Beef Cattle and Yak Industrial Technology System, China (Award # CARS-37), Fundamental Research Funds for the Central Universities, China (Southwest Minzu University, Award # 2020NQN26), and Sichuan Science and Technology Program, China (Award #s 2021YJ0269 and 2021YJ0266).

Author Contributions

Jiabo Wang: software, data curation, writing—original draft preparation, visualization, investigation.

Alexander E. Lipka: revision of the manuscript.

Jianming Yu: revision of the manuscript.

Zhiwu Zhang: conceptualization and revision of the manuscript.

Author information

Authors and Affiliations

  1. Key Laboratory of Qinghai-Tibetan Plateau Animal Genetic Resource Reservation and Utilization, Sichuan Province and Ministry of Education, Southwest Minzu University, Chengdu, Sichuan, China

    Jiabo Wang

  2. Department of Agronomy, Iowa State University, Ames, IA, USA

    Jianming Yu

  3. Department of Crop Sciences, University of Illinois, Urbana, IL, USA

    Alexander E. Lipka

  4. Department of Crop and Soil Sciences, Washington State University, Pullman, WA, USA

    Zhiwu Zhang

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  1. Jiabo Wang
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Corresponding author

Correspondence to Jiabo Wang .

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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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Wang, J., Yu, J., Lipka, A.E., Zhang, Z. (2022). Interpretation of Manhattan Plots and Other Outputs of 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_5

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  • DOI: https://doi.org/10.1007/978-1-0716-2237-7_5

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