Genome-wide association studies

Author(s)

• Torkamaneh, Davoud
• Belzile, François

Bibliographic Information

Genome-wide association studies

edited by Davoud Torkamaneh, François Belzile

（Methods in molecular biology / edited by John M. Walker, 2481）

Humana Press, c2022

Note

Includes bibliographical references and index

Description and Table of Contents

Description

This detailed collection explores genome-wide association studies (GWAS), which have revolutionized the investigation of complex traits over the past decade and have unveiled numerous useful genotype-phenotype associations in plants. The book describes 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. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of invaluable implementation advice that leads to the most fruitful research results. Authoritative and practical, Genome-Wide Association Studies serves as an extremely valuable resource for the plant research community by rendering GWAS analysis less challenging and more accessible to a broader group of users.

Table of Contents

Part I: Reviews and Overviews 1. Designing a Genome-Wide Association Study: Main Steps and Critical Decisions Francois Belzile and Davoud Torkamaneh 2. Preparation and Curation of Phenotypic Datasets Santiago Alvarez Prado, Fernando Hernandez, Ana Laura Achilli, and Agustina Amelong 3. Genotyping Platforms for Genome-Wide Association Studies: Options and Practical Considerations David L. Hyten 4. Genome-Wide Association Study Statistical Models: A Review Mohsen Yoosefzadeh-Najafabadi, Milad Eskandari, Francois Belzile, and Davoud Torkamaneh 5. Interpretation of Manhattan Plots and Other Outputs of Genome-Wide Association Studies Jiabo Wang, Jianming Yu, Alexander E. Lipka, and Zhiwu Zhang Part II: Phenotypic Data for GWAS 6. Preparation and Curation of Multi-Year, Multi-Location, Multi-Trait Datasets Amina Abed and Zakaria Kehel 7. Development, Preparation, and Curation of High-Throughput Phenotypic Data for Genome-Wide Association Studies: A Sample Pipeline in R Pasquale Tripodi 8. Preparation and Curation of Omics Data for Genome-Wide Association Studies Feng Zhu, Alisdair R. Fernie, and Federico Scossa Part III: Genotypic Data and Assessment of Population Structure 9. Producing High-Quality Single Nucleotide Polymorphism Data for Genome-Wide Association Studies Philipp E. Bayer, Mitchell Gill, Monica F. Danilevicz, and David Edwards 10. A Practical Guide to Using Structural Variants for Genome-Wide Association Studies Marc-Andre Lemay and Sidiki Malle 11. Data Integration, Imputation, and Meta-Analysis for Genome-Wide Association Studies Reem Joukhadar and Hans D. Daetwyler 12. Population Structure and Relatedness for Genome-Wide Association Studies Sidiki Malle Part IV: Bioinformatics Tools for GWAS 13. Performing Genome-Wide Association Studies with Multiple Models Using GAPIT Jiabo Wang, You Tang, and Zhiwu Zhang 14. Performing Genome-Wide Association Studies Using rMVP Xiaolei Liu, Lilin Yin, Haohao Zhang, Xinyun Li, and Shuhong Zhao Part V: Identification of Candidate Genes and Validation 15. Identification and Validation of Candidate Genes from Genome-Wide Association Studies Elise Albert and Christopher Sauvage 16. Biparental Crossing and QTL Mapping for Genome-Wide Association Studies Validation Pawan L. Kulwal and Ravinder Singh 17. Development of Breeder Friendly KASP Markers from Genome-Wide Association Studies Results Manar Makhoul and Christian Obermeier Part VI: Case Studies 18. Mapping Major Disease Resistance Genes in Soybean by Genome-Wide Association Studies Everton Geraldo Capote Ferreira and Francismar Correa Marcelino-Guimaraes 19. GWAS Case Studies in Wheat Deepmala Sehgal and Susanne Dreisigacker 20. Plant Microbiome-Based Genome-Wide Association Studies Siwen Deng, Michael A. Meier, Daniel Caddell, Jinliang Yang, and Devin Coleman-Derr

by "Nielsen BookData"

Details

• NCID: BC18701342
• ISBN:
  • 9781071622360
• Country Code: us
• Title Language Code: eng
• Text Language Code: eng
• Place of Publication: New York, N.Y.
• Pages/Volumes: xiii, 371 p.
• Size: 27 cm
• Classification:
  • DC23 : 572.86
• Subject Headings:
  • LCSH : Genomes
  • LCSH : Molecular genetics
• Parent Bibliography ID:
  • BA01223853