Overview
- This is an Open Access book published under the CC-BY 4.0 license
- Highlights statistical and machine learning models for complex genetic and environmental interactions
- Offers a practical approach using real and simulated datasets to illustrate their implementations
- It clearly explains these models and methods and presents softwares for their analysis
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Table of contents (15 chapters)
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Front Matter
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Back Matter
Keywords
About this book
This open access book brings together the latest genome base prediction models currently being used by statisticians, breeders and data scientists. It provides an accessible way to understand the theory behind each statistical learning tool, the required pre-processing, the basics of model building, how to train statistical learning methods, the basic R scripts needed to implement each statistical learning tool, and the output of each tool. To do so, for each tool the book provides background theory, some elements of the R statistical software for its implementation, the conceptual underpinnings, and at least two illustrative examples with data from real-world genomic selection experiments. Lastly, worked-out examples help readers check their own comprehension.
The book will greatly appeal to readers in plant (and animal) breeding, geneticists and statisticians, as it provides in a very accessible way the necessary theory, the appropriate R code, and illustrative examples for a complete understanding of each statistical learning tool. In addition, it weighs the advantages and disadvantages of each tool.
Authors and Affiliations
About the authors
Dr. Abelardo Montesinos López holds a PhD in Probability and Statistics from the Centro de Investigación en Matemáticas (CIMAT), Guanajuato, México. He is currently a Professor of Statistical Interference, Probability and Statistical Learning Methods at the Departamento de Matemáticas, CentroUniversitario de Ciencias Exactas e Ingenierías (CUCEI), Universidad de Guadalajara, Mexico. His areas of interest are: development of novel genomic prediction models for plant breeding, high dimensional data analysis, generalized linear mixed models, survival analysis, Bayesian analysis and multivariate analysis. He has contributed univariate and multivariate genomic prediction models for predicting breeding values in plants with normal, binary, count and ordinal phenotypes.
Dr. José Crossa is a distinguished Scientist at the Biometrics and Statistics Unit of the International Maize and Wheat Improvement Center (CIMMYT). He has contributed to the statistical analyses of plant breeding trials with an emphasis on modeling genotype x environment interactions, QTL x environment interactions and genomic x environment interactions. He has significantly advanced the integration of essential factors such as pedigree and trial data into genomic selection for crop breeding, by creating and describing sophisticated statistical models of proven effectiveness that have since been widely adopted. He is a Fellow of the Agronomy Society of America and of the Crop Science Society of America, Member of the Mexican Academy of Science, Member of the Mexican National Research System of the National Council of Research and Technology, invited professor at Universities in Mexico and Uruguay, and Adjunct Professor at the Department of Statistics and Department of Plant Science at the University of Nebraska-Lincoln, USA.
Bibliographic Information
Book Title: Multivariate Statistical Machine Learning Methods for Genomic Prediction
Authors: Osval Antonio Montesinos López, Abelardo Montesinos López, José Crossa
DOI: https://doi.org/10.1007/978-3-030-89010-0
Publisher: Springer Cham
eBook Packages: Biomedical and Life Sciences, Biomedical and Life Sciences (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s) 2022
Hardcover ISBN: 978-3-030-89009-4Published: 14 January 2022
Softcover ISBN: 978-3-030-89012-4Published: 14 January 2022
eBook ISBN: 978-3-030-89010-0Published: 14 January 2022
Edition Number: 1
Number of Pages: XXIV, 691
Number of Illustrations: 52 b/w illustrations, 61 illustrations in colour
Topics: Agriculture, Biostatistics, Plant Genetics and Genomics, Animal Genetics and Genomics, Statistics for Life Sciences, Medicine, Health Sciences