This book is open access under a CC BY 4.0 license 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.
