Genomic Prediction of Complex Traits: Methods and Protocols

This volume explores the conceptual framework and the practical issues related to genomic prediction of complex traits in human medicine and in animal and plant breeding. The book is organized into five parts. Part One reminds molecular genetics approaches intending to predict phenotypic variations.  Part Two presents the principles of genomic prediction of complex traits, and reviews factors that affect its reliability. Part Three describes genomic prediction methods, including machine-learning approaches, accounting for different degree of biological complexity, and reviews the associated computer-packages. Part Four reports on emerging trends such as phenomic prediction and incorporation into genomic prediction models of “omics” data and crop growth models. Part Five is dedicated to lessons learned from cases studies in the fields of human health and animal and plant breeding, and to methods for analysis of the economic effectiveness of genomic prediction. Written in the highly successful Methods in Molecular Biology series format, the book provides theoretical bases and practical guidelines for an informed decision making of practitioners and identifies pertinent routes for further methodological researches.  Cutting-edge and thorough, Complex Trait Predictions: Methods and Protocols is a valuable resource for scientists and researchers who are interested in learning more about this important and developing field. Chapters 3, 9, 13, 14, and 21 are available open access under a Creative Commons Attribution 4.0 International License via link.springer.com. 

Table of Contents:
Genetic Bases of Complex Traits: From Quantitative Trait Loci to Predictions.- Genomic Prediction of Complex Traits, Principles, Overview of Factors Affecting the Reliability of Genomic Prediction, and Algebra of the Reliability.- Building a Calibration Set for Genomic Prediction, Characteristics to be Considered and Optimization Approaches.- Genotyping, the Usefulness of Imputation to Increase SNP Density, Imputation Methods and Tools.- Overview of Genomic Prediction Methods and the Associated Assumptions on the Variance of Marker Effect, and on the Architecture of the Target Trait.- Overview of Major Computer Packages for Genomic Prediction of Complex Traits.- Genome-Enabled Prediction Methods Based on Machine Learning.- Genomic Prediction Methods Accounting for Non-Additive Genetic Effects.- Genome and Environment Based Prediction Models and Methods of Complex Traits Incorporating Genotype × Environment Interaction.- Accounting for Correlation between Traits in Genomic Prediction.- Incorporation of Trait-Specific Genetic Information into Genomic Prediction Models.- Incorporating Omics Data in Genomic Prediction.- Integration of Crop Growth Models and Genomic Prediction.- Phenomic Selection: A New and Efficient Alternative to Genomic Selection.- From Genotype to Phenotype: Polygenic Prediction of Complex Human Traits.- Genomic Prediction of Complex Traits in Animal Breeding with Long Breeding History, The Dairy Cattle Case.- Genomic Selection in Aquaculture Species.- Genomic Prediction of Complex Traits in Perennial Plants: A Case for Forest Trees.- Genomic Prediction of Complex Traits in Forage Plants Species: Perennial Grasses Case.- Genomic Prediction of Complex Traits in an Allogamous Annual Crop: The Case of Maize Single-Cross Hybrids.- Genomic Prediction: Progress and Perspective for Rice Improvement.- Analyzing the Economic Effectiveness of Genomic Selection Relative to Conventional Breeding Approaches.