Table of Contents: Linear Selection Indices in Modern Plant Breeding.

Linear Selection Indices in Modern Plant Breeding.

Bibliographic Details
Main Author:	Céron-Rojas, J. Jesus.
Other Authors:	Crossa, José., Gianola, Daniel.
Format:	eBook
Language:	English
Published:	Cham : Springer International Publishing AG, 2018.
Edition:	1st ed.
Subjects:	Electronic books.
Online Access:	Click to View

Table of Contents:

Intro
Foreword
References
Preface
References
Acknowledgments
Contents
Chapter 1: General Introduction
1.1 Standard Linear Selection Indices
1.1.1 Linear Phenotypic Selection Indices
1.1.2 Linear Marker Selection Indices
1.1.3 Linear Genomic Selection Indices
1.2 Eigen Selection Index Methods
1.2.1 Linear Phenotypic Eigen Selection Index Method
1.2.2 Linear Marker and Genomic Eigen Selection Index Methods
1.3 Multistage Linear Selection Indices
1.4 Stochastic Simulation of Four Linear Phenotypic Selection Indices
1.5 RIndSel: Selection Indices with R
1.6 The Lagrange Multiplier Method
References
Chapter 2: The Linear Phenotypic Selection Index Theory
2.1 Bases for Construction of the Linear Phenotypic Selection Index
2.2 The Net Genetic Merit and the LPSI
2.3 Fundamental Parameters of the LPSI
2.3.1 The LPSI Selection Response
2.3.2 The Maximized Selection Response
2.3.3 The LPSI Expected Genetic Gain Per Trait
2.3.4 Heritability of the LPSI
2.4 Statistical LPSI Properties
2.5 Particular Cases of the LPSI
2.5.1 The Base LPSI
2.5.2 The LPSI for Independent Traits
2.6 Criteria for Comparing LPSI Efficiency
2.7 Estimating Matrices G and P
2.8 Numerical Examples
2.8.1 Simulated Data
2.8.2 Estimated Matrices, LPSI, and Its Parameters
2.8.3 LPSI Efficiency Versus Base Index Efficiency
2.9 The LPSI and Its Relationship with the Quadratic Phenotypic Selection Index
2.9.1 The Quadratic Nonlinear Net Genetic Merit
2.9.2 The Quadratic Index
2.9.3 The Vector and the Matrix of Coefficients of the Quadratic Index
2.9.4 The Accuracy and Maximized Selection Response of the Quadratic Index
References
Chapter 3: Constrained Linear Phenotypic Selection Indices
3.1 The Null Restricted Linear Phenotypic Selection Index.
3.1.1 The Maximized RLPSI Parameters
3.1.2 Statistical Properties of the RLPSI
3.1.3 The RLPSI Matrix of Restrictions
3.1.4 Numerical Examples
3.2 The Predetermined Proportional Gains Linear Phenotypic Selection Index
3.2.1 The Maximized PPG-LPSI Parameters
3.2.2 Statistical Properties of the PPG-LPSI
3.2.3 There Is Only One Optimal PPG-LGSI
3.2.4 Numerical Examples
3.3 The Desired Gains Linear Phenotypic Selection Index
3.4 Applicability of the LPSI, RLPSI, and PPG-LPSI
References
Chapter 4: Linear Marker and Genome-Wide Selection Indices
4.1 The Linear Marker Selection Index
4.1.1 Basic Conditions for Constructing the LMSI
4.1.2 The LMSI Parameters
4.1.3 The Maximized LMSI Parameters
4.1.4 The LMSI for One Trait
4.1.5 Efficiency of LMSI Versus LPSI Efficiency for One Trait
4.1.6 Statistical LMSI Properties
4.2 The Genome-Wide Linear Selection Index
4.2.1 The GW-LMSI Parameters
4.2.2 Relationship Between the GW-LMSI and the LPSI
4.2.3 Statistical Properties of GW-LMSI
4.3 Estimating the LMSI Parameters
4.3.1 Estimating the Marker Score
4.3.2 Estimating the Variance of the Marker Score
4.3.3 Estimating LMSI Selection Response and Efficiency
4.3.4 Estimating the Variance of the Marker Score in the Multi-Trait Case
4.4 Estimating the GW-LMSI Parameters in the Asymptotic Context
4.5 Comparing LMSI Versus LPSI and GW-LMSI Efficiency
References
Chapter 5: Linear Genomic Selection Indices
5.1 The Linear Genomic Selection Index
5.1.1 Basic Conditions for Constructing the LGSI
5.1.2 Genomic Breeding Values and Marker Effects
5.1.3 The LGSI and Its Parameters
5.1.4 Maximizing LGSI Parameters
5.1.5 Relationship Between the LGSI and LPSI Selection Responses
5.1.6 Statistical LGSI Properties.
5.1.7 Genomic Covariance Matrix in the Training and Testing Population
5.1.8 Numerical Examples
5.2 The Combined Linear Genomic Selection Index
5.2.1 The CLGSI Parameters
5.2.2 Relationship Between the CLGSI and the LGSI
5.2.3 Statistical Properties of the CLGSI
5.2.4 Estimating the CLGSI Parameters
5.2.5 LGSI and CLGSI Efficiency Vs LMSI, GW-LMSI and LPSI Efficiency
References
Chapter 6: Constrained Linear Genomic Selection Indices
6.1 The Restricted Linear Genomic Selection Index
6.1.1 The Maximized RLGSI Parameters
6.1.2 Statistical Properties of RLGSI
6.1.3 Numerical Examples
6.2 The Predetermined Proportional Gain Linear Genomic Selection Index
6.2.1 Objective of the PPG-LGSI
6.2.2 The Maximized PPG-LGSI Parameters
6.2.3 Statistical Properties of the PPG-LGSI
6.2.4 Numerical Example
6.3 The Combined Restricted Linear Genomic Selection Index
6.3.1 The Maximized CRLGSI Parameters
6.3.2 Numerical Examples
6.4 The Combined Predetermined Proportional Gains Linear Genomic Selection Index
6.4.1 The Maximized CPPG-LGSI Parameters
6.4.2 Numerical Examples
References
Chapter 7: Linear Phenotypic Eigen Selection Index Methods
7.1 The Linear Phenotypic Eigen Selection Index Method
7.1.1 The ESIM Parameters
7.1.2 Statistical ESIM Properties
7.1.3 The ESIM and the Canonical Correlation Theory
7.1.4 Estimated ESIM Parameters and Their Sampling Properties
7.1.5 Numerical Examples
7.2 The Linear Phenotypic Restricted Eigen Selection Index Method
7.2.1 The RESIM Parameters
7.2.2 Estimating the RESIM Parameters
7.2.3 Numerical Examples
7.3 The Linear Phenotypic Predetermined Proportional Gain Eigen Selection Index Method
7.3.1 The PPG-ESIM Parameters
7.3.2 Estimating PPG-ESIM Parameters
7.3.3 Numerical Examples
References.
Chapter 8: Linear Molecular and Genomic Eigen Selection Index Methods
8.1 The Molecular Eigen Selection Index Method
8.1.1 The MESIM Parameters
8.1.2 Estimating MESIM Parameters
8.1.3 Numerical Examples
8.2 The Linear Genomic Eigen Selection Index Method
8.2.1 The GESIM Parameters
8.2.2 Numerical Examples
8.3 The Genome-Wide Linear Eigen Selection Index Method
8.3.1 The GW-ESIM Parameters
8.3.2 Estimating GW-ESIM Parameters
8.3.3 Numerical Examples
8.4 The Restricted Linear Genomic Eigen Selection Index Method
8.4.1 The RGESIM Parameters
8.4.2 Estimating RGESIM Parameters
8.4.3 Numerical Examples
8.5 The Predetermined Proportional Gain Linear Genomic Eigen Selection Index Method
8.5.1 The PPG-GESIM Parameters
8.5.2 Numerical Examples
References
Chapter 9: Multistage Linear Selection Indices
9.1 Multistage Linear Phenotypic Selection Index
9.1.1 The MLPSI Parameters for Two Stages
9.1.2 The Selection Intensities
9.1.3 Numerical Example
9.2 The Multistage Restricted Linear Phenotypic Selection Index
9.2.1 The MRLPSI Parameters for Two Stages
9.2.2 Numerical Examples
9.3 The Multistage Predetermined Proportional Gain Linear Phenotypic Selection Index
9.3.1 The MPPG-LPSI Parameters
9.3.2 Numerical Examples
9.4 The Multistage Linear Genomic Selection Index
9.4.1 The MLGSI Parameters
9.4.2 Estimating the Genomic Covariance Matrix
9.4.3 Numerical Examples
9.5 The Multistage Restricted Linear Genomic Selection Index (MRLGSI)
9.5.1 The MRLGSI Parameters
9.5.2 Numerical Examples
9.6 The Multistage Predetermined Proportional Gain Linear Genomic Selection Index
9.6.1 The OMPPG-LGSI Parameters
9.6.2 Numerical Examples
References
Chapter 10: Stochastic Simulation of Four Linear Phenotypic Selection Indices
10.1 Stochastic Simulation.
10.1.1 Breeding Design
10.1.2 Simulating Quantitative Traits
10.1.3 Simulated Scenarios
10.1.4 Inferences
10.2 Results
10.2.1 Realized Genetic Gains
10.2.2 Genetic Variances
10.2.3 Selection Accuracy
References
Chapter 11: RIndSel: Selection Indices with R
11.1 Background
11.2 Requirements, Installation, and Opening
11.3 First Module: Data Reading and Helping
11.4 Second Module: Capturing Parameters to Run
11.5 Selection Index
11.6 Experimental Design
11.7 Variable Selection
11.8 Response Variables
11.9 Molecular Selection Indices
11.10 How to Use RIndSel
11.11 RIndSel Output
References.

Linear Selection Indices in Modern Plant Breeding.

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