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Linear Selection Indices in Modern Plant Breeding.

Yazar:C�eron-Rojas, J. Jesus
Katkıda bulunan(lar):Crossa, Jos�e | Gianola, Daniel
Materyal türü: KonuKonuYayıncı: Cham : Springer International Publishing AG, 2018Telif hakkı tarihi: {copy}2018Tanım: 1 online resource (271 pages)İçerik türü:text Ortam türü:computer Taşıyıcı türü: online resourceISBN: 9783319912233Tür/Form:Electronic books.Ek fiziksel biçimler:Print version:: Linear Selection Indices in Modern Plant BreedingLOC classification: QH323.5Çevrimiçi kaynaklar: Click to View
İçindekiler:
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.
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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.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2022. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.

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