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Genetic Data Analysis for Plant and Animal Breeding 1st ed. 2017 [Kõva köide]

  • Formaat: Hardback, 400 pages, kõrgus x laius: 279x210 mm, 241 Illustrations, color; 119 Illustrations, black and white; XVII, 400 p. 360 illus., 241 illus. in color., 1 Hardback
  • Ilmumisaeg: 02-Oct-2017
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319551752
  • ISBN-13: 9783319551753
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  • Formaat: Hardback, 400 pages, kõrgus x laius: 279x210 mm, 241 Illustrations, color; 119 Illustrations, black and white; XVII, 400 p. 360 illus., 241 illus. in color., 1 Hardback
  • Ilmumisaeg: 02-Oct-2017
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319551752
  • ISBN-13: 9783319551753
This book fills the gap between textbooks of quantitative genetic theory, and software manuals that provide details on analytical methods but little context or perspective on which methods may be most appropriate for a particular application. Accordingly this book is composed of two sections. The first section (Chapters 1 to 8) covers topics of classical phenotypic data analysis for prediction of breeding values in animal and plant breeding programs. In the second section (Chapters 9 to 13) we provide the concept and overall review of available tools for using DNA markers for predictions of genetic merits in breeding populations. With advances in DNA sequencing technologies, genomic data, especially single nucleotide polymorphism (SNP) markers, have become available for animal and plant breeding programs in recent years. Analysis of DNA markers for prediction of genetic merit is a relatively new and active research area. The algorithms and software to implement these algorithms are changing rapidly. This section represents state-of-the-art knowledge on the tools and technologies available for genetic analysis of plants and animals. However, readers should be aware that the methods or statistical packages covered here may not be available or they might be out of date in a few years. Ultimately the book is intended for professional breeders interested in utilizing these tools and approaches in their breeding programs. Lastly, we anticipate the usage of this volume for advanced level graduate courses in agricultural and breeding courses.
1 Introduction to ASReml Software
1(48)
Why ASReml?
2(1)
ASReml Workflow
2(1)
Setting Up ConTEXT Editor to Create and Execute ASReml Command Files
3(1)
Starting with ASReml
3(14)
Data Field Definitions
8(1)
Transformation of Response Variables
9(1)
Data File and Job Control Qualifiers
10(4)
Specifying Terms in the Linear Model
14(1)
Variance Header Line and Random Model Terms
15(2)
Running ASReml
17(1)
ASReml Output Files
18(11)
Tabulation
26(1)
Prediction
27(2)
Processing Multiple Analyses with One Command File
29(8)
Linear Combinations of Variance Components
37(2)
A Brief Introduction to ASReml-R
39(10)
Data Set Used in the Analysis
40(3)
Fitting a Model in ASReml-R
43(6)
2 A Review of Linear Mixed Models
49(38)
Mixed Models Compared to Traditional ANOVA
50(12)
Balanced Data: ANOVA with SAS Proc GLM
51(2)
Balanced Data: ANOVA with R
53(2)
Balanced Data: ANOVA with ASReml
55(2)
Balanced Data: Mixed Models Analysis with SAS Proc MIXED
57(1)
Balanced Data: Mixed Models Analysis with R
57(2)
Balanced Data: Mixed Models Analysis with ASReml
59(1)
Hypothesis Testing with Mixed Models
60(1)
Prediction: BLUE and BLUP
61(1)
Unbalanced Data
62(12)
ANOVA with SAS Proc GLM
62(3)
Unbalanced Data: Mixed Models Analysis with SAS
65(9)
Mixed Models in a Nutshell: Theory and Concepts
74(7)
The Model
74(1)
Fixed and Random Effects
74(2)
Expectations and Variance-Covariance for the Random Effects
76(1)
A Trivial Example: Daughters Lactation Yield
77(2)
Solving the Model
79(1)
The Mixed Model Equations
80(1)
Estimability in Models with Multiple Fixed Effects
81(3)
Standard Errors and Accuracy of the Estimates
84(1)
A Brief Note on REML
85(2)
3 Variance Modeling in ASReml
87(20)
Variance Model Specifications
88(14)
Gamma and Sigma Parameterization in ASReml
88(1)
Homogenous Variance Models
89(2)
Heterogeneous R Variance Structures
91(7)
Heterogeneous G Variance Structures
98(4)
Initial Values
102(5)
4 Breeding Values
107(34)
Family Selection
108(1)
Causal Variance Components and Resemblance
109(3)
The GCA (Family) Model
112(1)
Analysis of Half-Sib Progeny Data Using GCA Model
113(8)
Variance Components and Their Linear Combinations
115(2)
Variation Among Family Means
117(1)
Within-Family Variation
118(2)
The Accuracy of Breeding Values
120(1)
Individual ("Animal") Model
121(6)
Animal Model for Half-Sib Family Data
122(5)
The Animal Model with Deep Pedigrees and Maternal Effects
127(7)
Accounting for Genetic Groups Effect in Predictions
134(4)
Treating Genetic Groups as a Fixed Effect in GCA model
134(2)
Fitting Genetic Groups as Pedigree Information in Individual Model
136(2)
Effect of Self-Fertilization on Variance Components
138(3)
5 Genetic Values
141(24)
Specific Combining Ability (SCA) and Genetic Values
142(1)
Diallel Mating Designs
142(14)
Diallel Example
144(5)
Specific Combining Ability (SCA) Effect
149(1)
Reciprocal Effects
150(2)
Interpretation of Observed Variances from Diallels
152(1)
Linear Combinations of Variances from Diallels
153(3)
Factorial Mating Designs
156(2)
Analysis of Cloned Progeny Test Data
158(7)
6 Multivariate Models
165(38)
Introduction
166(1)
Some Theory
166(5)
The Linear Mixed Model for Multivariate Models
167(4)
Maize RILs Multivariate Model
171(22)
Linear Combinations of Variances and Covariances
184(7)
Predictions from Multivariate Models
191(2)
The Animal Model in a Multivariate Re-visitation
193(10)
7 Spatial Analysis
203(24)
Background
204(1)
Modeling Spatial Effects
204(5)
Variance-Covariance Matrix of Residuals
205(3)
Model Selection
208(1)
Example of Spatial Analyses of Field Trial Data
209(18)
Heritability Estimate from Spatial Model
219(8)
8 Multi Environmental Trials
227(36)
Introduction
228(2)
MET: General Approach and Considerations
228(2)
Statistical Models
230(5)
Formulation of FA models in ASReml
233(2)
Example: Analysis of Pine Polymix MET Data
235(14)
Summarize Data for Each Site
235(1)
Analyze Each Site Separately to Obtain Variances
236(1)
Model 3 Cross-Classified ANOVA
237(1)
Model 4 Compound Symmetry
238(1)
Model 5 Heterogeneous Residuals and Block Effects
239(1)
Model 6 CORUH G Structure
239(1)
Models 7 and 8 US and CORGH Structures
240(1)
Model 9 FA1 Covariance Structure
240(2)
Model 10 FA1 Correlation Structure
242(3)
Model 11 XFA1 Structure
245(1)
Model 12 XFA2 Structure
246(2)
Model 13 XFA3 Structure
248(1)
MET Models with ASReml-R
248(1)
Genetic Prediction with FA Models
249(5)
Estimating Heritability and Reliability from FA Models
254(8)
Biplots from FA Models
262(1)
9 Exploratory Marker Data Analysis
263(24)
Marker Data and Some Definitions
264(5)
Allele Frequencies
266(1)
Hardy-Weinberg Equilibrium (HWE)
266(1)
Polymorphism Information Content
267(1)
Heterozygosity
267(1)
Linkage Disequilibrium (LD)
267(2)
Software and Tools for Processing Marker Data
269(12)
Introduction to the Synbreed Package
269(1)
Maritime Pine Data Example
270(6)
Recoding Loci and Imputing Missing Genotypes
276(1)
Genetics Package for Estimating Population Parameters
277(4)
Data Summary and Visualization
281(6)
Genetic Map
281(1)
Pairwise Linkage Disequilibrium
282(5)
10 Imputing Missing Genotypes
287(24)
Introduction
288(1)
The Idea Behind Imputation
289(1)
Pedigree Free Imputation
289(3)
Imputation from Densely Genotyped Reference Panel to Individuals Genotyped at Lower Density
292(12)
Imputation Without a Reference Panel
304(2)
Imputation with the Synbreed Package
306(5)
11 Genomic Relationships and GBLUP
311(44)
Realized Genomic Relationships
312(12)
Calculation of G Matrices
319(5)
Genomic BLUP
324(12)
GBLUP with the Synbreed Package
327(9)
Cross-Validation
336(14)
GBLUP with Replicated Family Data in ASReml
343(7)
Blended Genetic Relationships
350(5)
Example Calculation of H Matrix
351(4)
12 Genomic Selection
355(30)
Regression Models for Genomic Prediction
356(6)
A Brief Tour of Bayesian Concepts
357(4)
Choice of Statistical Models
361(1)
Bayesian Regression Examples with BGLR Package
362(23)
Model Fit Statistics and Model Convergence
367(4)
Choice of Priors
371(5)
Genetic Architecture
376(5)
Cross-Validation
381(4)
Index of Figures 385(4)
Literature Cited 389(6)
Index 395
Fikret Isik is a Professor of quantitative genetics in the Department of Forestry and Environmental Resources and the Associate Director of the Tree Improvement Program at North Carolina State University.





Jim Holland is a research geneticist with the United States Department of Agriculture - Agriculture Research Service, and a Professor in the Department of Crop and Soil Sciences at North Carolina State University. 









Christian Maltecca is an Associate Professor of quantitative genetics and breeding in the Animal Science department at North Carolina State University.