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000799822 019__ $$a1003317593$$a1003698451
000799822 020__ $$a9783319551777$$q(electronic book)
000799822 020__ $$a3319551779$$q(electronic book)
000799822 020__ $$z9783319551753
000799822 020__ $$z3319551752
000799822 035__ $$aSP(OCoLC)on1003515814
000799822 035__ $$aSP(OCoLC)1003515814$$z(OCoLC)1003317593$$z(OCoLC)1003698451
000799822 040__ $$aYDX$$beng$$erda$$cYDX$$dN$T$$dEBLCP$$dGW5XE$$dN$T$$dOCLCF$$dNJR
000799822 049__ $$aISEA
000799822 050_4 $$aQH430
000799822 08204 $$a576.5$$223
000799822 1001_ $$aIsik, Fikret,$$eauthor.
000799822 24510 $$aGenetic data analysis for plant and animal breeding /$$cFikret Isik, James Holland, Christian Maltecca.
000799822 264_1 $$aCham, Switzerland :$$bSpringer,$$c[2017].
000799822 264_4 $$c©2017
000799822 300__ $$a1 online resource.
000799822 336__ $$atext$$btxt$$2rdacontent
000799822 337__ $$acomputer$$bc$$2rdamedia
000799822 338__ $$aonline resource$$bcr$$2rdacarrier
000799822 504__ $$aIncludes bibliographical references and indexes.
000799822 5050_ $$aPreface; Software Requirements; Contents; About the Authors; Chapter 1: Introduction to ASReml Software; Why ASReml?; ASReml Workflow; Setting Up ConTEXT Editor to Create and Execute ASReml Command Files; Starting with ASReml; Data Field Definitions; Transformation of Response Variables; Data File and Job Control Qualifiers; Specifying Terms in the Linear Model; Variance Header Line and Random Model Terms; Running ASReml; ASReml Output Files; Tabulation; Prediction; Processing Multiple Analyses with One Command File; Linear Combinations of Variance Components; A Brief Introduction to ASReml-R
000799822 5058_ $$aData Set Used in the AnalysisFitting a Model in ASReml-R; Chapter 2: A Review of Linear Mixed Models; Mixed Models Compared to Traditional ANOVA; Balanced Data: ANOVA with SAS Proc GLM; Balanced Data: ANOVA with R; Balanced Data: ANOVA with ASReml; Balanced Data: Mixed Models Analysis with SAS Proc MIXED; Balanced Data: Mixed Models Analysis with R; Balanced Data: Mixed Models Analysis with ASReml; Hypothesis Testing with Mixed Models; Prediction: BLUE and BLUP; Unbalanced Data; ANOVA with SAS Proc GLM; Unbalanced Data: Mixed Models Analysis with SAS
000799822 5058_ $$aMixed Models in a Nutshell: Theory and ConceptsThe Model; Fixed and Random Effects; Expectations and Variance-Covariance for the Random Effects; A Trivial Example: Daughters Lactation Yield; Solving the Model; The Mixed Model Equations; Estimability in Models with Multiple Fixed Effects; Standard Errors and Accuracy of the Estimates; A Brief Note on REML; Chapter 3: Variance Modeling in ASReml; Variance Model Specifications; Gamma and Sigma Parameterization in ASReml; Homogenous Variance Models; Heterogeneous R Variance Structures; Sections May Have Different Residual Variances
000799822 5058_ $$aError Effects May Not Be IndependentHeterogeneous G Variance Structures; Block Diagonal G Structure; Nested and Interaction Terms in the G Structure; Effects in G Can Be Correlated; Correlated Effects Due to Genetics; Initial Values; Chapter 4: Breeding Values; Family Selection; Causal Variance Components and Resemblance; The GCA (Family) Model; Analysis of Half-Sib Progeny Data Using GCA Model; Variance Components and Their Linear Combinations; Variation Among Family Means; Within-Family Variation; The Accuracy of Breeding Values; Individual ("Animal) Model
000799822 5058_ $$aAnimal Model for Half-Sib Family DataThe Animal Model with Deep Pedigrees and Maternal Effects; Accounting for Genetic Groups Effect in Predictions; Treating Genetic Groups as a Fixed Effect in GCA model; Fitting Genetic Groups as Pedigree Information in Individual Model; Effect of Self-Fertilization on Variance Components; Chapter 5: Genetic Values; Specific Combining Ability (SCA) and Genetic Values; Diallel Mating Designs; Diallel Example; Specific Combining Ability (SCA) Effect; Reciprocal Effects; Interpretation of Observed Variances from Diallels
000799822 506__ $$aAccess limited to authorized users.
000799822 520__ $$aThis 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.
000799822 588__ $$aDescription based on print version record.
000799822 650_0 $$aGenetics.
000799822 7001_ $$aHolland, James,$$eauthor.
000799822 7001_ $$aMaltecca, Christian,$$eauthor.
000799822 77608 $$iPrint version:$$z3319551752$$z9783319551753$$w(OCoLC)972772184
000799822 852__ $$bebk
000799822 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-55177-7$$zOnline Access$$91397441.1
000799822 909CO $$ooai:library.usi.edu:799822$$pGLOBAL_SET
000799822 980__ $$aEBOOK
000799822 980__ $$aBIB
000799822 982__ $$aEbook
000799822 983__ $$aOnline
000799822 994__ $$a92$$bISE