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ARS Home » Southeast Area » Raleigh, North Carolina » Plant Science Research » Research » Publications at this Location » Publication #339856

Title: Genetic data analysis for plant and animal breeding

item ISIK, FIKRET - North Carolina State University
item Holland, Jim - Jim
item MALTECCA, CHRISTIAN - North Carolina State University

Submitted to: Book Chapter
Publication Type: Book / Chapter
Publication Acceptance Date: 6/30/2017
Publication Date: 9/12/2017
Citation: Isik, F., Holland, J.B., Maltecca, C. 2017. Genetic data analysis for plant and animal breeding. Springer, New York. 400 pp. Book Chapter.

Interpretive Summary: This book is intended as an advanced textbook on the use of statistical analysis on genetic and phenotypic data from breeding populations of crops, trees, and animals. The book includes step-by-step data analysis examples for readers to learn quickly and apply on their studies and work. This is the first book on 'how to' analyze genomic data for plant and animal breeding. It fills the gap between theory of quantitative genetics and software manuals for plant & animal breeding. This book also covers the latest methods of DNA marker analysis for plant and animal breeding.

Technical Abstract: This book is an advanced textbook covering the application of quantitative genetics theory to analysis of actual data (both trait and DNA marker information) for breeding populations of crops, trees, and animals. Chapter 1 is an introduction to basic software used for trait data analysis. Chapter 2 covers restricted maximum likelihood approaches to mixed linear models. Chapter 3 describes different covariance structures. Chapter 4 introduces estimation of breeding values from phenotypes with pedigree information. Chapter 5 generalizes breeding value estimation to include dominance and other non-additive genetic effects. Chapter 6 covers multivariate (multiple trait) models used to estimate genetic correlations. Chapter 7 describes appropriate residual covariance structures for field trials with correlated errors. Chapter 8 discusses multiple-environment trials and modeling genotype-by-environment interaction effects with mixed models including genetic correlations across environments. Chapter 9 introduces DNA marker data derived from sequence or SNP array technologies and describes software approaches to handling and manipulating such data for later use in breeding analysis. Chapter 10 covers imputation of missing genotype values in marker data sets. Chapter 11 describes the estimation of realized genetic relationships and their use in mixed models to predict breeding values as a form of genomic selection. Chapter 12 expands the ideas of genomic selection to alternative methods based on Bayesian concepts.