Linear Selection Indices in Modern Plant Breeding
Published on 11. October 2018
Book
Hardback
XXII, 256 pages
978-3-319-91222-6 (ISBN)
Description
This open access book focuses on the linear selection index (LSI) theory and its statistical properties. It addresses the single-stage LSI theory by assuming that economic weights are fixed and known - or fixed, but unknown - to predict the net genetic merit in the phenotypic, marker and genomic context. Further, it shows how to combine the LSI theory with the independent culling method to develop the multistage selection index theory. The final two chapters present simulation results and SAS and R codes, respectively, to estimate the parameters and make selections using some of the LSIs described. It is essential reading for plant quantitative geneticists, but is also a valuable resource for animal breeders.
More details
Edition
2018 ed.
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Illustrations
45 farbige Abbildungen
XXII, 256 p. 45 illus. in color.
Dimensions
Height: 241 mm
Width: 160 mm
Thickness: 21 mm
Weight
588 gr
ISBN-13
978-3-319-91222-6 (9783319912226)
DOI
10.1007/978-3-319-91223-3
Schweitzer Classification
Other editions
Additional editions
J. Jesus Céron-Rojas | José Crossa
Linear Selection Indices in Modern Plant Breeding
Book
12/2018
Springer
€53.49
Shipment within 7-9 days
Persons
J. Jesus Ceron-Rojas and José Crossa HiriartCIMMYT, Biometrics and Statistics Unit, Apdo. Postal 6-641, 06600 Mexico DF, Mexico
Content
General introduction.- The linear phenotypic selection index theory.- Constrained linear phenotypic selection indices.- Constrained linear phenotypic selection indices.- Linear marker and genomic selection indices.- Linear genomic selection indices.- Constrained linear genomic selection indices.- Linear phenotypic eigen selection index methods.- Linear molecular and genomic eigen selection index methods.- Multistage linear selection indices.- Stochastic simulation of four linear phenotypic selection indices.- RIndSel: Selection indices with R.