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Chapter 4. Linear marker and genome-wide selection indices

Author: Ceron Rojas, J.J.
Author: Crossa, J.
Year: 2018
ISBN: 978-3-319-91222-6 (Print)
ISBN: 978-3-319-91223-3 (Online)
URI: https://hdl.handle.net/10883/19803
Abstract: There are two main linear marker selection indices employed in marker-assisted selection (MAS) to predict the net genetic merit and to select individual candidates as parents for the next generation: the linear marker selection index (LMSI) and the genome-wide LMSI (GW-LMSI). Both indices maximize the selection response, the expected genetic gain per trait, and the correlation with the net genetic merit; however, applying the LMSI in plant or animal breeding requires genotyping the candidates for selection; performing a linear regression of phenotypic values on the coded values of the markers such that the selected markers are statistically linked to quantitative trait loci that explain most of the variability in the regression model; constructing the marker score, and combining the marker score with phenotypic information to predict and rank the net genetic merit of the candidates for selection. On the other hand, the GW-LMSI is a single-stage procedure that treats information at each individual marker as a separate trait. Thus, all marker information can be entered together with phenotypic information into the GW-LMSI, which is then used to predict the net genetic merit and select candidates. We describe the LMSI and GW-LMSI theory and show that both indices are direct applications of the linear phenotypic selection index theory to MAS. Using real and simulated data we validated the theory of both indices.
Format: PDF
Language: English
Publisher: Springer
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Type: Book Chapter
Place of Publication: Switzerland
Pages: 71-98
DOI: 10.1007/978-3-319-91223-3_4
Agrovoc: MARKER-ASSISTED SELECTION
Agrovoc: GENETIC GAIN
Agrovoc: LINEAR MODELS
Agrovoc: PHENOTYPIC VARIATION
Journal: Linear selection indices in modern plant breeding


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    Genetic Resources including germplasm collections, wild relatives, genotyping, genomics, and IP

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