Titlebook: Linear Selection Indices in Modern Plant Breeding; J. Jesus Céron-Rojas,José Crossa Book‘‘‘‘‘‘‘‘ 2018 The Editor(s) (if applicable) and Th

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General Introduction,em is called . In the first approach, the economic weights are fixed and known, whereas in the second approach the economic weights are fixed but unknown. This is the main difference between both approaches and implies that the eigen selection index methods include to the standard linear selection i

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The Linear Phenotypic Selection Index Theory,linear combination of the true unobservable breeding values of the traits weighted by their respective economic values, whereas the LPSI is a linear combination of several observable and optimally weighted phenotypic trait values. It is assumed that the net genetic merit and the LPSI have bivariate

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Linear Marker and Genome-Wide Selection Indices,al 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 ani

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Linear Genomic Selection Indices,tic merit and select individual candidates from a nonphenotyped testing population as parents of the next selection cycle. In the LGSI, phenotypic and marker data from the training population are fitted into a statistical model to estimate all individual available genome marker effects; these estima

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Constrained Linear Genomic Selection Indices,PG-LGSI respectively), which are a linear combination of genomic estimated breeding values (GEBVs) to predict the net genetic merit. They are the results of a direct application of the restricted and the predetermined proportional gain linear phenotypic selection index theory to the genomic selectio

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