A Genomic Selection Index Applied to Simulated and Real Data

Authors

J. Jesus Ceron-Rojas, Biometrics and Statistics Unit, International Maize and Wheat Improvement Center (CIMMYT), Mexico
Jose Crossa, Biometrics and Statistics Unit, International Maize and Wheat Improvement Center (CIMMYT), MexicoFollow
Vivi N. Arief, The University of Queensland, School of Agriculture and Food Sciences, St Lucia, QLD 4072, Brisbane, Australia
Kaye Basford, The University of Queensland, School of Agriculture and Food Sciences, St Lucia, QLD 4072, Brisbane, Australia
Jessica Rutkoski, Biometrics and Statistics Unit, International Maize and Wheat Improvement Center (CIMMYT), Mexico
Diego Jarquin, University of Nebraska-LincolnFollow
Gregorio Alvarado, Biometrics and Statistics Unit, International Maize and Wheat Improvement Center (CIMMYT), Mexico
Yoseph Beyene, Global Maize Program, CIMMYT, Village Market 00621, Nairobi, Kenya
Kassa Semagn, Global Maize Program, CIMMYT, Village Market 00621, Nairobi, Kenya
Ian DeLacy, The University of Queensland, School of Agriculture and Food Sciences, St Lucia, QLD 4072, Brisbane, Australia

Date of this Version

2015

Citation

Genes Genomes Genetics Volume 5 (October 2015) 2155-

Comments

Copyright © 2015 Ceron-Rojas et al.

Abstract

A genomic selection index (GSI) is a linear combination of genomic estimated breeding values that uses genomic markers to predict the net genetic merit and select parents from a nonphenotyped testing population. Some authors have proposed a GSI; however, they have not used simulated or real data to validate the GSI theory and have not explained how to estimate the GSI selection response and the GSI expected genetic gain per selection cycle for the unobserved traits after the first selection cycle to obtain information about the genetic gains in each subsequent selection cycle. In this paper, we develop the theory of a GSI and apply it to two simulated and four real data sets with four traits. Also, we numerically compare its efficiency with that of the phenotypic selection index (PSI) by using the ratio of the GSI response over the PSI response, and the PSI and GSI expected genetic gain per selection cycle for observed and unobserved traits, respectively. In addition, we used the Technow inequality to compare GSI vs. PSI efficiency. Results from the simulated data were confirmed by the real data, indicating that GSI was more efficient than PSI per unit of time.