A Genomic Selection Index Applied to Simulated and Real Data.
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.
Biometrics and Statistics Unit, International Maize and Wheat Improvement Center (CIMMYT), 06600, México Distrito Federal, México.
J Jesus
JJ
Ceron-Rojas
Biometrics and Statistics Unit, International Maize and Wheat Improvement Center (CIMMYT), 06600, México Distrito Federal, México j.crossa@cgiar.org.
José
http://orcid.org/0000-0001-9429-5855
J
Crossa
The University of Queensland, School of Agriculture and Food Sciences, St Lucia, QLD 4072, Brisbane, Australia.
Vivi N
VN
Arief
The University of Queensland, School of Agriculture and Food Sciences, St Lucia, QLD 4072, Brisbane, Australia.
Kaye
K
Basford
Biometrics and Statistics Unit, International Maize and Wheat Improvement Center (CIMMYT), 06600, México Distrito Federal, México International Programs of the College of Agriculture and Life Sciences, Cornell University, Ithaca, New York 14853.
Jessica
J
Rutkoski
Department of Agronomy and Horticulture, University of Nebraska, Lincoln, Nebraska 68583.
Diego
D
Jarquín
Biometrics and Statistics Unit, International Maize and Wheat Improvement Center (CIMMYT), 06600, México Distrito Federal, México.
Gregorio
G
Alvarado
Global Maize Program, CIMMYT, Village Market 00621, Nairobi, Kenya.
Yoseph
Y
Beyene
Global Maize Program, CIMMYT, Village Market 00621, Nairobi, Kenya.
Kassa
K
Semagn
The University of Queensland, School of Agriculture and Food Sciences, St Lucia, QLD 4072, Brisbane, Australia.
Ian
I
DeLacy
D000465:Algorithms; D003198:Computer Simulation; D066264:Datasets as Topic; D008957:Models, Genetic; D012641:Selection, Genetic
GenPred; genomic estimated breeding value; genomic selection; net genetic merit; selection index; selection response; shared data resource