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Reimagining Maize Inbred Potential: Identifying Breeding Crosses Using Genetic Variance of Simulated Progeny
Author(s) -
Beckett Travis J.,
Rocheford Torbert R.,
Mohammadi Mohsen
Publication year - 2019
Publication title -
crop science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.76
H-Index - 147
eISSN - 1435-0653
pISSN - 0011-183X
DOI - 10.2135/cropsci2018.08.0508
Subject(s) - biology , germplasm , trait , hybrid , test weight , inbred strain , population , agronomy , genetic gain , plant breeding , breeding program , best linear unbiased prediction , microbiology and biotechnology , crop , selection (genetic algorithm) , genetic variation , grain yield , genetics , cultivar , demography , artificial intelligence , sociology , computer science , gene , programming language
Proper choice of parents for breeding populations is essential in developing new maize ( Zea mays L.) inbreds for improved hybrid performance. Breeders have frequently chosen parental combinations based on mid‐parent (MP) value, or predicted progeny mean, to combine favorable traits from the two parents. When two breeding populations have the same MP value, an accurate prediction of progeny variance may reveal which population has a greater potential for genetic gain. In this study we used available inbred genotypes and new hybrid phenotypes from 246 inbreds with expired Plant Variety Protection certificates and 39 historically important North American dent inbreds, all test crossed to Iodent inbred PHP02. We used the R package ‘PopVar’ to simulate biparental populations and perform genome‐wide prediction to predict the progeny mean, genetic variance, and superior progeny mean for grain yield, grain moisture, and test weight within each virtual population. Breeding crosses were ranked based on the mean grain yield of superior progeny and correlated trait responses for test weight and moisture. Results show that combining germplasm from different proprietors in new breeding crosses can produce inbreds with improved performance in a hybrid test cross. The simulation and prediction model presented in this study may help breeders to identify parental pairs with the greatest potential for genetic gain in hybrid crop breeding programs.