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Genomic prediction of the general combining ability of maize lines ( Zea mays L.) and the performance of their single crosses
Author(s) -
VélezTorres Marcelina,
GarcíaZavala José Jesús,
HernándezRodríguez Martha,
LobatoOrtiz Ricardo,
LópezReynoso José Jesús,
BenítezRiquelme Ignacio,
MejíaContreras José Apolinar,
EsquivelEsquivel Gilberto,
MolinaGalán José Domingo,
PérezRodríguez Paulino,
Zhang Xuecai
Publication year - 2018
Publication title -
plant breeding
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.583
H-Index - 71
eISSN - 1439-0523
pISSN - 0179-9541
DOI - 10.1111/pbr.12597
Subject(s) - zea mays , biology , single nucleotide polymorphism , selection (genetic algorithm) , genomic selection , snp , genetics , genotyping , microbiology and biotechnology , genotype , agronomy , gene , computer science , machine learning
The objective of this study was to assess the effectiveness of genomic selection ( GS ) on predicting the general combining ability ( GCA ) of maize lines and the performance of their single crosses. Eight maize lines developed from the different self‐pollination generations of Chalqueño race, along with their 24 single crosses, were evaluated in the field during the years of 2011, 2012 and 2013. Genomic prediction results using genotyping‐by‐sequencing‐based single nucleotide polymorphisms showed that the GCA classification of the parental lines estimated from the SNP information was consistent with the phenotypic classification of the lines evaluated from the field trial data. The prediction accuracy values estimated from the cross‐validation method ranged from 0.49 to 0.61 in the different prediction models. Yield performance of the unevaluated single crosses was predicted based on their SNP information. The total genetic variance of the yield of the single crosses was most explained by the GCA effects. Compared with phenotyping method, GS is a more effective and efficient approach to predict the GCA of maize lines and their hybrid performance.