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Drought Tolerance In Maize: Indirect Selection Through Secondary Traits Versus Genomewide Selection

Cathrine Ziyomo, R. Bernardo
Published 2013 · Biology

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phenotyping maize (Zea mays L.) for drought tolerance is costly and time consuming. our objectives were to determine (i) the heritability, genetic variance, and genetic correlations for grain yield and secondary traits in maize under drought and (ii) the efficiency of indirect selection through secondary traits versus genomewide selection. Testcrosses of 238 recombinant inbreds from the intermated B73 × Mo17 population were evaluated in multilocation trials under managed drought and nondrought (control) conditions in Minnesota in 2009 and 2010. Mean grain yield under drought was 52% of the mean grain yield in the control experiments. Heritability for grain yield was 0.37 ± 0.08 under drought and 0.60 ± 0.04 in the control experiments. Indirect selection based on anthesis-silking interval, leaf senescence, leaf chlorophyll content, or grain yield in control conditions was not predicted to be more efficient than direct selection for grain yield under drought. Genomewide selection (with 998 markers) for grain yield under drought had a predicted relative efficiency of 1.24. Genetic correlations estimated from genomewide marker effects agreed well with correlations estimated from genetic covariances. Given that multiple cycles of marker-based selection can be done per year in maize and that genotyping is cheaper than phenotyping for drought tolerance, our results suggest that genomewide selection could increase genetic gains per unit time for grain yield under drought. Dep. of Agronomy and Plant Genetics, Univ. of Minnesota, 411 Borlaug Hall, 1991 Upper Buford Circle, Saint Paul, MN 55108. Received 26 Nov. 2012. *Corresponding author (bernardo@umn.edu). Abbreviations: ASI, anthesis-silking interval; h2, heritability on an entry-mean basis; IBM, intermated B73 × Mo17; QTL, quantitative trait loci; REML, restricted maximum likelihood; rMP, correlation between predicted genotypic values and observed phenotypic values; RR-BLUP, ridge regression-best linear unbiased prediction; SPAD, soil plant analysis development; VG, genetic variance. Published in Crop Sci. 52:1269–1275 (2012). doi: 10.2135/cropsci2012.11.0651 Freely available online through the author-supported open-access option. © Crop Science Society of America | 5585 Guilford Rd., Madison, WI 53711 USA All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Permission for printing and for reprinting the material contained herein has been obtained by the publisher. Published May 17, 2013April 19, 2013
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