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Estimating genotype × environment interaction for and genetic correlations among drought tolerance traits in maize via factor analytic multiplicative mixed models

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Crop Science Society of America (CSSA)

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Water deficit is one of the most common causes of severe crop-production losses worldwide in maize (Zea mays L.). The main goal of this study was to infer about genotype × environment interaction (G × E) and to estimate genetic correlations between drought tolerance traits in maize using factor analytic (FA) multiplicative mixed models in the context of multi-environment trial (MET) and multi-trait multi-environment trial (MTMET) analyses. The traits measured were: grain yield (GY), ears per plot (EPP), anthesis-silking interval (ASI), female flowering time (FFT), and male flowering time (MFT). Three-hundred and eight hybrids were evaluated in a total of eight trials conducted under water-stressed (WS) and well-watered (WW) conditions across 2 yr and two locations in Brazil. For most of the traits (GY, ASI, and FFT), the magnitude of the genetic variances differed across WS and WW conditions. Genetic correlations between water conditions for FFT and MFT were 0.81 and 0.82, respectively, indicating that it might be unnecessary to measure these traits in both water conditions. Grain yield and EPP showed moderate to high G × E, with genetic correlations of 0.57 and 0.39 between WS and WW conditions, respectively, which suggested that gene expression was not consistent across different water regimes. Therefore, it is necessary to evaluate these traits under both water conditions. Genetic correlations between pairs of traits, in general, were higher under WS conditions compared with WW conditions. Grain yield exhibited moderate correlations with EPP (r = 0.62) and FFT (r = −0.42) under WS conditions. The FA models can be a useful tool for MET and MTMET analyses in maize breeding programs for drought tolerance.

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DIAS, K. O. das G. et al. Estimating genotype × environment interaction for and genetic correlations among drought tolerance traits in maize via factor analytic multiplicative mixed models. Crop Science, [S.l.], v. 58, n. 1, p. 72-83, 2018. DOI: 10.2135/cropsci2016.07.0566

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