Mapping QTLs and meta-QTLs for two inflorescence architecture traits in multiple maize populations under different watering environments

QTL mapping for six ear leaf architecture traits under water‐stressed and well‐watered conditions in maize (Zea mays L.)

“…Then, the corresponding plants were harvested and subsequently air‐dried to measure ear weight (EW; the weight per ear including the cob), 100‐kernel weight (KW; the 100 kernels randomly chosen from the bulked kernels) and ear length (EL; the length per ear including the bald head). Relative to well‐watered conditions, the average rate of change (RC) of each trait under water‐stressed conditions was calculated as follows (Zhao et al., ):where T S is the average value of a trait under water‐stressed conditions and T W is the average value of the corresponding trait under well‐watered conditions.…”

“…TS141 originates from Reid yellow dent, and Langhuang and Chang7-2 originate from Tangsipingtou (Wu et al, 2014). Moreover, previous research suggests that Langhuang and Chang7-2 have a higher drought tolerance than TS141 (Peng, Zhao, Ren, & Li, 2014;Peng et al, 2013;Zhao, Peng, Zhang, Fang, & Wu, 2017).…”

Comparative QTL analysis for yield components and morphological traits in maize (<i>Zea mays</i> L.) under water-stressed and well-watered conditions

“…The collected phenotypic data from each watering condition were statistically analyzed. The general linear model for univariate (GLM-Univariate) tests was used for conducting an ANOVA (Zhao et al 2017), which was employed to evaluate the total and residual variances among F 2:3 populations for each trait. Pearson correlation analyses and dendrogram construction were performed using the IBM-SPSS Statistics 19.0 software (SPSS Inc., Chicago, IL, USA).…”

“…Maize grain yield (GY) is the primary objective of breeding for drought tolerance, however, the low heritability of GY, as it decreases under drought stress conditions, makes the direct selection for this trait inefficient (Su et al 2017). However, multiple morphological traits and yield components, namely, plant height (PH), ear height (EH), leaf area (LA), anthesis-silking interval (ASI), tassel branch number (TBN), ear weight (EW), cob weight (CW), 100-kernel weight (KW), and ear length (EL), are correlated with GY and drought tolerance can experience increased genetic variance and heritability under drought environments (Almeida et al 2014, Zhao et al 2017, 2018b, Ziyomo and Bernardo 2012. Moreover, drought stress inhibits growth of vegetative organs, limits photosynthesis, reduces flux of assimilates to the developing ears, slows down ear and silk growth and delays silk emergence (Bänziger et al 2000, Zhao et al 2017.…”

“…Genetic studies on maize PH, EH, ASI, TBN, EW, CW, KW, and EL have been extensively conducted using quantitative trait locus (QTL) mapping in biparental populations during drought (Almeida et al 2014, Lu et al 2010, Semagn et al 2013, Vargas et al 2006, Zhao et al 2017. QTLs/ genes have been identified for morphological traits and yield components in maize using map-based cloning strategies, such as Brachytic 2 (Br2) (Xing et al 2015), gln1-4 (Martin et al 2006), Opaque-2 (o2) (Schmidt et al 1992), unbranched2 (ub2) (Chuck et al 2014), and ZmCLA4 (Zhang et al 2014), which can be useful targets for marker assisted selection (MAS) in modern maize breeding programs.…”