Identification of QTLs for Yield and Drought-Related Traits in Maize: Assessment of Their Causal Relationships

Nikolić, Ana; Ignjatović-Micić, Dragana; Dodig, Dejan; Andjelkovic, Violeta; Lazic-Jancic, Vesna

doi:10.5504/bbeq.2012.0016

Cited by 16 publications

(6 citation statements)

References 32 publications

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“…Previous studies have also reported positive correlations between NDVI and GY ( Trachsel et al., 2016 ). Interestingly, significant positive correlations were observed between morphophysiological and yield-related traits in the present study ( Table S7 ), whereas only non-significant correlations were reported in an earlier study ( Nikolić et al., 2012 ).…”

Section: Discussioncontrasting

confidence: 76%

Mapping of QTLs for morphophysiological and yield traits under water-deficit stress and well-watered conditions in maize

et al. 2023

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Maize productivity is significantly impacted by drought; therefore, improvement of drought tolerance is a critical goal in maize breeding. To achieve this, a better understanding of the genetic basis of drought tolerance is necessary. Our study aimed to identify genomic regions associated with drought tolerance-related traits by phenotyping a mapping population of recombinant inbred lines (RILs) for two seasons under well-watered (WW) and water-deficit (WD) conditions. We also used single nucleotide polymorphism (SNP) genotyping through genotyping-by-sequencing to map these regions and attempted to identify candidate genes responsible for the observed phenotypic variation. Phenotyping of the RILs population revealed significant variability in most of the traits, with normal frequency distributions, indicating their polygenic nature. We generated a linkage map using 1,241 polymorphic SNPs distributed over 10 chromosomes (chrs), covering a total genetic distance of 5,471.55 cM. We identified 27 quantitative trait loci (QTLs) associated with various morphophysiological and yield-related traits, with 13 QTLs identified under WW conditions and 12 under WD conditions. We found one common major QTL (qCW2–1) for cob weight and a minor QTL (qCH1–1) for cob height that were consistently identified under both water regimes. We also detected one major and one minor QTL for the Normalized Difference Vegetation Index (NDVI) trait under WD conditions on chr 2, bin 2.10. Furthermore, we identified one major QTL (qCH1–2) and one minor QTL (qCH1–1) on chr 1 that were located at different genomic positions to those identified in earlier studies. We found co-localized QTLs for stomatal conductance and grain yield on chr 6 (qgs6–2 and qGY6–1), while co-localized QTLs for stomatal conductance and transpiration rate were identified on chr 7 (qgs7–1 and qTR7–1). We also attempted to identify the candidate genes responsible for the observed phenotypic variation; our analysis revealed that the major candidate genes associated with QTLs detected under water deficit conditions were related to growth and development, senescence, abscisic acid (ABA) signaling, signal transduction, and transporter activity in stress tolerance. The QTL regions identified in this study may be useful in designing markers that can be utilized in marker-assisted selection breeding. In addition, the putative candidate genes can be isolated and functionally characterized so that their role in imparting drought tolerance can be more fully understood.

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Section: Discussioncontrasting

confidence: 76%

Mapping of QTLs for morphophysiological and yield traits under water-deficit stress and well-watered conditions in maize

et al. 2023

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“…These plants also showed good stomatal conductance and high chlorophyll index in water stressed conditions. In addition, the NF-YB transcription factor mapped close to an SNP on chromosome 5 and was co-localized with a QTL for GY, which was previously identified by Nikolić et al ( 2012 ). Another transcription factor CAMTA is identified as a regulator of the photosynthetic machinery, where the T-DNA insertion line of AtCAMTAs has low photo system II efficiency under drought stress (Pandey et al, 2013 ).…”

Section: Discussionmentioning

confidence: 55%

“…ERF is another drought-responsive transcription factor stimulated under the effect of ABA but is also integrated with other two hormones—jasmonic acid and ethylene—which induce the closing of stomata (Cheng et al, 2013 ). The ERF transcription factor mapped close to three SNPs, distributed on chromosomes 5, 7, and 8 was co-localized with quantitative trait loci (QTLs) mapped for EL, GY, ASI, and KRN in earlier studies (Guo et al, 2008 ; Messmer et al, 2009 ; Nikolić et al, 2012 ; Figure 2 ).…”

Section: Discussionmentioning

confidence: 76%

Genomic Selection for Drought Tolerance Using Genome-Wide SNPs in Maize

et al. 2017

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Traditional breeding strategies for selecting superior genotypes depending on phenotypic traits have proven to be of limited success, as this direct selection is hindered by low heritability, genetic interactions such as epistasis, environmental-genotype interactions, and polygenic effects. With the advent of new genomic tools, breeders have paved a way for selecting superior breeds. Genomic selection (GS) has emerged as one of the most important approaches for predicting genotype performance. Here, we tested the breeding values of 240 maize subtropical lines phenotyped for drought at different environments using 29,619 cured SNPs. Prediction accuracies of seven genomic selection models (ridge regression, LASSO, elastic net, random forest, reproducing kernel Hilbert space, Bayes A and Bayes B) were tested for their agronomic traits. Though prediction accuracies of Bayes B, Bayes A and RKHS were comparable, Bayes B outperformed the other models by predicting highest Pearson correlation coefficient in all three environments. From Bayes B, a set of the top 1053 significant SNPs with higher marker effects was selected across all datasets to validate the genes and QTLs. Out of these 1053 SNPs, 77 SNPs associated with 10 drought-responsive transcription factors. These transcription factors were associated with different physiological and molecular functions (stomatal closure, root development, hormonal signaling and photosynthesis). Of several models, Bayes B has been shown to have the highest level of prediction accuracy for our data sets. Our experiments also highlighted several SNPs based on their performance and relative importance to drought tolerance. The result of our experiments is important for the selection of superior genotypes and candidate genes for breeding drought-tolerant maize hybrids.

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“…Sanguineti et al (1999) identified eight QTLs on chromosomes 1, 2, 7, 9, and 10 related to the LRWC of maize under drought stress. Nikoli c et al (2014) detected 14 QTLs for LRWC, accounting for 0.80%-69.4% of the phenotypic variation, on chromosomes 1-6, 8, and 10, out of which three of the QTLs on chromosome 2 partially overlapped with the QTLs identified by Sanguineti et al (1999). Although these QTLs have been identified in maize, the genes controlling LRWC are still unknown, especially regarding the underlying genetic mechanism (Aroca et al, 2003;Kamal et al, 2020).…”

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confidence: 98%

Bulk analysis by resequencing and RNA‐seq identifies candidate genes for maintaining leaf water content under water deficit in maize

Zhang

et al. 2021

Physiologia Plantarum

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Drought is one of the main abiotic stresses adversely affecting maize growth and grain yield. Identifying drought tolerance-related genes and breeding varieties with enhanced tolerance are effective strategies for minimizing the effects of drought stress. In this study, the leaf relative water content (LRWC) was used for evaluating drought tolerance. QTL-seq analysis of 419 F 2 individuals from a cross between ZhengT22 (the drought-tolerant line with high LRWC) and ZhengA88 (the drought-sensitive line with low LRWC) revealed four LRWC-related QTLs (qLRWC2, qLRWC10a, qLRWC10b, and qLRWC10c) in maize seedlings under water deficit. Of these QTLs, qLRWC2 was located in a 2.03-Mb interval on chromosome 2, whereas qLRWC10a, qLRWC10b, and qLRWC10c were located in 2.85-, 3.99-, and 2.05-Mb intervals, respectively, on chromosome 10, and the 93 genes contained the variation loci locating in the four QTLs regions. To identify the candidate genes within the QTLs, an RNA-seq analysis was performed for the parents exposed to water deficit. Seven genes with effective variation loci showed significant difference in expression either in ZhengA88 or ZhengT22 in response to water deficit. Moreover, among the genes, ZmPrx64, ZmCIPK, HSP90, and ABCG34 have all been shown to be related to water stress in the previous studies. Thus, they are primary considered as the potential candidate genes controlling LRWC under water deficit at the seeding stage of maize in this study. These findings will help clarify the molecular basis of drought tolerance in maize seedlings and may be relevant for future functional analysis and for breeding drought-tolerant maize varieties. | INTRODUCTIONDrought is a main abiotic constraint to crop growth and grain yield, accounting for approximately 70% potential yield loss for major crops worldwide (Ghatak et al., 2017;Wu et al., 2017). According to meteorologists estimation, the rates of grain yield reduction due to drought stress for the major crops will obviously increase and is expected to be nearly 90% in 2100 (Li et al., 2009). Hence, enhancing crop tolerance to drought is critical for sustainable production (Khan et al., 2020).Maize (Zea mays L.) is one of the most widely cultivated crops, offering feed for animals, food for humans, materials for industry, Fengqi Zhang and Jun Zhang contributed equally to this work.

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Identification of QTLs for Yield and Drought-Related Traits in Maize: Assessment of Their Causal Relationships

Cited by 16 publications

References 32 publications

Mapping of QTLs for morphophysiological and yield traits under water-deficit stress and well-watered conditions in maize

Mapping of QTLs for morphophysiological and yield traits under water-deficit stress and well-watered conditions in maize

Genomic Selection for Drought Tolerance Using Genome-Wide SNPs in Maize

Bulk analysis by resequencing and RNA‐seq identifies candidate genes for maintaining leaf water content under water deficit in maize

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