Drought stress and tropical maize: QTL-by-environment interactions and stability of QTLs across environments for yield components and secondary traits

Leveraging Multiomics Insights and Exploiting Wild Relatives' Potential for Drought and Heat Tolerance in Maize

Climate change, particularly drought and heat stress, may slash agricultural productivity by 25.7% by 2080, with maize being the hardest hit. Therefore, unraveling the molecular nature of plant responses to these stressors is vital for the development of climate-smart maize. This manuscript's primary objective was to examine how maize plants respond to these stresses, both individually and in combination. Additionally, the paper delved into harnessing the potential of maize wild relatives as a valuable genetic resource and leveraging AI-based technologies to boost maize resilience. The role of multiomics approaches particularly genomics and transcriptomics in dissecting the genetic basis of stress tolerance was also highlighted. The way forward was proposed to utilize a bunch of information obtained through omics technologies by an interdisciplinary state-of-the-art forward-looking big-data, cyberagriculture system, and AI-based approach to orchestrate the development of climate resilient maize genotypes.

Genome-wide association analysis of four yield-related traits using a maize (Zea mays L.) F1 population

Increasing the yield of maize F1 hybrid is one of the most important target for breeders. However, as a result of the genetic complexity and extremely low heritability, it is very difficult to directly dissect the genetic basis and molecular mechanisms of yield, and reports on genetic analysis of F1 hybrid yield are rare. Taking F1 hybrid as the research object and dividing the yield into different affect factors, this approach may be the best strategy for clarifying the genetic mechanism of yield. Therefore, in this study, a maize F1 population consisting of 300 hybrids with 17,652 single nucleotide polymorphisms (SNPs) markers was used for genome-wide association study (GWAS) to filtrate candidate genes associated with the four yield-related traits, i.e., kernel row number (KRN), kernel number per row (KNPR), ear tip-barrenness (ETB), and hundred kernel weight (HKW). Combined with the results of previous studies and functional annotation information of candidate genes, a total of six candidate genes were identified as being associated with the four traits, which were involved in plant growth and development, protein synthesis response, phytohormone biosynthesis and signal transduction. Our results improve the understanding of the genetic basis of the four yield-related traits and may be provide a new strategy for the genetic basis of maize yield.

Studies on Candidate Genes Related to Flowering Time in a Multiparent Population of Maize Derived from Tropical and Temperate Germplasm

A comprehensive study on maize flowering traits, focusing on the regulation of flowering time and the elucidation of molecular mechanisms underlying the genes controlling flowering, holds the potential to significantly enhance our understanding of the associated regulatory gene network. In this study, three tropical maize inbreds, CML384, CML171, and CML444, were used, along with a temperate maize variety, Shen137, as parental lines to cross with Ye107. The resulting F1s underwent seven consecutive generations of self-pollination through the single-seed descent (SSD) method to develop a multiparent population. To investigate the regulation of maize flowering time-related traits and to identify loci and candidate genes, a genome-wide association study (GWAS) was conducted. GWAS analysis identified 556 SNPs and 12 candidate genes that were significantly associated with flowering time-related traits. Additionally, an analysis of the effect of the estimated breeding values of the subpopulations on flowering time was conducted to further validate the findings of the present study. Collectively, this study offers valuable insights into novel candidate genes, contributing to an improved understanding of maize flowering time-related traits. This information holds practical significance for future maize breeding programs aimed at developing high-yielding hybrids.

Identification of quantitative trait loci (QTLs) regulating leaf SPAD value and trichome density in mungbean (Vigna radiata L.) using genotyping-by-sequencing (GBS) approach

Quantitative trait loci (QTL) mapping is used for the precise localization of genomic regions regulating various traits in plants. Two major QTLs regulating Soil Plant Analysis Development (SPAD) value (qSPAD-7-1) and trichome density (qTric-7-2) in mungbean were identified using recombinant inbred line (RIL) populations (PMR-1×Pusa Baisakhi) on chromosome 7. Functional analysis of QTL region identified 35 candidate genes for SPAD value (16 No) and trichome (19 No) traits. The candidate genes regulating trichome density on the dorsal leaf surface of the mungbean include VRADI07G24840, VRADI07G17780, and VRADI07G15650, which encodes for ZFP6, TFs bHLH DNA-binding superfamily protein, and MYB102, respectively. Also, candidate genes having vital roles in chlorophyll biosynthesis are VRADIO7G29860, VRADIO7G29450, and VRADIO7G28520, which encodes for s-adenosyl-L-methionine, FTSHI1 protein, and CRS2-associated factor, respectively. The findings unfolded the opportunity for the development of customized genotypes having high SPAD value and high trichome density having a possible role in yield and mungbean yellow vein mosaic India virus (MYMIV) resistance in mungbean.

Epigenetic variation in maize agronomical traits for breeding and trait improvement

Epigenetics-mediated breeding (Epibreeding) involves engineering crop traits and stress responses through the targeted manipulation of key epigenetic features to enhance agricultural productivity. While conventional breeding methods raise concerns about reduced genetic diversity, epibreeding propels crop improvement through epigenetic variations that regulate gene expression, ultimately impacting crop yield. Epigenetic regulation in crops encompasses various modes, including histone modification, DNA modification, RNA modification, non-coding RNA, and chromatin remodeling. This review summarizes the epigenetic mechanisms underlying major agronomic traits in maize and identifies candidate epigenetic landmarks in the maize breeding process. We propose a valuable strategy for improving maize yield through epibreeding, combining CRISPR/Cas-based epigenome editing technology and Synthetic Epigenetics (SynEpi). Finally, we discuss the challenges and opportunities associated with maize trait improvement through epibreeding.

Effects of Climate Change and Drought Tolerance on Maize Growth

Climate change is affecting all regions of the world with different climates, and the scale of damage is increasing due to the occurrence of various natural disasters. In particular, maize production is highly affected by abnormal climate events such as heat waves and droughts. Increasing temperatures can accelerate growth and shorten the growing season, potentially reducing productivity. Additionally, enhanced temperatures during the ripening period can accelerate the process, reducing crop yields. In addition, drought stress due to water deficit can greatly affect seedling formation, early plant growth, photosynthesis, reproductive growth, and yield, so proper water management is critical to maize growth. Maize, in particular, is tall and broad-leaved, so extreme drought stress at planting can cause leaves to curl and stunt growth. It is important to understand that severe drought can have a detrimental effect on the growth and reproduction of maize. In addition, high temperatures caused by drought stress can inhibit the induction of flowering in male flowers and cause factors that interfere with pollen development. It is therefore important to increase the productivity of all food crops, including maize, while maintaining them in the face of persistent drought caused by climate change. This requires a strategy to develop genetically modified crops and drought-tolerant maize that can effectively respond to climate change. The aim of this paper is to investigate the effects of climate change and drought tolerance on maize growth. We also reviewed molecular breeding techniques to develop drought-tolerant maize varieties in response to climate change.

Molecular mechanisms of drought resistance using genome-wide association mapping in maize (Zea mays L.)

BACKGROUND

Drought is a critical abiotic stress that influences maize yield and reduces grain yield when it occurs at the flowering or filling stage. To dissect the genetic architecture of grain yield under drought stress (DS), a genome-wide association analysis was conducted in a maize population composed of diverse inbred lines from five locations under well-watered and DS conditions at flowering in 2019 and 2020.

RESULTS

Using a fixed and random model circulating probability unification model, a total of 147 loci associated with grain yield or the drought resistance index (DRI) were identified, of which 54 loci were associated with a DRI with an average phenotypic variation explanation of 4.03%. Further, 10 of these loci explained more than 10% of the phenotypic variation. By integrating two public transcriptome datasets, 22 differentially expressed genes were considered as candidate genes, including the cloned gene ZmNAC49, which responds to drought by regulating stomatal density. Enrichment and protein interaction network showed that signaling pathways responded to drought resistance, including jasmonic acid and salicylic acid, mitogen-activated protein kinase, and abscisic acid-activated. Additionally, several transcription factors involved in DS were identified, including basic leucine zipper (GRMZM2G370026), NAC (GRMZM2G347043), and ethylene-responsive element binding protein (GRMZM2G169654).

CONCLUSIONS

In this study, we nominated several genes as candidate genes for drought resistance by intergrating association maping and transcription analysis. These results provide valuable information for understanding the genetic basis of drought tolerance at the mature stage and for designing drought-tolerant maize breeding.

Dynamic growth QTL action in diverse light environments: characterization of light regime-specific and stable QTL in Arabidopsis

Plant growth is a complex process affected by a multitude of genetic and environmental factors and their interactions. To identify genetic factors influencing plant performance under different environmental conditions, vegetative growth was assessed in Arabidopsis thaliana cultivated under constant or fluctuating light intensities, using high-throughput phenotyping and genome-wide association studies. Daily automated non-invasive phenotyping of a collection of 382 Arabidopsis accessions provided growth data during developmental progression under different light regimes at high temporal resolution. Quantitative trait loci (QTL) for projected leaf area, relative growth rate, and PSII operating efficiency detected under the two light regimes were predominantly condition-specific and displayed distinct temporal activity patterns, with active phases ranging from 2 d to 9 d. Eighteen protein-coding genes and one miRNA gene were identified as potential candidate genes at 10 QTL regions consistently found under both light regimes. Expression patterns of three candidate genes affecting projected leaf area were analysed in time-series experiments in accessions with contrasting vegetative leaf growth. These observations highlight the importance of considering both environmental and temporal patterns of QTL/allele actions and emphasize the need for detailed time-resolved analyses under diverse well-defined environmental conditions to effectively unravel the complex and stage-specific contributions of genes affecting plant growth processes.

Drought stress in maize: stress perception to molecular response and strategies for its improvement

Given the future demand for food crops, increasing crop productivity in drought-prone rainfed areas has become essential. Drought-tolerant varieties are warranted to solve this problem in major crops, with drought tolerance as a high-priority trait for future research. Maize is one such crop affected by drought stress, which limits production, resulting in substantial economic losses. It became a more serious issue due to global climate change. The most drought sensitive among all stages of maize is the reproductive stages and the most important for overall maize production. The exact molecular basis of reproductive drought sensitivity remains unclear due to genes' complex regulation of drought stress. Understanding the molecular biology and signaling of the unexplored area of reproductive drought tolerance will provide an opportunity to develop climate-smart drought-tolerant next-generation maize cultivars. In recent decades, significant progress has been made in maize to understand the drought tolerance mechanism. However, improving maize drought tolerance through breeding is ineffective due to the complex nature and multigenic control of drought traits. With the help of advanced breeding techniques, molecular genetics, and a precision genome editing approach like CRISPR-Cas, candidate genes for drought-tolerant maize can be identified and targeted. This review summarizes the effects of drought stress on each growth stage of maize, potential genes, and transcription factors that determine drought tolerance. In addition, we discussed drought stress sensing, its molecular mechanisms, different approaches to developing drought-resistant maize varieties, and how molecular breeding and genome editing will help with the current unpredictable climate change.

Genetic variation among elite inbred lines suggests potential to breed for BNI-capacity in maize

Biological nitrification inhibition (BNI) is a plant function where root systems release antibiotic compounds (BNIs) specifically aimed at suppressing nitrifiers to limit soil-nitrate formation in the root zone. Little is known about BNI-activity in maize (Zea mays L.), the most important food, feed, and energy crop. Two categories of BNIs are released from maize roots; hydrophobic and hydrophilic BNIs, that determine BNI-capacity in root systems. Zeanone is a recently discovered hydrophobic compound with BNI-activity, released from maize roots. The objectives of this study were to understand/quantify the relationship between zeanone activity and hydrophobic BNI-capacity. We assessed genetic variability among 250 CIMMYT maize lines (CMLs) characterized for hydrophobic BNI-capacity and zeanone activity, towards developing genetic markers linked to this trait in maize. CMLs with high BNI-capacity and ability to release zeanone from roots were identified. GWAS was performed using 27,085 SNPs (with unique positions on the B73v.4 reference genome, and false discovery rate = 10), and phenotypic information for BNI-capacity and zeanone production from root systems. Eighteen significant markers were identified; three associated with specific BNI-activity (SBNI), four with BNI-activity per plant (BNIPP), another ten were common between SBNI and BNIPP, and one with zeanone release. Further, 30 annotated genes were associated with the significant SNPs; most of these genes are involved in pathways of "biological process", and one (AMT5) in ammonium regulation in maize roots. Although the inbred lines in this study were not developed for BNI-traits, the identification of markers associated with BNI-capacity suggests the possibility of using these genomic tools in marker-assisted selection to improve hydrophobic BNI-capacity in maize.

Mapping and Functional Analysis of QTL for Kernel Number per Row in Tropical and Temperate-Tropical Introgression Lines of Maize (Zea mays L.)

Kernel number per row (KNR) is an essential component of maize (Zea mays L.) grain yield (GY), and understanding its genetic mechanism is crucial to improve GY. In this study, two F₇ recombinant inbred line (RIL) populations were created using a temperate-tropical introgression line TML418 and a tropical inbred line CML312 as female parents and a backbone maize inbred line Ye107 as the common male parent. Bi-parental quantitative trait locus (QTL) mapping and genome-wide association analysis (GWAS) were then performed on 399 lines of the two maize RIL populations for KNR in two different environments using 4118 validated single nucleotide polymorphism (SNP) markers. This study aimed to: (1) detect molecular markers and/or the genomic regions associated with KNR; (2) identify the candidate genes controlling KNR; and (3) analyze whether the candidate genes are useful in improving GY. The authors reported a total of 7 QTLs tightly linked to KNR through bi-parental QTL mapping and identified 21 SNPs significantly associated with KNR through GWAS. Among these, a highly confident locus qKNR7-1 was detected at two locations, Dehong and Baoshan, with both mapping approaches. At this locus, three novel candidate genes (Zm00001d022202, Zm00001d022168, Zm00001d022169) were identified to be associated with KNR. These candidate genes were primarily involved in the processes related to compound metabolism, biosynthesis, protein modification, degradation, and denaturation, all of which were related to the inflorescence development affecting KNR. These three candidate genes were not reported previously and are considered new candidate genes for KNR. The progeny of the hybrid Ye107 × TML418 exhibited strong heterosis for KNR, which the authors believe might be related to qKNR7-1. This study provides a theoretical foundation for future research on the genetic mechanism underlying KNR in maize and the use of heterotic patterns to develop high-yielding hybrids.

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

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.

Identifying yield-related genes in maize based on ear trait plasticity

BACKGROUND

Phenotypic plasticity is defined as the phenotypic variation of a trait when an organism is exposed to different environments, and it is closely related to genotype. Exploring the genetic basis behind the phenotypic plasticity of ear traits in maize is critical to achieve climate-stable yields, particularly given the unpredictable effects of climate change. Performing genetic field studies in maize requires development of a fast, reliable, and automated system for phenotyping large numbers of samples.

RESULTS

Here, we develop MAIZTRO as an automated maize ear phenotyping platform for high-throughput measurements in the field. Using this platform, we analyze 15 common ear phenotypes and their phenotypic plasticity variation in 3819 transgenic maize inbred lines targeting 717 genes, along with the wild type lines of the same genetic background, in multiple field environments in two consecutive years. Kernel number is chosen as the primary target phenotype because it is a key trait for improving the grain yield and ensuring yield stability. We analyze the phenotypic plasticity of the transgenic lines in different environments and identify 34 candidate genes that may regulate the phenotypic plasticity of kernel number.

CONCLUSIONS

Our results suggest that as an integrated and efficient phenotyping platform for measuring maize ear traits, MAIZTRO can help to explore new traits that are important for improving and stabilizing the yield. This study indicates that genes and alleles related with ear trait plasticity can be identified using transgenic maize inbred populations.

Genetic dissection of embryo size and weight related traits for enhancement of kernel oil in maize

Embryo is a key determinant of kernel-oil in maize. Higher calorific value of maize kernel is attributed to increment in kernel-oil and it stores in specialised structure called embryo. Understanding the genetic behaviour of embryo size and weight related-traits is inevitable task for genetic improvement of kernel-oil. Here, the six-basic generations (P₁, P₂, F₁, F₂, BC₁P₁ and BC₁P₂) of three crosses (CRPBIO-962 × EC932601, CRPBIO-973 × CRPBIO-966 and CRPBIO-966 × CRPBIO-979) between contrasting embryo-sized maize inbreds were field evaluated at three locations to decipher the genetics of twenty embryo, kernel and embryo-to-kernel related-traits through generation-mean-analysis (GMA). Combined ANOVA revealed the significance of all the traits among generations; however, location and generation × location were found to be non-significant (P > 0.05) for most of the traits. Significance (P < 0.05) of scaling and joint-scaling tests revealed the presence of non-allelic interactions. Elucidation of six-parameters disclosed the predominance of dominance main-effect (h) and dominance × dominance interaction-effect (l) for most of traits. The signs of (h) and (l) indicated the prevalence of duplicate-epistasis type across crosses and locations. Thus, the population improvement approaches along with heterosis breeding method could be effective for improvement of these traits. Quantitative inheritance pattern was observed for all the traits with high broad-sense heritability and better-stability across locations. The study also predicted one to three major-gene blocks/QTLs for embryo-traits and up to 11 major-gene blocks/QTLs for embryo-to-kernel traits. These findings could provide deep insights to strategize extensive breeding methods to improve embryo traits for enhancing kernel-oil in sustainable manner.

Effects of post-silking drought stress degree on grain yield and quality of waxy maize

BACKGROUND

Drought stress (DS) induced by post-silking have a major impact on the yield and quality of maize. In this study, the effects of different degrees of DS after pollination on grain filling, starch and protein metabolism, and functional properties were investigated using two waxy maize cultivars as materials. The levels of DS that were investigated were 'mild water stress' (WS1), 'moderate water stressed' (WS2), and 'severe waterstressed' (WS3).

RESULTS

Drought stress decreased grain fresh weight, dry weight, and moisture content in both cultivars during grain filling, and reduced kernel number, kernel weight, and grain yield at maturity. The effect on grain development and yield formation gradually increased with drought aggravation. The water stress (WS) treatment downregulated the enzymatic activities related to starch biosynthesis during grain-filling process, accompanied by a decrease in soluble sugar and starch deposition. The WS treatment increased the enzymatic activities involved in protein synthesis during grain-filling process, thereby increasing the protein content of grains. On average, WS2 and WS3 treatments reduced the pasting viscosities and increased the gelatinization temperatures of grains, with WS3 having the greatest effect. However, the changes of setback viscosity, gelatinization enthalpy, retrogradation enthalpy, and retrogradation percentage under WS treatment were inconsistent in both cultivars. Pearson correlation analysis showed that starch content was negatively correlated with gelatinization temperatures and positively correlated with pasting viscosities in both cultivars. However, grain pasting  and gelatinization properties have opposite correlations with protein content and starch content.

CONCLUSIONS

These findings suggest that post-silking DS regulated the grain-filling process and starch and protein biosynthesis, which influenced grain yield and quality. © 2022 Society of Chemical Industry.

Analysis of QTL mapping for germination and seedling response to drought stress in sunflower (Helianthus annuus L.)

Sunflower is an important oilseed crop across the world. It is considered as a moderately drought tolerant plant, however, its yield is still negatively affected by drought stress. Improving drought tolerance is of the outmost important for breeding. Although several studies have documented the relationship between the sunflower phenotype and genotype under drought stress, but relatively few studies have simultaneously investigated the molecular mechanisms of drought tolerance in the sunflower at different growth stages. In this study, we conducted quantitative trait locus (QTL) analysis for different sunflower traits during the germination and seedling stages. Eighteen phenotypic traits were evaluated under well-watered and drought stress conditions. We determined that the germination rate, germination potential, germination index, and root-to-shoot ratio can be used as effective indexes for drought tolerance selection and breeding. A total of 33 QTLs were identified on eight chromosomes (PVE: 0.016%-10.712% with LOD: 2.017-7.439). Within the confidence interval of the QTL, we identified 60 putative drought-related genes. Four genes located on chromosome 13 may function in both germination and seedling stages for drought response. Genes LOC110898128, LOC110898092, LOC110898071, and LOC110898072 were annotated as aquaporin SIP1-2-like, cytochrome P450 94C1, GABA transporter 1-like, and GABA transporter 1-like isoform X2, respectively. These genes will be used for further functional validation. This study provides insight into the molecular mechanisms of the sunflower's in response to drought stress. At the same time, it lays a foundation for sunflower drought tolerance breeding and genetic improvement.

Genetic dissection of QTLs for oil content in four maize DH populations

Oil is one of the main components in maize kernels. Increasing the total oil content (TOC) is favorable to optimize feeding requirement by improving maize quality. To better understand the genetic basis of TOC, quantitative trait loci (QTL) in four double haploid (DH) populations were explored. TOC exhibited continuously and approximately normal distribution in the four populations. The moderate to high broad-sense heritability (67.00-86.60%) indicated that the majority of TOC variations are controlled by genetic factors. A total of 16 QTLs were identified across all chromosomes in a range of 3.49-30.84% in term of phenotypic variation explained. Among them, six QTLs were identified as the major QTLs that explained phenotypic variation larger than 10%. Especially, qOC-1-3 and qOC-2-3 on chromosome 9 were recognized as the largest effect QTLs with 30.84% and 21.74% of phenotypic variance, respectively. Seventeen well-known genes involved in fatty acid metabolic pathway located within QTL intervals. These QTLs will enhance our understanding of the genetic basis of TOC in maize and offer prospective routes to clone candidate genes regulating TOC for breeding program to cultivate maize varieties with the better grain quality.

Nucleotide polymorphisms of the maize ZmFWL7 gene and their association with ear-related traits

Plant fw2.2-like (FWL) genes, encoding proteins harboring a placenta-specific eight domain, have been suggested to control fruit and grain size through regulating cell division, differentiation, and expansion. Here, we re-sequenced the nucleotide sequences of the maize ZmFWL7 gene, a member of the FWL family, in 256 elite maize inbred lines, and the associations of nucleotide polymorphisms in this locus with 11 ear-related traits were further detected. A total of 175 variants, including 159 SNPs and 16 InDels, were identified in the ZmFWL7 locus. Although the promoter and downstream regions showed higher nucleotide polymorphism, the coding region also possessed 61 SNPs and 6 InDels. Eleven polymorphic sites in the ZmFWL7 locus were found to be significantly associated with eight ear-related traits. Among them, two nonsynonymous SNPs (SNP2370 and SNP2898) showed significant association with hundred kernel weight (HKW), and contributed to 7.11% and 8.62% of the phenotypic variations, respectively. In addition, the SNP2898 was associated with kernel width (KW), and contributed to 7.57% of the phenotypic variations. Notably, the elite allele T of SNP2370 was absent in teosintes and landraces, while its frequency in inbred lines was increased to 12.89%. By contrast, the frequency of the elite allele A of SNP2898 was 3.12% in teosintes, and it was raised to 12.68% and 19.92% in landraces and inbred lines, respectively. Neutral tests show that this locus wasn’t artificially chosen during the process of domestication and genetic improvement. Our results revealed that the elite allelic variants in ZmFWL7 might possess potential for the genetic improvement of maize ear-related traits.

Earbox, an open tool for high-throughput measurement of the spatial organization of maize ears and inference of novel traits

BACKGROUND

Characterizing plant genetic resources and their response to the environment through accurate measurement of relevant traits is crucial to genetics and breeding. Spatial organization of the maize ear provides insights into the response of grain yield to environmental conditions. Current automated methods for phenotyping the maize ear do not capture these spatial features.

RESULTS

We developed EARBOX, a low-cost, open-source system for automated phenotyping of maize ears. EARBOX integrates open-source technologies for both software and hardware that facilitate its deployment and improvement for specific research questions. The imaging platform consists of a customized box in which ears are repeatedly imaged as they rotate via motorized rollers. With deep learning based on convolutional neural networks, the image analysis algorithm uses a two-step procedure: ear-specific grain masks are first created and subsequently used to extract a range of trait data per ear, including ear shape and dimensions, the number of grains and their spatial organisation, and the distribution of grain dimensions along the ear. The reliability of each trait was validated against ground-truth data from manual measurements. Moreover, EARBOX derives novel traits, inaccessible through conventional methods, especially the distribution of grain dimensions along grain cohorts, relevant for ear morphogenesis, and the distribution of abortion frequency along the ear, relevant for plant response to stress, especially soil water deficit.

CONCLUSIONS

The proposed system provides robust and accurate measurements of maize ear traits including spatial features. Future developments include grain type and colour categorisation. This method opens avenues for high-throughput genetic or functional studies in the context of plant adaptation to a changing environment.

Stubby or Slender? Ear Architecture Is Related to Drought Resistance in Maize

Ear architecture is determined by two stable heritable traits, kernel row number (KRN) and kernel number per row (KNPR), but its relationship with drought resistance is still vague. To this end, we obtained 16 and 11 hybrids with slender (less KRN but more KNPR) and stubby (more KRN but less KNPR) ears by intentionally crossbreeding, respectively. These hybrids were exposed to a seven-day water deficit (WD) since silk emergence coupled with synchronous (SP) and continuous pollination (CP) to alter the pollination time gaps on ears. The results showed that the emerged silks in CP were 9.1 and 9.0% less than in the SP treatment in the stubby and slender ears, respectively, suggesting the suppression of asynchronous pollination on silk emergence. The stubby ears performed higher silking rate and yield compared with the slender ears with or without drought stress. To eliminate the inherent difference in sink capacities, we selected four hybrids for each ear type with similar silk and kernel numbers for further analyses. Interestingly, the stubby ears were less affected in silking rate and thus performed higher yield under drought compared with the slender ears. The finding suggests that ear architecture matters in the determination of drought resistance that deserves more attention in breeding.

Genome-Wide Association Analysis for Candidate Genes Contributing to Kernel-Related Traits in Maize

Maize grain size is the main factor determining grain yield. Dissecting the genetic basis of maize grain size may help reveal the regulatory mechanism of maize seed development and yield formation. In this study, two associated populations were used for genome-wide association analysis of kernel length, kernel width, kernel thickness, and hundred-kernel weight from multiple locations in AM122 and AM180, respectively. Then, genome-wide association mapping was performed based on the maize 6H90K SNP chip. A total of 139 loci were identified as associated with the four traits with p < 1 × 10^-4 using two models (FarmCPU and MLM). The transcriptome data showed that 15 of them were expressed differentially in two maize-inbred lines KB182 (small kernel) and KB020 (big kernel) during kernel development. These candidate genes were enriched in regulating peroxidase activity, oxidoreductase, and leaf senescence. The molecular function was major in binding and catalytic activity. This study provided important reference information for exploring maize kernel development mechanisms and applying molecular markers in high-yield breeding.

Identification of a candidate gene underlying qHKW3, a QTL for hundred-kernel weight in maize

qHKW3, a quantitative trait locus for hundred-kernel weight, harbors the proposed causal gene Zm00001d044081, encoding a homeobox-leucine zipper protein (ATHB-4) that might affect kernel size and weight. Kernel size and weight are key traits that contribute greatly to grain yield per year in maize (Zea mays). Here, we developed the chromosome segment substitution line (CSSL), H15-6-2, with smaller kernel size and lower kernel weight across environments compared to the background line Ye478. Histological analysis suggested that a slower kernel filling rate of H15-6-2 contributes to its small-kernel size and reduced hundred-kernel weight. We identified a quantitative trait locus (QTL) explaining 23% of the phenotypic variation in hundred-kernel weight. This QTL, qHKW3, was fine mapped to an interval of approximately 40.66-kb harboring the gene Zm00001d044081. The upstream sequence and its expression level of Zm00001d044081 in kernels at 6 days after pollination (DAP) showed obvious differences between the near-isogenic lines HKW3^Ye478 and HKW3^H15-6-2. We further confirmed the effects of the Zm00001d044081 promoter on maize kernel size and weight in an independent association mapping panel with 513 lines by candidate regional association analysis. We propose that Zm00001d044081, which encodes the homeobox-leucine zipper protein ATHB-4, is the causal gene of qHKW3, representing an attractive target for the genetic improvement of maize yield.

Mutant lpa1 Analysis of ZmLPA1 Gene Regulates Maize Leaf-Angle Development through the Auxin Pathway

Maize plant type is one of the main factors determining maize yield, and leaf angle is an important aspect of plant type. The rice Loose Plant Architecture1 (LPA1) gene and Arabidopsis AtIDD15/SHOOT GRAVITROPISM5 (SGR5) gene are related to their leaf angle. However, the homologous ZmLPA1 in maize has not been studied. In this study, the changing of leaf angle, as well as gene expression in leaves in maize mutant lpa1 and wild-type ‘B73’ under different IAA concentrations were investigated. The regulation effect of IAA on the leaf angle of lpa1 was significantly stronger than that of the wild type. Transcriptome analysis showed that different exogenous IAA treatments had a common enrichment pathway—the indole alkaloid biosynthesis pathway—and among the differentially expressed genes, four genes—AUX1, AUX/IAA, ARF and SAUR—were significantly upregulated. This study revealed the regulation mechanism of ZmLPA1 gene on maize leaf angle and provided a promising gene resource for maize breeding.

Dissection of Maize Drought Tolerance at the Flowering Stage Using Genome-Wide Association Studies

Drought is one of the most critical environmental factors constraining maize production. When it occurs at the flowering stage, serious yield losses are caused, and often, the damage is irretrievable. In this study, anthesis to silk interval (ASI), plant height (PH), and ear biomass at the silking date (EBM) of 279 inbred lines were studied under both water-stress (WS) and well-water (WW) field conditions, for three consecutive years. Averagely, ASI was extended by 25.96%, EBM was decreased by 17.54%, and the PH was reduced by 12.47% under drought stress. Genome-wide association studies were carried out using phenotypic values under WS, WW, and drought-tolerance index (WS-WW or WS/WW) and applying a mixed linear model that controls both population structure and relative kinship. In total, 71, 159, and 21 SNPs, located in 32, 59, and 12 genes, were significantly (P < 10−5) associated with ASI, EBM, and PH, respectively. Only a few overlapped candidate genes were found to be associated with the same drought-related traits under different environments, for example, ARABIDILLO 1, glycoprotein, Tic22-like, and zinc-finger family protein for ASI; 26S proteasome non-ATPase and pyridoxal phosphate transferase for EBM; 11-ß-hydroxysteroid dehydrogenase, uncharacterised, Leu-rich repeat protein kinase, and SF16 protein for PH. Furthermore, most candidate genes were revealed to be drought-responsive in an association panel. Meanwhile, the favourable alleles/key variations were identified with a haplotype analysis. These candidate genes and their key variations provide insight into the genetic basis of drought tolerance, especially for the female inflorescence, and will facilitate drought-tolerant maize breeding.

Maize plant architecture trait QTL mapping and candidate gene identification based on multiple environments and double populations

BACKGROUND

The plant architecture traits of maize determine the yield. Plant height, ear position, leaf angle above the primary ear and internode length above the primary ear together determine the canopy structure and photosynthetic efficiency of maize and at the same time affect lodging and disease resistance. A flat and tall plant architecture confers an obvious advantage in the yield of a single plant but is not conducive to dense planting and results in high rates of lodging; thus, it has been gradually eliminated in production. Although using plants that are too compact, short and density tolerant can increase the yield per unit area to a certain extent, the photosynthetic efficiency of such plants is low, ultimately limiting yield increases. Genetic mapping is an effective method for the improvement of plant architecture to identify candidate genes for regulating plant architecture traits.

RESULTS

To find the best balance between the yield per plant and the yield per unit area of maize, in this study, the F2:3 pedigree population and a RIL population with the same male parent were used to identify QTL for plant height (PH), ear height (EH), leaf angle and internode length above the primary ear (LAE and ILE) in Changchun and Gongzhuling for 5 consecutive years (2016-2020). A total of 11, 13, 23 and 13 QTL were identified for PH, EH, LAE, and ILE, respectively. A pleiotropic consistent QTL for PH overlapped with that for EH on chromosome 3, with a phenotypic variation explanation rate from 6.809% to 21.96%. In addition, there were major consistent QTL for LAE and ILE, and the maximum phenotypic contribution rates were 24.226% and 30.748%, respectively. Three candidate genes were mined from the three consistent QTL regions and were involved in the gibberellin-activated signal pathway, brassinolide signal transduction pathway and auxin-activated signal pathway, respectively. Analysis of the expression levels of the three genes showed that they were actively expressed during the jointing stage of vigorous maize growth.

CONCLUSIONS

In this study, three consistent major QTL related to plant type traits were identified and three candidate genes were screened. These results lay a foundation for the cloning of related functional genes and marker-assisted breeding of related functional genes.

Genetic Architecture of Grain Yield-Related Traits in Sorghum and Maize

Grain size, grain number per panicle, and grain weight are crucial determinants of yield-related traits in cereals. Understanding the genetic basis of grain yield-related traits has been the main research object and nodal in crop science. Sorghum and maize, as very close C4 crops with high photosynthetic rates, stress tolerance and large biomass characteristics, are extensively used to produce food, feed, and biofuels worldwide. In this review, we comprehensively summarize a large number of quantitative trait loci (QTLs) associated with grain yield in sorghum and maize. We placed great emphasis on discussing 22 fine-mapped QTLs and 30 functionally characterized genes, which greatly hinders our deep understanding at the molecular mechanism level. This review provides a general overview of the comprehensive findings on grain yield QTLs and discusses the emerging trend in molecular marker-assisted breeding with these QTLs.

Is Photoprotection of PSII One of the Key Mechanisms for Drought Tolerance in Maize?

Drought is one of the most important abiotic stress factors limiting maize production worldwide. The objective of this study was to investigate whether photoprotection of PSII was associated with the degree of drought tolerance and yield in three maize hybrids (30Y87, 31R88, P3939). To do this, three maize hybrids were subjected to three cycles of drought, and we measured the activities of photosystem II (PSII) and photosystem I (PSI). In a second field experiment, three maize hybrids were subjected to drought by withholding irrigation, and plant water status, yield and yield attributes were measured. Drought stress decreased leaf water potential (Ψ_L) in three maize hybrids, and this reduction was more pronounced in hybrid P3939 (−40%) compared to that of 30Y87 (−30%). Yield and yield attributes of three maize hybrids were adversely affected by drought. The number of kernels and 100-kernel weight was the highest in maize hybrid 30Y87 (−56%, −6%), whereas these were lowest in hybrid P3939 (−88%, −23%). Drought stress reduced the quantum yield of PSII [Y(II)], photochemical quenching (qP), electron transport rate through PSII [ETR(II)] and NPQ, except in P3939. Among the components of NPQ, drought increased the Y(NPQ) with concomitant decrease in Y(NO) only in P3939, whereas Y(NO) increased in drought-stressed plants of hybrid 30Y87 and 31R88. However, an increase in cyclic electron flow (CEF) around PSI and Y(NPQ) in P3939 might have protected the photosynthetic machinery but it did not translate in yield. However, drought-stressed plants of 30Y87 might have sufficiently downregulated PSII to match the energy consumption in downstream biochemical processes. Thus, changes in PSII and PSI activity and development of NPQ through CEF are physiological mechanisms to protect the photosynthetic apparatus, but an appropriate balance between these physiological processes is required, without which plant productivity may decline.

Nucleotide Diversity of the Maize ZmCNR13 Gene and Association With Ear Traits

The maize (Zea mays L.) ZmCNR13 gene, encoding a protein of fw2.2-like (FWL) family, has been demonstrated to be involved in cell division, expansion, and differentiation. In the present study, the genomic sequences of the ZmCNR13 locus were re-sequenced in 224 inbred lines, 56 landraces and 30 teosintes, and the nucleotide polymorphism and selection signature were estimated. A total of 501 variants, including 415 SNPs and 86 Indels, were detected. Among them, 51 SNPs and 4 Indels were located in the coding regions. Although neutrality tests revealed that this locus had escaped from artificial selection during the process of maize domestication, the population of inbred lines possesses lower nucleotide diversity and decay of linkage disequilibrium. To estimate the association between sequence variants of ZmCNR13 and maize ear characteristics, a total of ten ear-related traits were obtained from the selected inbred lines. Four variants were found to be significantly associated with six ear-related traits. Among them, SNP2305, a non-synonymous mutation in exon 2, was found to be associated with ear weight, ear grain weight, ear diameter and ear row number, and explained 4.59, 4.61, 4.31, and 8.42% of the phenotypic variations, respectively. These results revealed that natural variations of ZmCNR13 might be involved in ear development and can be used in genetic improvement of maize ear-related traits.

Association mapping for general combining ability with yield, plant height and ear height using F1 population in maize

General combining ability (GCA) is an important index for inbred lines breeding of maize. To identify the genetic loci of GCA and associated agronomic traits, an association analysis with 195 SSRs was made in phenotypic traits of 240 F1 derived from 120 elite inbred lines containing current breeding resources of maize crossed with 2 testers (Zheng58 and Chang7-2) in two places in 2018. All of the 20 association loci detected for grain yield (GY), plant height (PH), ear height (EH) and GCA for the three traits in two places could explain a phenotypic variation range of 7.31%-9.29%. Among the 20 association loci, 9 (7.31%-9.04%) were associated with GY, 4 (7.22%-8.91%) were related to GCA of GY, 1 (7.56%) was associated with PH, and 3 (7.53%-8.96%) were related to EH. In addition, 3 loci (9.14%-9.29%) were associated with GCA of PH whereas no locus was identified for GCA of EH. In the comparison of the association loci detected in Baoding and Handan, interestingly, one locus (7.69% and 8.11%) was identified in both environments and one locus (7.52% and 7.82%) was identified for yield and GCA of yield. Therefore, the identification of GY-, PH-, EH- and GCA-related association loci could not only provide references for high yield breeding of maize, but also help us comprehend the relationships among GY, agricultural traits and GCA.

Assay system for mesocotyl elongation and hydrotropism of maize primary root in response to low moisture gradient

We designed and validated a test system that simulates a growth environment for Zea mays L. maize seedlings under conditions of low moisture gradient in darkness. This system allowed us to simultaneously measure mesocotyl elongation and the primary root hydrotropic response in seedlings before the emergence phase in a collection of maize hybrids. We found great variation in these two traits with statistically significant reduction of their elongations under the low moisture gradient condition that indicate the richness of maize genetic diversity. Hence, the objective of designing a new test system that evaluates the association between these underground traits with the potential use to measure other traits in maize seedlings related to early vigor was achieved.

Toward the Design of Potato Tolerant to Abiotic Stress

Potato is a major global crop that has an important role to play in food security, reducing poverty and improving human nutrition. Productivity in potato however is limited in many environments by its sensitivity to abiotic stresses such as elevated temperature, drought, frost, and salinity. In this chapter we focus on the effects of elevated temperature on potato yields as high temperature is the most important uncontrollable factor affecting growth and yield of potato. We describe some of the physiological impacts of elevated temperature and review recent findings about response mechanisms. We describe genetic approaches that could be used to identify allelic variants of genes that may be useful to breed for increased climate resilience, an approach that could be deployed with recent advances in potato breeding.

DeepCob: precise and high-throughput analysis of maize cob geometry using deep learning with an application in genebank phenomics

BACKGROUND

Maize cobs are an important component of crop yield that exhibit a high diversity in size, shape and color in native landraces and modern varieties. Various phenotyping approaches were developed to measure maize cob parameters in a high throughput fashion. More recently, deep learning methods like convolutional neural networks (CNNs) became available and were shown to be highly useful for high-throughput plant phenotyping. We aimed at comparing classical image segmentation with deep learning methods for maize cob image segmentation and phenotyping using a large image dataset of native maize landrace diversity from Peru.

RESULTS

Comparison of three image analysis methods showed that a Mask R-CNN trained on a diverse set of maize cob images was highly superior to classical image analysis using the Felzenszwalb-Huttenlocher algorithm and a Window-based CNN due to its robustness to image quality and object segmentation accuracy ([Formula: see text]). We integrated Mask R-CNN into a high-throughput pipeline to segment both maize cobs and rulers in images and perform an automated quantitative analysis of eight phenotypic traits, including diameter, length, ellipticity, asymmetry, aspect ratio and average values of red, green and blue color channels for cob color. Statistical analysis identified key training parameters for efficient iterative model updating. We also show that a small number of 10-20 images is sufficient to update the initial Mask R-CNN model to process new types of cob images. To demonstrate an application of the pipeline we analyzed phenotypic variation in 19,867 maize cobs extracted from 3449 images of 2484 accessions from the maize genebank of Peru to identify phenotypically homogeneous and heterogeneous genebank accessions using multivariate clustering.

CONCLUSIONS

Single Mask R-CNN model and associated analysis pipeline are widely applicable tools for maize cob phenotyping in contexts like genebank phenomics or plant breeding.

Harnessing translational research in wheat for climate resilience

Despite being the world's most widely grown crop, research investments in wheat (Triticum aestivum and Triticum durum) fall behind those in other staple crops. Current yield gains will not meet 2050 needs, and climate stresses compound this challenge. However, there is good evidence that heat and drought resilience can be boosted through translating promising ideas into novel breeding technologies using powerful new tools in genetics and remote sensing, for example. Such technologies can also be applied to identify climate resilience traits from among the vast and largely untapped reserve of wheat genetic resources in collections worldwide. This review describes multi-pronged research opportunities at the focus of the Heat and Drought Wheat Improvement Consortium (coordinated by CIMMYT), which together create a pipeline to boost heat and drought resilience, specifically: improving crop design targets using big data approaches; developing phenomic tools for field-based screening and research; applying genomic technologies to elucidate the bases of climate resilience traits; and applying these outputs in developing next-generation breeding methods. The global impact of these outputs will be validated through the International Wheat Improvement Network, a global germplasm development and testing system that contributes key productivity traits to approximately half of the global wheat-growing area.

Unoccupied aerial systems discovered overlooked loci capturing the variation of entire growing period in maize

Traditional phenotyping methods, coupled with genetic mapping in segregating populations, have identified loci governing complex traits in many crops. Unoccupied aerial systems (UAS)-based phenotyping has helped to reveal a more novel and dynamic relationship between time-specific associated loci with complex traits previously unable to be evaluated. Over 1,500 maize (Zea mays L.) hybrid row plots containing 280 different replicated maize hybrids from the Genomes to Fields (G2F) project were evaluated agronomically and using UAS in 2017. Weekly UAS flights captured variation in plant heights during the growing season under three different management conditions each year: optimal planting with irrigation (G2FI), optimal dryland planting without irrigation (G2FD), and a stressed late planting (G2LA). Plant height of different flights were ranked based on importance for yield using a random forest (RF) algorithm. Plant heights captured by early flights in G2FI trials had higher importance (based on Gini scores) for predicting maize grain yield (GY) but also higher accuracies in genomic predictions which fluctuated for G2FD (-0.06∼0.73), G2FI (0.33∼0.76), and G2LA (0.26∼0.78) trials. A genome-wide association analysis discovered 52 significant single nucleotide polymorphisms (SNPs), seven were found consistently in more than one flights or trial; 45 were flight or trial specific. Total cumulative marker effects for each chromosome's contributions to plant height also changed depending on flight. Using UAS phenotyping, this study showed that many candidate genes putatively play a role in the regulation of plant architecture even in relatively early stages of maize growth and development.

Mapping and Validation of a Stable Quantitative Trait Locus Conferring Maize Resistance to Gibberella Ear Rot

Gibberella ear rot (GER), a prevalent disease caused by Fusarium graminearum, can result in significant yield loss and carcinogenic mycotoxin contamination in maize worldwide. However, only a few quantitative trait loci (QTLs) for GER resistance have been reported. In this study, we evaluated a Chinese recombinant inbred line (RIL) population comprising 204 lines, developed from a cross between a resistant parent DH4866 and a susceptible line T877, in three field trials under artificial inoculation with F. graminearum. The RIL population and their parents were genotyped with an Affymetrix microarray CGMB56K SNP Array. Based on the genetic linkage map constructed using 1,868 bins as markers, 11 QTLs, including five stable QTLs, were identified by individual environment analysis. Joint multiple environments analysis and epistatic interaction analysis revealed six additive and six epistatic (additive × additive) QTLs, respectively. None of the QTLs could explain more than 10% of phenotypic variation, suggesting that multiple minor-effect QTLs contributed to the genetic component of resistance to GER, and both additive and epistatic effects contributed to the genetic architecture of resistance to GER. A novel QTL, qGER4.09, with the largest effect, identified and validated using 588 F₂ individuals, was colocalized with genomic regions for Fusarium ear rot and Aspergillus ear rot, indicating that this genetic locus likely confers resistance to multiple pathogens and can potentially be utilized in breeding maize varieties aimed at improving the resistance not only to GER but also other ear rot diseases.

Stomatal Traits and Barley (Hordeum vulgare L.) Forage Yield in Drought Conditions of Northeastern Mexico

Infrared technology is a practical, fast, non-destructive method that helps in forecasting plant development and can be used to select physiological traits, instead of other methodologies that require more time and breeding efforts. According to the statistical analyses and the relationship between the direct and indirect effects of the variables, this technology could serve as the basis for implementing a genotype selection methodology. Awnless barley was assessed in a randomized block design with three replicates in two crop seasons at Mexico's northeastern region. Two samplings were carried out during crop development: at 75 and 90 days after sowing. The infrared and stomatal sensors were used to identify the direct and indirect effects of stomata's traits on dry forage yield. The data were analyzed in a subdivided plot design, using mean comparison tests, correlation coefficients and path analyses, finding significant differences (p < 0.05) among localities and among samplings. Dry forage yield was significant and positively correlated with plant height (r = 0.578) and canopy temperature (r = 0.724), and negatively correlated with the leaf upper side stomatal width (r = -0.409) and the leaf lower side stomatal width (r = -0.641), chlorophyll content and vegetation index. Temperature, chlorophyll, density and leaf lower side stomatal index had the strongest direct effects on yield. Therefore, the infrared technology appears as a way to select high yielding awnless forage barley, to obtain the correlation, the positive direct effect of temperature and the negative effect of chlorophyll. Due to their direct effects, low density and low leaf underside stomatal indexes can also help in the indirect selection of higher yielding forage barley genotypes.

Identification of Genetic Loci Associated With Crude Protein Content and Fiber Composition in Alfalfa (Medicago sativa L.) Using QTL Mapping

Forage quality determined mainly by protein content and fiber composition has a crucial influence on digestibility and nutrition intake for animal feeding. To explore the genetic basis of quality traits, we conducted QTL mapping based on the phenotypic data of crude protein (CP), neutral detergent fiber (NDF), acid detergent fiber (ADF), and lignin of an F1 alfalfa population generated by crossing of two alfalfa parents with significant difference in quality. In total, 83 QTLs were identified with contribution to the phenotypic variation (PVE) ranging from 1.45 to 14.35%. Among them, 47 QTLs interacted significantly with environment and 12 QTLs were associated with more than one trait. Epistatic effect was also detected for 73 pairs of QTLs with PVE of 1.08-14.06%. The results suggested that the inheritance of quality-related traits was jointly affected by additive, epistasis and environment. In addition, 83.33% of the co-localized QTLs were shared by ADF and NDF with the same genetic direction, while the additive effect of crude protein-associated QTLs was opposite to that fiber composition on the same locus, suggesting that the loci may antagonistically contribute to protein content and fiber composition. Further analysis of a QTL related to all the three traits of fiber composition (qNDF1C, qADF1C-2, and qlignin1C-2) showed that five candidate genes were homologs of cellulose synthase-like protein A1 in Medicago truncatula, indicating the potential role in fiber synthesis. For the protein-associated loci we identified, qCP4C-1 was located in the shortest region (chr 4.3 39.3-39.4 Mb), and two of the seven corresponding genes in this region were predicted to be E3 ubiquitin-protein ligase in protein metabolism. Therefore, our results provide some reliable regions significantly associated with alfalfa quality, and identification of the key genes would facilitate marker-assisted selection for favorable alleles in breeding program of alfalfa quality improvement.

Genomic Architecture of Phenotypic Plasticity in Response to Water Stress in Tetraploid Wheat

Phenotypic plasticity is one of the main mechanisms of adaptation to abiotic stresses via changes in critical developmental stages. Altering flowering phenology is a key evolutionary strategy of plant adaptation to abiotic stresses, to achieve the maximum possible reproduction. The current study is the first to apply the linear regression residuals as drought plasticity scores while considering the variation in flowering phenology and traits under non-stress conditions. We characterized the genomic architecture of 17 complex traits and their drought plasticity scores for quantitative trait loci (QTL) mapping, using a mapping population derived from a cross between durum wheat (Triticum turgidum ssp. durum) and wild emmer wheat (T. turgidum ssp. dicoccoides). We identified 79 QTLs affected observed traits and their plasticity scores, of which 33 reflected plasticity in response to water stress and exhibited epistatic interactions and/or pleiotropy between the observed and plasticity traits. Vrn-B3 (TaTF1) residing within an interval of a major drought-escape QTL was proposed as a candidate gene. The favorable alleles for most of the plasticity QTLs were contributed by wild emmer wheat, demonstrating its high potential for wheat improvement. Our study presents a new approach for the quantification of plant adaptation to various stresses and provides new insights into the genetic basis of wheat complex traits under water-deficit stress.

QTL controlling fiber quality traits under salt stress in upland cotton (Gossypium hirsutum L.)

QTL for fiber quality traits under salt stress discerned candidate genes controlling fatty acid metabolism. Salinity stress seriously affects plant growth and limits agricultural productivity of crop plants. To dissect the genetic basis of response to salinity stress, a recombinant inbred line population was developed to compare fiber quality in upland cotton (Gossypium hirsutum L.) under salt stress and normal conditions. Based on three datasets of (1) salt stress, (2) normal growth, and (3) the difference value between salt stress and normal conditions, 51, 70, and 53 QTL were mapped, respectively. Three QTL for fiber length (FL) (qFL-Chr1-1, qFL-Chr5-5, and qFL-Chr24-4) were detected under both salt and normal conditions and explained 4.26%, 9.38%, and 3.87% of average phenotypic variation, respectively. Seven genes within intervals of two stable QTL (qFL-Chr1-1 and qFL-Chr5-5) were highly expressed in lines with extreme long fiber. A total of 35 QTL clusters comprised of 107 QTL were located on 18 chromosomes and exhibited pleiotropic effects. Thereinto, two clusters were responsible for improving five fiber quality traits, and 6 influenced FL and fiber strength (FS). The QTL with positive effect for fiber length exhibited active effects on fatty acid synthesis and elongation, but the ones with negative effect played passive roles on fatty acid degradation under salt stress.

Genetic dissection of maize drought tolerance for trait improvement

Maize is one of the most important crops, but its production is threatened by drought stress worldwide. Thus, increased drought tolerance has been a major goal of maize breeding. Conventional breeding strategies have led to significantly increase of maize yields; however, these strategies often fail to meet the need for drought stress tolerance enhancement. Here, we focus on progress related to the genetic dissection of drought tolerance in maize at different developmental stages achieved through linkage mapping and association mapping. Moreover, recent molecular breeding systems, including transgenic, genome-wide marker-assisted selection, and genome editing technologies, have provided a more direct, efficient, and accurate approach for trait improvement. We also provide perspectives on future directions regarding multi-omics studies and maize improvement. Overall, the application of acquired knowledge will facilitate maize breeding to meet the challenges.

Identification and Functional Verification of Cold Tolerance Genes in Spring Maize Seedlings Based on a Genome-Wide Association Study and Quantitative Trait Locus Mapping

Maize (Zea mays L.) is a tropical crop, and low temperature has become one of the main abiotic stresses for maize growth and development, affecting many maize growth processes. The main area of maize production in China, Jilin province, often suffers from varying degrees of cold damage in spring, which seriously affects the quality and yield of maize. In the face of global climate change and food security concerns, discovering cold tolerance genes, developing cold tolerance molecular markers, and creating cold-tolerant germplasm have become urgent for improving maize resilience against these conditions and obtaining an increase in overall yield. In this study, whole-genome sequencing and genotyping by sequencing were used to perform genome-wide association analysis (GWAS) and quantitative trait locus (QTL) mapping of the two populations, respectively. Overall, four single-nucleotide polymorphisms (SNPs) and 12 QTLs were found to be significantly associated with cold tolerance. Through joint analysis, an intersection of GWAS and QTL mapping was found on chromosome 3, on which the Zm00001d002729 gene was identified as a potential factor in cold tolerance. We verified the function of this target gene through overexpression, suppression of expression, and genetic transformation into maize. We found that Zm00001d002729 overexpression resulted in better cold tolerance in this crop. The identification of genes associated with cold tolerance contributes to the clarification of the underlying mechanism of this trait in maize and provides a foundation for the adaptation of maize to colder environments in the future, to ensure food security.

Genetic Dissection of the General Combining Ability of Yield-Related Traits in Maize

Maize yield components including row number, kernel number per row, kernel thickness, kernel width, kernel length, 100-kernel weight, and volume weight affect grain yield directly. Previous studies mainly focused on dissecting the genetic basis of per se performances for yield-related traits, but the genetic basis of general combining ability (GCA) for these traits is still unclear. In the present study, 328 RILs were crossed as males to two testers according to the NCII mating design, resulting in a hybrid panel composed of 656 hybrids. Both the hybrids and parental lines were evaluated in four environments in 2015 and 2016. Correlation analysis showed the performances of GCA effects were significantly correlated to the per se performances of RILs for all yield-related traits (0.17 ≤ r ≤ 0.64, P > 0.01). Only 17 of 95 QTL could be detected for both per se performances of RILs and GCA effects for eight yield-related traits. The QTL qKN7-1 and qHKW1-3, which could explain more than 10% of the variation in the GCA effects of KN and HKW, were also detected for per se performances for the traits. The pleiotropic loci qRN3-1 and qRN6, which together explained 14.92% of the observed variation in GCA effects for RN, were associated with the GCA effects of KW and HKW, but not with per se performances for these traits. In contrast, Incw1, which was related to seed weight in maize, was mapped to the region surrounding MK2567 at the qHKW5-2 locus, but no GCA effect was detected. The QTL identified in present study for per se performances and corresponding GCA effects for yield-related traits might be useful for maize hybrid breeding.

Mapping of QTL for Grain Yield Components Based on a DH Population in Maize

The elite maize hybrid Zhengdan 958 (ZD958), which has high and stable yield and extensive adaptability, is widely grown in China. To elucidate the genetic basis of yield and its related traits in this elite hybrid, a set of doubled haploid (DH) lines derived from ZD958 were evaluated in four different environments at two locations over two years, and a total of 49 quantitative trait loci (QTL) and 24 pairs of epistatic interactions related to yield and yield components were detected. Furthermore, 21 QTL for six investigated phenotypic traits were detected across two different sites. Combining the results of these QTL in each environment and across both sites, three main QTL hotspots were found in chromosomal bins 2.02, 2.05–2.06, and 6.05 between the simple sequence repeat (SSR) markers umc1165-bnlg1017, umc1065-umc1637, and nc012-bnlg345, respectively. The existence of three QTL hotspots associated with various traits across multiple environments could be explained by pleiotropic QTL or multiple tightly linked QTL. These genetic regions could provide targets for genetic improvement, fine mapping, and marker-assisted selection in future studies.

Analysis of the genetic architecture of maize kernel size traits by combined linkage and association mapping

Kernel size-related traits are the most direct traits correlating with grain yield. The genetic basis of three kernel traits of maize, kernel length (KL), kernel width (KW) and kernel thickness (KT), was investigated in an association panel and a biparental population. A total of 21 single nucleotide polymorphisms (SNPs) were detected to be most significantly (P < 2.25 × 10^-6 ) associated with these three traits in the association panel under four environments. Furthermore, 50 quantitative trait loci (QTL) controlling these traits were detected in seven environments in the intermated B73 × Mo17 (IBM) Syn10 doubled haploid (DH) population, of which eight were repetitively identified in at least three environments. Combining the two mapping populations revealed that 56 SNPs (P < 1 × 10^-3 ) fell within 18 of the QTL confidence intervals. According to the top significant SNPs, stable-effect SNPs and the co-localized SNPs by association analysis and linkage mapping, a total of 73 candidate genes were identified, regulating seed development. Additionally, seven miRNAs were found to situate within the linkage disequilibrium (LD) regions of the co-localized SNPs, of which zma-miR164e was demonstrated to cleave the mRNAs of Arabidopsis CUC1, CUC2 and NAC6 in vitro. Overexpression of zma-miR164e resulted in the down-regulation of these genes above and the failure of seed formation in Arabidopsis pods, with the increased branch number. These findings provide insights into the mechanism of seed development and the improvement of molecular marker-assisted selection (MAS) for high-yield breeding in maize.

De novo Assembly of Transcriptomes From a B73 Maize Line Introgressed With a QTL for Resistance to Gray Leaf Spot Disease Reveals a Candidate Allele of a Lectin Receptor-Like Kinase

Gray leaf spot (GLS) disease in maize, caused by the fungus Cercospora zeina, is a threat to maize production globally. Understanding the molecular basis for quantitative resistance to GLS is therefore important for food security. We developed a de novo assembly pipeline to identify candidate maize resistance genes. Near-isogenic maize lines with and without a QTL for GLS resistance on chromosome 10 from inbred CML444 were produced in the inbred B73 background. The B73-QTL line showed a 20% reduction in GLS disease symptoms compared to B73 in the field (p = 0.01). B73-QTL leaf samples from this field experiment conducted under GLS disease pressure were RNA sequenced. The reads that did not map to the B73 or C. zeina genomes were expected to contain novel defense genes and were de novo assembled. A total of 141 protein-coding sequences with B73-like or plant annotations were identified from the B73-QTL plants exposed to C. zeina. To determine whether candidate gene expression was induced by C. zeina, the RNAseq reads from C. zeina-challenged and control leaves were mapped to a master assembly of all of the B73-QTL reads, and differential gene expression analysis was conducted. Combining results from both bioinformatics approaches led to the identification of a likely candidate gene, which was a novel allele of a lectin receptor-like kinase named L-RLK-CML that (i) was induced by C. zeina, (ii) was positioned in the QTL region, and (iii) had functional domains for pathogen perception and defense signal transduction. The 817AA L-RLK-CML protein had 53 amino acid differences from its 818AA counterpart in B73. A second "B73-like" allele of L-RLK was expressed at a low level in B73-QTL. Gene copy-specific RT-qPCR confirmed that the l-rlk-cml transcript was the major product induced four-fold by C. zeina. Several other expressed defense-related candidates were identified, including a wall-associated kinase, two glutathione s-transferases, a chitinase, a glucan beta-glucosidase, a plasmodesmata callose-binding protein, several other receptor-like kinases, and components of calcium signaling, vesicular trafficking, and ethylene biosynthesis. This work presents a bioinformatics protocol for gene discovery from de novo assembled transcriptomes and identifies candidate quantitative resistance genes.

Identification of QTLs for resistance to maize rough dwarf disease using two connected RIL populations in maize

Maize rough dwarf disease (MRDD) is a significant viral disease caused by rice black-streaked dwarf virus (RBSDV) in China, which results in 30% yield losses in affected summer maize-growing areas. In this study, two connected recombinant inbred line (RIL) populations were constructed to elucidate the genetic basis of resistance during two crop seasons. Ten quantitative trait loci (QTLs) for resistance to MRDD were detected in the two RILs. Individual QTLs accounted for 4.97-23.37% of the phenotypic variance explained (PVE). The resistance QTL (qZD-MRDD8-1) with the largest effect was located in chromosome bin 8.03, representing 16.27-23.37% of the PVE across two environments. Interestingly, one pair of common significant QTLs was located in the similar region on chromosome 4 in both populations, accounting for 7.11-9.01% of the PVE in Zheng58×D863F (RIL-ZD) and 9.43-13.06% in Zheng58×ZS301 (RIL-ZZ). A total of five QTLs for MRDD resistance trait showed significant QTL-by-Environment interactions (QEI). Two candidate genes associated with resistance (GDSL-lipase and RPP13-like gene) which were higher expressed in resistant inbred line D863F than in susceptible inbred line Zheng58, were located in the physical intervals of the major QTLs on chromosomes 4 and 8, respectively. The identified QTLs will be studied further for application in marker-assisted breeding in maize genetic improvement of MRDD resistance.

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

Drought significantly influences maize morphology and yield potential. The elucidation of the genetic mechanisms of yield components and morphological traits, and tightly linked molecular markers under drought stress are thus of great importance in marker assisted selection (MAS) breeding. Here, we identified 32 QTLs for grain weight per ear, kernel ratio, and ear height-to-plant height ratio across two F_2:3 populations under both drought and non-drought conditions by single-environment mapping with composite interval mapping (CIM), of which 21 QTLs were mapped under water-stressed conditions. We identified 29 QTLs by joint analysis of all environments with mixed-linear-model-based composite interval mapping (MCIM), 14 QTLs involved in QTL-by-environment interactions, and 11 epistatic interactions. Further analysis simultaneously identified 20 stable QTLs (sQTLs) by CIM and MCIM could be useful for genetic improvement of these traits via QTL pyramiding. Remarkably, bin 1.07-1.10/6.05/8.03/8.06 exhibited four pleiotropic sQTLs that were consistent with phenotypic correlations among traits, supporting the pleiotropy of QTLs and playing important roles in conferring growth and yield advantages under contrasting watering conditions. These findings provide information on the genetic mechanisms responsible for yield components and morphological traits that are affected by different watering conditions. Furthermore, these alleles provide useful targets for MAS.

Identification of a candidate gene underlying qKRN5b for kernel row number in Zea mays L

A quantitative trait locus for kernel row number, qKRN5, was dissected into two tightly linked loci, qKRN5a and qKRN5b. Fine mapping, comparative analysis of nucleotide sequences and gene expression established the endonuclease/exonuclease/phosphatase family protein-encoding gene Zm00001d013603 as a causal gene of qKRN5b. Maize grain yield is determined by agronomically important traits that are controlled by interactions among and between genes and environmental factors. Considerable efforts have been made to identify major quantitative trait loci (QTLs) for yield-related traits; however, few were previously isolated and characterized in maize. In this study, we divided a QTL for kernel row number (KRN), qKRN5, into two tightly linked loci, qKRN5a and qKRN5b, using advanced backcross populations derived from near-isogenic lines. KRN was greater in individuals that were homozygous for the NX531 allele, which showed coupling-phase linkage. The major QTL qKRN5b had an additive effect of approximately one kernel row. Furthermore, fine mapping narrowed qKRN5b within a 147.2-kb region. The upstream sequence Zm00001d013603 and its expression in the ear inflorescence showed obvious differences between qKRN5b near-isogenic lines. In situ hybridization located Zm00001d013603 on the primordia of the spikelet pair meristems and spikelet meristems, but not in the inflorescence meristem, which indicates a role in regulating the initiation of reproductive axillary meristems of ear inflorescences. Expression analysis and nucleotide sequence alignment revealed that Zm00001d013603, which encodes an endonuclease/exonuclease/phosphatase family protein that hydrolyzes phosphatidyl inositol diphosphates, is the causal gene of qKRN5b. These results provide insight into the genetic basis of KRN and have potential value for enhancing maize grain yield.

Mapping quantitative trait loci for yield-related traits and predicting candidate genes for grain weight in maize

Quantitative trait loci (QTLs) mapped in different genetic populations are of great significance for marker-assisted breeding. In this study, an F_2:3 population were developed from the crossing of two maize inbred lines SG-5 and SG-7 and applied to QTL mapping for seven yield-related traits. The seven traits included 100-kernel weight, ear length, ear diameter, cob diameter, kernel row number, ear weight, and grain weight per plant. Based on an ultra-high density linkage map, a total of thirty-three QTLs were detected for the seven studied traits with composite interval mapping (CIM) method, and fifty-four QTLs were indentified with genome-wide composite interval mapping (GCIM) methods. For these QTLs, Fourteen were both detected by CIM and GCIM methods. Besides, eight of the thirty QTLs detected by CIM were identical to those previously mapped using a F₂ population (generating from the same cross as the mapping population in this study), and fifteen were identical to the reported QTLs in other recent studies. For the fifty-four QTLs detected by GCIM, five of them were consistent with the QTLs mapped in the F₂ population of SG-5 × SG-7, and twenty one had been reported in other recent studies. The stable QTLs associated with grain weight were located on maize chromosomes 2, 5, 7, and 9. In addition, differentially expressed genes (DEGs) between SG-5 and SG-7 were obtained from the transcriptomic profiling of grain at different developmental stages and overlaid onto the stable QTLs intervals to predict candidate genes for grain weight in maize. In the physical intervals of confirmed QTLs qKW-7, qEW-9, qEW-10, qGWP-6, qGWP-8, qGWP-10, qGWP-11 and qGWP-12, there were 213 DEGs in total. Finally, eight genes were predicted as candidate genes for grain size/weight. In summary, the stable QTLs would be reliable and the candidate genes predicted would be benefit for maker assisted breeding or cloning.