Maize Panel Research Articles

Integrative multi-omic analysis identifies genes associated with cuticular wax biogenesis in adult maize leaves.

Studying the genetic basis of leaf wax composition and its correlation with leaf cuticular conductance (gc) is crucial for improving crop productivity. The leaf cuticle, which comprises a cutin matrix and various waxes, functions as an extracellular hydrophobic layer, protecting against water loss upon stomatal closure. To address the limited understanding of genes associated with the natural variation of adult leaf cuticular waxes and their connection to gc, we conducted statistical genetic analyses using leaf transcriptomic, metabolomic, and physiological data sets collected from a maize (Zea mays L.) panel of ∼300 inbred lines. Through a random forest analysis with 60 cuticular wax traits, it was shown that high molecular weight wax esters play an important role in predicting gc. Integrating results from genome-wide and transcriptome-wide studies (GWAS and TWAS) via a Fisher's combined test revealed 231 candidate genes detected by all three association tests. Among these, 11 genes exhibit known or predicted roles in cuticle-related processes. Throughout the genome, multiple hotspots consisting of GWAS signals for several traits from one or more wax classes were discovered, identifying four additional plausible candidate genes and providing insights into the genetic basis of correlated wax traits. Establishing a partially shared genetic architecture, we identified 35 genes for both gc and at least one wax trait, with four considered plausible candidates. Our study enhances the understanding of how adult leaf cuticle wax composition relates to gc and implicates both known and novel candidate genes as potential targets for optimizing productivity in maize.

A phased small interfering RNA-derived pathway mediates lead stress tolerance in maize.

Phased small interfering RNAs (phasiRNAs) are a distinct class of endogenous small interfering RNAs, which regulate plant growth, development and environmental stress response. To determine the effect of phasiRNAs on maize (Zea mays L.) tolerance to lead (Pb) stress, the roots of 305 maize lines under Pb treatment were subjected to generation of individual databases of small RNAs. We identified 55 high-confidence phasiRNAs derived from 13 PHAS genes (genes producing phasiRNAs) in this maize panel, of which 41 derived from nine PHAS loci were negatively correlated with Pb content in the roots. The potential targets of the 41 phasiRNAs were enriched in ion transport and import. Only the expression of PHAS_1 (ZmTAS3j, Trans-Acting Short Interference RNA3) was regulated by its cis-expression quantitative trait locus and thus affected the Pb content in the roots. Using the Nicotiana benthamiana (N. benthamiana) transient expression system, 5'-rapid amplification of cDNA ends, and Arabidopsis heterologously expressed, we verified that ZmTAS3j was cleaved by zma-miR390 and thus generated tasiRNA targeting ARF genes (tasiARFs), and that the 5' and 3' zma-miR390 target sites of ZmTAS3j were both necessary for efficient biosynthesis and functional integrity of tasiARFs. We validated the involvement of the zma-miR390-ZmTAS3j-tasiARF-ZmARF3-ZmHMA3 pathway in Pb accumulation in maize seedlings using genetic, molecular, and cytological methods. Moreover, the increased Pb tolerance in ZmTAS3j-overexpressed lines was likely attributed to the zma-miR390-ZmTAS3j-tasiARF-ZmARF3-SAURs pathway, which elevated indole acetic acid levels and thus reactive oxygen species scavenging capacity in maize roots. Our study reveals the importance of the TAS3-derived tasiRNA pathway in plant adaptation to Pb stress.

Genetic variability of maize (Zea mays) germplasm from Iran: genotyping with a maize 600K SNP array and genome-wide scanning for selection signatures

Context Maize (Zea mays L.) is one of the most economically important plants of the cereal family; it has value as human food, livestock feed, and as a component of industrial products. Aims This study focused on genetic diversity and existence of genetic divergence among promising maize inbred lines in Iran. Methods A commercial maize 600K SNP (single-nucleotide polymorphism) array was used to inspect genetic variability among 93 maize inbred lines. Key results The rate of transition mutation was twice as high as transversion mutation, and the density of detected SNPs was greater close to telomere regions of maize chromosomes. Considering the fluctuation of observed, expected and total heterozygosity and fixation index values across maize chromosomes, as well as polymorphism information content values, there is a high level of genetic variability among the studied maize panel. In addition, discriminant analysis of the principal components revealed four subpopulations in which the subpopulation ‘Line’ was distinct from other subpopulations and had no genomic overlap with them. Selection signature analysis revealed 177 regions harbouring 75 genes that differentiate among subgroups. Detected genes had a role in the mitogen-activated protein kinase signalling pathway, spliceosome, protein processing in endoplasmic reticulum, and hormone signal transduction. Conclusions We conclude that remarkable genetic diversity and differentiation exists among the studied maize subpopulations. The most differentiated SNPs among the subpopulations were associated with important biological processing genes and pathways. Implications The findings provide valuable insights for future maize breeding programs through exploitation of heterosis, as well as marker-assisted selection.

Genetic architecture of kernel-related traits in sweet and waxy maize revealed by genome-wide association analysis.

Maize (Zea mays L.) is one of the most important crops worldwide, the kernel size-related traits are the major components of maize grain yield. To dissect the genetic architecture of four kernel-related traits of 100-kernel weight, kernel length, kernel width, and kernel diameter, a genome-wide association study (GWAS) was conducted in the waxy and sweet maize panel comprising of 447 maize inbred lines re-sequenced at the 5× coverage depth. GWAS analysis was carried out with the mixed linear model using 1,684,029 high-quality SNP markers. In total, 49 SNPs significantly associated with the four kernel-related traits were identified, including 46 SNPs on chromosome 3, two SNPs on chromosome 4, and one SNP on chromosome 7. Haplotype regression analysis identified 338 haplotypes that significantly affected these four kernel-related traits. Genomic selection (GS) results revealed that a set of 10,000 SNPs and a training population size of 30% are sufficient for the application of GS in waxy and sweet maize breeding for kernel weight and kernel size. Forty candidate genes associated with the four kernel-related traits were identified, including both Zm00001d000707 and Zm00001d044139 expressed in the kernel development tissues and stages with unknown functions. These significant SNPs and important haplotypes provide valuable information for developing functional markers for the implementation of marker-assisted selection in breeding. The molecular mechanism of Zm00001d000707 and Zm00001d044139 regulating these kernel-related traits needs to be investigated further.

Multi-trait and multi-environment genomic prediction for flowering traits in maize: a deep learning approach.

Maize (Zea mays L.), the third most widely cultivated cereal crop in the world, plays a critical role in global food security. To improve the efficiency of selecting superior genotypes in breeding programs, researchers have aimed to identify key genomic regions that impact agronomic traits. In this study, the performance of multi-trait, multi-environment deep learning models was compared to that of Bayesian models (Markov Chain Monte Carlo generalized linear mixed models (MCMCglmm), Bayesian Genomic Genotype-Environment Interaction (BGGE), and Bayesian Multi-Trait and Multi-Environment (BMTME)) in terms of the prediction accuracy of flowering-related traits (Anthesis-Silking Interval: ASI, Female Flowering: FF, and Male Flowering: MF). A tropical maize panel of 258 inbred lines from Brazil was evaluated in three sites (Cambira-2018, Sabaudia-2018, and Iguatemi-2020 and 2021) using approximately 290,000 single nucleotide polymorphisms (SNPs). The results demonstrated a 14.4% increase in prediction accuracy when employing multi-trait models compared to the use of a single trait in a single environment approach. The accuracy of predictions also improved by 6.4% when using a single trait in a multi-environment scheme compared to using multi-trait analysis. Additionally, deep learning models consistently outperformed Bayesian models in both single and multiple trait and environment approaches. A complementary genome-wide association study identified associations with 26 candidate genes related to flowering time traits, and 31 marker-trait associations were identified, accounting for 37%, 37%, and 22% of the phenotypic variation of ASI, FF and MF, respectively. In conclusion, our findings suggest that deep learning models have the potential to significantly improve the accuracy of predictions, regardless of the approach used and provide support for the efficacy of this method in genomic selection for flowering-related traits in tropical maize.

Genome-Wide Association Study for Root Morphology and Phosphorus Acquisition Efficiency in Diverse Maize Panels.

Maximizing soil exploration through modifications of the root system is a strategy for plants to overcome phosphorus (P) deficiency. Genome-wide association with 561 tropical maize inbred lines from Embrapa and DTMA panels was undertaken for root morphology and P acquisition traits under low- and high-P concentrations, with 353,540 SNPs. P supply modified root morphology traits, biomass and P content in the global maize panel, but root length and root surface area changed differentially in Embrapa and DTMA panels. This suggests that different root plasticity mechanisms exist for maize adaptation to low-P conditions. A total of 87 SNPs were associated to phenotypic traits in both P conditions at -log10(p-value) ≥ 5, whereas only seven SNPs reached the Bonferroni significance. Among these SNPs, S9_137746077, which is located upstream of the gene GRMZM2G378852 that encodes a MAPKKK protein kinase, was significantly associated with total seedling dry weight, with the same allele increasing root length and root surface area under P deficiency. The C allele of S8_88600375, mapped within GRMZM2G044531 that encodes an AGC kinase, significantly enhanced root length under low P, positively affecting root surface area and seedling weight. The broad genetic diversity evaluated in this panel suggests that candidate genes and favorable alleles could be exploited to improve P efficiency in maize breeding programs of Africa and Latin America.

Genome‐wide association study for bacterial leaf streak resistance in maize

AbstractMaize (Zea mays L.), one of the most important crops worldwide, is affected by foliar diseases that limit its global production. Bacterial diseases have increased in Argentina during the last years. The aim of this work was to explore a maize panel provided by the International Maize and Wheat Improvement Center (CIMMYT) to identify alleles associated with resistance to bacterial leaf streak (BLS) of maize, a disease caused by Xanthomonas vasicola pv. vasculorum, in central Argentina. A diverse panel of 200 maize lines was evaluated for resistance to bacterial diseases in four environments of central Argentina in 2020 and 2021. The predictor of the genetic merit does not include environmental effects and 46,990 single‐nucleotide polymorphisms obtained by genotyping‐by‐sequencing were used in the genome‐wide association study (GWAS). The 10 lines with the lowest severity across environments belonged to different environmental adaptation programs defined by CIMMYT. The GWAS allowed us to identify 11 genomic regions associated with BLS, located in chromosomes 1, 2, 5, 7, 8, and 9. Five of those regions, located in bins 1.04, 2.01, 5.03, 8.06, and 9.03, were associated with plant defense candidate genes such as strictosidine synthase‐like 11, protein‐serine/threonine phosphatase, and a putative Leucine‐Rich repeats receptor‐like serine/threonine‐protein kinase gene. Our study provides potential resistance alleles to BLS that can be incorporated to improve maize breeding programs.

Genome-Wide Association Analysis Identified Newly Natural Variation for Photosynthesis-Related Traits in a Large Maize Panel

High-yielding cultivars with high water use efficiency are a main target in maize breeding; yet, no comprehensive study about the genetic regulation of photosynthesis, or about the ranges of variability for gas exchange-related parameters in maize has been published. Here, a large maize panel of 731 inbred lines previously genotyped was evaluated to elucidate the genetic basis of photosynthesis-related parameters, measured 15–30 days after silking, across three years. Large phenotypic and genotypic variations were observed in this panel with dramatic fluctuations in heritability for various traits. We detected 27 minor Quantitative Trait Loci (QTL), comprising 39 significant trait-Single Nucleotide Polymorphisms (SNPs), located mostly on chromosomes 5 and 8. Most candidates genes were novel, though a few of them were functionally associated with the theory of source-to-sink translocation presented in previous studies. Genomic selection of favorable alleles to enhance photosynthesis, along with other tools, could be a practical, and a promising, approach in the future. Leaf net photosynthesis and stomatal conductance are the most promising targets for breeding programs. We did not find any marker associated with intrinsic water use efficiency, which highlights that developing maize cultivars with more efficient use of water by genomic selection is not straightforward; however, given the high heritability value for this trait, phenotypic selection could be implemented.

Genome‐wide association analysis of chilling‐tolerant germination in a new maize association mapping panel

AbstractMaize is a crop that is highly susceptible to the negative effects of low temperature. Low temperature can delay seed germination and cause a decrease in seed vigor, which seriously affects seedling emergence and yield. In this study, 190 maize accessions (inbred lines) with strong germination potential at normal temperature (25°C) were selected from more than 500 accessions to construct a new association mapping panel to further investigate germination under chilling stress (5°C). We re‐sequenced the genomes of the 190 diverse accessions and obtained 4,886,919 high‐quality SNPs. We then used this data to analyze population structure, perform principal components analysis, and construct a phylogenetic tree of the new maize panel. The relative germination rate (RGR) and relative germination index (RGI) are two traits that are significantly related to chilling‐tolerant germination. Genome‐wide association analysis showed that RGR and RGI shared a major QTL, and they also shared the top SNP. There were a total of 26 significant SNPs in common. These SNPs hit directly or indirectly within 37 candidate genes. Among these 37 gene candidates, eight are homologs of genes previously reported to be related to both germination and low‐temperature stress, and another 12 genes related to low‐temperature stress or other abiotic stresses such as drought, salinity, oxidative, and high light stress. In addition, RGR and RGI had another 15 and 26 significant SNPs, respectively, which were associated with 17 and 92 candidate genes, respectively. Further qRT‐PCR analysis using 26 chilling‐tolerant and 22 chilling‐sensitive accessions implied that Zm00001eb272370, Zm00001eb272390, and Zm00001eb272400 associated with the top SNP, may play different roles during cold‐germination. Thus, our study not only established a new association mapping panel suitable for investigation of germination at low temperature but also provided valuable genetic resources for future studies to improve chilling‐tolerant maize varieties.

Genome-wide association analysis identified newly natural variation of photosynthesis-related traits in a large maize panel

Genome-wide association analysis identified newly natural variation of photosynthesis-related traits in a large maize panel

Genome-wide association analysis identified newly natural variation of photosynthesis-related traits in a large maize panel

Genome-wide association analysis identified newly natural variation of photosynthesis-related traits in a large maize panel

Genome-wide association analysis identified newly natural variation of photosynthesis-related traits in a large maize panel

Genome-wide association analysis identified newly natural variation of photosynthesis-related traits in a large maize panel

Genome-wide association analysis identified newly natural variation of photosynthesis-related traits in a large maize panel

Genome-wide association analysis identified newly natural variation of photosynthesis-related traits in a large maize panel

Prediction of evolutionary constraint by genomic annotations improves functional prioritization of genomic variants in maize

BackgroundCrop improvement through cross-population genomic prediction and genome editing requires identification of causal variants at high resolution, within fewer than hundreds of base pairs. Most genetic mapping studies have generally lacked such resolution. In contrast, evolutionary approaches can detect genetic effects at high resolution, but they are limited by shifting selection, missing data, and low depth of multiple-sequence alignments. Here we use genomic annotations to accurately predict nucleotide conservation across angiosperms, as a proxy for fitness effect of mutations.ResultsUsing only sequence analysis, we annotate nonsynonymous mutations in 25,824 maize gene models, with information from bioinformatics and deep learning. Our predictions are validated by experimental information: within-species conservation, chromatin accessibility, and gene expression. According to gene ontology and pathway enrichment analyses, predicted nucleotide conservation points to genes in central carbon metabolism. Importantly, it improves genomic prediction for fitness-related traits such as grain yield, in elite maize panels, by stringent prioritization of fewer than 1% of single-site variants.ConclusionsOur results suggest that predicting nucleotide conservation across angiosperms may effectively prioritize sites most likely to impact fitness-related traits in crops, without being limited by shifting selection, missing data, and low depth of multiple-sequence alignments. Our approach—Prediction of mutation Impact by Calibrated Nucleotide Conservation (PICNC)—could be useful to select polymorphisms for accurate genomic prediction, and candidate mutations for efficient base editing. The trained PICNC models and predicted nucleotide conservation at protein-coding SNPs in maize are publicly available in CyVerse (https://doi.org/10.25739/hybz-2957).

Combining datasets for maize root seedling traits increases the power of GWAS and genomic prediction accuracies.

The identification of genomic regions associated with root traits and the genomic prediction of untested genotypes can increase the rate of genetic gain in maize breeding programs targeting roots traits. Here, we combined two maize association panels with different genetic backgrounds to identify single nucleotide polymorphisms (SNPs) associated with root traits, and used a genome-wide association study (GWAS) and to assess the potential of genomic prediction for these traits in maize. For this, we evaluated 377 lines from the Ames panel and 302 from the Backcrossed Germplasm Enhancement of Maize (BGEM) panel in a combined panel of 679 lines. The lines were genotyped with 232 460 SNPs, and four root traits were collected from 14-day-old seedlings. We identified 30 SNPs significantly associated with root traits in the combined panel, whereas only two and six SNPs were detected in the Ames and BGEM panels, respectively. Those 38 SNPs were in linkage disequilibrium with 35 candidate genes. In addition, we found higher prediction accuracy in the combined panel than in the Ames or BGEM panel. We conclude that combining association panels appears to be a useful strategy to identify candidate genes associated with root traits in maize and improve the efficiency of genomic prediction.

Haplotype associated RNA expression (HARE) improves prediction of complex traits in maize.

Genomic prediction typically relies on associations between single-site polymorphisms and traits of interest. This representation of genomic variability has been successful for predicting many complex traits. However, it usually cannot capture the combination of alleles in haplotypes and it has generated little insight about the biological function of polymorphisms. Here we present a novel and cost-effective method for imputing cis haplotype associated RNA expression (HARE), studied their transferability across tissues, and evaluated genomic prediction models within and across populations. HARE focuses on tightly linked cis acting causal variants in the immediate vicinity of the gene, while excluding trans effects from diffusion and metabolism. Therefore, HARE estimates were more transferrable across different tissues and populations compared to measured transcript expression. We also showed that HARE estimates captured one-third of the variation in gene expression. HARE estimates were used in genomic prediction models evaluated within and across two diverse maize panels–a diverse association panel (Goodman Association panel) and a large half-sib panel (Nested Association Mapping panel)–for predicting 26 complex traits. HARE resulted in up to 15% higher prediction accuracy than control approaches that preserved haplotype structure, suggesting that HARE carried functional information in addition to information about haplotype structure. The largest increase was observed when the model was trained in the Nested Association Mapping panel and tested in the Goodman Association panel. Additionally, HARE yielded higher within-population prediction accuracy as compared to measured expression values. The accuracy achieved by measured expression was variable across tissues, whereas accuracy by HARE was more stable across tissues. Therefore, imputing RNA expression of genes by haplotype is stable, cost-effective, and transferable across populations.

On the genetic architecture in a public tropical maize panel of the symbiosis between corn and plant growth-promoting bacteria aiming to improve plant resilience.

The online version contains supplementary material available at 10.1007/s11032-021-01257-6.

Comparison of non-overlapping maize populations of unequal sizes for resistance to maize lethal necrosis

ABSTRACT Contrast between marker-assisted backcross (MABC) and doubled haploid (DH) methods in transferring genes for resistance to maize lethal necrosis (MLN) in maize (Zea mays L.) is not well understood. The MLN is caused by co-infection of maize plant by maize chlorotic mottle virus and sugarcane mosaic virus. Two maize panels consisting of four BC3F2 and six DH populations, separately developed through marker-assisted selection from crosses between susceptible CIMMYT lines and MLN-resistant donor parent (KS23-6), were used in the current study. The two populations were of different population structures with unequal sizes. Experiments were conducted under artificial MLN inoculations for two seasons in 2018. Analyses of variance revealed significant variations among genotypes in both panels (p ≤ 0.001). Levene’s and Welch’s tests found that variances and means of the BC3F2 and DH populations were highly unequal (p ≤ 0.001). The study identified genotypes with reduced MLN infections in both populations; however, lower means for MLN severity and area under disease progress curve (AUDPC) values, and higher heritability estimates were obtained in the DH populations than in the BC3F2 populations. Additionally, the DH populations showed higher relative genetic gains for resistance to MLN compared with the BC3F2 populations. The current study detected superiority of DH over MABC populations for breeding for resistance to MLN. Nevertheless, the results observed in the present study warrant further investigations using the same genetic materials with identical population sizes.

An integrated framework reinstating the environmental dimension for GWAS and genomic selection in crops

Identifying mechanisms and pathways involved in gene–environment interplay and phenotypic plasticity is a long-standing challenge. It is highly desirable to establish an integrated framework with an environmental dimension for complex trait dissection and prediction. A critical step is to identify an environmental index that is both biologically relevant and estimable for new environments. With extensive field-observed complex traits, environmental profiles, and genome-wide single nucleotide polymorphisms for three major crops (maize, wheat, and oat), we demonstrated that identifying such an environmental index (i.e., a combination of environmental parameter and growth window) enables genome-wide association studies and genomic selection of complex traits to be conducted with an explicit environmental dimension. Interestingly, genes identified for two reaction-norm parameters (i.e., intercept and slope) derived from flowering time values along the environmental index were less colocalized for a diverse maize panel than for wheat and oat breeding panels, agreeing with the different diversity levels and genetic constitutions of the panels. In addition, we showcased the usefulness of this framework for systematically forecasting the performance of diverse germplasm panels in new environments. This general framework and the companion CERIS-JGRA analytical package should facilitate biologically informed dissection of complex traits, enhanced performance prediction in breeding for future climates, and coordinated efforts to enrich our understanding of mechanisms underlying phenotypic variation.

A worldwide maize panel revealed new genetic variation for cold tolerance.

A large association panel of 836 maize inbreds revealed a broader genetic diversity of cold tolerance, as predominantly favorable QTL with small effects were identified, indicating that genomic selection is the most promising option for breeding maize for cold tolerance. Maize (Zea mays L.) has limited cold tolerance, and breeding for cold tolerance is a noteworthy bottleneck for reaching the high potential of maize production in temperate areas. In this study, we evaluate a large panel of 836 maize inbred lines to detect genetic loci and candidate genes for cold tolerance at the germination and seedling stages. Genetic variation for cold tolerance was larger than in previous reports with moderately high heritability for most traits. We identified 187 significant single-nucleotide polymorphisms (SNPs) that were integrated into 159 quantitative trait loci (QTL) for emergence and traits related to early growth. Most of the QTL have small effects and are specific for each environment, with the majority found under control conditions. Favorable alleles are more frequent in 120 inbreds including all germplasm groups, but mainly from Minnesota and Spain. Therefore, there is a large, potentially novel, genetic variability in the germplasm groups represented by these inbred lines. Most of the candidate genes are involved in metabolic processes and intracellular membrane-bounded organelles. We expect that further evaluations of germplasm with broader genetic diversity could identify additional favorable alleles for cold tolerance. However, it is not likely that further studies will find favorable alleles with large effects for improving cold tolerance in maize.