Zea mays (L.) P1 locus for cob glume color identified as a post-domestication selection target with an effect on temperate maize genomes

Novel Combination of the Biophysical, Nutritional, and Nutraceutical Properties in Subtropical Pigmented Maize Hybrids

Maize (Zea mays L.) represents the main caloric source for much of the world's population. Pigmented maize varieties are an excellent source of nutraceutical compounds: blue and yellow maize are rich in anthocyanins as well as carotenoids and phenolic acids, respectively. However, blue maize is usually grown in small quantities as a specialty crop because it lacks the qualities and adaptations of commercial white and yellow varieties. Here, a new high-yield variety of blue maize called Vitamaiz was developed from inbred lines of subtropical blue, white, and yellow maize. The aim of this study was to characterize the nutraceutical and physical properties of 30 Vitamaiz hybrids in two subtropical locations. Kernel physical traits, nutrient composition, and nutraceutical components (free phenolic acids, FPA; cell wall-bound phenolic acids, BPA; total monomeric anthocyanin content, TAC; antioxidant capacity, AOX by oxygen radical absorbance capacity assay, and total carotenoid content, TCC) were evaluated. The biophysical traits of the hybrids were suitable for nixtamalized and flour maize industries. High levels of FPA (228 mg GAE/100 g), BPA (635 mg GAE/100 g), and AOX (2.0 and 8.1 mM Trolox equivalent/100 g for FPA and BPA, respectively) were also detected with elevated TAC levels (274 mg C3G/kg dw) and AOX activity (3.1 mM Trolox equivalent/100 g). This is the first study to characterize Blue × Yellow maize hybrids that adapt to subtropical environments.

Development of high-resolution multiple-SNP arrays for genetic analyses and molecular breeding through genotyping by target sequencing and liquid chip

Genotyping platforms, as critical supports for genomics, genetics, and molecular breeding, have been well implemented at national institutions/universities in developed countries and multinational seed companies that possess high-throughput, automatic, large-scale, and shared facilities. In this study, we integrated an improved genotyping by target sequencing (GBTS) system with capture-in-solution (liquid chip) technology to develop a multiple single-nucleotide polymorphism (mSNP) approach in which mSNPs can be captured from a single amplicon. From one 40K maize mSNP panel, we developed three types of markers (40K mSNPs, 251K SNPs, and 690K haplotypes), and generated multiple panels with various marker densities (1K-40K mSNPs) by sequencing at different depths. Comparative genetic diversity analysis was performed with genic versus intergenic markers and di-allelic SNPs versus non-typical SNPs. Compared with the one-amplicon-one-SNP system, mSNPs and within-mSNP haplotypes are more powerful for genetic diversity detection, linkage disequilibrium decay analysis, and genome-wide association studies. The technologies, protocols, and application scenarios developed for maize in this study will serve as a model for the development of mSNP arrays and highly efficient GBTS systems in animals, plants, and microorganisms.

Playing with colours: genetics and regulatory mechanisms for anthocyanin pathway in cereals

Cereals form the most important source of energy in our food. Currently, demand for coloured food grains is significantly increasing globally because of their antioxidant properties and enhanced nutritional value. Coloured grains of major and minor cereals are due to accumulation of secondary metabolites like carotenoids and flavonoids such as anthocyanin, proanthocyanin, phlobaphenes in pericarp, aleurone, lemma, testa or seed coat of grains. Differential accumulation of colour in grains is regulated by several regulatory proteins and enzymes involved in flavonoid and caroteniod biosynthesis. MYB and bHLH gene family members are the major regulators of these pathways. Genes for colour across various cereals have been extensively studied; however, only a few functional and allele-specific markers to be utilized directly in breeding programmes are reported so far. In this review, while briefly discussing the well studied and explored carotenoid pathway, we focus on a much more complex anthocyanin pathway that is found across cereals. The genes and their orthologs that are responsible for encoding key regulators of anthocyanin biosynthesis are discussed. This review also focuses on the genetic factors that influence colour change in different cereal crops, and the available/reported markers that can be used in breeding programs for utilizing this pathway for enhancing food and nutritional security.

Detection of QTNs for kernel moisture concentration and kernel dehydration rate before physiological maturity in maize using multi-locus GWAS

Maize is China’s largest grain crop. Mechanical grain harvesting is the key technology in maize production, and the kernel moisture concentration (KMC) is the main controlling factor in mechanical maize harvesting in China. The kernel dehydration rate (KDR) is closely related to the KMC. Thus, it is important to conduct genome-wide association studies (GWAS) of the KMC and KDR in maize, detect relevant quantitative trait nucleotides (QTNs), and mine relevant candidate genes. Here, 132 maize inbred lines were used to measure the KMC every 5 days from 10 to 40 days after pollination (DAP) in order to calculate the KDR. These lines were genotyped using a maize 55K single-nucleotide polymorphism array. QTNs for the KMC and KDR were detected based on five methods (mrMLM, FASTmrMLM, FASTmrEMMA, pLARmEB, and ISIS EM-BLASSO) in the package mrMLM. A total of 334 significant QTNs were found for both the KMC and KDR, including 175 QTNs unique to the KMC and 178 QTNs unique to the KDR; 116 and 58 QTNs were detected among the 334 QTNs by two and more than two methods, respectively; and 9 and 5 QTNs among 58 QTNs were detected in 2 and 3 years, respectively. A significant enrichment in cellular component was revealed by Gene Ontology enrichment analysis of candidate genes in the intervals adjacent to the 14 QTNs and this category contained five genes. The information provided in this study may be useful for further mining of genes associated with the KMC and KDR in maize.

Genome-wide association study of maize plant architecture using F1 populations

Genome-wide association study of maize plant architecture using F₁ populations can better dissect various genetic effects that can provide precise guidance for genetic improvement in maize breeding. Maize grain yield has increased at least eightfold during the past decades. Plant architecture, including plant height, leaf angle, leaf length, and leaf width, has been changed significantly to adapt to higher planting density. Although the genetic architecture of these traits has been dissected using different populations, the genetic basis remains unclear in the F₁ population. In this work, we perform a genome-wide association study of the four traits using 573 F₁ hybrids with a mixed linear model approach and QTXNetwork mapping software. A total of 36 highly significant associated quantitative trait SNPs were identified for these traits, which explained 51.86-79.92% of the phenotypic variation and were contributed mainly by additive, dominance, and environment-specific effects. Heritability as a result of environmental interaction was more important for leaf angle and leaf length, while major effects (a, aa, and d) were more important for leaf width and plant height. The potential breeding values of the superior lines and superior hybrids were also predicted, and these values can be applied in maize breeding by direct selection of superior genotypes for the associated quantitative trait SNPs. A total of 108 candidate genes were identified for the four traits, and further analysis was performed to screen the potential genes involved in the development of maize plant architecture. Our results provide new insights into the genetic architecture of the four traits, and will be helpful in marker-assisted breeding for maize plant architecture.

Identification and functional characterization of the AGO1 ortholog in maize

Eukaryotic Argonaute proteins play primary roles in miRNA and siRNA pathways that are essential for numerous developmental and biological processes. However, the functional roles of the four ZmAGO1 genes have not yet been characterized in maize (Zea mays L.). In the present study, ZmAGO1a was identified from four putative ZmAGO1 genes for further characterization. Complementation of the Arabidopsis ago1-27 mutant with ZmAGO1a indicated that constitutive overexpression of ZmAGO1a could restore the smaller rosette, serrated leaves, later flowering and maturation, lower seed set, and darker green leaves at late stages of the mutant to the wild-type phenotype. The expression profiles of ZmAGO1a under five different abiotic stresses indicated that ZmAGO1a shares expression patterns similar to those of Argonaute genes in rice, Arabidopsis, and wheat. Further, variation in ZmAGO1a alleles among diverse maize germplasm that resulted in several amino acid changes revealed genetic diversity at this locus. The present data suggest that ZmAGO1a might be an important AGO1 ortholog in maize. The results presented provide further insight into the function of ZmAGO1a.

Genetic dissection of maize plant architecture with an ultra-high density bin map based on recombinant inbred lines

Background

Plant architecture attributes, such as plant height, ear height, and internode number, have played an important role in the historical increases in grain yield, lodging resistance, and biomass in maize (Zea mays L.). Analyzing the genetic basis of variation in plant architecture using high density QTL mapping will be of benefit for the breeding of maize for many traits. However, the low density of molecular markers in existing genetic maps has limited the efficiency and accuracy of QTL mapping. Genotyping by sequencing (GBS) is an improved strategy for addressing a complex genome via next-generation sequencing technology. GBS has been a powerful tool for SNP discovery and high-density genetic map construction. The creation of ultra-high density genetic maps using large populations of advanced recombinant inbred lines (RILs) is an efficient way to identify QTL for complex agronomic traits.

Results

A set of 314 RILs derived from inbreds Ye478 and Qi319 were generated and subjected to GBS. A total of 137,699,000 reads with an average of 357,376 reads per individual RIL were generated, which is equivalent to approximately 0.07-fold coverage of the maize B73 RefGen_V3 genome for each individual RIL. A high-density genetic map was constructed using 4183 bin markers (100-Kb intervals with no recombination events). The total genetic distance covered by the linkage map was 1545.65 cM and the average distance between adjacent markers was 0.37 cM with a physical distance of about 0.51 Mb. Our results demonstrated a relatively high degree of collinearity between the genetic map and the B73 reference genome. The quality and accuracy of the bin map for QTL detection was verified by the mapping of a known gene, pericarp color 1 (P1), which controls the color of the cob, with a high LOD value of 80.78 on chromosome 1. Using this high-density bin map, 35 QTL affecting plant architecture, including 14 for plant height, 14 for ear height, and seven for internode number were detected across three environments. Interestingly, pQTL10, which influences all three of these traits, was stably detected in three environments on chromosome 10 within an interval of 14.6 Mb. Two MYB transcription factor genes, GRMZM2G325907 and GRMZM2G108892, which might regulate plant cell wall metabolism are the candidate genes for qPH10.

Conclusions

Here, an ultra-high density accurate linkage map for a set of maize RILs was constructed using a GBS strategy. This map will facilitate identification of genes and exploration of QTL for plant architecture in maize. It will also be helpful for further research into the mechanisms that control plant architecture while also providing a basis for marker-assisted selection.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-2555-z) contains supplementary material, which is available to authorized users.