QTL identification of grain protein concentration and its genetic correlation with starch concentration and grain weight using two populations in maize (Zea mays L.)

Dissecting the genetic architecture of polygenic nutritional traits in maize through meta-QTL analysis

Maize, as a staple crop, contributes significantly to global nutritional security. However, improving its nutritional quality, including grain zinc (GZn), grain iron (GFe), kernel oil (KO), protein quality (PQ), and content (PC), is difficult due to the complex and polygenic nature of these traits. In traditional quantitative trait loci (QTLs) mapping, different populations tested across variable environments have resulted in heterogeneous findings, highlighting the challenge of QTL instability. Therefore, we tested whether Meta-QTL (MQTL) analysis enables the identification of stable QTLs with broader allelic coverage and higher mapping resolution for effective marker-assisted selection (MAS) of complex traits. A comprehensive literature search revealed 29 mapping studies encompassing 308 QTLs for the targeted traits. A total of 34 stable MQTLs were identified, with an average CI of 4.59 cM. These MQTLs were located on all ten maize chromosomes, with phenotypic variance explained (PVE %) ranging from 7.3 % (MQTL1_2) to 49.0 % (MQTL3_2). Furthermore, the analysis revealed six MAS-friendly and five hotspot MQTLs. Besides, 591 CGs were identified underlying these MQTLs, of which 14 have known roles in grain filling, metal homeostasis, and fatty acid biosynthesis in maize. In silico analysis confirmed the tissue-specific expression of these 14 CGs. MQTL analysis effectively refined the genomic regions (4.86 folds) linked with nutritional quality and identified stable MQTLs and CGs. These findings will be useful for developing nutritionally enriched varieties through MAS and genetic engineering.

GWAS and Meta-QTL Analysis of Kernel Quality-Related Traits in Maize

The quality of corn kernels is crucial for their nutritional value, making the enhancement of kernel quality a primary objective of contemporary corn breeding efforts. This study utilized 260 corn inbred lines as research materials and assessed three traits associated with grain quality. A genome-wide association study (GWAS) was conducted using the best linear unbiased estimator (BLUE) for quality traits, resulting in the identification of 23 significant single nucleotide polymorphisms (SNPs). Additionally, nine genes associated with grain quality traits were identified through gene function annotation and prediction. Furthermore, a total of 697 quantitative trait loci (QTL) related to quality traits were compiled from 27 documents, followed by a meta-QTL analysis that revealed 40 meta-QTL associated with these traits. Among these, 19 functional genes and reported candidate genes related to quality traits were detected. Three significant SNPs identified by GWAS were located within the intervals of these QTL, while the remaining eight significant SNPs were situated within 2 Mb of the QTL. In summary, the findings of this study provide a theoretical framework for analyzing the genetic basis of corn grain quality-related traits and for enhancing corn quality.

Linkage mapping and genomic prediction of grain quality traits in tropical maize (Zea mays L.)

The suboptimal productivity of maize systems in sub-Saharan Africa (SSA) is a pressing issue, with far-reaching implications for food security, nutrition, and livelihood sustainability within the affected smallholder farming communities. Dissecting the genetic basis of grain protein, starch and oil content can increase our understanding of the governing genetic systems, improve the efficacy of future breeding schemes and optimize the end-use quality of tropical maize. Here, four bi-parental maize populations were evaluated in field trials in Kenya and genotyped with mid-density single nucleotide polymorphism (SNP) markers. Genotypic (G), environmental (E) and G×E variations were found to be significant for all grain quality traits. Broad sense heritabilities exhibited substantial variation (0.18-0.68). Linkage mapping identified multiple quantitative trait loci (QTLs) for the studied grain quality traits: 13, 7, 33, 8 and 2 QTLs for oil content, protein content, starch content, grain texture and kernel weight, respectively. The co-localization of QTLs identified in our research suggests the presence of shared genetic factors or pleiotropic effects, implying that specific genomic regions influence the expression of multiple grain quality traits simultaneously. Genomic prediction accuracies were moderate to high for the studied traits. Our findings highlight the polygenic nature of grain quality traits and demonstrate the potential of genomic selection to enhance genetic gains in maize breeding. Furthermore, the identified genomic regions and single nucleotide polymorphism markers can serve as the groundwork for investigating candidate genes that regulate grain quality traits in tropical maize. This, in turn, can facilitate the implementation of marker-assisted selection (MAS) in breeding programs focused on improving grain nutrient levels.

Genetic basis of maize kernel protein content revealed by high-density bin mapping using recombinant inbred lines

Maize with a high kernel protein content (PC) is desirable for human food and livestock fodder. However, improvements in its PC have been hampered by a lack of desirable molecular markers. To identify quantitative trait loci (QTL) and candidate genes for kernel PC, we employed a genotyping-by-sequencing strategy to construct a high-resolution linkage map with 6,433 bin markers for 275 recombinant inbred lines (RILs) derived from a high-PC female Ji846 and low-PC male Ye3189. The total genetic distance covered by the linkage map was 2180.93 cM, and the average distance between adjacent markers was 0.32 cM, with a physical distance of approximately 0.37 Mb. Using this linkage map, 11 QTLs affecting kernel PC were identified, including qPC7 and qPC2-2, which were identified in at least two environments. For the qPC2-2 locus, a marker named IndelPC2-2 was developed with closely linked polymorphisms in both parents, and when tested in 30 high and 30 low PC inbred lines, it showed significant differences (P = 1.9E-03). To identify the candidate genes for this locus, transcriptome sequencing data and PC best linear unbiased estimates (BLUE) for 348 inbred lines were combined, and the expression levels of the four genes were correlated with PC. Among the four genes, Zm00001d002625, which encodes an S-adenosyl-L-methionine-dependent methyltransferase superfamily protein, showed significantly different expression levels between two RIL parents in the endosperm and is speculated to be a potential candidate gene for qPC2-2. This study will contribute to further research on the mechanisms underlying the regulation of maize PC, while also providing a genetic basis for marker-assisted selection in the future.

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

Starch is the principal carbohydrate source in maize kernels. Understanding the genetic basis of starch content (SC) benefits greatly in improving maize yield and optimizing end-use quality. Here, four double haploid (DH) populations were generated and were used to identify quantitative trait loci (QTLs) associated with SC. The phenotype of SC exhibited continuous and approximate normal distribution in each population. A total of 13 QTLs for SC in maize kernels was detected in a range of 3.65-16.18% of phenotypic variation explained (PVE). Among those, only some partly overlapped with QTLs previously known to be related to SC. Meanwhile, 12 genes involved in starch synthesis and metabolism located within QTLs were identified in this study. These QTLs will lay the foundation to explore candidate genes regulating SC in maize kernel and facilitate the application of molecular marker-assisted selection for a breeding program to cultivate maize varieties with a deal of grain quality.

Genetic basis of kernel starch content decoded in a maize multi-parent population

Starch is the most abundant storage carbohydrate in maize kernels and provides calories for humans and other animals as well as raw materials for various industrial applications. Decoding the genetic basis of natural variation in kernel starch content is needed to manipulate starch quantity and quality via molecular breeding to meet future needs. Here, we identified 50 unique single quantitative trait loci (QTLs) for starch content with 18 novel QTLs via single linkage mapping, joint linkage mapping and a genome-wide association study in a multi-parent population containing six recombinant inbred line populations. Only five QTLs explained over 10% of phenotypic variation in single populations. In addition to a few large-effect and many small-effect additive QTLs, limited pairs of epistatic QTLs also contributed to the genetic basis of the variation in kernel starch content. A regional association study identified five non-starch-pathway genes that were the causal candidate genes underlying the identified QTLs for starch content. The pathway-driven analysis identified ZmTPS9, which encodes a trehalose-6-phosphate synthase in the trehalose pathway, as the causal gene for the QTL qSTA4-2, which was detected by all three statistical analyses. Knockout of ZmTPS9 increased kernel starch content and, in turn, kernel weight in maize, suggesting potential applications for ZmTPS9 in maize starch and yield improvement. These findings extend our knowledge about the genetic basis of starch content in maize kernels and provide valuable information for maize genetic improvement of starch quantity and quality.

Analysis of Nutritional Quality Attributes and Their Inter-Relationship in Maize Inbred Lines for Sustainable Livelihood

The present investigation was planned to understand the variability and inter-relationship among various nutritional quality attributes of maize kernels to identify potential donors of the respective traits for future hybridization programs. Sixty-three maize inbred lines were processed for the estimation of protein, starch, fat, sugar, 100-kernel weight, specific gravity, and moisture level of the grain. The results reveal that a wide variability among protein, starch, 100-kernel weight, specific gravity, and fat was seen, with special emphasis on the protein concentration that varied from 8.83 to 15.54%, starch (67.43–75.31%), and 100-kernel weight (9.14–36.11 gm). Factor analysis revealed that the protein concentration, starch, and 100-kernel weight, the three major components, comprise 68.58% of the kernel variability. Protein exhibited a significant negative correlation with starch and 100-kernel weight, indicating that an increase in the protein concentration will down-regulate the starch and 100-kernel weight. The inbred lines are proposed as donors for the development of high cultivars for their respective traits, viz., high protein (DMR WNC NY 403 and DMR WNC NY 404), high starch concentration (DMR WNC NY 2163, DMR WNC NY 2219, DMR WNC NY 2234, DMR WNC NY 2408, DMR WNC NY 2437, and DMR WNC NY 2466), high 100-kernel wt. (DMR WNC NY 2113, DMR WNC NY 2213, DMR WNC NY 2233, DMR WNC NY 2234, DMR WNC NY 2414, DMR WNC NY 2435, DMR WNC NY 2465, and DMR WNC NY 2474), sugar (DMR WNC NY 2417), and specific gravity (DMR WNC NY 2418). Genetic distance analysis revealed that DMR WNC NY 397 and DMR WNC NY 404 are the farthest apart inbred lines, having major differences in their protein, fat, starch, and sugar contents, followed by DMR WNC NY 2436 and DMR WNC NY 2394, DMR WNC NY 2212 and DMR WNC NY 2430, DMR WNC NY 396 and DMR WNC NY 2415, DMR WNC NY 404 and DMR WNC NY 2144, and DMR WNC NY403 and DMR WNC NY 2115. Moreover, this study proposes that these possible combinations of lines (in a breeding program) would result in genetic variability with simultaneous high values for the respective characteristics.

Breeding and biotechnological interventions for trait improvement: status and prospects

Present review describes the molecular tools and strategies deployed in the trait discovery and improvement of major crops. The prospects and challenges associated with these approaches are discussed. Crop improvement relies on modulating the genes and genomic regions underlying key traits, either directly or indirectly. Direct approaches include overexpression, RNA interference, genome editing, etc., while breeding majorly constitutes the indirect approach. With the advent of latest tools and technologies, these strategies could hasten the improvement of crop species. Next-generation sequencing, high-throughput genotyping, precision editing, use of space technology for accelerated growth, etc. had provided a new dimension to crop improvement programmes that work towards delivering better varieties to cope up with the challenges. Also, studies have widened from understanding the response of plants to single stress to combined stress, which provides insights into the molecular mechanisms regulating tolerance to more than one stress at a given point of time. Altogether, next-generation genetics and genomics had made tremendous progress in delivering improved varieties; however, the scope still exists to expand its horizon to other species that remain underutilized. In this context, the present review systematically analyses the different genomics approaches that are deployed for trait discovery and improvement in major species that could serve as a roadmap for executing similar strategies in other crop species. The application, pros, and cons, and scope for improvement of each approach have been discussed with examples, and altogether, the review provides comprehensive coverage on the advances in genomics to meet the ever-growing demands for agricultural produce.

Genetic architecture of kernel composition in global sorghum germplasm

Background

Sorghum [Sorghum bicolor (L.) Moench] is an important cereal crop for dryland areas in the United States and for small-holder farmers in Africa. Natural variation of sorghum grain composition (protein, fat, and starch) between accessions can be used for crop improvement, but the genetic controls are still unresolved. The goals of this study were to quantify natural variation of sorghum grain composition and to identify single-nucleotide polymorphisms (SNPs) associated with variation in grain composition concentrations.

Results

In this study, we quantified protein, fat, and starch in a global sorghum diversity panel using near-infrared spectroscopy (NIRS). Protein content ranged from 8.1 to 18.8%, fat content ranged from 1.0 to 4.3%, and starch content ranged from 61.7 to 71.1%. Durra and bicolor-durra sorghum from Ethiopia and India had the highest protein and fat and the lowest starch content, while kafir sorghum from USA, India, and South Africa had the lowest protein and the highest starch content. Genome-wide association studies (GWAS) identified quantitative trait loci (QTL) for sorghum protein, fat, and starch. Previously published RNAseq data was used to identify candidate genes within a GWAS QTL region. A putative alpha-amylase 3 gene, which has previously been shown to be associated with grain composition traits, was identified as a strong candidate for protein and fat variation.

Conclusions

We identified promising sources of genetic material for manipulation of grain composition traits, and several loci and candidate genes that may control sorghum grain composition. This survey of grain composition in sorghum germplasm and identification of protein, fat, and starch QTL contributes to our understanding of the genetic basis of natural variation in sorghum grain nutritional traits.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-3403-x) contains supplementary material, which is available to authorized users.

Genome-Wide Association Study Identifies Candidate Genes for Starch Content Regulation in Maize Kernels

Kernel starch content is an important trait in maize (Zea mays L.) as it accounts for 65-75% of the dry kernel weight and positively correlates with seed yield. A number of starch synthesis-related genes have been identified in maize in recent years. However, many loci underlying variation in starch content among maize inbred lines still remain to be identified. The current study is a genome-wide association study that used a set of 263 maize inbred lines. In this panel, the average kernel starch content was 66.99%, ranging from 60.60 to 71.58% over the three study years. These inbred lines were genotyped with the SNP50 BeadChip maize array, which is comprised of 56,110 evenly spaced, random SNPs. Population structure was controlled by a mixed linear model (MLM) as implemented in the software package TASSEL. After the statistical analyses, four SNPs were identified as significantly associated with starch content (P ≤ 0.0001), among which one each are located on chromosomes 1 and 5 and two are on chromosome 2. Furthermore, 77 candidate genes associated with starch synthesis were found within the 100-kb intervals containing these four QTLs, and four highly associated genes were within 20-kb intervals of the associated SNPs. Among the four genes, Glucose-1-phosphate adenylyltransferase (APS1; Gene ID GRMZM2G163437) is known as an important regulator of kernel starch content. The identified SNPs, QTLs, and candidate genes may not only be readily used for germplasm improvement by marker-assisted selection in breeding, but can also elucidate the genetic basis of starch content. Further studies on these identified candidate genes may help determine the molecular mechanisms regulating kernel starch content in maize and other important cereal crops.

Verification of QTL for grain starch content and its genetic correlation with oil content using two connected RIL populations in high-oil maize

Grain oil content is negatively correlated with starch content in maize in general. In this study, 282 and 263 recombinant inbred lines (RIL) developed from two crosses between one high-oil maize inbred and two normal dent maize inbreds were evaluated for grain starch content and its correlation with oil content under four environments. Single-trait QTL for starch content in single-population and joint-population analysis, and multiple-trait QTL for both starch and oil content were detected, and compared with the result obtained in the two related F(2∶3) populations. Totally, 20 single-population QTL for grain starch content were detected. No QTL was simultaneously detected across all ten cases. QTL at bins 5.03 and 9.03 were all detected in both populations and in 4 and 5 cases, respectively. Only 2 of the 16 joint-population QTL had significant effects in both populations. Three single-population QTL and 8 joint-population QTL at bins 1.03, 1.04-1.05, 3.05, 8.04-8.05, 9.03, and 9.05 could be considered as fine-mapped. Common QTL across F(2∶3) and RIL generations were observed at bins 5.04, 8.04 and 8.05 in population 1 (Pop.1), and at bin 5.03 in population 2 (Pop.2). QTL at bins 3.02-3.03, 3.05, 8.04-8.05 and 9.03 should be focused in high-starch maize breeding. In multiple-trait QTL analysis, 17 starch-oil QTL were detected, 10 in Pop.1 and 7 in Pop.2. And 22 single-trait QTL failed to show significance in multiple-trait analysis, 13 QTL for starch content and 9 QTL for oil content. However, QTL at bins 1.03, 6.03-6.04 and 8.03-8.04 might increase grain starch content and/or grain oil content without reduction in another trait. Further research should be conducted to validate the effect of these QTL in the simultaneous improvement of grain starch and oil content in maize.

Sequence polymorphism characteristics in the See2β gene from maize key inbred lines and derived lines in China

Research on the sequence polymorphism characteristics of key genes is important in the early identification of maize inbred lines. The See2β gene functions in remobilizing leaf nitrogen and exporting it to developing grain during foliar senescence. We analyzed See2β sequences from 49 inbred lines representing four key lines and their derivatives: Huangzaosi, Mo17, Dan340, and Ye478. We found that the See2β gene had one insertion and two deletions in most Huangzaosi lines and three insertions in the Mo17 group; that the Huangzaosi, Dan340, and Ye478 lines, but not the Mo17 line, had unique indels; and that the See2β gene in lines derived from the same key inbred line had higher sequence homology, according to phenetic analysis of the inbred lines derived from Huangzaosi and Mo17. Thus, a candidate inbred line could be preliminarily identified using markers closely linked to the See2β gene combined with sequence alignment of See2β.

Variation characteristics of the nitrate reductase gene of key inbred maize lines and derived lines in China

Key inbred lines have played a fundamental role in maize genetics and breeding. Research on variation characteristics of key genes from key inbred lines and from derived lines is important for early identification and evaluation of inbred maize lines. The nitrate reductase (NR) gene, which plays a central role in nitrate acquisition, was the target gene for this research. Forty-one inbred maize lines were investigated, including four key inbred lines: Huangzaosi, Mo17, Dan340, and Ye478. Through multiple sequence alignment with the NR gene from B73, used as a control, we found that: 1) the NR gene of most inbred lines from Huangzaosi and from derived lines had two insertion fragments and two replacement fragments; 2) the NR gene of most inbred lines from Mo17 and derived lines had one insertion fragment and one replacement fragment; 3) there were two common variations and eight common SNPs in the NR gene of the four key lines. Huangzaosi and Mo17 also had three common variations compared with the other key lines. Moreover, Mo17 had some unique variations; there were no unique variations in the other key lines, even for SNPs, and 4) phylogenetic tree analysis showed that the NR gene of the derived lines from the same key line had higher sequence homology. Based on the above NR gene variation characteristics and sequence homology of key inbred lines and derived lines, a candidate inbred line can be preliminarily selected and evaluated by marker development and/or sequence alignment of the NR gene.