Genetic properties of the MAGIC maize population: a new platform for high definition QTL mapping in Zea mays

QTL mapping for plant height and ear height using bi-parental immortalized heterozygous populations in maize

Introduction

Plant height (PH) and ear height (EH) are key plant architectural traits in maize, which will affect the photosynthetic efficiency, high plant density tolerance, suitability for mechanical harvesting.

Methods

QTL mapping were conducted for PH and EH using a recombinant inbred line (RIL) population and two corresponding immortalized backcross (IB) populations obtained from crosses between the RIL population and the two parental lines.

Results

A total of 17 and 15 QTL were detected in the RIL and IB populations, respectively. Two QTL, qPH1-1 (qEH1-1) and qPH1-2 (qEH1-4) in the RIL, were simultaneously identified for PH and EH. Combing reported genome-wide association and cloned PH-related genes, co-expression network analyses were constructed, then five candidate genes with high confidence in major QTL were identified including Zm00001d011117 and Zm00001d011108, whose homologs have been confirmed to play a role in determining PH in maize and soybean.

Discussion

QTL mapping used a immortalized backcross population is a new strategy. These identified genes in this study can provide new insights for improving the plant architecture in maize.

Natural uORF variation in plants

Taking advantage of natural variation promotes our understanding of phenotypic diversity and trait evolution, ultimately accelerating plant breeding, in which the identification of causal variations is critical. To date, sequence variations in the coding region and transcription level polymorphisms caused by variations in the promoter have been prioritized. An upstream open reading frame (uORF) in the 5' untranslated region (5' UTR) regulates gene expression at the post-transcription or translation level. In recent years, studies have demonstrated that natural uORF variations shape phenotypic diversity. This opinion article highlights recent researches and speculates on future directions for natural uORF variation in plants.

Development and evaluation of the utility of GenoBaits Peanut 40K for a peanut MAGIC population

Population and genotype data are essential for genetic mapping. The multi-parent advanced generation intercross (MAGIC) population is a permanent mapping population used for precisely mapping quantitative trait loci. Moreover, genotyping-by-target sequencing (GBTS) is a robust high-throughput genotyping technology characterized by its low cost, flexibility, and limited requirements for information management and support. In this study, an 8-way MAGIC population was constructed using eight elite founder lines. In addition, GenoBaits Peanut 40K was developed and utilized for the constructed MAGIC population. A subset (297 lines) of the MAGIC population at the S2 stage was genotyped using GenoBaits Peanut 40K. Furthermore, these lines and the eight parents were analyzed in terms of pod length, width, area, and perimeter. A total of 27 single nucleotide polymorphisms (SNPs) were revealed to be significantly associated with peanut pod size-related traits according to a genome-wide association study. The GenoBaits Peanut 40K provided herein and the constructed MAGIC population will be applicable for future research to identify the key genes responsible for important peanut traits.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-023-01417-w.

Discovering useful genetic variation in the seed parent gene pool for sorghum improvement

Multi-parent populations contain valuable genetic material for dissecting complex, quantitative traits and provide a unique opportunity to capture multi-allelic variation compared to the biparental populations. A multi-parent advanced generation inter-cross (MAGIC) B-line (MBL) population composed of 708 F6 recombinant inbred lines (RILs), was recently developed from four diverse founders. These selected founders strategically represented the four most prevalent botanical races (kafir, guinea, durra, and caudatum) to capture a significant source of genetic variation to study the quantitative traits in grain sorghum [Sorghum bicolor (L.) Moench]. MBL was phenotyped at two field locations for seven yield-influencing traits: panicle type (PT), days to anthesis (DTA), plant height (PH), grain yield (GY), 1000-grain weight (TGW), tiller number per meter (TN) and yield per panicle (YPP). High phenotypic variation was observed for all the quantitative traits, with broad-sense heritabilities ranging from 0.34 (TN) to 0.84 (PH). The entire population was genotyped using Diversity Arrays Technology (DArTseq), and 8,800 single nucleotide polymorphisms (SNPs) were generated. A set of polymorphic, quality-filtered markers (3,751 SNPs) and phenotypic data were used for genome-wide association studies (GWAS). We identified 52 marker-trait associations (MTAs) for the seven traits using BLUPs generated from replicated plots in two locations. We also identified desirable allelic combinations based on the plant height loci (Dw1, Dw2, and Dw3), which influences yield related traits. Additionally, two novel MTAs were identified each on Chr1 and Chr7 for yield traits independent of dwarfing genes. We further performed a multi-variate adaptive shrinkage analysis and 15 MTAs with pleiotropic effect were identified. The five best performing MBL progenies were selected carrying desirable allelic combinations. Since the MBL population was designed to capture significant diversity for maintainer line (B-line) accessions, these progenies can serve as valuable resources to develop superior sorghum hybrids after validation of their general combining abilities via crossing with elite pollinators. Further, newly identified desirable allelic combinations can be used to enrich the maintainer germplasm lines through marker-assisted backcross breeding.

Genome-wide association studies: an intuitive solution for SNP identification and gene mapping in trees

Analysis of natural diversity in wild/cultivated plants can be used to understand the genetic basis for plant breeding programs. Recent advancements in DNA sequencing have expanded the possibilities for genetically altering essential features. There have been several recently disclosed statistical genetic methods for discovering the genes impacting target qualities. One of these useful methods is the genome-wide association study (GWAS), which effectively identifies candidate genes for a variety of plant properties by examining the relationship between a molecular marker (such as SNP) and a target trait. Conventional QTL mapping with highly structured populations has major limitations. The limited number of recombination events results in poor resolution for quantitative traits. Only two alleles at any given locus can be studied simultaneously. Conventional mapping approach fails to work in perennial plants and vegetatively propagated crops. These limitations are sidestepped by association mapping or GWAS. The flexibility of GWAS comes from the fact that the individuals being examined need not be linked to one another, allowing for the use of all meiotic and recombination events to increase resolution. Phenotyping, genotyping, population structure analysis, kinship analysis, and marker-trait association analysis are the fundamental phases of GWAS. With the rapid development of sequencing technologies and computational methods, GWAS is becoming a potent tool for identifying the natural variations that underlie complex characteristics in crops. The use of high-throughput sequencing technologies along with genotyping approaches like genotyping-by-sequencing (GBS) and restriction site associated DNA (RAD) sequencing may be highly useful in fast-forward mapping approach like GWAS. Breeders may use GWAS to quickly unravel the genomes through QTL and association mapping by taking advantage of natural variances. The drawbacks of conventional linkage mapping can be successfully overcome with the use of high-resolution mapping and the inclusion of multiple alleles in GWAS.

A rapid method to quantify vein density in C4 plants using starch staining

C4 photosynthesis has evolved multiple times in the angiosperms and typically involves alterations to the biochemistry, cell biology and development of leaves. One common modification found in C4 plants compared with the ancestral C3 state is an increase in vein density such that the leaf contains a larger proportion of bundle sheath cells. Recent findings indicate that there may be significant intraspecific variation in traits such as vein density in C4 plants but to use such natural variation for trait-mapping, rapid phenotyping would be required. Here we report a high-throughput method to quantify vein density that leverages the bundle sheath-specific accumulation of starch found in C4 species. Starch staining allowed high-contrast images to be acquired permitting image analysis with MATLAB- and Python-based programmes. The method works for dicotyledons and monocotolydons. We applied this method to Gynandropsis gynandra where significant variation in vein density was detected between natural accessions, and Zea mays where no variation was apparent in the genotypically diverse lines assessed. We anticipate this approach will be useful to map genes controlling vein density in C4 species demonstrating natural variation for this trait.

Identification of Candidate QTLs and Genes for Ear Diameter by Multi-Parent Population in Maize

Ear diameter (ED) is a critical component of grain yield (GY) in maize (Zea mays L.). Studying the genetic basis of ED in maize is of great significance in enhancing maize GY. Against this backdrop, this study was framed to (1) map the ED-related quantitative trait locus (QTL) and SNPs associated with ED; and (2) identify putative functional genes that may affect ED in maize. To accomplish this, an elite maize inbred line, Ye107, which belongs to the Reid heterotic group, was used as a common parent and crossed with seven elite inbred lines from three different heterotic groups (Suwan1, Reid, and nonReid) that exhibited abundant genetic variation in ED. This led to the construction of a multi-parent population consisting of 1215 F₇ recombinant inbred lines (F₇RILs). A genome-wide association study (GWAS) and linkage analysis were then conducted for the multi-parent population using 264,694 high-quality SNPs generated via the genotyping-by-sequencing method. Our study identified a total of 11 SNPs that were significantly associated with ED through the GWAS, and three QTLs were revealed by the linkage analysis for ED. The major QTL on chromosome 1 was co-identified in the region by the GWAS at SNP_143985532. SNP_143985532, located upstream of the Zm00001d030559 gene, encodes a callose synthase that is expressed in various tissues, with the highest expression level in the maize ear primordium. Haplotype analysis indicated that the haplotype B (allele AA) of Zm00001d030559 was positively correlated with ED. The candidate genes and SNPs identified in this study provide crucial insights for future studies on the genetic mechanism of maize ED formation, cloning of ED-related genes, and genetic improvement of ED. These results may help develop important genetic resources for enhancing maize yield through marker-assisted breeding.

Identifying genomic regions determining shoot and root traits related to nitrogen uptake efficiency in a multiparent advanced generation intercross (MAGIC) winter wheat population in a high-throughput phenotyping facility

In the context of a continuously increasing human population that needs to be fed, with environmental protection in mind, nitrogen use efficiency (NUE) improvement is becoming very important. To understand the natural variation of traits linked to nitrogen uptake efficiency (UPE), one component of NUE, the multiparent advanced generation intercross (MAGIC) winter wheat population WM-800 was phenotyped under two contrasting nitrogen (N) levels in a high-throughput phenotyping facility for six weeks. Three biomass-related, three root-related, and two reflectance-related traits were measured weekly under each treatment. Subsequently, the population was genetically analysed using a total of 13,060 polymorphic haplotypes and singular SNPs for a genome-wide association study (GWAS). In total, we detected 543 quantitative trait loci (QTL) across all time points and traits, which were pooled into 42 stable QTL (sQTL; present in at least three of the six weeks). Besides Rht-B1 and Rht-D1, candidate genes playing a role in gibberellic acid-regulated growth and nitrate transporter genes from the NPF gene family, like NRT 1.1, were linked to sQTL. Two novel sQTL on chromosomes 5 A and 6D showed pleiotropic effects on several traits. The high number of N-specific sQTL indicates that selection for UPE is useful specifically under N-limited conditions.

Which mouse multiparental population is right for your study? The Collaborative Cross inbred strains, their F1 hybrids, or the Diversity Outbred population

Multiparental populations (MPPs) encompass greater genetic diversity than traditional experimental crosses of two inbred strains, enabling broader surveys of genetic variation underlying complex traits. Two such mouse MPPs are the Collaborative Cross (CC) inbred panel and the Diversity Outbred (DO) population, which are descended from the same eight inbred strains. Additionally, the F1 intercrosses of CC strains (CC-RIX) have been used and enable study designs with replicate outbred mice. Genetic analyses commonly used by researchers to investigate complex traits in these populations include characterizing how heritable a trait is, i.e. its heritability, and mapping its underlying genetic loci, i.e. its quantitative trait loci (QTLs). Here we evaluate the relative merits of these populations for these tasks through simulation, as well as provide recommendations for performing the quantitative genetic analyses. We find that sample populations that include replicate animals, as possible with the CC and CC-RIX, provide more efficient and precise estimates of heritability. We report QTL mapping power curves for the CC, CC-RIX, and DO across a range of QTL effect sizes and polygenic backgrounds for samples of 174 and 500 mice. The utility of replicate animals in the CC and CC-RIX for mapping QTLs rapidly decreased as traits became more polygenic. Only large sample populations of 500 DO mice were well-powered to detect smaller effect loci (7.5-10%) for highly complex traits (80% polygenic background). All results were generated with our R package musppr, which we developed to simulate data from these MPPs and evaluate genetic analyses from user-provided genotypes.

Exploring the Potential of Multiomics and Other Integrative Approaches for Improving Waterlogging Tolerance in Plants

Soil flooding has emerged as a serious threat to modern agriculture due to the rapid global warming and climate change, resulting in catastrophic crop damage and yield losses. The most detrimental effects of waterlogging in plants are hypoxia, decreased nutrient uptake, photosynthesis inhibition, energy crisis, and microbiome alterations, all of which result in plant death. Although significant advancement has been made in mitigating waterlogging stress, it remains largely enigmatic how plants perceive flood signals and translate them for their adaptive responses at a molecular level. With the advent of multiomics, there has been significant progress in understanding and decoding the intricacy of how plants respond to different stressors which have paved the way towards the development of climate-resistant smart crops. In this review, we have provided the overview of the effect of waterlogging in plants, signaling (calcium, reactive oxygen species, nitric oxide, hormones), and adaptive responses. Secondly, we discussed an insight into past, present, and future prospects of waterlogging tolerance focusing on conventional breeding, transgenic, multiomics, and gene-editing approaches. In addition, we have also highlighted the importance of panomics for developing waterlogging-tolerant cultivars. Furthermore, we have discussed the role of high-throughput phenotyping in the screening of complex waterlogging-tolerant traits. Finally, we addressed the current challenges and future perspectives of waterlogging signal perception and transduction in plants, which warrants future investigation.

A Combination of QTL Mapping and GradedPool-Seq to Dissect Genetic Complexity for Gibberella Ear Rot Resistance in Maize Using an IBM Syn10 DH Population

Gibberella ear rot (GER) caused by Fusarium graminearum (teleomorph Gibberella zeae) is one of the most devastating maize diseases that reduces grain yield and quality worldwide. Utilization of host genetic resistance has become one of the most suitable strategies to control GER. In this study, a set of 246 diverse inbred lines derived from the intermated B 73 × Mo 17 doubled haploid population (IBM Syn10 DH) were used to detect quantitative trait loci (QTL) associated with resistance to GER. Meanwhile, a GradedPool-Seq (GPS) approach was performed to identify genomic variations involved in GER resistance. Using artificial inoculation across multiple environments, GER severity of the population was observed with wide phenotypic variation. Based on the linkage mapping, a total of 14 resistant QTLs were detected, accounting for 5.11 to 14.87% of the phenotypic variation, respectively. In GPS analysis, five significant single nucleotide polymorphisms (SNPs) associated with GER resistance were identified. Combining QTL mapping and GPS analysis, a peak-value SNP on chromosome 4 from GPS was overlapped with the QTL qGER4.2, suggesting that the colocalized region could be the most possible target location conferring resistance to GER. Subsequently, seven candidate genes were identified within the peak SNP, linking them to GER resistance. These findings are useful for exploring the complicated genetic variations in maize GER resistance. The genomic regions and genes identified herein provide a list of candidate targets for further investigation, in addition to the combined strategy that can be used for quantitative traits in plant species.

The Use of DArTseq Technology to Identify Markers Related to the Heterosis Effects in Selected Traits in Maize

Spectacular scientific advances in the area of molecular biology and the development of modern biotechnological tools have had a significant impact on the development of maize heterosis breeding. One technology based on next-generation sequencing is DArTseq. The plant material used for the research consisted of 13 hybrids resulting from the crossing of inbred maize lines. A two-year field experiment was established at two Polish breeding stations: Smolice and Łagiewniki. Nine quantitative traits were observed: cob length, cob diameter, core length, core diameter, number of rows of grain, number of grains in a row, mass of grain from the cob, weight of one thousand grains, and yield. The isolated DNA was subjected to DArTseq genotyping. Association mapping was performed using a method based on the mixed linear model. A total of 81602 molecular markers (28571 SNPs and 53031 SilicoDArTs) were obtained as a result of next-generation sequencing. Out of 81602, 15409 (13850 SNPs and 1559 SilicoDArTs) were selected for association analysis. The 105 molecular markers (8 SNPs and 97 SilicoDArTs) were associated with the heterosis effect of at least one trait in at least one environment. A total of 186 effects were observed. The number of statistically significant relationships between the molecular marker and heterosis effect varied from 8 (for cob length) and 9 (for yield) to 42 (for the number of rows of grain). Of particular note were three markers (2490222, 2548691 and 7058267), which were significant in 17, 8 and 6 cases, respectively. Two of them (2490222 and 7058267) were associated with the heterosis effects of yield in three of the four environments.

Development of a MAGIC population and high-resolution quantitative trait mapping for nicotine content in tobacco

Multiparent Advanced Generation Inter-Cross (MAGIC) population is an ideal genetic and breeding material for quantitative trait locus (QTL) mapping and molecular breeding. In this study, a MAGIC population derived from eight tobacco parents was developed. Eight parents and 560 homozygous lines were genotyped by a 430K single-nucleotide polymorphism (SNP) chip assay and phenotyped for nicotine content under different conditions. Four QTLs associated with nicotine content were detected by genome-wide association mapping (GWAS), and one major QTL, named qNIC7-1, was mapped repeatedly under different conditions. Furthermore, by combining forward mapping, bioinformatics analysis and gene editing, we identified an ethylene response factor (ERF) transcription factor as a candidate gene underlying the major QTL qNIC7-1 for nicotine content in tobacco. A presence/absence variation (PAV) at qNIC7-1 confers changes in nicotine content. Overall, the large size of this MAGIC population, diverse genetic composition, balanced parental contributions and high levels of recombination all contribute to its value as a genetic and breeding resource. The application of the tobacco MAGIC population for QTL mapping and detecting rare allelic variation was demonstrated using nicotine content as a proof of principle.

Flame resistant cotton lines generated by synergistic epistasis in a MAGIC population

Textiles made from cotton fibers are flammable and thus often include flame retardant additives for consumer safety. Transgressive segregation in multi-parent populations facilitates new combinations of alleles of genes and can result in traits that are superior to those of any of the parents. A screen of 257 recombinant inbred lines from a multi-parent advanced generation intercross (MAGIC) population for naturally enhance flame retardance (FR) was conducted. All eleven parents, like all conventional white fiber cotton cultivars produce flammable fabric. MAGIC recombinant inbred lines (RILs) that produced fibers with significantly lower heat release capacities (HRC) as measured by microscale combustion calorimetry (MCC) were identified and the stability of the phenotypes of the outliers were confirmed when the RILs were grown at an additional location. Of the textiles fabricated from the five superior RILs, four exhibited the novel characteristic of inherent flame resistance. When exposed to open flame by standard 45° incline flammability testing, these four fabrics self-extinguished. To determine the genetic architecture of this novel trait, linkage, epistatic and multi-locus genome wide association studies (GWAS) were conducted with 473k SNPs identified by whole genome sequencing (WGS). Transcriptomes of developing fiber cells from select RILs were sequenced (RNAseq). Together, these data provide insight into the genetic mechanism of the unexpected emergence of flame-resistant cotton by transgressive segregation in a breeding program. The incorporation of this trait into global cotton germplasm by breeding has the potential to greatly reduce the costs and impacts of flame-retardant chemicals.

Association analysis revealed loci linked to post-drought recovery and traits related to persistence of smooth bromegrass (Bromus inermis)

Association analysis has been proven as a powerful tool for the genetic dissection of complex traits. This study was conducted to identify association of recovery, persistence, and summer dormancy with sequence related amplified polymorphism (SRAP) markers in 36 smooth bromegrass genotypes under two moisture conditions and find stable associations. In this study, a diverse panel of polycross-derived progenies of smooth bromegrass was phenotyped under normal and water deficit regimes for three consecutive years. Under water deficit, dry matter yield of cut 1 was approximately reduced by 36, 39, and 37% during 2013, 2014, and 2015, respectively, compared with the normal regime. For dry matter yield of cut 2, these reductions were approximately 38, 60, and 56% in the same three consecutive years relative to normal regime. Moreover, water deficit decreased the RY and PER of the genotypes by 35 and 28%, respectively. Thirty primer combinations were screened by polymerase chain reaction (PCR). From these, 541 polymorphic bands were developed and subjected to association analysis using the mixed linear model (MLM). Population structure analysis identified five main subpopulations possessing significant genetic differences. Association analysis identified 69 and 46 marker-trait associations under normal and water deficit regimes, respectively. Some of these markers were associated with more than one trait; which can be attributed to pleiotropic effects or tightly linked genes affecting several traits. In normal and water-deficit regimes, these markers could potentially be incorporated into marker-assisted selection and targeted trait introgression for the improvement of drought tolerance of smooth bromegrass.

The Italian Research on the Molecular Characterization of Maize Kernel Development

The study of the genetic control of maize seed development and seed-related pathways has been one of the most important themes approached by the Italian scientific community. Maize has always attracted the interest of the Italian community of agricultural genetics since its beginning, as some of its founders based their research projects on and developed their “schools” by adopting maize as a reference species. Some of them spent periods in the United States, where maize was already becoming a model system, to receive their training. In this manuscript we illustrate the research work carried out in Italy by different groups that studied maize kernels and underline their contributions in elucidating fundamental aspects of caryopsis development through the characterization of maize mutants. Since the 1980s, most of the research projects aimed at the comprehension of the genetic control of seed development and the regulation of storage products’ biosyntheses and accumulation, and have been based on forward genetics approaches. We also document that for some decades, Italian groups, mainly based in Northern Italy, have contributed to improve the knowledge of maize genomics, and were both fundamental for further international studies focused on the correct differentiation and patterning of maize kernel compartments and strongly contributed to recent advances in maize research.

Exploring Chickpea Germplasm Diversity for Broadening the Genetic Base Utilizing Genomic Resourses

Legume crops provide significant nutrition to humans as a source of protein, omega-3 fatty acids as well as specific macro and micronutrients. Additionally, legumes improve the cropping environment by replenishing the soil nitrogen content. Chickpeas are the second most significant staple legume food crop worldwide behind dry bean which contains 17%–24% protein, 41%–51% carbohydrate, and other important essential minerals, vitamins, dietary fiber, folate, β-carotene, anti-oxidants, micronutrients (phosphorus, calcium, magnesium, iron, and zinc) as well as linoleic and oleic unsaturated fatty acids. Despite these advantages, legumes are far behind cereals in terms of genetic improvement mainly due to far less effort, the bottlenecks of the narrow genetic base, and several biotic and abiotic factors in the scenario of changing climatic conditions. Measures are now called for beyond conventional breeding practices to strategically broadening of narrow genetic base utilizing chickpea wild relatives and improvement of cultivars through advanced breeding approaches with a focus on high yield productivity, biotic and abiotic stresses including climate resilience, and enhanced nutritional values. Desirable donors having such multiple traits have been identified using core and mini core collections from the cultivated gene pool and wild relatives of Chickpea. Several methods have been developed to address cross-species fertilization obstacles and to aid in inter-specific hybridization and introgression of the target gene sequences from wild Cicer species. Additionally, recent advances in “Omics” sciences along with high-throughput and precise phenotyping tools have made it easier to identify genes that regulate traits of interest. Next-generation sequencing technologies, whole-genome sequencing, transcriptomics, and differential genes expression profiling along with a plethora of novel techniques like single nucleotide polymorphism exploiting high-density genotyping by sequencing assays, simple sequence repeat markers, diversity array technology platform, and whole-genome re-sequencing technique led to the identification and development of QTLs and high-density trait mapping of the global chickpea germplasm. These altogether have helped in broadening the narrow genetic base of chickpeas.

SoyMAGIC: An Unprecedented Platform for Genetic Studies and Breeding Activities in Soybean

Multi-Parent Advanced Generation Inter-Cross (MAGIC) populations are emerging genetic platforms for high-resolution and fine mapping of quantitative traits, such as agronomic and seed composition traits in soybean (Glycine max L.). We have established an eight-parent MAGIC population, comprising 721 recombinant inbred lines (RILs), through conical inter-mating of eight soybean lines. The parental lines were genetically diverse elite cultivars carrying different agronomic and seed composition characteristics, including amino acids and fatty acids, as well as oil and protein concentrations. This study aimed to introduce soybean MAGIC (SoyMAGIC) population as an unprecedented platform for genotypic and phenotypic investigation of agronomic and seed quality traits in soybean. The RILs were evaluated for important seed composition traits using replicated field trials during 2020 and 2021. To measure the seed composition traits, near-infrared reflectance (NIR) was employed. The RILs were genotyped using genotyping-by-sequencing (GBS) method to decipher the genome and discover single-nucleotide polymorphic (SNP) markers among the RILs. A high-density linkage map was constructed through inclusive composite interval mapping (ICIM). The linkage map was 3,770.75 cM in length and contained 12,007 SNP markers. Chromosomes 11 and 18 were recorded as the shortest and longest linkage groups with 71.01 and 341.15 cM in length, respectively. Observed transgressive segregation of the selected traits and higher recombination frequency across the genome confirmed the capability of MAGIC population in reshuffling the diversity in the soybean genome among the RILs. The assessment of haplotype blocks indicated an uneven distribution of the parents' genomes in RILs, suggesting cryptic influence against or in favor of certain parental genomes. The SoyMAGIC population is a recombined genetic material that will accelerate further genomic studies and the development of soybean cultivars with improved seed quality traits through the development and implementation of reliable molecular-based toolkits.

Concerted cis and trans effects underpin heightened defense gene expression in multi-herbivore-resistant maize lines

In maize (Zea mays ssp. mays), agriculturally damaging herbivores include lepidopteran insects, such as the European corn borer (Ostrinia nubilalis), and distantly related arthropods, like the two-spotted spider mite (Tetranychus urticae). A small number of maize lines, including B96 and B75, are highly resistant to both herbivores, and B96 is also resistant to thrips. Using T. urticae as a representative pest that causes significant leaf tissue damage, we examined the gene expression responses of these lines to herbivory in comparison with each other and with the susceptible line B73. Upon herbivory, the most resistant line, B96, showed the strongest gene expression response, with a dramatic upregulation of genes associated with jasmonic acid biosynthesis and signaling, as well as the biosynthesis of specialized herbivore deterrent compounds, such as death acids and benzoxazinoids. Extending this work with allele-specific expression analyses in F₁ hybrids, we inferred that the concerted upregulation of many defense genes, including the majority of benzoxazinoid biosynthetic genes in B96, as compared with B73, for the herbivore treatment, resulted from an assemblage of trans control and multiple cis effects acting with similar directionality on gene expression. Further, at the level of individual and potentially rate limiting genes in several major defense pathways, cis and trans effects acted in a reinforcing manner to result in exceptionally high expression in B96. Our study provides a comprehensive resource of cis elements for maize lines important in breeding efforts for herbivore resistance, and reveals potential genetic underpinnings of the origins of multi-herbivore resistance in plant populations.

Genetic insights in pearl millet breeding in the genomic era: challenges and prospects

Pearl millet, a vital staple food and an important cereal, is emerging as crop having various end-uses as feed, food as well as fodder. Advancement in high-throughput sequencing technology has boosted up pearl millet genomic research in past few years. The available draft genome of pearl millet providing an insight into the advancement of several breeding lines. Comparative and functional genomics have untangled several loci and genes regulating adaptive and agronomic traits in pearl millet. Additionally, the knowledge achieved has far away from being applicable in real breeding practices. We believe that the best path ahead is to adopt genome-based approaches for tailored designing of pearl millet as multi-functional crop with outstanding agronomic traits for various end uses. Presently review highlight several novel concepts and techniques in crop breeding, and summarize the recent advances in pearl millet genomic research, peculiarly genome-wide association dissections of several novel alleles and genes for agronomically important traits.

Using breeding and quantitative genetics to understand the C4 pathway

Reducing photorespiration in C3 crops could significantly increase rates of photosynthesis and yield. One method to achieve this would be to integrate C4 photosynthesis into C3 species. This objective is challenging as it involves engineering incompletely understood traits into C3 leaves, including complex changes to their biochemistry, cell biology, and anatomy. Quantitative genetics and selective breeding offer underexplored routes to identify regulators of these processes. We first review examples of natural intraspecific variation in C4 photosynthesis as well as the potential for hybridization between C3 and C4 species. We then discuss how quantitative genetic approaches including artificial selection and genome-wide association could be used to better understand the C4 syndrome and in so doing guide the engineering of the C4 pathway into C3 crops.

High-Throughput Phenotyping Accelerates the Dissection of the Phenotypic Variation and Genetic Architecture of Shank Vascular Bundles in Maize (Zea mays L.)

The vascular bundle of the shank is an important ‘flow’ organ for transforming maize biological yield to grain yield, and its microscopic phenotypic characteristics and genetic analysis are of great significance for promoting the breeding of new varieties with high yield and good quality. In this study, shank CT images were obtained using the standard process for stem micro-CT data acquisition at resolutions up to 13.5 μm. Moreover, five categories and 36 phenotypic traits of the shank including related to the cross-section, epidermis zone, periphery zone, inner zone and vascular bundle were analyzed through an automatic CT image process pipeline based on the functional zones. Next, we analyzed the phenotypic variations in vascular bundles at the base of the shank among a group of 202 inbred lines based on comprehensive phenotypic information for two environments. It was found that the number of vascular bundles in the inner zone (IZ_VB_N) and the area of the inner zone (IZ_A) varied the most among the different subgroups. Combined with genome-wide association studies (GWAS), 806 significant single nucleotide polymorphisms (SNPs) were identified, and 1245 unique candidate genes for 30 key traits were detected, including the total area of vascular bundles (VB_A), the total number of vascular bundles (VB_N), the density of the vascular bundles (VB_D), etc. These candidate genes encode proteins involved in lignin, cellulose synthesis, transcription factors, material transportation and plant development. The results presented here will improve the understanding of the phenotypic traits of maize shank and provide an important phenotypic basis for high-throughput identification of vascular bundle functional genes of maize shank and promoting the breeding of new varieties with high yield and good quality.

The Use of DArTseq Technology to Identify New SNP and SilicoDArT Markers Related to the Yield-Related Traits Components in Maize

In the last decade, many scientists have used molecular biology methods in their research to locate the grain-yield-determining loci and yield structure characteristics in maize. Large-scale molecular analyses in maize do not only focus on the identification of new markers and quantitative trait locus (QTL) regions. DNA analysis in the selection of parental components for heterotic crosses is a very important tool for breeders. The aim of this research was to identify and select new markers for maize (SNP and SilicoDArT) linked to genes influencing the size of the yield components in maize. The plant material used for the research was 186 inbred maize lines. The field experiment was established in twolocations. The yield and six yield components were analyzed. For identification of SNP and SilicoDArT markers related to the yield and yield components, next-generation sequencing was used. As a result of the biometric measurements analysis, differentiation in the average elevation of the analyzed traits for the lines in both locations was found. The above-mentioned results indicate the existence of genotype–environment interactions. The analysis of variance for the observed quality between genotypes indicated a statistically significant differentiation between genotypes and a statistically significant differentiation for all the observed properties betweenlocations. A canonical variable analysis was applied to present a multi-trait assessment of the similarity of the tested maize genotypes in a lower number of dimensions with the lowest possible loss of information. No grouping of lines due to the analyzed was observed. As a result of next-generation sequencing, the molecular markers SilicoDArT (53,031) and SNP (28,571) were obtained. The genetic distance between the analyzed lines was estimated on the basis of these markers. Out of 81,602 identified SilicoDArT and SNP markers, 15,409 (1559 SilicoDArT and 13,850 SNPs) significantly related to the analyzed yield components were selected as a result of association mapping. The greatest numbers of molecular markers were associated with cob length (1203), cob diameter (1759), core length (1201) and core diameter (2326). From 15,409 markers significantly related to the analyzed traits of the yield components, 18 DArT markers were selected, which were significant for the same four traits (cob length, cob diameter, core length, core diameter) in both Kobierzyce and Smolice. These markers were used for physical mapping. As a result of the analyses, it was found that 6 out of 18 (1818; 14,506; 2317; 3233; 11,657; 12,812) identified markers are located inside genes. These markers are located on chromosomes 8, 9, 7, 3, 5, and 1, respectively.

Newly Developed MAGIC Population Allows Identification of Strong Associations and Candidate Genes for Anthocyanin Pigmentation in Eggplant

Multi-parent advanced generation inter-cross (MAGIC) populations facilitate the genetic dissection of complex quantitative traits in plants and are valuable breeding materials. We report the development of the first eggplant MAGIC population (S3 Magic EGGplant InCanum, S3MEGGIC; 8-way), constituted by the 420 S3 individuals developed from the intercrossing of seven cultivated eggplant (Solanum melongena) and one wild relative (S. incanum) parents. The S3MEGGIC recombinant population was genotyped with the eggplant 5k probes SPET platform and phenotyped for anthocyanin presence in vegetative plant tissues (PA) and fruit epidermis (FA), and for the light-insensitive anthocyanic pigmentation under the calyx (PUC). The 7,724 filtered high-confidence single-nucleotide polymorphisms (SNPs) confirmed a low residual heterozygosity (6.87%), a lack of genetic structure in the S3MEGGIC population, and no differentiation among subpopulations carrying a cultivated or wild cytoplasm. Inference of haplotype blocks of the nuclear genome revealed an unbalanced representation of the founder genomes, suggesting a cryptic selection in favour or against specific parental genomes. Genome-wide association study (GWAS) analysis for PA, FA, and PUC detected strong associations with two myeloblastosis (MYB) genes similar to MYB113 involved in the anthocyanin biosynthesis pathway, and with a COP1 gene which encodes for a photo-regulatory protein and may be responsible for the PUC trait. Evidence was found of a duplication of an ancestral MYB113 gene with a translocation from chromosome 10 to chromosome 1 compared with the tomato genome. Parental genotypes for the three genes were in agreement with the identification of the candidate genes performed in the S3MEGGIC population. Our new eggplant MAGIC population is the largest recombinant population in eggplant and is a powerful tool for eggplant genetics and breeding studies.

Modeling allelic diversity of multiparent mapping populations affects detection of quantitative trait loci

The search for quantitative trait loci that explain complex traits such as yield and drought tolerance has been ongoing in all crops. Methods such as biparental quantitative trait loci mapping and genome-wide association studies each have their own advantages and limitations. Multiparent advanced generation intercross populations contain more recombination events and genetic diversity than biparental mapping populations and are better able to estimate effect sizes of rare alleles than association mapping populations. Here, we discuss the results of using a multiparent advanced generation intercross population of doubled haploid maize lines created from 16 diverse founders to perform quantitative trait loci mapping. We compare 3 models that assume bi-allelic, founder, and ancestral haplotype allelic states for quantitative trait loci. The 3 methods have differing power to detect quantitative trait loci for a variety of agronomic traits. Although the founder approach finds the most quantitative trait loci, all methods are able to find unique quantitative trait loci, suggesting that each model has advantages for traits with different genetic architectures. A closer look at a well-characterized flowering time quantitative trait loci, qDTA8, which contains vgt1, highlights the strengths and weaknesses of each method and suggests a potential epistatic interaction. Overall, our results reinforce the importance of considering different approaches to analyzing genotypic datasets, and shows the limitations of binary SNP data for identifying multiallelic quantitative trait loci.

Analysis of genotype-by-environment interactions in a maize mapping population

Genotype-by-environment interactions are a significant challenge for crop breeding as well as being important for understanding the genetic basis of environmental adaptation. In this study, we analyzed genotype-by-environment interactions in a maize multiparent advanced generation intercross population grown across 5 environments. We found that genotype-by-environment interactions contributed as much as genotypic effects to the variation in some agronomically important traits. To understand how genetic correlations between traits change across environments, we estimated the genetic variance–covariance matrix in each environment. Changes in genetic covariances between traits across environments were common, even among traits that show low genotype-by-environment variance. We also performed a genome-wide association study to identify markers associated with genotype-by-environment interactions but found only a small number of significantly associated markers, possibly due to the highly polygenic nature of genotype-by-environment interactions in this population.

New genomic regions associated with white mold resistance in dry bean using a MAGIC population

Dry bean (Phaseolus vulgaris L.) production in many regions is threatened by white mold (WM) [Sclerotinia sclerotiorum (Lib.) de Bary]. Seed yield losses can be up to 100% under conditions favorable for the pathogen. The low heritability, polygenic inheritance, and cumbersome screening protocols make it difficult to breed for improved genetic resistance. Some progress in understanding genetic resistance and germplasm improvement has been accomplished, but cultivars with high levels of resistance are yet to be released. A WM multiparent advanced generation inter-cross (MAGIC) population (n = 1060) was developed to facilitate mapping and breeding efforts. A seedling straw test screening method provided a quick assay to phenotype the population for response to WM isolate 1980. Nineteen MAGIC lines were identified with improved resistance. For genome-wide association studies (GWAS), the data was transformed into three phenotypic distributions-quantitative, polynomial, and binomial-and coupled with ∼52,000 single-nucleotide polymorphisms (SNPs). The three phenotypic distributions identified 30 significant genomic intervals [-log₁₀ (P value) ≥ 3.0]. However, across distributions, four new genomic regions as well as two regions previously reported were found to be associated with resistance. Cumulative R² values were 57% for binomial distribution using 13 genomic intervals, 41% for polynomial using eight intervals, and 40% for quantitative using 11 intervals. New resistant germplasm as well as new genomic regions associated with resistance are now available for further investigation.

Genetic dissection of maize plant architecture using a novel nested association mapping population

The leaf angle (LA), plant height (PH), and ear height (EH) are key plant architectural traits influencing maize (Zea mays L.) yield. However, their genetic determinants have not yet been well-characterized. Here, we developed a maize advanced backcross-nested association mapping population in Henan Agricultural University (HNAU-NAM1) comprised of 1,625 BC₁ F₄ /BC₂ F₄ lines. These were obtained by crossing a diverse set of 12 representative inbred lines with the common GEMS41 line, which were then genotyped using the MaizeSNP9.4K array. Genetic diversity and phenotypic distribution analyses showed considerable levels of genetic variation. We obtained 18-88 quantitative trait loci (QTLs) associated with LA, PH, and EH by using three complementary mapping methods, named as separate linkage mapping, joint linkage mapping, and genome-wide association studies. Our analyses enabled the identification of ten QTL hot-spot regions associated with the three traits, which were distributed on nine different chromosomes. We further selected 13 major QTLs that were simultaneously detected by three methods and deduced the candidate genes, of which eight were not reported before. The newly constructed HNAU-NAM1 population in this study will further broaden our insights into understanding of genetic regulation of plant architecture, thus will help to improve maize yield and provide an invaluable resource for maize functional genomics and breeding research.

A generic hidden Markov model for multiparent populations

A common step in the analysis of multiparent populations (MPPs) is genotype reconstruction: identifying the founder origin of haplotypes from dense marker data. This process often makes use of a probability model for the pattern of founder alleles along chromosomes, including the relative frequency of founder alleles and the probability of exchanges among them, which depend on a model for meiotic recombination and on the mating design for the population. While the precise experimental design used to generate the population may be used to derive a precise characterization of the model for exchanges among founder alleles, this can be tedious, particularly given the great variety of experimental designs that have been proposed. We describe an approximate model that can be applied for a variety of MPPs. We have implemented the approach in the R/qtl2 software, and we illustrate its use in applications to publicly available data on Diversity Outbred and Collaborative Cross mice.

Genomic interrogation of a MAGIC population highlights genetic factors controlling fiber quality traits in cotton

Cotton (Gossypium hirsutum L.) fiber is the most important resource of natural and renewable fiber for the textile industry. However, the understanding of genetic components and their genome-wide interactions controlling fiber quality remains fragmentary. Here, we sequenced a multiple-parent advanced-generation inter-cross (MAGIC) population, consisting of 550 individuals created by inter-crossing 11 founders, and established a mosaic genome map through tracing the origin of haplotypes that share identity-by-descent (IBD). We performed two complementary GWAS methods—SNP-based GWAS (sGWAS) and IBD-based haplotype GWAS (hGWAS). A total of 25 sQTLs and 14 hQTLs related to cotton fiber quality were identified, of which 26 were novel QTLs. Two major QTLs detected by both GWAS methods were responsible for fiber strength and length. The gene Ghir_D11G020400 (GhZF14) encoding the MATE efflux family protein was identified as a novel candidate gene for fiber length. Beyond the additive QTLs, we detected prevalent epistatic interactions that contributed to the genetics of fiber quality, pinpointing another layer for trait variance. This study provides new targets for future molecular design breeding of superior fiber quality.

Wang and colleagues use a complementary GWAS approach to identify genetic loci associated with cotton fiber quality. Using a multiparent advanced-generation inter-cross population, 26 new QTLs related to cotton fiber quality were found.

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.

An IBD-based mixed model approach for QTL mapping in multiparental populations

The identity-by-descent (IBD)-based mixed model approach introduced in this study can detect quantitative trait loci (QTLs) referring to the parental origin and simultaneously account for multilevel relatedness of individuals within and across families. This unified approach is proved to be a powerful approach for all kinds of multiparental population (MPP) designs. Multiparental populations (MPPs) have become popular for quantitative trait loci (QTL) detection. Tools for QTL mapping in MPPs are mostly developed for specific MPPs and do not generalize well to other MPPs. We present an IBD-based mixed model approach for QTL mapping in all kinds of MPP designs, e.g., diallel, Nested Association Mapping (NAM), and Multiparental Advanced Generation Intercross (MAGIC) designs. The first step is to compute identity-by-descent (IBD) probabilities using a general Hidden Markov model framework, called reconstructing ancestry blocks bit by bit (RABBIT). Next, functions of IBD information are used as design matrices, or genetic predictors, in a mixed model approach to estimate variance components for multiallelic genetic effects associated with parents. Family-specific residual genetic effects are added, and a polygenic effect is structured by kinship relations between individuals. Case studies of simulated diallel, NAM, and MAGIC designs proved that the advanced IBD-based multi-QTL mixed model approach incorporating both kinship relations and family-specific residual variances (IBD.MQMkin_F) is robust across a variety of MPP designs and allele segregation patterns in comparison to a widely used benchmark association mapping method, and in most cases, outperformed or behaved at least as well as other tools developed for specific MPP designs in terms of mapping power and resolution. Successful analyses of real data cases confirmed the wide applicability of our IBD-based mixed model methodology.

Precision Mapping of a Maize MAGIC Population Identified a Candidate Gene for the Senescence-Associated Physiological Traits

Senescence is an important trait in maize (Zea mais L.), a key crop that provides nutrition values and a renewable source of bioenergy worldwide. Genome-wide association studies (GWAS) can be used to identify causative genetic variants that influence the major physiological measures of senescence, which is used by plants as a defense mechanism against abiotic and biotic stresses affecting its performance. We measured four physiological and two agronomic traits that affect senescence. Six hundred seventy-two recombinant inbred lines (RILs) were evaluated in two consecutive years. Thirty-six candidate genes were identified by genome-wide association study (GWAS), and 11 of them were supported by additional evidence for involvement in senescence-related processes including proteolysis, sugar transport, and sink activity. We identified a candidate gene, Zm00001d043586, significantly associated with chlorophyll, and independently studied its transcription expression in an independent panel. Our results showed that Zm00001d043586 affects chlorophyl rate degradation, a key determinant of senescence, at late plant development stages. These results contribute to better understand the genetic relationship of the important trait senescence with physiology related parameters in maize and provide new putative molecular markers that can be used in marker assisted selection for line development.

Crafting for a better MAGIC: systematic design and test for Multiparental Advanced Generation Inter-Cross population

Multiparental Advanced Generation Inter-Cross (MAGIC) populations are valuable crop resources with a wide array of research uses including genetic mapping of complex traits, management of genetic resources and breeding of new varieties. Multiple founders are crossed to create a rich mosaic of highly recombined founder genomes in the MAGIC recombinant inbred lines (RILs). Many variations of MAGIC population designs exist; however, a large proportion of the currently available populations have been created empirically and based on similar designs. In our evaluations of five MAGIC populations, we found that the choice of designs has a large impact on the recombination landscape in the RILs. The most popular design used in many MAGIC populations has been shown to have a bias in recombinant haplotypes and low level of unique recombinant haplotypes, and therefore is not recommended. To address this problem and provide a remedy for the future, we have developed the “magicdesign” R package for creating and testing any MAGIC population design via simulation. A Shiny app version of the package is available as well. Our “magicdesign” package provides a unifying tool and a framework for creativity and innovation in MAGIC population designs. For example, using this package, we demonstrate that MAGIC population designs can be found which are very effective in creating haplotype diversity without the requirement for very large crossing programs. Furthermore, we show that interspersing cycles of crossing with cycles of selfing is effective in increasing haplotype diversity. These approaches are applicable in species that are hard to cross or in which resources are limited.

Genome-wide association studies of yield-related traits in high-latitude japonica rice

BACKGROUND

Heilongjiang Province is a high-quality japonica rice cultivation area in China. One in ten bowls of Chinese rice is produced here. Increasing yield is one of the main aims of rice production in this area. However, yield is a complex quantitative trait composed of many factors. The purpose of this study was to determine how many genetic loci are associated with yield-related traits. Genome-wide association studies (GWAS) were performed on 450 accessions collected from northeast Asia, including Russia, Korea, Japan and Heilongjiang Province of China. These accessions consist of elite varieties and landraces introduced into Heilongjiang Province decade ago.

RESULTS

After resequencing of the 450 accessions, 189,019 single nucleotide polymorphisms (SNPs) were used for association studies by two different models, a general linear model (GLM) and a mixed linear model (MLM), examining four traits: days to heading (DH), plant height (PH), panicle weight (PW) and tiller number (TI). Over 25 SNPs were found to be associated with each trait. Among them, 22 SNPs were selected to identify candidate genes, and 2, 8, 1 and 11 SNPs were found to be located in 3' UTR region, intron region, coding region and intergenic region, respectively.

CONCLUSIONS

All SNPs detected in this research may become candidates for further fine mapping and may be used in the molecular breeding of high-latitude rice.

PATRIOT: A Pipeline for Tracing Identity-by-Descent for Chromosome Segments to Improve Genomic Prediction in Self-Pollinating Crop Species

The lowering genotyping cost is ushering in a wider interest and adoption of genomic prediction and selection in plant breeding programs worldwide. However, improper conflation of historical and recent linkage disequilibrium between markers and genes restricts high accuracy of genomic prediction (GP). Multiple ancestors may share a common haplotype surrounding a gene, without sharing the same allele of that gene. This prevents parsing out genetic effects associated with the underlying allele of that gene among the set of ancestral haplotypes. We present "Parental Allele Tracing, Recombination Identification, and Optimal predicTion" (i.e., PATRIOT) approach that utilizes marker data to allow for a rapid identification of lines carrying specific alleles, increases the accuracy of genomic relatedness and diversity estimates, and improves genomic prediction. Leveraging identity-by-descent relationships, PATRIOT showed an improvement in GP accuracy by 16.6% relative to the traditional rrBLUP method. This approach will help to increase the rate of genetic gain and allow available information to be more effectively utilized within breeding programs.

The identification of grain size genes by RapMap reveals directional selection during rice domestication

Cloning quantitative trait locus (QTL) is time consuming and laborious, which hinders the understanding of natural variation and genetic diversity. Here, we introduce RapMap, a method for rapid multi-QTL mapping by employing F2 gradient populations (F2GPs) constructed by minor-phenotypic-difference accessions. The co-segregation standard of the single-locus genetic models ensures simultaneous integration of a three-in-one framework in RapMap i.e. detecting a real QTL, confirming its effect, and obtaining its near-isogenic line-like line (NIL-LL). We demonstrate the feasibility of RapMap by cloning eight rice grain-size genes using 15 F2GPs in three years. These genes explain a total of 75% of grain shape variation. Allele frequency analysis of these genes using a large germplasm collection reveals directional selection of the slender and long grains in indica rice domestication. In addition, major grain-size genes have been strongly selected during rice domestication. We think application of RapMap in crops will accelerate gene discovery and genomic breeding.

Impacts of environmental conditions, and allelic variation of cytosolic glutamine synthetase on maize hybrid kernel production

Cytosolic glutamine synthetase (GS1) is the enzyme mainly responsible of ammonium assimilation and reassimilation in maize leaves. The agronomic potential of GS1 in maize kernel production was investigated by examining the impact of an overexpression of the enzyme in the leaf cells. Transgenic hybrids exhibiting a three-fold increase in leaf GS activity were produced and characterized using plants grown in the field. Several independent hybrids overexpressing Gln1-3, a gene encoding cytosolic (GS1), in the leaf and bundle sheath mesophyll cells were grown over five years in different locations. On average, a 3.8% increase in kernel yield was obtained in the transgenic hybrids compared to controls. However, we observed that such an increase was simultaneously dependent upon both the environmental conditions and the transgenic event for a given field trial. Although variable from one environment to another, significant associations were also found between two GS1 genes (Gln1-3 and Gln1-4) polymorphic regions and kernel yield in different locations. We propose that the GS1 enzyme is a potential lead for producing high yielding maize hybrids using either genetic engineering or marker-assisted selection. However, for these hybrids, yield increases will be largely dependent upon the environmental conditions used to grow the plants.

Amiour et al. use a multi-year field trial evaluation and association mapping to determine if increased enzyme activity and native allelic variations at the GS1 loci in maize contribute to differences in grain yield. Overexpression of GS1 and polymorphisms in the corresponding loci were associated with kernel yield, indicating that GS1 expression can directly control kernel production and that GS1 has a potential lead in the production of high yielding maize hybrids depending on environmental conditions.

Regional Heritability Mapping of Quantitative Trait Loci Controlling Traits Related to Growth and Productivity in Popcorn (Zea mays L.)

The method of regional heritability mapping (RHM) has become an important tool in the identification of quantitative trait loci (QTLs) controlling traits of interest in plants. Here, RHM was first applied in a breeding population of popcorn, to identify the QTLs and candidate genes involved in grain yield, plant height, kernel popping expansion, and first ear height, as well as determining the heritability of each significant genomic region. The study population consisted of 98 S1 families derived from the 9th recurrent selection cycle (C-9) of the open-pollinated variety UENF-14, which were genetically evaluated in two environments (ENV1 and ENV2). Seventeen and five genomic regions were mapped by the RHM method in ENV1 and ENV2, respectively. Subsequent genome-wide analysis based on the reference genome B73 revealed associations with forty-six candidate genes within these genomic regions, some of them are considered to be biologically important due to the proteins that they encode. The results obtained by the RHM method have the potential to contribute to knowledge on the genetic architecture of the growth and yield traits of popcorn, which might be used for marker-assisted selection in breeding programs.

Megabase-scale presence-absence variation with Tripsacum origin was under selection during maize domestication and adaptation

BACKGROUND

Structural variants (SVs) significantly drive genome diversity and environmental adaptation for diverse species. Unlike the prevalent small SVs (< kilobase-scale) in higher eukaryotes, large-size SVs rarely exist in the genome, but they function as one of the key evolutionary forces for speciation and adaptation.

RESULTS

In this study, we discover and characterize several megabase-scale presence-absence variations (PAVs) in the maize genome. Surprisingly, we identify a 3.2 Mb PAV fragment that shows high integrity and is present as complete presence or absence in the natural diversity panel. This PAV is embedded within the nucleolus organizer region (NOR), where the suppressed recombination is found to maintain the PAV against the evolutionary variation. Interestingly, by analyzing the sequence of this PAV, we not only reveal the domestication trace from teosinte to modern maize, but also the footprints of its origin from Tripsacum, shedding light on a previously unknown contribution from Tripsacum to the speciation of Zea species. The functional consequence of the Tripsacum segment migration is also investigated, and environmental fitness conferred by the PAV may explain the whole segment as a selection target during maize domestication and improvement.

CONCLUSIONS

These findings provide a novel perspective that Tripsacum contributes to Zea speciation, and also instantiate a strategy for evolutionary and functional analysis of the "fossil" structure variations during genome evolution and speciation.

A forward genetics approach integrating genome-wide association study and expression quantitative trait locus mapping to dissect leaf development in maize (Zea mays)

The characterization of the genetic basis of maize (Zea mays) leaf development may support breeding efforts to obtain plants with higher vigor and productivity. In this study, a mapping panel of 197 biparental and multiparental maize recombinant inbred lines (RILs) was analyzed for multiple leaf traits at the seedling stage. RNA sequencing was used to estimate the transcription levels of 29 573 gene models in RILs and to derive 373 769 single nucleotide polymorphisms (SNPs), and a forward genetics approach combining these data was used to pinpoint candidate genes involved in leaf development. First, leaf traits were correlated with gene expression levels to identify transcript-trait correlations. Then, leaf traits were associated with SNPs in a genome-wide association (GWA) study. An expression quantitative trait locus mapping approach was followed to associate SNPs with gene expression levels, prioritizing candidate genes identified based on transcript-trait correlations and GWAs. Finally, a network analysis was conducted to cluster all transcripts in 38 co-expression modules. By integrating forward genetics approaches, we identified 25 candidate genes highly enriched for specific functional categories, providing evidence supporting the role of vacuolar proton pumps, cell wall effectors, and vesicular traffic controllers in leaf growth. These results tackle the complexity of leaf trait determination and may support precision breeding in maize.

Maize DNA Methylation in Response to Drought Stress Is Involved in Target Gene Expression and Alternative Splicing

DNA methylation is important for plant growth, development, and stress response. To understand DNA methylation dynamics in maize roots under water stress (WS), we reanalyzed DNA methylation sequencing data to profile DNA methylation and the gene expression landscape of two inbred lines with different drought sensitivities, as well as two of their derived recombination inbred lines (RILs). Combined with genotyping-by-sequencing, we found that the inheritance pattern of DNA methylation between RILs and parental lines was sequence-dependent. Increased DNA methylation levels were observed under WS and the methylome of drought-tolerant inbred lines were much more stable than that of the drought-sensitive inbred lines. Distinctive differentially methylated genes were found among diverse genetic backgrounds, suggesting that inbred lines with different drought sensitivities may have responded to stress in varying ways. Gene body DNA methylation showed a negative correlation with gene expression but a positive correlation with exon splicing events. Furthermore, a positive correlation of a varying extent was observed between small interfering RNA (siRNA) and DNA methylation, which at different genic regions. The response of siRNAs under WS was consistent with the differential DNA methylation. Taken together, our data can be useful in deciphering the roles of DNA methylation in plant drought-tolerance variations and in emphasizing its function in alternative splicing.

Opportunities for Improving Waterlogging Tolerance in Cereal Crops-Physiological Traits and Genetic Mechanisms

Waterlogging occurs when soil is saturated with water, leading to anaerobic conditions in the root zone of plants. Climate change is increasing the frequency of waterlogging events, resulting in considerable crop losses. Plants respond to waterlogging stress by adventitious root growth, aerenchyma formation, energy metabolism, and phytohormone signalling. Genotypes differ in biomass reduction, photosynthesis rate, adventitious roots development, and aerenchyma formation in response to waterlogging. We reviewed the detrimental effects of waterlogging on physiological and genetic mechanisms in four major cereal crops (rice, maize, wheat, and barley). The review covers current knowledge on waterlogging tolerance mechanism, genes, and quantitative trait loci (QTL) associated with waterlogging tolerance-related traits, the conventional and modern breeding methods used in developing waterlogging tolerant germplasm. Lastly, we describe candidate genes controlling waterlogging tolerance identified in model plants Arabidopsis and rice to identify homologous genes in the less waterlogging-tolerant maize, wheat, and barley.

Exploring the genic resources underlying metabolites through mGWAS and mQTL in wheat: From large-scale gene identification and pathway elucidation to crop improvement

Common wheat (Triticum aestivum L.) is a leading cereal crop, but has lagged behind with respect to the interpretation of the molecular mechanisms of phenotypes compared with other major cereal crops such as rice and maize. The recently available genome sequence of wheat affords the pre-requisite information for efficiently exploiting the potential molecular resources for decoding the genetic architecture of complex traits and identifying valuable breeding targets. Meanwhile, the successful application of metabolomics as an emergent large-scale profiling methodology in several species has demonstrated this approach to be accessible for reaching the above goals. One such productive avenue is combining metabolomics approaches with genetic designs. However, this trial is not as widespread as that for sequencing technologies, especially when the acquisition, understanding, and application of metabolic approaches in wheat populations remain more difficult and even arguably underutilized. In this review, we briefly introduce the techniques used in the acquisition of metabolomics data and their utility in large-scale identification of functional candidate genes. Considerable progress has been made in delivering improved varieties, suggesting that the inclusion of information concerning these metabolites and genes and metabolic pathways enables a more explicit understanding of phenotypic traits and, as such, this procedure could serve as an -omics-informed roadmap for executing similar improvement strategies in wheat and other species.

Sorghum breeding in the genomic era: opportunities and challenges

The importance and potential of the multi-purpose crop sorghum in global food security have not yet been fully exploited, and the integration of the state-of-art genomics and high-throughput technologies into breeding practice is required. Sorghum, a historically vital staple food source and currently the fifth most important major cereal, is emerging as a crop with diverse end-uses as food, feed, fuel and forage and a model for functional genetics and genomics of tropical grasses. Rapid development in high-throughput experimental and data processing technologies has significantly speeded up sorghum genomic researches in the past few years. The genomes of three sorghum lines are available, thousands of genetic stocks accessible and various genetic populations, including NAM, MAGIC, and mutagenised populations released. Functional and comparative genomics have elucidated key genetic loci and genes controlling agronomical and adaptive traits. However, the knowledge gained has far away from being translated into real breeding practices. We argue that the way forward is to take a genome-based approach for tailored designing of sorghum as a multi-functional crop combining excellent agricultural traits for various end uses. In this review, we update the new concepts and innovation systems in crop breeding and summarise recent advances in sorghum genomic researches, especially the genome-wide dissection of variations in genes and alleles for agronomically important traits. Future directions and opportunities for sorghum breeding are highlighted to stimulate discussion amongst sorghum academic and industrial communities.

Genome-wide association studies: assessing trait characteristics in model and crop plants

GWAS involves testing genetic variants across the genomes of many individuals of a population to identify genotype–phenotype association. It was initially developed and has proven highly successful in human disease genetics. In plants genome-wide association studies (GWAS) initially focused on single feature polymorphism and recombination and linkage disequilibrium but has now been embraced by a plethora of different disciplines with several thousand studies being published in model and crop species within the last decade or so. Here we will provide a comprehensive review of these studies providing cases studies on biotic resistance, abiotic tolerance, yield associated traits, and metabolic composition. We also detail current strategies of candidate gene validation as well as the functional study of haplotypes. Furthermore, we provide a critical evaluation of the GWAS strategy and its alternatives as well as future perspectives that are emerging with the emergence of pan-genomic datasets.

Development of an Aus-Derived Nested Association Mapping (Aus-NAM) Population in Rice

A genetic resource for studying genetic architecture of agronomic traits and environmental adaptation is essential for crop improvements. Here, we report the development of a rice nested association mapping population (aus-NAM) using 7 aus varieties as diversity donors and T65 as the common parent. Aus-NAM showed broad phenotypic variations. To test whether aus-NAM was useful for quantitative trait loci (QTL) mapping, known flowering genes (Ehd1, Hd1, and Ghd7) in rice were characterized using single-family QTL mapping, joint QTL mapping, and the methods based on genome-wide association study (GWAS). Ehd1 was detected in all the seven families and all the methods. On the other hand, Hd1 and Ghd7 were detected in some families, and joint QTL mapping and GWAS-based methods resulted in weaker and uncertain peaks. Overall, the high allelic variations in aus-NAM provide a valuable genetic resource for the rice community.

DArTseq-Based High-Throughput SilicoDArT and SNP Markers Applied for Association Mapping of Genes Related to Maize Morphology

Today, agricultural productivity is essential to meet the needs of a growing population, and is also a key tool in coping with climate change. Innovative plant breeding technologies such as molecular markers, phenotyping, genotyping, the CRISPR/Cas method and next-generation sequencing can help agriculture meet the challenges of the 21st century more effectively. Therefore, the aim of the research was to identify single-nucleotide polymorphisms (SNPs) and SilicoDArT markers related to select morphological features determining the yield in maize. The plant material consisted of ninety-four inbred lines of maize of various origins. These lines were phenotyped under field conditions. A total of 14 morphological features was analyzed. The DArTseq method was chosen for genotyping because this technique reduces the complexity of the genome by restriction enzyme digestion. Subsequently, short fragment sequencing was used. The choice of a combination of restrictases allowed the isolation of highly informative low copy fragments of the genome. Thanks to this method, 90% of the obtained DArTseq markers are complementary to the unique sequences of the genome. All the observed features were normally distributed. Analysis of variance indicated that the main effect of lines was statistically significant (p < 0.001) for all 14 traits of study. Thanks to the DArTseq analysis with the use of next-generation sequencing (NGS) in the studied plant material, it was possible to identify 49,911 polymorphisms, of which 33,452 are SilicoDArT markers and the remaining 16,459 are SNP markers. Among those mentioned, two markers associated with four analyzed traits deserved special attention: SNP (4578734) and SilicoDArT (4778900). SNP marker 4578734 was associated with the following features: anthocyanin coloration of cob glumes, number of days from sowing to anthesis, number of days from sowing to silk emergence and anthocyanin coloration of internodes. SilicoDArT marker 4778900 was associated with the following features: number of days from sowing to anthesis, number of days from sowing to silk emergence, tassel: angle between the axis and lateral branches and plant height. Sequences with a length of 71 bp were used for physical mapping. The BLAST and EnsemblPlants databases were searched against the maize genome to identify the positions of both markers. Marker 4578734 was localized on chromosome 7, the closest gene was Zm00001d022467, approximately 55 Kb apart, encoding anthocyanidin 3-O-glucosyltransferase. Marker 4778900 was located on chromosome 7, at a distance of 45 Kb from the gene Zm00001d045261 encoding starch synthase I. The latter observation indicated that these flanking SilicoDArT and SNP markers were not in a state of linkage disequilibrium.

Genomic Prediction of Grain Yield in a Barley MAGIC Population Modeling Genotype per Environment Interaction

Multi-parent Advanced Generation Inter-crosses (MAGIC) lines have mosaic genomes that are generated shuffling the genetic material of the founder parents following pre-defined crossing schemes. In cereal crops, these experimental populations have been extensively used to investigate the genetic bases of several traits and dissect the genetic bases of epistasis. In plants, genomic prediction models are usually fitted using either diverse panels of mostly unrelated accessions or individuals of biparental families and several empirical analyses have been conducted to evaluate the predictive ability of models fitted to these populations using different traits. In this paper, we constructed, genotyped and evaluated a barley MAGIC population of 352 individuals developed with a diverse set of eight founder parents showing contrasting phenotypes for grain yield. We combined phenotypic and genotypic information of this MAGIC population to fit several genomic prediction models which were cross-validated to conduct empirical analyses aimed at examining the predictive ability of these models varying the sizes of training populations. Moreover, several methods to optimize the composition of the training population were also applied to this MAGIC population and cross-validated to estimate the resulting predictive ability. Finally, extensive phenotypic data generated in field trials organized across an ample range of water regimes and climatic conditions in the Mediterranean were used to fit and cross-validate multi-environment genomic prediction models including G×E interaction, using both genomic best linear unbiased prediction and reproducing kernel Hilbert space along with a non-linear Gaussian Kernel. Overall, our empirical analyses showed that genomic prediction models trained with a limited number of MAGIC lines can be used to predict grain yield with values of predictive ability that vary from 0.25 to 0.60 and that beyond QTL mapping and analysis of epistatic effects, MAGIC population might be used to successfully fit genomic prediction models. We concluded that for grain yield, the single-environment genomic prediction models examined in this study are equivalent in terms of predictive ability while, in general, multi-environment models that explicitly split marker effects in main and environmental-specific effects outperform simpler multi-environment models.

New genomic approaches for enhancing maize genetic improvement

Maize (Zea mays) is one of the most widely grown crops in the world, with an annual global production of over 1147 million tons. Genomics approaches are thought to be the best solution for accelerating yield improvement to meet the challenges of a growing population and global climate change. Here, we review current approaches to the exploration of novel genetic variation in genomes, DNA modifications, and transcription levels of cultivated maize, landraces, and wild relatives. We discuss applications of genetic engineering to maize yield improvement and highlight future directions for maize genomics studies.