Satellite-enabled enviromics to enhance crop improvement.

Enviromics refers to the characterization of micro- and macroenvironments based on large-scale environmental datasets. By providing genotypic recommendations with predictive extrapolation at a site-specific level, enviromics could inform plant breeding decisions across varying conditions and anticipate productivity in a changing climate. Enviromics-based integration of statistics, envirotyping (i.e., determining environmental factors), and remote sensing could help unravel the complex interplay of genetics, environment, and management. To support this goal, exhaustive envirotyping to generate precise environmental profiles would significantly improve predictions of genotype performance and genetic gain in crops. Already, informatics management platforms aggregate diverse environmental datasets obtained using optical, thermal, radar, and light detection and ranging (LiDAR)sensors that capture detailed information about vegetation, surface structure, and terrain. This wealth of information, coupled with freely available climate data, fuels innovative enviromics research. While enviromics holds immense potential for breeding, a few obstacles remain, such as the need for (1) integrative methodologies to systematically collect field data to scale and expand observations across the landscape with satellite data; (2) state-of-the-art AI models for data integration, simulation, and prediction; (3) cyberinfrastructure for processing big data across scales and providing seamless interfaces to deliver forecasts to stakeholders; and (4) collaboration and data sharing among farmers, breeders, physiologists, geoinformatics experts, and programmers across research institutions. Overcoming these challenges is essential for leveraging the full potential of big data captured by satellites to transform 21st century agriculture and crop improvement through enviromics.

Comprehensive improvement of nutrients and volatile compounds of black/purple rice by extrusion-puffing technology.

Introduction: Black/purple rice is a pigmented rice variety that contains high levels of anthocyanins, flavonoids, and other valuable bioactive compounds. Owing to its robust anti-inflammatory and antioxidant properties, black/purple rice exerts a beneficial effect on human health. Extrusion puffing technology has emerged as a promising means of improving rice flavor with lesser effect on nutrient content. In this study, metabolomics approach was used to conduct comprehensive metabolomics analyses aimed at examining the impact of extrusion puffing on black/purple rice nutritional value and flavor. Methods: Firstly, the basic nutrient composition contents and extrudate characteristics of black/purple rice and Extrusion puffed black/purple rice were conducted. Then metabolomics profiling analyses of black/purple rice samples were performed to explore the impact of the extrusion puffing process on nutrient content and bioactive properties, in which we quantitatively determined the flavonoids and evaluated relative contents of volatile compounds. Results: These analyses revealed that following extrusion puffing, black/purple rice exhibited significant improvements in the content of nutrients including flavonoids, minerals, and proteins together. Extrusion puffing additionally increased the diversity of volatile compounds within black/purple rice. Discussion: These results suggest that extrusion puffing represents an effective means of substantially improving the functional and nutritional properties of black/purple rice, offering beneficial effects on consumer health. Overall, these data provide novel insights into the quality of extrusion puffed black/purple rice that will guide future efforts to establish how extrusion puffing can alter the nutrient content in a range of foods, thereby supporting the further development of a range of healthy food products.

Integrative transcriptome and metabolome analysis reveals the mechanisms of light-induced pigmentation in purple waxy maize.

Introduction: Waxy maize, mainly consumed at the immature stage, is a staple and vegetable food in Asia. The pigmentation in the kernel of purple waxy maize enhances its nutritional and market values. Light, a critical environmental factor, affects anthocyanin biosynthesis and results in pigmentation in different parts of plants, including in the kernel. SWL502 is a light-sensitive waxy maize inbred line with purple kernel color, but the regulatory mechanism of pigmentation in the kernel resulting in purple color is still unknown. Methods: In this study, cyanidin, peonidin, and pelargonidin were identified as the main anthocyanin components in SWL502, evaluated by the ultra-performance liquid chromatography (UPLC) method. Investigation of pigment accumulation in the kernel of SWL502 was performed at 12, 17, and 22 days after pollination (DAP) under both dark and light treatment conditions via transcriptome and metabolome analyses. Results: Dark treatment affected genes and metabolites associated with metabolic pathways of amino acid, carbohydrate, lipid, and galactose, biosynthesis of phenylpropanoid and terpenoid backbone, and ABC transporters. The expression of anthocyanin biosynthesis genes, such as 4CL2, CHS, F3H, and UGT, was reduced under dark treatment. Dynamic changes were identified in genes and metabolites by time-series analysis. The genes and metabolites involved in photosynthesis and purine metabolism were altered in light treatment, and the expression of genes and metabolites associated with carotenoid biosynthesis, sphingolipid metabolism, MAPK signaling pathway, and plant hormone signal transduction pathway were induced by dark treatment. Light treatment increased the expression level of major transcription factors such as LRL1, myc7, bHLH125, PIF1, BH093, PIL5, MYBS1, and BH074 in purple waxy maize kernels, while dark treatment greatly promoted the expression level of transcription factors RVE6, MYB4, MY1R1, and MYB145. Discussion: This study is the first report to investigate the effects of light on waxy maize kernel pigmentation and the underlying mechanism at both transcriptome and metabolome levels, and the results from this study are valuable for future research to better understand the effects of light on the regulation of plant growth.

Climate change challenges plant breeding.

Plant breeding is important to cope with climate change impacts, complementing crop management and policy interventions to ensure global food production. However, changes in environmental factors also affect the objectives, efficiency, and genetic gains of the current plant breeding system. In this review, we summarize the challenges prompted by climate change to breeding climate-resilient crops and the limitations of the next-generation breeding approach in addressing climate change. It is anticipated that the integration of multi-disciplines and technologies into three schemes of genotyping, phenotyping, and envirotyping will result in the delivery of climate change-ready crops in less time.

Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction.

The first paradigm of plant breeding involves direct selection-based phenotypic observation, followed by predictive breeding using statistical models for quantitative traits constructed based on genetic experimental design and, more recently, by incorporation of molecular marker genotypes. However, plant performance or phenotype (P) is determined by the combined effects of genotype (G), envirotype (E), and genotype by environment interaction (GEI). Phenotypes can be predicted more precisely by training a model using data collected from multiple sources, including spatiotemporal omics (genomics, phenomics, and enviromics across time and space). Integration of 3D information profiles (G-P-E), each with multidimensionality, provides predictive breeding with both tremendous opportunities and great challenges. Here, we first review innovative technologies for predictive breeding. We then evaluate multidimensional information profiles that can be integrated with a predictive breeding strategy, particularly envirotypic data, which have largely been neglected in data collection and are nearly untouched in model construction. We propose a smart breeding scheme, integrated genomic-enviromic prediction (iGEP), as an extension of genomic prediction, using integrated multiomics information, big data technology, and artificial intelligence (mainly focused on machine and deep learning). We discuss how to implement iGEP, including spatiotemporal models, environmental indices, factorial and spatiotemporal structure of plant breeding data, and cross-species prediction. A strategy is then proposed for prediction-based crop redesign at both the macro (individual, population, and species) and micro (gene, metabolism, and network) scales. Finally, we provide perspectives on translating smart breeding into genetic gain through integrative breeding platforms and open-source breeding initiatives. We call for coordinated efforts in smart breeding through iGEP, institutional partnerships, and innovative technological support.

Epistasis Activation Contributes Substantially to Heterosis in Temperate by Tropical Maize Hybrids.

Epistasis strongly affects the performance of superior maize hybrids. In this study, a multiple-hybrid population, consisting of three hybrid maize sets with varied interparental divergence, was generated by crossing 28 temperate and 23 tropical inbred lines with diverse genetic backgrounds. We obtained 1,154 tested hybrids. Among these tested hybrids, heterosis increased steadily as the heterotic genetic distance increased. Mid-parent heterosis was significantly higher in the temperate by tropical hybrids than in the temperate by temperate hybrids. Genome-wide prediction and association mapping was performed for grain weight per plant (GWPP) and days to silking (DTS) using 20K high-quality SNPs, showing that epistatic effects played a more prominent role than dominance effects in temperate by tropical maize hybrids. A total of 33 and 420 epistatic QTL were identified for GWPP and DTS, respectively, in the temperate by tropical hybrids. Protein-protein interaction network and gene-set enrichment analyses showed that epistatic genes were involved in protein interactions, which play an important role in photosynthesis, biological transcription pathways, and protein synthesis. We showed that the interaction of many minor-effect genes in the hybrids could activate the transcription activators of epistatic genes, resulting in a cascade of amplified yield heterosis. The multiple-hybrid population design enhanced our understanding of heterosis in maize, providing an insight into the acceleration of hybrid maize breeding by activating epistatic effects.

A telomere-to-telomere gap-free reference genome of watermelon and its mutation library provide important resources for gene discovery and breeding.

Watermelon, Citrullus lanatus, is the world's third largest fruit crop. Reference genomes with gaps and a narrow genetic base hinder functional genomics and genetic improvement of watermelon. Here, we report the assembly of a telomere-to-telomere gap-free genome of the elite watermelon inbred line G42 by incorporating high-coverage and accurate long-read sequencing data with multiple assembly strategies. All 11 chromosomes have been assembled into single-contig pseudomolecules without gaps, representing the highest completeness and assembly quality to date. The G42 reference genome is 369 321 829 bp in length and contains 24 205 predicted protein-coding genes, with all 22 telomeres and 11 centromeres characterized. Furthermore, we established a pollen-EMS mutagenesis protocol and obtained over 200 000 M1 seeds from G42 . In a sampling pool, 48 monogenic phenotypic mutations, selected from 223 M1 and 78 M2 mutants with morphological changes, were confirmed. The average mutation density was 1 SNP/1.69 Mb and 1 indel/4.55 Mb per M1 plant and 1 SNP/1.08 Mb and 1 indel/6.25 Mb per M2 plant. Taking advantage of the gap-free G42 genome, 8039 mutations from 32 plants sampled from M1 and M2 families were identified with 100% accuracy, whereas only 25% of the randomly selected mutations identified using the 97103v2 reference genome could be confirmed. Using this library and the gap-free genome, two genes responsible for elongated fruit shape and male sterility (ClMS1) were identified, both caused by a single base change from G to A. The validated gap-free genome and its EMS mutation library provide invaluable resources for functional genomics and genetic improvement of watermelon.

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

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

In pursuit of a better world: crop improvement and the CGIAR.

The CGIAR crop improvement (CI) programs, unlike commercial CI programs, which are mainly geared to profit though meeting farmers' needs, are charged with meeting multiple objectives with target populations that include both farmers and the community at large. We compiled the opinions from >30 experts in the private and public sector on key strategies, methodologies, and activities that could the help CGIAR meet the challenges of providing farmers with improved varieties while simultaneously meeting the goals of: (i) nutrition, health, and food security; (ii) poverty reduction, livelihoods, and jobs; (iii) gender equality, youth, and inclusion; (iv) climate adaptation and mitigation; and (v) environmental health and biodiversity. We review the crop improvement processes starting with crop choice, moving through to breeding objectives, production of potential new varieties, selection, and finally adoption by farmers. The importance of multidisciplinary teams working towards common objectives is stressed as a key factor to success. The role of the distinct disciplines, actors, and their interactions throughout the process from crop choice through to adoption by farmers is discussed and illustrated.

Genetic variation and population structure in China summer maize germplasm.

Maize (Zea mays L.) germplasm in China Summer maize ecological region (CSM) or central corn-belt of China is diverse but has not been systematically characterized at molecular level. In this study, genetic variation, genome diversity, linkage disequilibrium patterns, population structure, and characteristics of different heterotic groups were studied using 525,141 SNPs obtained by Genotyping-By-Sequencing (GBS) for 490 inbred lines collected from researchers at CSM region. The SNP density is lower near centromere, but higher near telomere region of maize chromosome, the degree of linkage disequilibrium (r2) vary at different chromosome regions. Majority of the inbred lines (66.05%) show pairwise relative kinship near zero, indicating a large genetic diversity in the CSM breeding germplasm. Using 4849 tagSNPs derived from 3618 haplotype blocks, the 490 inbred lines were delineated into 3 supergroups, 6 groups, and 10 subgroups using ADMIXTURE software. A procedure of assigning inbred lines into heterotic groups using genomic data and tag-SNPs was developed and validated. Genome differentiation among different subgroups measured by Fst, and the genetic diversity within each subgroup measured by GD are both large. The share of heterotic groups that have significant North American germplasm contribution: P, SS, IDT, and X, accounts about 54% of the CSM breeding germplasm collection and has increased significantly in the last two decades. Two predominant types of heterotic pattern in CSM region are: M-Reid group × TSPT group, and X subgroup × Local subgroups.

Identification of major QTL for waterlogging tolerance in maize using genome-wide association study and bulked sample analysis.

Waterlogging has increasingly become one of the major constraints to maize (Zea mays L.) production in some maize growing areas as it seriously decreases the yield. Waterlogging tolerance in maize germplasm provides a basis for maize waterlogging improvement. In this study, nine seedling traits, plant height (PH), root length (RL), shoot dry weight (SDW), root dry weight (RDW), adventitious root number (ARN), node number of brace root (BRNN), brace root number (BRN), brace root dry weigh (BRDW), survival rate (SR), and the secondary traits that were defined as relative phenotypic value of seedling traits under waterlogging and control treatments were used in a natural population that contain 365 inbred lines to evaluate the waterlogging tolerance of tropical maize. The result showed that maize waterlogging tolerance was genetically controlled and seedling traits were significantly different between the control and waterlogging treatments. PH, RL, SDW, and RDW are important seedling traits for waterlogging tolerance identification. Some tropical maize inbred lines were identified with extreme waterlogging tolerance that can provide an important germplasm resource for breeding. Population structure analysis showed that two major phylogenetic subgroups in tropical maize could be identified. Genome-wide association study (GWAS) using 39,266 single nucleotide polymorphisms (SNPs) across the whole genome identified 49 trait-SNPs distributed on over all 10 chromosomes excluding chromosome 10. Seventy-one significant SNPs, distributed on all 10 chromosomes excluding chromosome 5, were identified by extend bulked sample analysis (Ext-BSA) based on the inbred lines with extreme phenotypes. GWAS and Ext-BSA identified the same loci on bin1.07, bin6.01, bin2.09, bin6.04, bin7.02, and bin7.03. Nine genes were proposed as potential candidate genes. Cloning and functional validation of these genes would be helpful for understanding the molecular mechanism of waterlogging tolerance in maize.

Flowering time regulation model revisited by pooled sequencing of mass selection populations.

Maize is one of the most broadly cultivated crops throughout the world, and flowering time is a major adaptive trait for its diffusion. The biggest challenge in understanding maize flowering genetic architecture is that the trait is confounded with population structure. To eliminate the effect, we revisited the flower time genetic network by using a tropical maize population Pop32, which was under mass selection for adaptation to early flowering time in China for six generations from tropical to temperate regions. The days to anthesis (DTA) of the initial (Pop32C0), intermedia (Pop32C3), and final population (Pop32C5) was 90.77, 84.63, and 79.72 days on average, respectively. To examine the genetic mechanism and identify the genetic loci underlying this rapid change in flowering time of Pop32, we bulked 30 individuals from C0, C3, and C5 to conduct the whole genome sequencing. And we finally identified 4,973,810 high-quality single nucleotide polymorphisms (SNPs) and 6,517 genes with allele frequency significantly changed during the artificial improvement process. We speculate that these genes might participate in the adaptive improvement process and control flowering time. To identify the candidate genes for flowering time from the gene set with allele frequency changed, we carried out weighted gene co-expression network analysis (WGCNA), and identified four co-expression modules that highly associated with the flowering time development, as well as constructed the co-expression network of key flowering time genes. Gene Ontology (GO) enrichment analysis revealed that the GO terms photosynthesis/light reaction, carbohydrate binding, auxin mediated signaling pathway, response to temperature stimulus that are closely connected with flowering time. Furthermore, targeted GWAS revealed the genes are significantly connected with the flowering time. qRT-PCR of four candidate genes GRMZM2G019879, GRMZM2G055905, GRMZM2G058158, and GRMZM2G171365 showed that their expression level is similar to the flowering time genes, which playing a key role in maize flowering time transition. This study revealed that the changes of flowering time in mass selection process may be strongly associated with the variations of allele frequency changes, and we identified some important candidate genes for flowering time, which will provide a new insight for the rapid improvement of maize important agronomic traits and promote the gene cloning of maize flowering time.

Genomic resources in plant breeding for sustainable agriculture.

Climate change during the last 40 years has had a serious impact on agriculture and threatens global food and nutritional security. From over half a million plant species, cereals and legumes are the most important for food and nutritional security. Although systematic plant breeding has a relatively short history, conventional breeding coupled with advances in technology and crop management strategies has increased crop yields by 56 % globally between 1965-85, referred to as the Green Revolution. Nevertheless, increased demand for food, feed, fiber, and fuel necessitates the need to break existing yield barriers in many crop plants. In the first decade of the 21st century we witnessed rapid discovery, transformative technological development and declining costs of genomics technologies. In the second decade, the field turned towards making sense of the vast amount of genomic information and subsequently moved towards accurately predicting gene-to-phenotype associations and tailoring plants for climate resilience and global food security. In this review we focus on genomic resources, genome and germplasm sequencing, sequencing-based trait mapping, and genomics-assisted breeding approaches aimed at developing biotic stress resistant, abiotic stress tolerant and high nutrition varieties in six major cereals (rice, maize, wheat, barley, sorghum and pearl millet), and six major legumes (soybean, groundnut, cowpea, common bean, chickpea and pigeonpea). We further provide a perspective and way forward to use genomic breeding approaches including marker-assisted selection, marker-assisted backcrossing, haplotype based breeding and genomic prediction approaches coupled with machine learning and artificial intelligence, to speed breeding approaches. The overall goal is to accelerate genetic gains and deliver climate resilient and high nutrition crop varieties for sustainable agriculture.

Large-Scale Analysis of Combining Ability and Heterosis for Development of Hybrid Maize Breeding Strategies Using Diverse Germplasm Resources.

Understanding combining ability and heterosis among diverse maize germplasm resources is important for breeding hybrid maize (Zea mays L.). Using 28 temperate and 23 tropical maize inbreds that represent different ecotypes and worldwide diversity of maize germplasm, we first developed a large-scale multiple-hybrid population (MHP) with 724 hybrids, which could be divided into three subsets, 325 temperate diallel hybrids and 136 tropical diallel hybrids generated in Griffing IV, and 263 temperate by tropical hybrids generated in NCD II. All the parental lines and hybrids were evaluated for 11 traits in replicated tests across two locations and three years. Several widely used inbreds showed strong general combining ability (GCA), and their derived hybrids showed strong specific combining ability (SCA). Heterosis is a quantifiable, trait-dependent and environment-specific phenotype, and the response of parental lines and their hybrids to environments resulted in various levels of heterosis. For all the tested traits except plant height and hundred grain weight (HGW), NCD II (temperate × tropical) hybrids showed higher average heterosis than the temperate and tropical diallel hybrids, with higher hybrid performance for ear length, ear diameter, and HGW. Tropical maize germplasm can be used to improve the yield potential for temperate lines. Grain number per row and grain number per ear were two most important traits that determined yield heterosis, which can be used as direct selection criteria for yield heterosis. The hybrids from heterotic groups, Reid × SPT, Reid × LRC, SPT × PA, and Lancaster × LRC, contributed highly significant positive SCA effects and strong heterosis to yield-related traits, and the heterotic patterns identified in this study were potentially useful for commercial maize breeding. Heterosis was more significantly and positively correlated with SCA than GCA, indicating that SCA can be used in heterosis prediction to develop potential hybrids in commercial maize breeding. The results of the present study not only contribute to developing breeding strategies, but also improve targeted breeding efficiency by using both temperate and tropical maize to broaden genetic basis. Large sets of parental lines with available genotypic information can be shared and used in worldwide hybrid breeding programs through an open-source breeding strategy. Potential applications of the reported results in developing hybrid maize breeding strategies were also discussed.

Complex Genetic System Involved in Fusarium Ear Rot Resistance in Maize as Revealed by GWAS, Bulked Sample Analysis, and Genomic Prediction.

Fusarium ear rot (FER) caused by Fusarium verticillioides is one of the most prevalent maize diseases in China and worldwide. Resistance to FER is a complex trait controlled by multiple genes highly affected by environment. In this paper, genome-wide association study (GWAS), bulked sample analysis (BSA), and genomic prediction were performed for understanding FER resistance using 509 diverse inbred lines, which were genotyped by 37,801 high-quality single-nucleotide polymorphisms (SNPs). Ear rot evaluation was performed using artificial inoculation in four environments in China: Xinxiang, Henan, and Shunyi, Beijing, during 2017 and 2018. Significant phenotypic and genetic variation for FER severity was observed, and FER resistance was significantly correlated among the four environments with a generalized heritability of 0.78. GWAS identified 23 SNPs that were associated with FER resistance, 2 of which (1_226233417 on chromosome 1 and 10_14501044 on chromosome 10) were associated at threshold of 2.65 × 10-7 [-log(0.01/37,801)]. Using BSA, resistance quantitative trait loci were identified on chromosomes 3, 4, 7, 9, and 10 at the 90% confidence level and on chromosomes 3 and 10 at the 95% confidence level. A key region, bin 10.03, was detected by both GWAS and BSA. Genomic prediction for FER resistance showed that the prediction accuracy by trait-related markers was higher than that by randomly selected markers under different levels of marker density. Marker-assisted selection using genomic prediction could be an efficient strategy for genetic improvement for complex traits like FER resistance.

Enhancing Genetic Gain through Genomic Selection: From Livestock to Plants.

Although long-term genetic gain has been achieved through increasing use of modern breeding methods and technologies, the rate of genetic gain needs to be accelerated to meet humanity's demand for agricultural products. In this regard, genomic selection (GS) has been considered most promising for genetic improvement of the complex traits controlled by many genes each with minor effects. Livestock scientists pioneered GS application largely due to livestock's significantly higher individual values and the greater reduction in generation interval that can be achieved in GS. Large-scale application of GS in plants can be achieved by refining field management to improve heritability estimation and prediction accuracy and developing optimum GS models with the consideration of genotype-by-environment interaction and non-additive effects, along with significant cost reduction. Moreover, it would be more effective to integrate GS with other breeding tools and platforms for accelerating the breeding process and thereby further enhancing genetic gain. In addition, establishing an open-source breeding network and developing transdisciplinary approaches would be essential in enhancing breeding efficiency for small- and medium-sized enterprises and agricultural research systems in developing countries. New strategies centered on GS for enhancing genetic gain need to be developed.

Combined linkage and association mapping reveal QTL for host plant resistance to common rust (Puccinia sorghi) in tropical maize.

BACKGROUND: Common rust, caused by Puccinia sorghi, is an important foliar disease of maize that has been associated with up to 50% grain yield loss. Development of resistant maize germplasm is the ideal strategy to combat P. sorghi. RESULTS: Association mapping performed using a mixed linear model (MLM), integrating population structure and family relatedness identified 25 QTL (P < 3.12 × 10- 5) that were associated with resistance to common rust and distributed on chromosomes 1, 3, 5, 6, 8, and 10. We identified three QTLs associated with all three disease parameters (final disease rating, mean disease rating, and area under disease progress curve) located on chromosomes 1, 3, and 8. A total of 5 QTLs for resistance to common rust were identified in the RIL population. Nine candidate genes located on chromosomes 1, 5, 6, 8, and 10 for resistance to common rust associated loci were identified through detailed annotation. CONCLUSIONS: Using a diverse set of inbred lines genotyped with high density markers and evaluated for common rust resistance in multiple environments, it was possible to identify QTL significantly associated with resistance to common rust and several candidate genes. The results point to the need for fine mapping common rust resistance by targeting regions identified in common between this study and others using diverse germplasm.

Genome-wide association study dissects yield components associated with low-phosphorus stress tolerance in maize.

KEY MESSAGE: Phosphorus deficiency in soil is a worldwide constraint threatening maize production. Through a genome-wide association study, we identified molecular markers and associated candidate genes and molecular pathways for low-phosphorus stress tolerance. Phosphorus deficiency in soils will severely affect maize (Zea mays L.) growth and development, thus decreasing the final yield. Deciphering the genetic basis of yield-related traits can benefit our understanding of maize tolerance to low-phosphorus stress. However, considering that yield-related traits should be evaluated under field condition with large populations rather than under hydroponic condition at a single-plant level, searching for appropriate field experimental sites and target traits for low-phosphorus stress tolerance is still very challenging. In this study, a genome-wide association analysis using two natural populations was performed to detect candidate genes in response to low-phosphorus stress at two experimental sites representative of different climate and soil types. In total, 259 candidate genes were identified and these candidate genes are mainly involved in four major pathways: transcriptional regulation, reactive oxygen scavenging, hormone regulation, and remodeling of cell wall. Among these candidate genes, 98 showed differential expression by transcriptome data. Based on a haplotype analysis of grain number under phosphorus deficiency condition, the positive haplotypes with favorable alleles across five loci increased grain number by 42% than those without favorable alleles. For further verifying the feasibility of genomic selection for improving maize low-phosphorus tolerance, we also validated the predictive ability of five genomic selection methods and suggested that moderate-density SNPs were sufficient to make accurate predictions for low-phosphorus tolerance traits. All these results will facilitate elucidating genetic basis of maize tolerance to low-phosphorus stress and improving marker-assisted selection efficiency in breeding process.

Construction of the third-generation Zea mays haplotype map.

Background: Characterization of genetic variations in maize has been challenging, mainly due to deterioration of collinearity between individual genomes in the species. An international consortium of maize research groups combined resources to develop the maize haplotype version 3 (HapMap 3), built from whole-genome sequencing data from 1218 maize lines, covering predomestication and domesticated Zea mays varieties across the world. Results: A new computational pipeline was set up to process more than 12 trillion bp of sequencing data, and a set of population genetics filters was applied to identify more than 83 million variant sites. Conclusions: We identified polymorphisms in regions where collinearity is largely preserved in the maize species. However, the fact that the B73 genome used as the reference only represents a fraction of all haplotypes is still an important limiting factor.