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.

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.

Natural antisense transcripts are significantly involved in regulation of drought stress in maize.

Natural antisense transcripts (NATs) are a prominent and complex class of regulatory RNAs. Using strand-specific RNA sequencing, we identified 1769 sense and antisense transcript pairs (NAT pairs) in two maize inbreds with different sensitivity to drought, as well as in two derivative recombination inbred lines (RILs). A significantly higher proportion of NATs relative to non-NATs are specifically expressed under water stress (WS). Surprisingly, expression of sense and antisense transcripts produced by NAT pairs is significantly correlated, particularly under WS. We found an unexpected large proportion of NATs with protein coding potential, as estimated by ribosome release scores. Small RNAs significantly accumulate within NAT pairs, with 21 nt smRNA particularly enriched in overlapping regions of these pairs of genes. The abundance of these smRNAs is significantly altered in the leafbladeless1 mutant, suggesting that these genes may be regulated by the tasiRNA pathway. Further, NATs are significantly hypomethylated and include fewer transposable element sequences relative to non-NAT genes. NAT gene regions also exhibit higher levels of H3K36me3, H3K9ac, and H3K4me3, but lower levels of H3K27me3, indicating that NAT gene pairs generally exhibit an open chromatin configuration. Finally, NAT pairs in 368 diverse maize inbreds and 19 segregating populations were specifically enriched for polymorphisms associated with drought tolerance. Taken together, the data highlight the potential impact of that small RNAs and histone modifications have in regulation of NAT expression, and the significance of NATs in response to WS.

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.

Genome-wide characterization of non-reference transposable element insertion polymorphisms reveals genetic diversity in tropical and temperate maize.

BACKGROUND: Maize was originally domesticated in a tropical environment but is now widely cultivated at temperate latitudes. Temperate and tropical maize populations have diverged both genotypically and phenotypically. Tropical maize lines grown in temperate environments usually exhibit delayed flowering, pollination, and seed set, which reduces their grain yield relative to temperate adapted maize lines. One potential mechanism by which temperate maize may have adapted to a new environment is novel transposable element insertions, which can influence gene regulation. Recent advances in sequencing technology have made it possible to study variation in transposon content and insertion location in large sets of maize lines. RESULTS: In total, 274,408 non-redundant TEs (NRTEs) were identified using resequencing data generated from 83 maize inbred lines. The locations of DNA TEs and copia-superfamily retrotransposons showed significant positive correlations with gene density and genetic recombination rates, whereas gypsy-superfamily retrotransposons showed a negative correlation with these two parameters. Compared to tropical maize, temperate maize had fewer unique NRTEs but higher insertion frequency, lower background recombination rates, and higher linkage disequilibrium, with more NRTEs close to flowering and stress-related genes in the genome. Association mapping demonstrated that the presence/absence of 48 NRTEs was associated with flowering time and that expression of neighboring genes differed between haplotypes where a NRTE was present or absent. CONCLUSIONS: This study suggests that NRTEs may have played an important role in creating the variation in gene regulation that enabled the rapid adaptation of maize to diverse environments.

Enhancing genetic gain in the era of molecular breeding.

As one of the important concepts in conventional quantitative genetics and breeding, genetic gain can be defined as the amount of increase in performance that is achieved annually through artificial selection. To develop pro ducts that meet the increasing demand of mankind, especially for food and feed, in addition to various industrial uses, breeders are challenged to enhance the potential of genetic gain continuously, at ever higher rates, while they close the gaps that remain between the yield potential in breeders' demonstration trials and the actual yield in farmers' fields. Factors affecting genetic gain include genetic variation available in breeding materials, heritability for traits of interest, selection intensity, and the time required to complete a breeding cycle. Genetic gain can be improved through enhancing the potential and closing the gaps, which has been evolving and complemented with modern breeding techniques and platforms, mainly driven by molecular and genomic tools, combined with improved agronomic practice. Several key strategies are reviewed in this article. Favorable genetic variation can be unlocked and created through molecular and genomic approaches including mutation, gene mapping and discovery, and transgene and genome editing. Estimation of heritability can be improved by refining field experiments through well-controlled and precisely assayed environmental factors or envirotyping, particularly for understanding and controlling spatial heterogeneity at the field level. Selection intensity can be significantly heightened through improvements in the scale and precision of genotyping and phenotyping. The breeding cycle time can be shortened by accelerating breeding procedures through integrated breeding approaches such as marker-assisted selection and doubled haploid development. All the strategies can be integrated with other widely used conventional approaches in breeding programs to enhance genetic gain. More transdisciplinary approaches, team breeding, will be required to address the challenge of maintaining a plentiful and safe food supply for future generations. New opportunities for enhancing genetic gain, a high efficiency breeding pipeline, and broad-sense genetic gain are also discussed prospectively.

Strand-specific RNA-Seq transcriptome analysis of genotypes with and without low-phosphorus tolerance provides novel insights into phosphorus-use efficiency in maize.

BACKGROUND: Phosphorus (P) stress is a global problem in maize production. Although macro/microarray technologies have greatly increased our general knowledge of maize responses to P stress, a greater understanding of the diversity of responses in maize genotypes is still needed. RESULTS: In this study, we first evaluated the tolerance to low P of 560 accessions under field conditions, and selected the low P-tolerant line CCM454 and the low P-sensitive line 31778 for further research. We then generated 24 strand-specific RNA libraries from shoots and roots of CCM454 and 31778 that had been subjected to P stress for 2 and 8 days. The P deficiency-responsive genes common to CCM454 and 31778 were involved in various metabolic processes, including acid phosphatase (APase) activity. Determination of root-secretory APase activities showed that the induction of APase by P stress occurred much earlier in CCM454 than that in 31778. Gene Ontology analysis of differentially expressed genes (DEGs) and CAT/POD activities between CCM454 and 31778 under P-sufficient and -deficient conditions demonstrated that CCM454 has a greater ability to eliminate reactive oxygen species (ROS) than 31778. In addition, 16 miRNAs in roots and 12 miRNAs in shoots, including miRNA399s, were identified as DEGs between CCM454 and 31778. CONCLUSIONS: The results indicate that the tolerance to low P of CCM454 is mainly due to the rapid responsiveness to P stress and efficient elimination of ROS. Our findings increase the understanding of the molecular events involved in the diversity of responses to P stress among maize accessions.

Bulked sample analysis in genetics, genomics and crop improvement.

Biological assay has been based on analysis of all individuals collected from sample populations. Bulked sample analysis (BSA), which works with selected and pooled individuals, has been extensively used in gene mapping through bulked segregant analysis with biparental populations, mapping by sequencing with major gene mutants and pooled genomewide association study using extreme variants. Compared to conventional entire population analysis, BSA significantly reduces the scale and cost by simplifying the procedure. The bulks can be built by selection of extremes or representative samples from any populations and all types of segregants and variants that represent wide ranges of phenotypic variation for the target trait. Methods and procedures for sampling, bulking and multiplexing are described. The samples can be analysed using individual markers, microarrays and high-throughput sequencing at all levels of DNA, RNA and protein. The power of BSA is affected by population size, selection of extreme individuals, sequencing strategies, genetic architecture of the trait and marker density. BSA will facilitate plant breeding through development of diagnostic and constitutive markers, agronomic genomics, marker-assisted selection and selective phenotyping. Applications of BSA in genetics, genomics and crop improvement are discussed with their future perspectives.

Envirotyping for deciphering environmental impacts on crop plants.

Global climate change imposes increasing impacts on our environments and crop production. To decipher environmental impacts on crop plants, the concept "envirotyping" is proposed, as a third "typing" technology, complementing with genotyping and phenotyping. Environmental factors can be collected through multiple environmental trials, geographic and soil information systems, measurement of soil and canopy properties, and evaluation of companion organisms. Envirotyping contributes to crop modeling and phenotype prediction through its functional components, including genotype-by-environment interaction (GEI), genes responsive to environmental signals, biotic and abiotic stresses, and integrative phenotyping. Envirotyping, driven by information and support systems, has a wide range of applications, including environmental characterization, GEI analysis, phenotype prediction, near-iso-environment construction, agronomic genomics, precision agriculture and breeding, and development of a four-dimensional profile of crop science involving genotype (G), phenotype (P), envirotype (E) and time (T) (developmental stage). In the future, envirotyping needs to zoom into specific experimental plots and individual plants, along with the development of high-throughput and precision envirotyping platforms, to integrate genotypic, phenotypic and envirotypic information for establishing a high-efficient precision breeding and sustainable crop production system based on deciphered environmental impacts.

Combined linkage and association mapping identifies a major QTL (qRtsc8-1), conferring tar spot complex resistance in maize.

KEY MESSAGE: A major QTL ( qRtsc8 - 1 ) conditioning resistance to tar spot complex of maize and occurring at a frequency of 3.5 % across 890 maize inbred lines. Tar spot complex (TSC) is a highly destructive disease of maize found in some countries in America. Identification of TSC resistant germplasm and elucidating the genetic mechanism of resistance is crucial for the use of host resistance to manage this disease. We evaluated 890 elite maize inbred lines in multiple environments and used genome wide association analysis (GWAS) with genotypic data from Illumina MaizeSNP50 BeadChip containing 56 K SNPs to dissect the genetics of TSC resistance. GWAS results were validated through linkage analysis in three bi-parental populations derived from different resistant and susceptible parents. Through GWAS, three TSC resistance loci were identified on chromosome 2, 7 and 8 (-log10 (p) > 5.99). A major quantitative resistance locus (QTL) designated qRtsc8-1, was detected on maize chromosome bin 8.03. qRtsc8-1, was confirmed in three independent bi-parental populations and it accounted for 18-43 % of the observed phenotypic variation for TSC. A rare haplotype within the qRtsc8-1 region, occurring at a frequency of 3.5 % increased TSC resistance by 14 %. Candidate gene analysis revealed that a leucine-rich repeat receptor-like protein (LRR-RLKs) gene family maybe the candidate gene for qRtsc8-1. Identification and localization of a major locus conditioning TSC resistance provides the foundation for fine mapping qRtsc8-1 and developing functional markers for improving TSC resistance in maize breeding programs. To the best of our knowledge, this is the first report of a major QTL for TSC resistance.

Identification and functional characterization of the AGO1 ortholog in maize.

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

Identification of candidate genes for drought tolerance by whole-genome resequencing in maize.

BACKGROUND: Drought stress is one of the major limiting factors for maize production. With the availability of maize B73 reference genome and whole-genome resequencing of 15 maize inbreds, common variants (CV) and clustering analyses were applied to identify non-synonymous SNPs (nsSNPs) and corresponding candidate genes for drought tolerance. RESULTS: A total of 524 nsSNPs that were associated with 271 candidate genes involved in plant hormone regulation, carbohydrate and sugar metabolism, signaling molecules regulation, redox reaction and acclimation of photosynthesis to environment were detected by CV and cluster analyses. Most of the nsSNPs identified were clustered in bin 1.07 region that harbored six previously reported QTL with relatively high phenotypic variation explained for drought tolerance. Genes Ontology (GO) analysis of candidate genes revealed that there were 35 GO terms related to biotic stimulus and membrane-bounded organelle, showing significant differences between the candidate genes and the reference B73 background. Changes of expression level in these candidate genes for drought tolerance were detected using RNA sequencing for fertilized ovary, basal leaf meristem tissue and roots collected under drought stressed and well-watered conditions. The results indicated that 70% of candidate genes showed significantly expression changes under two water treatments and our strategies for mining candidate genes are feasible and relatively efficient. CONCLUSIONS: Our results successfully revealed candidate nsSNPs and associated genes for drought tolerance by comparative sequence analysis of 16 maize inbred lines. Both methods we applied were proved to be efficient for identifying candidate genes for complex traits through the next-generation sequencing technologies (NGS). These selected genes will not only facilitate understanding of genetic basis of drought stress response, but also accelerate genetic improvement through marker-assisted selection in maize.

Kernel number as a positive target trait for prediction of hybrid performance under low-nitrogen stress as revealed by diallel analysis under contrasting nitrogen conditions.

Environmental sustainability concerns make improving yield under lower N input a desirable breeding goal. To evaluate genetic variation and heterosis for low-N tolerance breeding, 28 F1 hybrids from a diallel scheme, along with their eight parental lines, were tested for agronomic traits including kernel number per ear (KNE) and grain yield per plant (GY), in replicated plots over two years under low-nitrogen (LN, without nitrogen application) and normal-nitrogen (NN, 220 kg N ha(-1)) conditions. Taken together the heritability in this and our previous studies, the correlation with grain yield, and the sensitivity to the stress for target trait selection, KNE was a good secondary target trait for LN selection in maize breeding. KNE also showed much higher mid-parent heterosis than hundred-kernel weight under both nitrogen levels, particularly under LN, indicating that KNE contributed the majority of GY heterosis, particularly under LN. Therefore, KNE can be used as a positive target trait for hybrid performance prediction in LN tolerance breeding. Our results also suggest that breeding hybrids for LN tolerance largely relies on phenotypic evaluation of hybrids under LN condition and yield under LN might be improved more by selection for KNE than by direct selection for GY per se.

Meta-analysis and candidate gene mining of low-phosphorus tolerance in maize.

Plants with tolerance to low-phosphorus (P) can grow better under low-P conditions, and understanding of genetic mechanisms of low-P tolerance can not only facilitate identifying relevant genes but also help to develop low-P tolerant cultivars. QTL meta-analysis was conducted after a comprehensive review of the reports on QTL mapping for low-P tolerance-related traits in maize. Meta-analysis produced 23 consensus QTL (cQTL), 17 of which located in similar chromosome regions to those previously reported to influence root traits. Meanwhile, candidate gene mining yielded 215 genes, 22 of which located in the cQTL regions. These 22 genes are homologous to 14 functionally characterized genes that were found to participate in plant low-P tolerance, including genes encoding miR399s, Pi transporters and purple acid phosphatases. Four cQTL loci (cQTL2-1, cQTL5-3, cQTL6-2, and cQTL10-2) may play important roles for low-P tolerance because each contains more original QTL and has better consistency across previous reports.

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.

Genome-wide association analysis for nine agronomic traits in maize under well-watered and water-stressed conditions.

Drought can cause severe reduction in maize production, and strongly threatens crop yields. To dissect this complex trait and identify superior alleles, 350 tropical and subtropical maize inbred lines were genotyped using a 1536-SNP array developed from drought-related genes and an array of 56,110 random SNPs. The inbred lines were crossed with a common tester, CML312, and the testcrosses were phenotyped for nine traits under well-watered and water-stressed conditions in seven environments. Using genome-wide association mapping with correction for population structure, 42 associated SNPs (P ≤ 2.25 × 10(-6) 0.1/N) were identified, located in 33 genes for 126 trait × environment × treatment combinations. Of these genes, three were co-localized to drought-related QTL regions. Gene GRMZM2G125777 was strongly associated with ear relative position, hundred kernel weight and timing of male and female flowering, and encodes NAC domain-containing protein 2, a transcription factor expressed in different tissues. These results provide some good information for understanding the genetic basis for drought tolerance and further studies on identified candidate genes should illuminate mechanisms of drought tolerance and provide tools for designing drought-tolerant maize cultivars tailored to different environmental scenarios.

Combined small RNA and degradome sequencing reveals novel miRNAs and their targets in response to low nitrate availability in maize.

BACKGROUND AND AIMS: MicroRNAs (miRNAs) play an important role in the responses and adaptation of plants to many stresses including low nitrogen (LN). Characterizing relevant miRNAs will improve our understanding of nitrogen (N) use efficiency and LN tolerance and thus contribute to sustainable maize production. The objective of this study was to identify novel and known miRNAs and their targets involved in the response and adaptation of maize (Zea mays) to LN stress. METHODS: MiRNAs and their targets were identified by combined analysis of deep sequencing of small RNA and degradome libraries. The identity of target genes was confirmed by gene-specific RNA ligase-mediated rapid amplification of 5' cDNA ends (RLM-RACE) and by quantitative expression analysis. KEY RESULTS: Over 150 million raw reads of small RNA and degradome sequence data were generated. A total of 46 unique mature miRNA sequences belonging to 23 maize miRNA families were sequenced. Eighty-five potentially new miRNAs were identified, with corresponding miRNA* also identified for 65 of them. Twenty-five new miRNAs showed >2-fold relative change in response to LN. In addition to known miR169 species, two novel putative miR169 species were identified. Deep sequencing of miRNAs and the degradome, and RLM-RACE and quantitative polymerase chain reaction (PCR) analyses of their targets showed that miRC10- and miRC68-mediated target cleavage may play a major role among miR169 families in the adaptation to LN by maize seedlings. CONCLUSIONS: Small RNA and degradome sequencing combined with quantitative reverse transcription-PCR and RLM-RACE verification enabled the efficient identification of miRNAs and their target genes. The generated data sets and the two novel miR169 species that were identified will contribute to our understanding of the physiological basis of adaptation to LN stress in maize plants.

Joint linkage-linkage disequilibrium mapping is a powerful approach to detecting quantitative trait loci underlying drought tolerance in maize.

This paper describes two joint linkage-linkage disequilibrium (LD) mapping approaches: parallel mapping (independent linkage and LD analysis) and integrated mapping (datasets analyzed in combination). These approaches were achieved using 2,052 single nucleotide polymorphism (SNP) markers, including 659 SNPs developed from drought-response candidate genes, screened across three recombinant inbred line (RIL) populations and 305 diverse inbred lines, with anthesis-silking interval (ASI), an important trait for maize drought tolerance, as the target trait. Mapping efficiency was improved significantly due to increased population size and allele diversity and balanced allele frequencies. Integrated mapping identified 18 additional quantitative trait loci (QTL) not detected by parallel mapping. The use of haplotypes improved mapping efficiency, with the sum of phenotypic variation explained (PVE) increasing from 5.4% to 23.3% for single SNP-based analysis. Integrated mapping with haplotype further improved the mapping efficiency, and the most significant QTL had a PVE of up to 34.7%. Normal allele frequencies for 113 of 277 (40.8%) SNPs with minor allele frequency (<5%) in 305 lines were recovered in three RIL populations, three of which were significantly associated with ASI. The candidate genes identified by two significant haplotype loci included one for a SET domain protein involved in the control of flowering time and the other encoding aldo/keto reductase associated with detoxification pathways that contribute to cellular damage due to environmental stress. Joint linkage-LD mapping is a powerful approach for detecting QTL underlying complex traits, including drought tolerance.

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.