Genome-wide selection and genetic improvement during modern maize breeding

Genome-wide association studies reveal the coordinated regulatory networks underlying photosynthesis and wood formation in Populus

Photosynthesis and wood formation underlie the ability of trees to provide renewable resources and perform ecological functions; however, the genetic basis and regulatory pathways coordinating these two linked processes remain unclear. Here, we used a systems genetics strategy, integrating genome-wide association studies, transcriptomic analyses, and transgenic experiments, to investigate the genetic architecture of photosynthesis and wood properties among 435 unrelated individuals of Populus tomentosa, and unravel the coordinated regulatory networks resulting in two trait categories. We detected 222 significant single-nucleotide polymorphisms, annotated to 177 candidate genes, for 10 traits of photosynthesis and wood properties. Epistasis uncovered 74 epistatic interactions for phenotypes. Strikingly, we deciphered the coordinated regulation patterns of pleiotropic genes underlying phenotypic variations for two trait categories. Furthermore, expression quantitative trait nucleotide mapping and coexpression analysis were integrated to unravel the potential transcriptional regulatory networks of candidate genes coordinating photosynthesis and wood properties. Finally, heterologous expression of two pleiotropic genes, PtoMYB62 and PtoMYB80, in Arabidopsis thaliana demonstrated that they control regulatory networks balancing photosynthesis and stem secondary cell wall components, respectively. Our study provides insights into the regulatory mechanisms coordinating photosynthesis and wood formation in poplar, and should facilitate genetic breeding in trees via molecular design.

Local adaptation contributes to gene expression divergence in maize

Gene expression links genotypes to phenotypes, so identifying genes whose expression is shaped by selection will be important for understanding the traits and processes underlying local adaptation. However, detecting local adaptation for gene expression will require distinguishing between divergence due to selection and divergence due to genetic drift. Here, we adapt a QST−FST framework to detect local adaptation for transcriptome-wide gene expression levels in a population of diverse maize genotypes. We compare the number and types of selected genes across a wide range of maize populations and tissues, as well as selection on cold-response genes, drought-response genes, and coexpression clusters. We identify a number of genes whose expression levels are consistent with local adaptation and show that genes involved in stress response show enrichment for selection. Due to its history of intense selective breeding and domestication, maize evolution has long been of interest to researchers, and our study provides insight into the genes and processes important for in local adaptation of maize.

Integration of omics data to unravel root microbiome recruitment

The plant microbiome plays an essential role in supporting plant growth and health, but plant molecular mechanisms underlying its recruitment are still unclear. Multi-omics data integration methods can be used to unravel new signalling relationships. Here, we review the effects of plant genetics and root exudates on root microbiome recruitment, and discuss methodological advances in data integration approaches that can help us to better understand and optimise the crop-microbiome interaction for a more sustainable agriculture.

Using high-throughput multiple optical phenotyping to decipher the genetic architecture of maize drought tolerance

BACKGROUND

Drought threatens the food supply of the world population. Dissecting the dynamic responses of plants to drought will be beneficial for breeding drought-tolerant crops, as the genetic controls of these responses remain largely unknown.

RESULTS

Here we develop a high-throughput multiple optical phenotyping system to noninvasively phenotype 368 maize genotypes with or without drought stress over a course of 98 days, and collected multiple optical images, including color camera scanning, hyperspectral imaging, and X-ray computed tomography images. We develop high-throughput analysis pipelines to extract image-based traits (i-traits). Of these i-traits, 10,080 were effective and heritable indicators of maize external and internal drought responses. An i-trait-based genome-wide association study reveals 4322 significant locus-trait associations, representing 1529 quantitative trait loci (QTLs) and 2318 candidate genes, many that co-localize with previously reported maize drought responsive QTLs. Expression QTL (eQTL) analysis uncovers many local and distant regulatory variants that control the expression of the candidate genes. We use genetic mutation analysis to validate two new genes, ZmcPGM2 and ZmFAB1A, which regulate i-traits and drought tolerance. Moreover, the value of the candidate genes as drought-tolerant genetic markers is revealed by genome selection analysis, and 15 i-traits are identified as potential markers for maize drought tolerance breeding.

CONCLUSION

Our study demonstrates that combining high-throughput multiple optical phenotyping and GWAS is a novel and effective approach to dissect the genetic architecture of complex traits and clone drought-tolerance associated genes.

Seeking for an optimal strategy to avoid arsenic and cadmium over-accumulation in crops: Soil management vs cultivar selection in a case study with maize

Soil management and cultivar selection are two strategies to reduce the accumulation risk of heavy metals in crops. However, it is still an open question which of these two strategies is more efficient for the safe utilization of contaminated soil. In this study, the available bio-concentration factors (aBCF) of arsenic (As) and cadmium (Cd) among 39 maize cultivars were determined through a field experiment. The effect of soil management was mimicked by choosing diverse sampling sites having different soil available contents of As and Cd. The aBCF of As and Cd in grain ranged from 0.02 to 0.13 and 1.17 to 42.2, respectively. The accumulation ability of As and Cd was classified among different maize cultivars. Soil pH and total As controlled the level of available As in soils, while soil pH dominated available Cd in soil. A soil pH of 6.5 was recommended to simultaneously minimize soil available As and Cd by managing soil conditions. The quantitative effects of cultivar and soil management on grain As and Cd were expressed as Q [Grain As] = 0.746Q [Cultivar]-0.126Q [pH]+0.276Q [As_available] (R² = 0.648, P = 1.00 × 10^-37) and Q [Grain Cd] = 0.913Q [Cultivar]-0.192Q [pH]+0.071Q [SOC] (R² = 0.782, P = 1.00 × 10^-37), respectively. Cultivar selection contributed stronger than soil management to decrease the As and Cd levels in maize grains. A feasible method to seek for a more efficient strategy was proposed for the safe utilization of contaminated soil.

Integration of light and hormone signaling pathways in the regulation of plant shade avoidance syndrome

As sessile organisms, plants are unable to move or escape from their neighboring competitors under high-density planting conditions. Instead, they have evolved the ability to sense changes in light quantity and quality (such as a reduction in photoactive radiation and drop in red/far-red light ratios) and evoke a suite of adaptative responses (such as stem elongation, reduced branching, hyponastic leaf orientation, early flowering and accelerated senescence) collectively termed shade avoidance syndrome (SAS). Over the past few decades, much progress has been made in identifying the various photoreceptor systems and light signaling components implicated in regulating SAS, and in elucidating the underlying molecular mechanisms, based on extensive molecular genetic studies with the model dicotyledonous plant Arabidopsis thaliana. Moreover, an emerging synthesis of the field is that light signaling integrates with the signaling pathways of various phytohormones to coordinately regulate different aspects of SAS. In this review, we present a brief summary of the various cross-talks between light and hormone signaling in regulating SAS. We also present a perspective of manipulating SAS to tailor crop architecture for breeding high-density tolerant crop cultivars.

A cis-regulatory atlas in maize at single-cell resolution

cis-regulatory elements (CREs) encode the genomic blueprints of spatiotemporal gene expression programs enabling highly specialized cell functions. Using single-cell genomics in six maize organs, we determined the cis- and trans-regulatory factors defining diverse cell identities and coordinating chromatin organization by profiling transcription factor (TF) combinatorics, identifying TFs with non-cell-autonomous activity, and uncovering TFs underlying higher-order chromatin interactions. Cell-type-specific CREs were enriched for enhancer activity and within unmethylated long terminal repeat retrotransposons. Moreover, we found cell-type-specific CREs are hotspots for phenotype-associated genetic variants and were targeted by selection during modern maize breeding, highlighting the biological implications of this CRE atlas. Through comparison of maize and Arabidopsis thaliana developmental trajectories, we identified TFs and CREs with conserved and divergent chromatin dynamics, showcasing extensive evolution of gene regulatory networks. In addition to this rich dataset, we developed single-cell analysis software, Socrates, which can be used to understand cis-regulatory variation in any species.

A cis-regulatory atlas in maize at single-cell resolution

cis-regulatory elements (CREs) encode the genomic blueprints of spatiotemporal gene expression programs enabling highly specialized cell functions. Using single-cell genomics in six maize organs, we determined the cis- and trans-regulatory factors defining diverse cell identities and coordinating chromatin organization by profiling transcription factor (TF) combinatorics, identifying TFs with non-cell-autonomous activity, and uncovering TFs underlying higher-order chromatin interactions. Cell-type-specific CREs were enriched for enhancer activity and within unmethylated long terminal repeat retrotransposons. Moreover, we found cell-type-specific CREs are hotspots for phenotype-associated genetic variants and were targeted by selection during modern maize breeding, highlighting the biological implications of this CRE atlas. Through comparison of maize and Arabidopsis thaliana developmental trajectories, we identified TFs and CREs with conserved and divergent chromatin dynamics, showcasing extensive evolution of gene regulatory networks. In addition to this rich dataset, we developed single-cell analysis software, Socrates, which can be used to understand cis-regulatory variation in any species.

How to build a crop plant: Defining the cis-regulatory landscape of maize

The functional regulatory elements of agronomically important plant genomes have been long sought after. Marand et. al. generate a comprehensive atlas of cis-regulatory elements at single cell resolution in maize, providing a powerful resource for inquiries into the rules of multicellular development and for precision crop engineering.

Genetic architecture affecting maize agronomic traits identified by variance heterogeneity association mapping

Conventional genome-wide association studies (GWAS) focused on the phenotypic mean differences (mGWAS) but often ignored genetic variants influencing differences in the variance between genotypes. In this study, we performed variance heterogeneity GWAS (vGWAS) analysis for 13 previously measured agronomic traits in a maize population. We discovered a total of 129 significant SNPs. We demonstrated that the genetic loci influencing mean differences and variance heterogeneity formed distinct groups, suggesting that breeders were able to independently select for phenotype mean and variance values. Moreover, vGWAS served as a tractable approach to effectively identify 214 epistatic interaction pairs. In addition, we documented four agronomic traits with decreasing phenotype variance during modern maize breeding history and identified the potential genetic variants influencing this process. In summary, we discovered additional non-additive effects contributing to missing heritability and valuable genetic variants used for breeding varieties with desired phenotypic variance.

PopAmaranth: A population genetic genome browser for grain amaranths and their wild relatives

The combination of genomic, physiological, and population genetic research has accelerated the understanding and improvement of numerous crops. For non-model crops the lack of interdisciplinary research hinders their improvement. Grain amaranth is an ancient nutritious pseudocereal that has been domesticated three times in different regions of the Americas. We present and employ PopAmaranth, a population genetic genome browser, which provides an accessible representation of the genetic variation of the three grain amaranth species (A. hypochondriacus, A. cruentus, and A. caudatus) and two wild relatives (A. hybridus and A. quitensis) along the A. hypochondriacus reference sequence. We performed population-scale diversity and selection analysis from whole-genome sequencing data of 88 curated genetically and taxonomically unambiguously classified accessions. We employ the platform to show that genetic diversity in the water stress-related MIF1 gene declined during amaranth domestication and provide evidence for convergent saponin reduction between amaranth and quinoa. PopAmaranth is available through amaranthGDB at amaranthgdb.org/popamaranth.html.

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.

Developmental genetics of maize vegetative shoot architecture

More than 1.1 billion tonnes of maize grain were harvested across 197 million hectares in 2019 (FAOSTAT 2020). The vast global productivity of maize is largely driven by denser planting practices, higher yield potential per area of land, and increased yield potential per plant. Shoot architecture, the three-dimensional structural arrangement of the above-ground plant body, is critical to maize grain yield and biomass. Structure of the shoot is integral to all aspects of modern agronomic practices. Here, the developmental genetics of the maize vegetative shoot is reviewed. Plant architecture is ultimately determined by meristem activity, developmental patterning, and growth. The following topics are discussed: shoot apical meristem, leaf architecture, axillary meristem and shoot branching, and intercalary meristem and stem activity. Where possible, classical and current studies in maize developmental genetics, as well as recent advances leveraged by "-omics" analyses, are highlighted within these sections.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-021-01208-1.

New insights into the response of maize to fluctuations in the light environment

Light is the most important environmental cue signaling the transition from skotomorphogenesis to photomorphogenesis, thus affecting plant development and metabolic activity. How the light response mechanisms of maize seedlings respond to fluctuations in the light environment has not been well characterized to date. In this study, we built a gene coexpression network from a dynamic transcriptomic map of maize seedlings exposed to different light environments. Coexpression analysis identified ten modules and multiple genes that closely correlate with photosynthesis and characterized hub genes associated with regulatory networks, duplication events, domestication and improvement. In addition, we identified that 38% of hub genes underwent duplication events, 74% of which are related to photosynthesis. Moreover, we captured the dynamic expression atlas of gene sets involved in the chloroplast photosynthetic apparatus and photosynthetic carbon assimilation in different light environments, which should help to elucidate the key mechanisms and regulatory networks that underlie photosynthesis in maize. Insights from this study provide a valuable resource to better understand the genetic mechanisms of the response to fluctuations in the light environment in maize.

Genome assembly and population genomic analysis provide insights into the evolution of modern sweet corn

Sweet corn is one of the most important vegetables in the United States and Canada. Here, we present a de novo assembly of a sweet corn inbred line Ia453 with the mutated shrunken2-reference allele (Ia453-sh2). This mutation accumulates more sugar and is present in most commercial hybrids developed for the processing and fresh markets. The ten pseudochromosomes cover 92% of the total assembly and 99% of the estimated genome size, with a scaffold N50 of 222.2 Mb. This reference genome completely assembles the large structural variation that created the mutant sh2-R allele. Furthermore, comparative genomics analysis with six field corn genomes highlights differences in single-nucleotide polymorphisms, structural variations, and transposon composition. Phylogenetic analysis of 5,381 diverse maize and teosinte accessions reveals genetic relationships between sweet corn and other types of maize. Our results show evidence for a common origin in northern Mexico for modern sweet corn in the U.S. Finally, population genomic analysis identifies regions of the genome under selection and candidate genes associated with sweet corn traits, such as early flowering, endosperm composition, plant and tassel architecture, and kernel row number. Our study provides a high-quality reference-genome sequence to facilitate comparative genomics, functional studies, and genomic-assisted breeding for sweet corn.

Functional gene assessment of bread wheat: breeding implications in Ningxia Province

BACKGROUND

The overall genetic distribution and divergence of cloned genes among bread wheat varieties that have occurred during the breeding process over the past few decades in Ningxia Province, China, are poorly understood. Here, we report the genetic diversities of 44 important genes related to grain yield, quality, adaptation and resistance in 121 Ningxia and 86 introduced wheat cultivars and advanced lines.

RESULTS

The population structure indicated characteristics of genetic components of Ningxia wheat, including landraces of particular genetic resources, introduced varieties with rich genetic diversities and modern cultivars in different periods. Analysis of allele frequencies showed that the dwarfing alleles Rht-B1b at Rht-B1 and Rht-D1b at Rht-D1, 1BL/1RS translocation, Hap-1 at GW2-6B and Hap-H at Sus2-2B are very frequently present in modern Ningxia cultivars and in introduced varieties from other regions but absent in landraces. This indicates that the introduced wheat germplasm with numerous beneficial genes is vital for broadening the genetic diversity of Ningxia wheat varieties. Large population differentiation between modern cultivars and landraces has occurred in adaptation genes. Founder parents carry excellent allele combinations of important genes, with a higher number of favorable alleles than modern cultivars. Gene flow analysis showed that six founder parents have greatly contributed to breeding improvement in Ningxia Province, particularly Zhou 8425B, for yield-related genes.

CONCLUSIONS

Varieties introduced from other regions with rich genetic diversity and landraces with well-adapted genetic resources have been applied to improve modern cultivars. Founder parents, particularly Zhou 8425B, for yield-related genes have contributed greatly to wheat breeding improvement in Ningxia Province. These findings will greatly benefit bread wheat breeding in Ningxia Province as well as other areas with similar ecological environments.

Integrating multi-omics data for crop improvement

Our agricultural systems are now in urgent need to secure food for a growing world population. To meet this challenge, we need a better characterization of plant genetic and phenotypic diversity. The combination of genomics, transcriptomics and metabolomics enables a deeper understanding of the mechanisms underlying the complex architecture of many phenotypic traits of agricultural relevance. We review the recent advances in plant genomics to see how these can be integrated with broad molecular profiling approaches to improve our understanding of plant phenotypic variation and inform crop breeding strategies.

Plant-ImputeDB: an integrated multiple plant reference panel database for genotype imputation

Genotype imputation is a process that estimates missing genotypes in terms of the haplotypes and genotypes in a reference panel. It can effectively increase the density of single nucleotide polymorphisms (SNPs), boost the power to identify genetic association and promote the combination of genetic studies. However, there has been a lack of high-quality reference panels for most plants, which greatly hinders the application of genotype imputation. Here, we developed Plant-ImputeDB (http://gong_lab.hzau.edu.cn/Plant_imputeDB/), a comprehensive database with reference panels of 12 plant species for online genotype imputation, SNP and block search and free download. By integrating genotype data and whole-genome resequencing data of plants from various studies and databases, the current Plant-ImputeDB provides high-quality reference panels of 12 plant species, including ∼69.9 million SNPs from 34 244 samples. It also provides an easy-to-use online tool with the option of two popular tools specifically designed for genotype imputation. In addition, Plant-ImputeDB accepts submissions of different types of genomic variations, and provides free and open access to all publicly available data in support of related research worldwide. In general, Plant-ImputeDB may serve as an important resource for plant genotype imputation and greatly facilitate the research on plant genetic research.

BnaGVD: A genomic variation database of rapeseed (Brassica napus)

Rapeseed (Brassica napus L.) is a typical polyploid crop and one of the most important oilseed crops worldwide. With the rapid progress on high-throughput sequencing technologies and the reduction of sequencing cost, large-scale genomic data of a specific crop have become available. However, raw sequence data are mostly deposited in the sequence read archive of the National Center of Biotechnology Information (NCBI) and the European Nucleotide Archive (ENA), which is freely accessible to all researchers. Extensive tools for practical purposes should be developed to efficiently utilize these large raw data. Here, we report a web-based rapeseed genomic variation database (BnaGVD, http://rapeseed.biocloud.net/home) from which genomic variations, such as single nucleotide polymorphisms (SNPs) and insertions/deletions (InDels) across a world-wide collection of rapeseed accessions, can be referred. The current release of the BnaGVD contains 34,591,899 high-quality SNPs and 12,281,923 high-quality InDels and provides search tools to retrieve genomic variations and gene annotations across 1,007 accessions of worldwide rapeseed germplasm. We implement a variety of built-in tools (e.g., BnaGWAS, BnaPCA, and BnaStructure) to help users perform in-depth analyses. We recommend this web resource for accelerating studies on the functional genomics and screening of molecular markers for rapeseed breeding.

Genome structure variation analyses of peach reveal population dynamics and a 1.67 Mb causal inversion for fruit shape

BACKGROUND

Structural variations (SVs), a major resource of genomic variation, can have profound consequences on phenotypic variation, yet the impacts of SVs remain largely unexplored in crops.

RESULTS

Here, we generate a high-quality de novo genome assembly for a flat-fruit peach cultivar and produce a comprehensive SV map for peach, as a high proportion of genomic sequence is occupied by heterozygous SVs in the peach genome. We conduct population-level analyses that indicate SVs have undergone strong purifying selection during peach domestication, and find evidence of positive selection, with a significant preference for upstream and intronic regions during later peach improvement. We perform a SV-based GWAS that identifies a large 1.67-Mb heterozygous inversion that segregates perfectly with flat-fruit shape. Mechanistically, this derived allele alters the expression of the PpOFP2 gene positioned near the proximal breakpoint of the inversion, and we confirm in transgenic tomatoes that PpOFP2 is causal for flat-fruit shape.

CONCLUSIONS

Thus, beyond introducing new genomics resources for peach research, our study illustrates how focusing on SV data can drive basic functional discoveries in plant science.

Proteome-wide Systems Genetics to Identify Functional Regulators of Complex Traits

Proteomic technologies now enable the rapid quantification of thousands of proteins across genetically diverse samples. Integration of these data with systems-genetics analyses is a powerful approach to identify new regulators of economically important or disease-relevant phenotypes in various populations. In this review, we summarize the latest proteomic technologies and discuss technical challenges for their use in population studies. We demonstrate how the analysis of correlation structure and loci mapping can be used to identify genetic factors regulating functional protein networks and complex traits. Finally, we provide an extensive summary of the use of proteome-wide systems genetics throughout fungi, plant, and animal kingdoms and discuss the power of this approach to identify candidate regulators and drug targets in large human consortium studies.

Genome-Wide Association Analysis Reveals Loci and Candidate Genes Involved in Fiber Quality Traits Under Multiple Field Environments in Cotton (Gossypium hirsutum)

Fiber length, fiber strength, and fiber micronaire are the main fiber quality parameters in cotton. Thus, mining the elite and stable loci/alleles related to fiber quality traits and elucidating the relationship between the two may accelerate genetic improvement of fiber quality in cotton. Here, genome-wide association analysis (GWAS) was performed for fiber quality parameters based on phenotypic data, and 56,010 high-quality single nucleotide polymorphisms (SNPs) using 242 upland cotton accessions under 12 field environments were obtained. Phenotypic analysis exhibited that fiber length (FL) had a positive correlation with fiber strength (FS) and had a negative correlation with fiber micronaire (Mic). Genetic analysis also indicated that FL, FS, and Mic had high heritability of more than 80%. A total of 67 stable quantitative trait loci (QTLs) were identified through GWAS analysis, including 31 for FL, 21 for FS, and 22 for Mic. Of them, three pairs homologous QTLs were detected between A and D subgenomes, and seven co-located QTLs with two fiber quality parameters were found. Compared with the reported QTLs, 34 co-located with previous studies, and 33 were newly revealed. Integrated with transcriptome analysis, we selected 256, 244, and 149 candidate genes for FL, FS, and Mic, respectively. Gene Ontology (GO) analysis showed that most of the genes located in QTLs interval of the three fiber quality traits were involved in sugar biosynthesis, sugar metabolism, microtubule, and cytoskeleton organization, which played crucial roles in fiber development. Through correlation analysis between haplotypes and phenotypes, three genes (GH_A05G1494, GH_D11G3097, and GH_A05G1082) predominately expressed in fiber development stages were indicated to be potentially responsible for FL, FS, and Mic, respectively. The GH_A05G1494 encoded a protein containing SGS-domain, which is related to tubulin-binding and ubiquitin-protein ligase binding. The GH_D11G3097 encoded 20S proteasome beta subunit G1, and was involved in the ubiquitin-dependent protein catabolic process. The GH_A05G1082 encoded RAN binding protein 1 with a molecular function of GTPase activator activity. These results provide new insights and candidate loci/genes for the improvement of fiber quality in cotton.