European maize genomes highlight intraspecies variation in repeat and gene content

Genetic Improvement in Sunflower Breeding—Integrated Omics Approach

Foresight in climate change and the challenges ahead requires a systematic approach to sunflower breeding that will encompass all available technologies. There is a great scarcity of desirable genetic variation, which is in fact undiscovered because it has not been sufficiently researched as detection and designing favorable genetic variation largely depends on thorough genome sequencing through broad and deep resequencing. Basic exploration of genomes is insufficient to find insight about important physiological and molecular mechanisms unique to crops. That is why integrating information from genomics, epigenomics, transcriptomics, proteomics, metabolomics and phenomics enables a comprehensive understanding of the molecular mechanisms in the background of architecture of many important quantitative traits. Omics technologies offer novel possibilities for deciphering the complex pathways and molecular profiling through the level of systems biology and can provide important answers that can be utilized for more efficient breeding of sunflower. In this review, we present omics profiling approaches in order to address their possibilities and usefulness as a potential breeding tools in sunflower genetic improvement.

Plant NLR diversity: the known unknowns of pan-NLRomes

Plants and pathogens constantly adapt to each other. As a consequence, many members of the plant immune system, and especially the intracellular nucleotide-binding site leucine-rich repeat receptors, also known as NOD-like receptors (NLRs), are highly diversified, both among family members in the same genome, and between individuals in the same species. While this diversity has long been appreciated, its true extent has remained unknown. With pan-genome and pan-NLRome studies becoming more and more comprehensive, our knowledge of NLR sequence diversity is growing rapidly, and pan-NLRomes provide powerful platforms for assigning function to NLRs. These efforts are an important step toward the goal of comprehensively predicting from sequence alone whether an NLR provides disease resistance, and if so, to which pathogens.

A capture-based assay for detection and characterization of transposon polymorphisms in maize

Transposons can create allelic diversity that affects gene expression and phenotypic diversity. The detection of transposon polymorphisms at a genome-wide scale across a large population is difficult. Here we developed a targeted sequencing approach to monitor transposon polymorphisms of interest. This approach can interrogate the presence or absence of transposons reliably across various genotypes. Using this approach, we genotyped a set of 965 transposon-related presence/absence polymorphisms in a diverse panel of 16 maize (Zea mays L.) inbred lines that are representative of the major maize breeding groups. About 70% of the selected regions can be effectively assayed in each genotype. The consistency between the capture-based assay and PCR-based assay are 98.6% based on analysis of 24 randomly selected transposon polymorphisms. By integrating the transposon polymorphisms data with gene expression data, ∼18% of the assayed transposon polymorphisms were found to be associated with variable gene expression levels. A detailed analysis of 18 polymorphisms in a larger association panel confirmed the effects of 10 polymorphisms, with one of them having stronger association with expression than nearby SNP markers. The effects of seven polymorphisms were tested using a luciferase-based expression assay, and one was confirmed. Together, this study demonstrates that the targeted sequencing assay is an effective way to explore transposon function in a high-throughput manner.

Zea mays RNA-seq estimated transcript abundances are strongly affected by read mapping bias

Background

Genetic variation for gene expression is a source of phenotypic variation for natural and agricultural species. The common approach to map and to quantify gene expression from genetically distinct individuals is to assign their RNA-seq reads to a single reference genome. However, RNA-seq reads from alleles dissimilar to this reference genome may fail to map correctly, causing transcript levels to be underestimated. Presently, the extent of this mapping problem is not clear, particularly in highly diverse species. We investigated if mapping bias occurred and if chromosomal features associated with mapping bias. Zea mays presents a model species to assess these questions, given it has genotypically distinct and well-studied genetic lines.

Results

In Zea mays, the inbred B73 genome is the standard reference genome and template for RNA-seq read assignments. In the absence of mapping bias, B73 and a second inbred line, Mo17, would each have an approximately equal number of regulatory alleles that increase gene expression. Remarkably, Mo17 had 2–4 times fewer such positively acting alleles than did B73 when RNA-seq reads were aligned to the B73 reference genome. Reciprocally, over one-half of the B73 alleles that increased gene expression were not detected when reads were aligned to the Mo17 genome template. Genes at dissimilar chromosomal ends were strongly affected by mapping bias, and genes at more similar pericentromeric regions were less affected. Biased transcript estimates were higher in untranslated regions and lower in splice junctions. Bias occurred across software and alignment parameters.

Conclusions

Mapping bias very strongly affects gene transcript abundance estimates in maize, and bias varies across chromosomal features. Individual genome or transcriptome templates are likely necessary for accurate transcript estimation across genetically variable individuals in maize and other species.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07577-3.

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.

Dissecting cis-regulatory control of quantitative trait variation in a plant stem cell circuit

Cis-regulatory mutations underlie important crop domestication and improvement traits^1,2. However, limited allelic diversity has hindered functional dissection of the large number of cis-regulatory elements and their potential interactions, thereby precluding a deeper understanding of how cis-regulatory variation impacts traits quantitatively. Here, we engineered over 60 promoter alleles in two tomato fruit size genes^3,4 to characterize cis-regulatory sequences and study their functional relationships. We found that targeted mutations in conserved promoter sequences of SlCLV3, a repressor of stem cell proliferation^5,6, have a weak impact on fruit locule number. Pairwise combinations of these mutations mildly enhance this phenotype, revealing additive and synergistic relationships between conserved regions and further suggesting even higher-order cis-regulatory interactions within the SlCLV3 promoter. In contrast, SlWUS, a positive regulator of stem cell proliferation repressed by SlCLV3 (refs. ^5,6), is more tolerant to promoter perturbations. Our results show that complex interplay among cis-regulatory variants can shape quantitative variation, and suggest that empirical dissections of this hidden complexity can guide promoter engineering to predictably modify crop traits.

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.

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.

How the pan-genome is changing crop genomics and improvement

Crop genomics has seen dramatic advances in recent years due to improvements in sequencing technology, assembly methods, and computational resources. These advances have led to the development of new tools to facilitate crop improvement. The study of structural variation within species and the characterization of the pan-genome has revealed extensive genome content variation among individuals within a species that is paradigm shifting to crop genomics and improvement. Here, we review advances in crop genomics and how utilization of these tools is shifting in light of pan-genomes that are becoming available for many crop species.

How the pan-genome is changing crop genomics and improvement

Crop genomics has seen dramatic advances in recent years due to improvements in sequencing technology, assembly methods, and computational resources. These advances have led to the development of new tools to facilitate crop improvement. The study of structural variation within species and the characterization of the pan-genome has revealed extensive genome content variation among individuals within a species that is paradigm shifting to crop genomics and improvement. Here, we review advances in crop genomics and how utilization of these tools is shifting in light of pan-genomes that are becoming available for many crop species.

Probably Correct: Rescuing Repeats with Short and Long Reads

Ever since the introduction of high-throughput sequencing following the human genome project, assembling short reads into a reference of sufficient quality posed a significant problem as a large portion of the human genome-estimated 50-69%-is repetitive. As a result, a sizable proportion of sequencing reads is multi-mapping, i.e., without a unique placement in the genome. The two key parameters for whether or not a read is multi-mapping are the read length and genome complexity. Long reads are now able to span difficult, heterochromatic regions, including full centromeres, and characterize chromosomes from "telomere to telomere". Moreover, identical reads or repeat arrays can be differentiated based on their epigenetic marks, such as methylation patterns, aiding in the assembly process. This is despite the fact that long reads still contain a modest percentage of sequencing errors, disorienting the aligners and assemblers both in accuracy and speed. Here, I review the proposed and implemented solutions to the repeat resolution and the multi-mapping read problem, as well as the downstream consequences of reference choice, repeat masking, and proper representation of sex chromosomes. I also consider the forthcoming challenges and solutions with regards to long reads, where we expect the shift from the problem of repeat localization within a single individual to the problem of repeat positioning within pangenomes.

Discovery of beneficial haplotypes for complex traits in maize landraces

Genetic variation is of crucial importance for crop improvement. Landraces are valuable sources of diversity, but for quantitative traits efficient strategies for their targeted utilization are lacking. Here, we map haplotype-trait associations at high resolution in ~1000 doubled-haploid lines derived from three maize landraces to make their native diversity for early development traits accessible for elite germplasm improvement. A comparative genomic analysis of the discovered haplotypes in the landrace-derived lines and a panel of 65 breeding lines, both genotyped with 600k SNPs, points to untapped beneficial variation for target traits in the landraces. The superior phenotypic performance of lines carrying favorable landrace haplotypes as compared to breeding lines with alternative haplotypes confirms these findings. Stability of haplotype effects across populations and environments as well as their limited effects on undesired traits indicate that our strategy has high potential for harnessing beneficial haplotype variation for quantitative traits from genetic resources.

Genetic variations present in landraces are critical for crop genetic improvement. Here, the authors map haplotype-trait associations in ~1000 doubled haploid lines derived from three European maize landraces and identify beneficial haplotypes for quantitative traits that are not present in breeding lines.