Integrating GWAS and TWAS to elucidate the genetic architecture of maize leaf cuticular conductance

Genome-wide association analysis and transgenic characterization for amylose content regulating gene in tuber of Dioscorea zingiberensis

BACKGROUND

Amylose, a prebiotic found in yams is known to be beneficial for the gut microflora and is particularly advantageous for diabetic patients' diet. However, the genetic machinery underlying amylose production remains elusive. A comprehensive characterization of the genetic basis of amylose content in yam tubers is a prerequisite for accelerating the genetic engineering of yams with respect to amylose content variation.

RESULTS

To uncover the genetic variants underlying variation in amylose content, we evaluated amylose content in freshly harvested tubers from 150 accessions of Dioscorea zingibensis. With 30,000 high-quality single nucleotide polymorphisms (SNP), we performed a genome-wide association analysis (GWAS). The population structure analysis classified the D. zingiberensis accessions into three groups. A total of 115 significant loci were detected on four chromosomes. Of these, 112 significant SNPs (log10(p) = 5, q-value < 0.004) were clustered in a narrow window on the chromosome 6 (chr6). The peak SNP at the position 75,609,202 on chr6 could explain 63.15% of amylose variation in the population and fell into the first exon of the ADP-glucose pyrophosphorylase (AGPase) small subunit gene, causing a non-synonymous modification of the resulting protein sequence. Allele segregation analysis showed that accessions with the rare G allele had a higher amylose content than those harboring the common A allele. However, AGPase, a key enzyme precursor of amylose biosynthesis, was not expressed differentially between accessions with A and G alleles. Overexpression of the two variants of AGPase in Arabidopsis thaliana resulted in a significantly higher amylose content in lines transformed with the AGPase-G allele.

CONCLUSIONS

Overall, this study showed that a major genetic variant in AGPase probably enhances the enzyme activity leading to high amylose content in D. zingiberensis tuber. The results provide valuable insights for the development of amylose-enriched genotypes.

The era of panomics-driven gene discovery in plants

Panomics is an approach to integrate multiple 'omics' datasets, generated using different individuals or natural variations. Considering their diverse phenotypic spectrum, the phenome is inherently associated with panomics-based science, which is further combined with genomics, transcriptomics, metabolomics, and other omics techniques, either independently or collectively. Panomics has been accelerated through recent technological advancements in the field of genomics that enable the detection of population-wide structural variations (SVs) and hence offer unprecedented insights into the genetic variations contributing to important agronomic traits. The present review provides the recent trends of panomics-driven gene discovery toward various traits related to plant development, stress tolerance, accumulation of specialized metabolites, and domestication/dedomestication. In addition, the success stories are highlighted in the broader context of enhancing crop productivity.

A genome-wide association study identifies genes associated with cuticular wax metabolism in maize

The plant cuticle is essential in plant defense against biotic and abiotic stresses. To systematically elucidate the genetic architecture of maize (Zea mays L.) cuticular wax metabolism, 2 cuticular wax-related traits, the chlorophyll extraction rate (CER) and water loss rate (WLR) of 389 maize inbred lines, were investigated and a genome-wide association study (GWAS) was performed using 1.25 million single nucleotide polymorphisms (SNPs). In total, 57 nonredundant quantitative trait loci (QTL) explaining 5.57% to 15.07% of the phenotypic variation for each QTL were identified. These QTLs contained 183 genes, among which 21 strong candidates were identified based on functional annotations and previous publications. Remarkably, 3 candidate genes that express differentially during cuticle development encode β-ketoacyl-CoA synthase (KCS). While ZmKCS19 was known to be involved in cuticle wax metabolism, ZmKCS12 and ZmKCS3 functions were not reported. The association between ZmKCS12 and WLR was confirmed by resequencing 106 inbred lines, and the variation of WLR was significant between different haplotypes of ZmKCS12. In this study, the loss-of-function mutant of ZmKCS12 exhibited wrinkled leaf morphology, altered wax crystal morphology, and decreased C32 wax monomer levels, causing an increased WLR and sensitivity to drought. These results confirm that ZmKCS12 plays a vital role in maize C32 wax monomer synthesis and is critical for drought tolerance. In sum, through GWAS of 2 cuticular wax-associated traits, this study reveals comprehensively the genetic architecture in maize cuticular wax metabolism and provides a valuable reference for the genetic improvement of stress tolerance in maize.

Data-driven approaches to improve water-use efficiency and drought resistance in crop plants

With the increasing population, there lies a pressing demand for food, feed and fibre, while the changing climatic conditions pose severe challenges for agricultural production worldwide. Water is the lifeline for crop production; thus, enhancing crop water-use efficiency (WUE) and improving drought resistance in crop varieties are crucial for overcoming these challenges. Genetically-driven improvements in yield, WUE and drought tolerance traits can buffer the worst effects of climate change on crop production in dry areas. While traditional crop breeding approaches have delivered impressive results in increasing yield, the methods remain time-consuming and are often limited by the existing allelic variation present in the germplasm. Significant advances in breeding and high-throughput omics technologies in parallel with smart agriculture practices have created avenues to dramatically speed up the process of trait improvement by leveraging the vast volumes of genomic and phenotypic data. For example, individual genome and pan-genome assemblies, along with transcriptomic, metabolomic and proteomic data from germplasm collections, characterised at phenotypic levels, could be utilised to identify marker-trait associations and superior haplotypes for crop genetic improvement. In addition, these omics approaches enable the identification of genes involved in pathways leading to the expression of a trait, thereby providing an understanding of the genetic, physiological and biochemical basis of trait variation. These data-driven gene discoveries and validation approaches are essential for crop improvement pipelines, including genomic breeding, speed breeding and gene editing. Herein, we provide an overview of prospects presented using big data-driven approaches (including artificial intelligence and machine learning) to harness new genetic gains for breeding programs and develop drought-tolerant crop varieties with favourable WUE and high-yield potential traits.

Effects of Climate Change and Drought Tolerance on Maize Growth

Climate change is affecting all regions of the world with different climates, and the scale of damage is increasing due to the occurrence of various natural disasters. In particular, maize production is highly affected by abnormal climate events such as heat waves and droughts. Increasing temperatures can accelerate growth and shorten the growing season, potentially reducing productivity. Additionally, enhanced temperatures during the ripening period can accelerate the process, reducing crop yields. In addition, drought stress due to water deficit can greatly affect seedling formation, early plant growth, photosynthesis, reproductive growth, and yield, so proper water management is critical to maize growth. Maize, in particular, is tall and broad-leaved, so extreme drought stress at planting can cause leaves to curl and stunt growth. It is important to understand that severe drought can have a detrimental effect on the growth and reproduction of maize. In addition, high temperatures caused by drought stress can inhibit the induction of flowering in male flowers and cause factors that interfere with pollen development. It is therefore important to increase the productivity of all food crops, including maize, while maintaining them in the face of persistent drought caused by climate change. This requires a strategy to develop genetically modified crops and drought-tolerant maize that can effectively respond to climate change. The aim of this paper is to investigate the effects of climate change and drought tolerance on maize growth. We also reviewed molecular breeding techniques to develop drought-tolerant maize varieties in response to climate change.

Multi-omics revolution to promote plant breeding efficiency

Crop production is the primary goal of agricultural activities, which is always taken into consideration. However, global agricultural systems are coming under increasing pressure from the rising food demand of the rapidly growing world population and changing climate. To address these issues, improving high-yield and climate-resilient related-traits in crop breeding is an effective strategy. In recent years, advances in omics techniques, including genomics, transcriptomics, proteomics, and metabolomics, paved the way for accelerating plant/crop breeding to cope with the changing climate and enhance food production. Optimized omics and phenotypic plasticity platform integration, exploited by evolving machine learning algorithms will aid in the development of biological interpretations for complex crop traits. The precise and progressive assembly of desire alleles using precise genome editing approaches and enhanced breeding strategies would enable future crops to excel in combating the changing climates. Furthermore, plant breeding and genetic engineering ensures an exclusive approach to developing nutrient sufficient and climate-resilient crops, the productivity of which can sustainably and adequately meet the world's food, nutrition, and energy needs. This review provides an overview of how the integration of omics approaches could be exploited to select crop varieties with desired traits.

Genome-wide association study and genetic mapping of BhWAX conferring mature fruit cuticular wax in wax gourd

BACKGROUND

Wax gourd [Benincasa hispida (Thunb) Cogn. (2n = 2x = 24)] is an economically important vegetable crop of genus Benincasa in the Cucurbitaceae family. Fruit is the main consumption organ of wax gourd. The mature fruit cuticular wax (MFCW) is an important trait in breeding programs, which is also of evolutionary significance in wax gourd. However, the genetic architecture of this valuable trait remains unrevealed.

RESULTS

In this study, genetic analysis revealed that the inheritance of MFCW was controlled by a single gene, with MFCW dominant over non-MFCW, and the gene was primarily named as BhWAX. Genome-wide association study (GWAS) highlighted a 1.1 Mb interval on chromosome 9 associated with MFCW in wax gourd germplasm resources. Traditional fine genetic mapping delimited BhWAX to a 0.5 Mb region containing 12 genes. Based on the gene annotation, expression analysis and co-segregation analysis, Bhi09G001428 that encodes a membrane bound O-acyltransferase (MBOAT) was proposed as the candidate gene for BhWAX. Moreover, it was demonstrated that the efficiency of a cleaved amplified polymorphic sequences (CAPS) marker in the determination of MFCW in wax gourd reached 80%.

CONCLUSIONS

In closing, the study identified the candidate gene controlling MFCW and provided an efficient molecular marker for the trait in wax gourd for the first time, which will not only be beneficial for functional validation of the gene and marker-assisted breeding of wax gourd, but also lay a foundation for analysis of its evolutionary meaning among cucurbits.

Integration of multi-omics technologies for crop improvement: Status and prospects

With the rapid development of next-generation sequencing (NGS), multi-omics techniques have been emerging as effective approaches for crop improvement. Here, we focus mainly on addressing the current status and future perspectives toward omics-related technologies and bioinformatic resources with potential applications in crop breeding. Using a large amount of omics-level data from the functional genome, transcriptome, proteome, epigenome, metabolome, and microbiome, clarifying the interaction between gene and phenotype formation will become possible. The integration of multi-omics datasets with pan-omics platforms and systems biology could predict the complex traits of crops and elucidate the regulatory networks for genetic improvement. Different scales of trait predictions and decision-making models will facilitate crop breeding more intelligent. Potential challenges that integrate the multi-omics data with studies of gene function and their network to efficiently select desirable agronomic traits are discussed by proposing some cutting-edge breeding strategies for crop improvement. Multi-omics-integrated approaches together with other artificial intelligence techniques will contribute to broadening and deepening our knowledge of crop precision breeding, resulting in speeding up the breeding process.