Recent advances in molecular marker techniques: Insight into QTL mapping, GWAS and genomic selection in plants

Haplotype-based breeding: A new insight in crop improvement

Haplotype-based breeding (HBB) is one of the cutting-edge technologies in the realm of crop improvement due to the increasing availability of Single Nucleotide Polymorphisms identified by Next Generation Sequencing technologies. The complexity of the data can be decreased with fewer statistical tests and a lower probability of spurious associations by combining thousands of SNPs into a few hundred haplotype blocks. The presence of strong genomic regions in breeding lines of most crop species facilitates the use of haplotypes to improve the efficiency of genomic and marker-assisted selection. Haplotype-based breeding as a Genomic Assisted Breeding (GAB) approach harnesses the genome sequence data to pinpoint the allelic variation used to hasten the breeding cycle and circumvent the challenges associated with linkage drag. This review article demonstrates ways to identify candidate genes, superior haplotype identification, haplo-pheno analysis, and haplotype-based marker-assisted selection. The crop improvement strategies that utilize superior haplotypes will hasten the breeding progress to safeguard global food security.

Application of bulk segregant RNA-Seq (BSR-Seq) and allele-specific primers to study soybean powdery mildew resistance

BACKGROUND

Powdery mildew (PM) is one of the important soybean diseases, and host resistance could practically contribute to soybean PM management. To date, only the Rmd locus on chromosome (Chr) 16 was identified through traditional QTL mapping and GWAS, and it remains unclear if the bulk segregant RNA-Seq (BSR-Seq) methodology is feasible to explore additional PM resistance that might exist in other varieties.

RESULTS

BSR-Seq was applied to contrast genotypes and gene expressions between the resistant bulk (R bulk) and the susceptible bulk (S bulk), as well as the parents. The ∆(SNP-index) and G' value identified several QTL and significant SNPs/Indels on Chr06, Chr15, and Chr16. Differentially expressed genes (DEGs) located within these QTL were identified using HISAT2 and Kallisto, and allele-specific primers (AS-primers) were designed to validate the accuracy of phenotypic prediction. While the AS-primers on Chr06 or Chr15 cannot distinguish the resistant and susceptible phenotypes, AS-primers on Chr16 exhibited 82% accuracy prediction with an additive effect, similar to the SSR marker Satt431.

CONCLUSIONS

Evaluation of additional AS-primers in the linkage disequilibrium (LD) block on Chr16 further confirmed the resistant locus, derived from the resistant parental variety 'Kaohsiung 11' ('KS11'), not only overlaps with the Rmd locus with unique up-regulated LRR genes (Glyma.16G213700 and Glyma.16G215100), but also harbors a down-regulated MLO gene (Glyma.16G145600). Accordingly, this study exemplified the feasibility of BSR-Seq in studying biotrophic disease resistance in soybean, and showed the genetic makeup of soybean variety 'KS11' comprising the Rmd locus and one MLO gene.

A comprehensive review on Gossypium hirsutum resistance against cotton leaf curl virus

Cotton (Gossypium hirsutum L.) is a significant fiber crop. Being a major contributor to the textile industry requires continuous care and attention. Cotton is subjected to various biotic and abiotic constraints. Among these, biotic factors including cotton leaf curl virus (CLCuV) are dominant. CLCuV is a notorious disease of cotton and is acquired, carried, and transmitted by the whitefly (Bemisia tabaci). A cotton plant affected with CLCuV may show a wide range of symptoms such as yellowing of leaves, thickening of veins, upward or downward curling, formation of enations, and stunted growth. Though there are many efforts to protect the crop from CLCuV, long-term results are not yet obtained as CLCuV strains are capable of mutating and overcoming plant resistance. However, systemic-induced resistance using a gene-based approach remained effective until new virulent strains of CLCuV (like Cotton Leaf Curl Burewala Virus and others) came into existence. Disease control by biological means and the development of CLCuV-resistant cotton varieties are in progress. In this review, we first discussed in detail the evolution of cotton and CLCuV strains, the transmission mechanism of CLCuV, the genetic architecture of CLCuV vectors, and the use of pathogen and nonpathogen-based approaches to control CLCuD. Next, we delineate the uses of cutting-edge technologies like genome editing (with a special focus on CRISPR-Cas), next-generation technologies, and their application in cotton genomics and speed breeding to develop CLCuD resistant cotton germplasm in a short time. Finally, we delve into the current obstacles related to cotton genome editing and explore forthcoming pathways for enhancing precision in genome editing through the utilization of advanced genome editing technologies. These endeavors aim to enhance cotton's resilience against CLCuD.

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.

Recent advances in artificial intelligence, mechanistic models, and speed breeding offer exciting opportunities for precise and accelerated genomics-assisted breeding

Given the challenges of population growth and climate change, there is an urgent need to expedite the development of high-yielding stress-tolerant crop cultivars. While traditional breeding methods have been instrumental in ensuring global food security, their efficiency, precision, and labour intensiveness have become increasingly inadequate to address present and future challenges. Fortunately, recent advances in high-throughput phenomics and genomics-assisted breeding (GAB) provide a promising platform for enhancing crop cultivars with greater efficiency. However, several obstacles must be overcome to optimize the use of these techniques in crop improvement, such as the complexity of phenotypic analysis of big image data. In addition, the prevalent use of linear models in genome-wide association studies (GWAS) and genomic selection (GS) fails to capture the nonlinear interactions of complex traits, limiting their applicability for GAB and impeding crop improvement. Recent advances in artificial intelligence (AI) techniques have opened doors to nonlinear modelling approaches in crop breeding, enabling the capture of nonlinear and epistatic interactions in GWAS and GS and thus making this variation available for GAB. While statistical and software challenges persist in AI-based models, they are expected to be resolved soon. Furthermore, recent advances in speed breeding have significantly reduced the time (3-5-fold) required for conventional breeding. Thus, integrating speed breeding with AI and GAB could improve crop cultivar development within a considerably shorter timeframe while ensuring greater accuracy and efficiency. In conclusion, this integrated approach could revolutionize crop breeding paradigms and safeguard food production in the face of population growth and climate change.

Genetic Mapping of the Root Mycobiota in Rice and its Role in Drought Tolerance

BACKGROUND

Rice is the second most produced crop worldwide, but is highly susceptible to drought. Micro-organisms can potentially alleviate the effects of drought. The aim of the present study was to unravel the genetic factors involved in the rice-microbe interaction, and whether genetics play a role in rice drought tolerance. For this purpose, the composition of the root mycobiota was characterized in 296 rice accessions (Oryza sativa L. subsp. indica) under control and drought conditions. Genome wide association mapping (GWAS) resulted in the identification of ten significant (LOD > 4) single nucleotide polymorphisms (SNPs) associated with six root-associated fungi: Ceratosphaeria spp., Cladosporium spp., Boudiera spp., Chaetomium spp., and with a few fungi from the Rhizophydiales order. Four SNPs associated with fungi-mediated drought tolerance were also found. Genes located around those SNPs, such as a DEFENSIN-LIKE (DEFL) protein, EXOCYST TETHERING COMPLEX (EXO70), RAPID ALKALINIZATION FACTOR-LIKE (RALFL) protein, peroxidase and xylosyltransferase, have been shown to be involved in pathogen defense, abiotic stress responses and cell wall remodeling processes. Our study shows that rice genetics affects the recruitment of fungi, and that some fungi affect yield under drought. We identified candidate target genes for breeding to improve rice-fungal interactions and hence drought tolerance.

Whole Genome Wide SSR Markers Identification Based on ddRADseq Data

The advent of advanced NGS technologies have led to the generation of enormous amount of sequence data which further aid in the discovery of the various type of markers such as SSRs, SNPs, InDels, etc. Among all these markers, microsatellite SSR markers can be mined from the ddRADseq data as certain properties of SSR markers make them ideal markers for study. These assist researchers and breeders in diversity analysis and producing new varieties with desired traits. To extract the markers, first, the ddRADseq data is assembled into consensus sequences using STACKS program which are further assembled for mining microsatellites using QDD along with MISA tool.

Barley Resistance to Fusarium graminearum Infections: From Transcriptomics to Field with Food Safety Concerns

Global climate change and the urgency to transform food crops require substantial breeding efforts to meet the food security challenges. Barley, an important cereal, has remained a preferential host of phytotoxic diseases caused by the Fusarium graminearum that not only severely reduces the crop yield but also compromises its food quality due to the accumulation of mycotoxins. To develop resistance against Fusarium infections, a better understanding of the host-pathogen interaction is inevitable and could be tracked through molecular insights. Here, we focused precisely on the potential gene targets that are exclusive to this devastating pathosystem and could be harnessed for fast breeding of barley. We also discuss the eco-friendly applications of nanobio hybrid and the CRISPR technology for barley protection. This review covers the critical information gaps within the subject and may be useful for the sustainable improvement of barley from the perspective of food and environmental safety concerns.

Pea Breeding for Resistance to Rhizospheric Pathogens

Pea (Pisum sativum L.) is a grain legume widely cultivated in temperate climates. It is important in the race for food security owing to its multipurpose low-input requirement and environmental promoting traits. Pea is key in nitrogen fixation, biodiversity preservation, and nutritional functions as food and feed. Unfortunately, like most crops, pea production is constrained by several pests and diseases, of which rhizosphere disease dwellers are the most critical due to their long-term persistence in the soil and difficulty to manage. Understanding the rhizosphere environment can improve host plant root microbial association to increase yield stability and facilitate improved crop performance through breeding. Thus, the use of various germplasm and genomic resources combined with scientific collaborative efforts has contributed to improving pea resistance/cultivation against rhizospheric diseases. This improvement has been achieved through robust phenotyping, genotyping, agronomic practices, and resistance breeding. Nonetheless, resistance to rhizospheric diseases is still limited, while biological and chemical-based control strategies are unrealistic and unfavourable to the environment, respectively. Hence, there is a need to consistently scout for host plant resistance to resolve these bottlenecks. Herein, in view of these challenges, we reflect on pea breeding for resistance to diseases caused by rhizospheric pathogens, including fusarium wilt, root rots, nematode complex, and parasitic broomrape. Here, we will attempt to appraise and harmonise historical and contemporary knowledge that contributes to pea resistance breeding for soilborne disease management and discuss the way forward.

Applications of Artificial Intelligence in Climate-Resilient Smart-Crop Breeding

Recently, Artificial intelligence (AI) has emerged as a revolutionary field, providing a great opportunity in shaping modern crop breeding, and is extensively used indoors for plant science. Advances in crop phenomics, enviromics, together with the other “omics” approaches are paving ways for elucidating the detailed complex biological mechanisms that motivate crop functions in response to environmental trepidations. These “omics” approaches have provided plant researchers with precise tools to evaluate the important agronomic traits for larger-sized germplasm at a reduced time interval in the early growth stages. However, the big data and the complex relationships within impede the understanding of the complex mechanisms behind genes driving the agronomic-trait formations. AI brings huge computational power and many new tools and strategies for future breeding. The present review will encompass how applications of AI technology, utilized for current breeding practice, assist to solve the problem in high-throughput phenotyping and gene functional analysis, and how advances in AI technologies bring new opportunities for future breeding, to make envirotyping data widely utilized in breeding. Furthermore, in the current breeding methods, linking genotype to phenotype remains a massive challenge and impedes the optimal application of high-throughput field phenotyping, genomics, and enviromics. In this review, we elaborate on how AI will be the preferred tool to increase the accuracy in high-throughput crop phenotyping, genotyping, and envirotyping data; moreover, we explore the developing approaches and challenges for multiomics big computing data integration. Therefore, the integration of AI with “omics” tools can allow rapid gene identification and eventually accelerate crop-improvement programs.

Genome-wide association study, haplotype analysis, and genomic prediction reveal the genetic basis of yield-related traits in soybean (Glycine max L.)

Identifying the genetic components underlying yield-related traits in soybean is crucial for improving its production and productivity. Here, 211 soybean genotypes were evaluated across six environments for four yield-related traits, including seed yield per plant (SYP), number of pods per plant number of seeds per plant and 100-seed weight (HSW). Genome-wide association study (GWAS) and genomic prediction (GP) analyses were performed using 12,617 single nucleotide polymorphism markers from NJAU 355K SoySNP Array. A total of 57 SNPs were significantly associated with four traits across six environments and a combined environment using five Genome-wide association study models. Out of these, six significant SNPs were consistently identified in more than three environments using multiple GWAS models. The genomic regions (±670 kb) flanking these six consistent SNPs were considered stable QTL regions. Gene annotation and in silico expression analysis revealed 15 putative genes underlying the stable QTLs that might regulate soybean yield. Haplotype analysis using six significant SNPs revealed various allelic combinations regulating diverse phenotypes for the studied traits. Furthermore, the GP analysis revealed that accurate breeding values for the studied soybean traits is attainable at an earlier generation. Our study paved the way for increasing soybean yield performance within a short breeding cycle.

Genetics as a key to improving crop photosynthesis

Since the basic biochemical mechanisms of photosynthesis are remarkably conserved among plant species, genetic modification approaches have so far been the main route to improve the photosynthetic performance of crops. Yet, phenotypic variation observed in wild species and between varieties of crop species implies there is standing natural genetic variation for photosynthesis, offering a largely unexplored resource to use for breeding crops with improved photosynthesis and higher yields. The reason this has not yet been explored is that the variation probably involves thousands of genes, each contributing only a little to photosynthesis, making them hard to identify without proper phenotyping and genetic tools. This is changing, though, and increasingly studies report on quantitative trait loci for photosynthetic phenotypes. So far, hardly any of these quantitative trait loci have been used in marker assisted breeding or genomic selection approaches to improve crop photosynthesis and yield, and hardly ever have the underlying causal genes been identified. We propose to take the genetics of photosynthesis to a higher level, and identify the genes and alleles nature has used for millions of years to tune photosynthesis to be in line with local environmental conditions. We will need to determine the physiological function of the genes and alleles, and design novel strategies to use this knowledge to improve crop photosynthesis through conventional plant breeding, based on readily available crop plant germplasm. In this work, we present and discuss the genetic methods needed to reveal natural genetic variation, and elaborate on how to apply this to improve crop photosynthesis.

Genome-Wide Association Study and Genomic Selection for Proteinogenic Methionine in Soybean Seeds

Soybean [Glycine max (L.) Merr.] seeds have an amino acid profile that provides excellent viability as a food and feed protein source. However, low concentrations of an essential amino acid, methionine, limit the nutritional utility of soybean protein. The objectives of this study were to identify genomic associations and evaluate the potential for genomic selection (GS) for methionine content in soybean seeds. We performed a genome-wide association study (GWAS) that utilized 311 soybean accessions from maturity groups IV and V grown in three locations in 2018 and 2019. A total of 35,570 single nucleotide polymorphisms (SNPs) were used to identify genomic associations with proteinogenic methionine content that was quantified by high-performance liquid chromatography (HPLC). Across four environments, 23 novel SNPs were identified as being associated with methionine content. The strongest associations were found on chromosomes 3 (ss715586112, ss715586120, ss715586126, ss715586203, and ss715586204), 8 (ss715599541 and ss715599547) and 16 (ss715625009). Several gene models were recognized within proximity to these SNPs, such as a leucine-rich repeat protein kinase and a serine/threonine protein kinase. Identification of these linked SNPs should help soybean breeders to improve protein quality in soybean seeds. GS was evaluated using k-fold cross validation within each environment with two SNP sets, the complete 35,570 set and a subset of 248 SNPs determined to be associated with methionine through GWAS. Average prediction accuracy (r ²) was highest using the SNP subset ranging from 0.45 to 0.62, which was a significant improvement from the complete set accuracy that ranged from 0.03 to 0.27. This indicated that GS utilizing a significant subset of SNPs may be a viable tool for soybean breeders seeking to improve methionine content.

Genetic architecture of variation in Arabidopsis thaliana rosettes

Rosette morphology across Arabidopsis accessions exhibits considerable variation. Here we report a high-throughput phenotyping approach based on automatic image analysis to quantify rosette shape and dissect the underlying genetic architecture. Shape measurements of the rosettes in a core set of Recombinant Inbred Lines from an advanced mapping population (Multiparent Advanced Generation Inter-Cross or MAGIC) derived from inter-crossing 19 natural accessions. Image acquisition and analysis was scaled to extract geometric descriptors from time stamped images of growing rosettes. Shape analyses revealed heritable morphological variation at early juvenile stages and QTL mapping resulted in over 116 chromosomal regions associated with trait variation within the population. Many QTL linked to variation in shape were located near genes related to hormonal signalling and signal transduction pathways while others are involved in shade avoidance and transition to flowering. Our results suggest rosette shape arises from modular integration of sub-organ morphologies and can be considered a functional trait subjected to selective pressures of subsequent morphological traits. On an applied aspect, QTLs found will be candidates for further research on plant architecture.

Genome-wide association study of cassava starch paste properties

An understanding of cassava starch paste properties (CSPP) can contribute to the selection of clones with differentiated starches. This study aimed to identify genomic regions associated with CSPP using different genome-wide association study (GWAS) methods (MLM, MLMM, and Farm-CPU). The GWAS was performed using 23,078 single-nucleotide polymorphisms (SNPs). The rapid viscoanalyzer (RVA) parameters were pasting temperature (PastTemp), peak viscosity (PeakVisc), hot-paste viscosity (Hot-PVisc), cool-paste viscosity (Cold-PVisc), final viscosity (FinalVis), breakdown (BreDow), and setback (Setback). Broad phenotypic and molecular diversity was identified based on the genomic kinship matrix. The broad-sense heritability estimates (h²) ranged from moderate to high magnitudes (0.66 to 0.76). The linkage disequilibrium (LD) declined to between 0.3 and 2.0 Mb (r² <0.1) for most chromosomes, except chromosome 17, which exhibited an extensive LD. Thirteen SNPs were found to be significantly associated with CSPP, on chromosomes 3, 8, 17, and 18. Only the BreDow trait had no associated SNPs. The regional marker-trait associations on chromosome 18 indicate a LD block between 2907312 and 3567816 bp and that SNP S18_3081635 was associated with SetBack, FinalVis, and Cold-PVisc (all three GWAS methods) and with Hot-PVisc (MLM), indicating that this SNP can track these four traits simultaneously. The variance explained by the SNPs ranged from 0.13 to 0.18 for SetBack, FinalVis, and Cold-PVisc and from 0.06 to 0.09 for PeakVisc and Hot-PVisc. The results indicated additive effects of the genetic control of Cold-PVisc, FinalVis, Hot-PVisc, and SetBack, especially on the large LD block on chromosome 18. One transcript encoding the glycosyl hydrolase family 35 enzymes on chromosome 17 and one encoding the mannose-p-dolichol utilization defect 1 protein on chromosome 18 were the most likely candidate genes for the regulation of CSPP. These results underline the potential for the assisted selection of high-value starches to improve cassava root quality through breeding programs.

Features and applications of haplotypes in crop breeding

Climate change with altered pest-disease dynamics and rising abiotic stresses threatens resource-constrained agricultural production systems worldwide. Genomics-assisted breeding (GAB) approaches have greatly contributed to enhancing crop breeding efficiency and delivering better varieties. Fast-growing capacity and affordability of DNA sequencing has motivated large-scale germplasm sequencing projects, thus opening exciting avenues for mining haplotypes for breeding applications. This review article highlights ways to mine haplotypes and apply them for complex trait dissection and in GAB approaches including haplotype-GWAS, haplotype-based breeding, haplotype-assisted genomic selection. Improvement strategies that efficiently deploy superior haplotypes to hasten breeding progress will be key to safeguarding global food security.

Recent advancements in molecular marker-assisted selection and applications in plant breeding programmes

Background

DNA markers improved the productivity and accuracy of classical plant breeding by means of marker-assisted selection (MAS). The enormous number of quantitative trait loci (QTLs) mapping read for different plant species have given a plenitude of molecular marker-gene associations.

Main body of the abstract

In this review, we have discussed the positive aspects of molecular marker-assisted selection and its precise applications in plant breeding programmes. Molecular marker-assisted selection has considerably shortened the time for new crop varieties to be brought to the market. To explore the information about DNA markers, many reviews have been published in the last few decades; all these reviews were intended by plant breeders to obtain information on molecular genetics. In this review, we intended to be a synopsis of recent developments of DNA markers and their application in plant breeding programmes and devoted to early breeders with little or no knowledge about the DNA markers. The progress made in molecular plant breeding, plant genetics, genomics selection, and editing of genome contributed to the comprehensive understanding of DNA markers and provides several proofs on the genetic diversity available in crop plants and greatly complemented plant breeding devices.

Short conclusion

MAS has revolutionized the process of plant breeding with acceleration and accuracy, which is continuously empowering plant breeders around the world.

Predictions from algorithmic modeling result in better decisions than from data modeling for soybean iron deficiency chlorosis

In soybean variety development and genetic improvement projects, iron deficiency chlorosis (IDC) is visually assessed as an ordinal response variable. Linear Mixed Models for Genomic Prediction (GP) have been developed, compared, and used to select continuous plant traits such as yield, height, and maturity, but can be inappropriate for ordinal traits. Generalized Linear Mixed Models have been developed for GP of ordinal response variables. However, neither approach addresses the most important questions for cultivar development and genetic improvement: How frequently are the 'wrong' genotypes retained, and how often are the 'correct' genotypes discarded? The research objective reported herein was to compare outcomes from four data modeling and six algorithmic modeling GP methods applied to IDC using decision metrics appropriate for variety development and genetic improvement projects. Appropriate metrics for decision making consist of specificity, sensitivity, precision, decision accuracy, and area under the receiver operating characteristic curve. Data modeling methods for GP included ridge regression, logistic regression, penalized logistic regression, and Bayesian generalized linear regression. Algorithmic modeling methods include Random Forest, Gradient Boosting Machine, Support Vector Machine, K-Nearest Neighbors, Naïve Bayes, and Artificial Neural Network. We found that a Support Vector Machine model provided the most specific decisions of correctly discarding IDC susceptible genotypes, while a Random Forest model resulted in the best decisions of retaining IDC tolerant genotypes, as well as the best outcomes when considering all decision metrics. Overall, the predictions from algorithmic modeling result in better decisions than from data modeling methods applied to soybean IDC.

G2PMineR: A Genome to Phenome Literature Review Approach

There is a gap in the conceptual framework linking genes to phenotypes (G2P) for non-model organisms, as most non-model organisms do not yet have genomic resources readily available. To address this, researchers often perform literature reviews to understand G2P linkages by curating a list of likely gene candidates, hinging upon other studies already conducted in closely related systems. Sifting through hundreds to thousands of articles is a cumbersome task that slows down the scientific process and may introduce bias into a study. To fill this gap, we created G2PMineR, a free and open source literature mining tool developed specifically for G2P research. This R package uses automation to make the G2P review process efficient and unbiased, while also generating hypothesized associations between genes and phenotypes within a taxonomical framework. We applied the package to a literature review for drought-tolerance in plants. The analysis provides biologically meaningful results within the known framework of drought tolerance in plants. Overall, the package is useful for conducting literature reviews for genome to phenome projects, and also has broad appeal to scientists investigating a wide range of study systems as it can conduct analyses under the auspices of three different kingdoms (Plantae, Animalia, and Fungi).

Genetic characterization of an almond germplasm collection and volatilome profiling of raw and roasted kernels

Almond is appreciated for its nutraceutical value and for the aromatic profile of the kernels. In this work, an almond collection composed of 96 Sicilian accessions complemented with 10 widely cultivated cultivars was phenotyped for the production of volatile organic compounds using a proton-transfer time-of-flight mass spectrometer and genotyped using the Illumina Infinium^®18 K Peach SNP array. The profiling of the aroma was carried out on fresh and roasted kernels enabling the detection of 150 mass peaks. Sixty eight, for the most related with sulfur compounds, furan containing compounds, and aldehydes formed by Strecker degradation, significantly increased during roasting, while the concentration of fifty-four mass peaks, for the most belonging to alcohols and terpenes, significantly decreased. Four hundred and seventy-one robust SNPs were selected and employed for population genetic studies. Structure analysis detected three subpopulations with the Sicilian accessions characterized by a different genetic stratification compared to those collected in Apulia (South Italy) and the International cultivars. The linkage-disequilibrium (LD) decay across the genome was equal to r² = 0.083. Furthermore, a high level of collinearity (r² = 0.96) between almond and peach was registered confirming the high synteny between the two genomes. A preliminary application of a genome-wide association analysis allowed the detection of significant marker-trait associations for 31 fresh and 33 roasted almond mass peaks respectively. An accurate genetic and phenotypic characterization of novel germplasm can represent a valuable tool for the set-up of marker-assisted selection of novel cultivars with an enhanced aromatic profile.

Harnessing High-throughput Phenotyping and Genotyping for Enhanced Drought Tolerance in Crop Plants

Development of drought-tolerant cultivars is one of the challenging tasks for the plant breeders due to its complex inheritance and polygenic regulation. Evaluating genetic material for drought tolerance is a complex process due to its spatiotemporal interactions with environmental factors. The conventional breeding approaches are costly, lengthy, and inefficient to achieve the expected gain in drought tolerance. In this regard, genomics-assisted breeding (GAB) offers promise to develop cultivars with improved drought tolerance in a more efficient, quicker, and cost-effective manner. The success of GAB depends upon the precision in marker-trait association and estimation of genomic estimated breeding values (GEBVs), which mostly depends on coverage and precision of genotyping and phenotyping. A wide gap between the discovery and practical use of quantitative trait loci (QTL) for crop improvement has been observed for many important agronomical traits. Such a limitation could be due to the low accuracy in QTL detection, mainly resulting from low marker density and manually collected phenotypes of complex agronomic traits. Increasing marker density using the high-throughput genotyping (HTG), and accurate and precise phenotyping using high-throughput digital phenotyping (HTP) platforms can improve the precision and power of QTL detection. Therefore, both HTG and HTP can enhance the practical utility of GAB along with a faster characterization of germplasm and breeding material. In the present review, we discussed how the recent innovations in HTG and HTP would assist in the breeding of improved drought-tolerant varieties. We have also discussed strategies, tools, and analytical advances made on the HTG and HTP along with their pros and cons.

Molecular characterization of imidazolinone-resistant Brassica rapa × B. napus hybrids

Herbicide-resistant oilseed rape (Brassica napus) cultivation in our country entails the risk of gene transfer to related wild species. One of these species is the wild turnip (B. rapa), an important weed of winter crops widely distributed in the Pampas region. Despite hybridization risks, Clearfield ® oilseed rape is available in Argentina. In 2008, a B. rapa population, which was sympatric to an imidazolinone-resistant and a conventional oilseed rape cultivar, was located on a farm in the main cropping area of the country. Herbicide-resistant individuals were found in the progeny of this population in a herbicide screening test. Therefore, a molecular characterization using cleaved amplified polymorphic sequence (CAPS) and simple sequence repeat (SSR) markers was conducted on these plants to determine their hybrid nature and to establish the origin of the imidazolinone resistance trait. The results of this study, along with information of field records, confirmed that the resistant plants were first generation interspecific hybrids. Imidazolinone resistance had been effectively transferred from the herbicide-resistant oilseed rape, even in the particular situation of pollen competition. Oilseed rape resistant cultivars are becoming more common in the country. So, considering that seed loss and crop volunteers are common in these species, it is crucial to avoid the dispersion of new resistant weed biotypes as they reduce the effectiveness of chemical control technologies.

Classification of Takifugu rubripes, T. chinensis and T. pseudommus by genotyping-by-sequencing

Takifugu rubripes is more expensive than other species of the genus because of its high protein content and special flavor. However, it is easily confused with imported T. chinensis and T. pseudommus because they have similar morphological characteristics. We identified single nucleotide polymorphism (SNP) markers of T. rubripes by genotyping-by-sequencing (GBS) and evaluated their ability to distinguish among T. rubripes, T. chinensis, and T. pseudommus. In all, 18 polymorphic SNPs were subjected to phylogenetic analyses of the three Takifugu species. Additionally, we subjected a second set of samples to Sanger sequencing to verify that the polymorphic SNPs could be used to evaluate the genetic variation among the three Takifugu species. A phylogenetic tree that included the analyzed sequence of set A, which is referred to as the reference sequence, and a validation sequence of set B with 18 SNPs were produced. Based on this phylogenetic tree and STRUCTURE analyses, T. rubripes, T. chinensis and T. pseudommus have low genetic variation and should be considered the same gene pool. Our findings suggest that further studies are needed to estimate the genetic association of the three Takifugu species.

Comparative analyses of 32 complete plastomes of Tef (Eragrostis tef ) accessions from Ethiopia: phylogenetic relationships and mutational hotspots

Eragrostis tef is an important cereal crop in Ethiopia with excellent storage properties, high–quality food, and the unique ability to thrive in extreme environmental conditions. However, the application of advanced molecular tools for breeding and conservation of these species is extremely limited. Therefore, developing chloroplast genome resources and high-resolution molecular markers are valuable to E. tef population and biogeographic studies. In the current study, we assembled and compared the complete plastomes of 32 E. tef accessions. The size of the plastomes ranged from 134,349 to 134,437 bp with similar GC content (∼38.3%). Genomes annotations revealed 112 individual genes, including 77 protein-coding, 31 tRNA, and 4 rRNA genes. Comparison of E. tef plastomes revealed a low degree of intraspecific sequence variations and no structural differentiations. Furthermore, we found 34 polymorphic sites (13 cpSSRs, 12 InDels, and 9 SNPs) that can be used as valuable DNA barcodes. Among them, the majority (88%) of the polymorphic sites were identified in the noncoding genomic regions. Nonsynonymous (ka) and synonymous (ks) substitution analysis showed that all PCGs were under purifying selection (ka/ks <1). The phylogenetic analyses of the whole plastomes and polymorphic region sequences were able to distinguish the accession from the southern population, indicating its potential to be used as a super-barcode. In conclusion, the newly generated plastomes and polymorphic markers developed here could be a useful genomic resource in molecular breeding, population genetics and the biogeographical study of E. tef.

Advances in Omics Approaches for Abiotic Stress Tolerance in Tomato

Tomato, one of the most important crops worldwide, has a high demand in the fresh fruit market and processed food industries. Despite having considerably high productivity, continuous supply as per the market demand is hard to achieve, mostly because of periodic losses occurring due to biotic as well as abiotic stresses. Although tomato is a temperate crop, it is grown in almost all the climatic zones because of widespread demand, which makes it challenge to adapt in diverse conditions. Development of tomato cultivars with enhanced abiotic stress tolerance is one of the most sustainable approaches for its successful production. In this regard, efforts are being made to understand the stress tolerance mechanism, gene discovery, and interaction of genetic and environmental factors. Several omics approaches, tools, and resources have already been developed for tomato growing. Modern sequencing technologies have greatly accelerated genomics and transcriptomics studies in tomato. These advancements facilitate Quantitative trait loci (QTL) mapping, genome-wide association studies (GWAS), and genomic selection (GS). However, limited efforts have been made in other omics branches like proteomics, metabolomics, and ionomics. Extensive cataloging of omics resources made here has highlighted the need for integration of omics approaches for efficient utilization of resources and a better understanding of the molecular mechanism. The information provided here will be helpful to understand the plant responses and the genetic regulatory networks involved in abiotic stress tolerance and efficient utilization of omics resources for tomato crop improvement.

Functional genomic approaches to improve crop plant heat stress tolerance

Heat stress as a yield limiting issue has become a major threat for food security as global warming progresses. Being sessile, plants cannot avoid heat stress. They respond to heat stress by activating complex molecular networks, such as signal transduction, metabolite production and expressions of heat stress-associated genes. Some plants have developed an intricate signalling network to respond and adapt it. Heat stress tolerance is a polygenic trait, which is regulated by various genes, transcriptional factors, proteins and hormones. Therefore, to improve heat stress tolerance, a sound knowledge of various mechanisms involved in the response to heat stress is required. The classical breeding methods employed to enhance heat stress tolerance has had limited success. In this era of genomics, next generation sequencing techniques, availability of genome sequences and advanced biotechnological tools open several windows of opportunities to improve heat stress tolerance in crop plants. This review discusses the potential of various functional genomic approaches, such as genome wide association studies, microarray, and suppression subtractive hybridization, in the process of discovering novel genes related to heat stress, and their functional validation using both reverse and forward genetic approaches. This review also discusses how these functionally validated genes can be used to improve heat stress tolerance through plant breeding, transgenics and genome editing approaches.

Understanding the Role of the WRKY Gene Family under Stress Conditions in Pigeonpea (Cajanus Cajan L.)

Pigeonpea (Cajanus cajan L.), a protein-rich legume, is a major food component of the daily diet for residents in semi-arid tropical regions of the word. Pigeonpea is also known for its high level of tolerance against biotic and abiotic stresses. In this regard, understanding the genes involved in stress tolerance has great importance. In the present study, identification, and characterization of WRKY, a large transcription factor gene family involved in numerous biological processes like seed germination, metabolism, plant growth, biotic and abiotic stress responses was performed in pigeonpea. A total of 94 WRKY genes identified in the pigeonpea genome were extensively characterized for gene-structures, localizations, phylogenetic distribution, conserved motif organizations, and functional annotation. Phylogenetic analysis revealed three major groups (I, II, and III) of pigeonpea WRKY genes. Subsequently, expression profiling of 94 CcWRKY genes across different tissues like root, nodule, stem, petiole, petal, sepal, shoot apical meristem (SAM), mature pod, and mature seed retrieved from the available RNAseq data identified tissue-specific WRKY genes with preferential expression in the vegetative and reproductive stages. Gene co-expression networks identified four WRKY genes at the center of maximum interaction which may play a key role in the entire WRKY regulations. Furthermore, quantitative real-time polymerase chain reaction (qRT-PCR) expression analysis of WRKY genes in root and leaf tissue samples from plants under drought and salinity stress identified differentially expressed WRKY genes. The study will be helpful to understand the evolution, regulation, and distribution of the WRKY gene family, and additional exploration for the development of stress tolerance cultivars in pigeonpea and other legumes crops.

Application of genetics and biotechnology for improving medicinal plants

Plant tissue culture has been used for conservation, micropropagation, and in planta overproduction of some pharma molecules of medicinal plants. New biotechnology-based breeding methods such as targeted genome editing methods are able to create custom-designed medicinal plants with different secondary metabolite profiles. For a long time, humans have used medicinal plants for therapeutic purposes and in food and other industries. Classical biotechnology techniques have been exploited in breeding medicinal plants. Now, it is time to apply faster biotechnology-based breeding methods (BBBMs) to these valuable plants. Assessment of the genetic diversity, conservation, proliferation, and overproduction are the main ways by which genetics and biotechnology can help to improve medicinal plants faster. Plant tissue culture (PTC) plays an important role as a platform to apply other BBBMs in medicinal plants. Agrobacterium-mediated gene transformation and artificial polyploidy induction are the main BBBMs that are directly dependent on PTC. Manageable regulation of endogens and/or transferred genes via engineered zinc-finger proteins or transcription activator-like effectors can help targeted manipulation of secondary metabolite pathways in medicinal plants. The next-generation sequencing techniques have great potential to study the genetic diversity of medicinal plants through restriction-site-associated DNA sequencing (RAD-seq) technique and also to identify the genes and enzymes that are involved in the biosynthetic pathway of secondary metabolites through precise transcriptome profiling (RNA-seq). The sequence-specific nucleases of transcription activator-like effector nucleases (TALENs), zinc-finger nucleases, and clustered regularly interspaced short palindromic repeats-associated (Cas) are the genome editing methods that can produce user-designed medicinal plants. These current targeted genome editing methods are able to manage plant synthetic biology and open new gates to medicinal plants to be introduced into appropriate industries.

Mapping of Quantitative Trait Loci for Growth and Carcass-Related Traits in Chickens Using a Restriction-Site Associated DNA Sequencing Method

In the present study, quantitative trait loci (QTLs) analysis was performed to identify the chromosomal positions of growth and carcass-related trait QTLs using 319 F₂ chickens obtained from intercrosses of an Oh-Shamo male and four White Plymouth Rock females. Body weight was measured weekly until the birds were 7 weeks old. Carcass-related traits were also measured at this timepoint. A genetic linkage map was constructed using 545 single nucleotide polymorphism (SNP) markers that were developed using a restriction-site associated DNA sequencing method. The linkage map included the 23 autosomes and the Z chromosome. Using simple interval QTL mapping, we were able to identify 10 significant and suggestive main-effect QTLs for growth and carcass-related traits present on chromosomes 1, 2, 3, 5, 8, 19, 24, and Z. These loci explained 5.60–16.52% of the phenotypic variances. The chromosomal positions of the 10 QTLs overlapped with those of previously reported QTLs, whereas the targeted traits varied. Our QTLs will aid future breeding programs in improving growth and meat yield of chickens (e.g., via marker-assisted selection), particularly in the Japanese brand chicken industry.

De Novo Transcriptomic Analysis and Development of EST–SSRs for Styrax japonicus

Styrax japonicus sieb. et Zucc. is widely distributed in China with ornamental and medicinal values. However, the transcriptome of S. japonicus has not yet been reported. In this study, we carried out the first transcriptome analysis of S. japonicus and developed a set of expressed sequence tag–simple sequence repeats (EST–SSRs). We obtained 338,570,222 clean reads in total, of which the mean GC content was 41.58%. In total, 136,071 unigenes were obtained having an average length of 611 bp and 71,226 unigenes were favorably annotated in the database. In total, we identified 55,977 potential EST–SSRs from 38,611 unigenes, of which there was 1 SSR per 6.73 kb. The di-nucleotide repeats (40.40%) were the most identified SSRs. One set of 60 primer pairs was randomly selected, and the amplified products in S. japonicus were validated; 28 primer pairs successfully produced clear amplicons. A total of 21 (35%) polymorphic genic SSR markers were identified between two populations. In total, 15 alleles were detected and the average number was 6. The average of observed heterozygosity and expected heterozygosity was 0.614 and 0.552, respectively. The polymorphism information content (PIC) value fluctuated between 0.074 and 0.855, with a mean value of 0.504, which was also the middle level. This study provides useful information for diversity studies and resource assessments of S. japonicus.

Next Generation Sequencing-Based Molecular Marker Development: A Case Study in Betula Alnoides

Betula alnoides is a fast-growing valuable indigenous tree species with multiple uses in the tropical and warm subtropical regions in South-East Asia and southern China. It has been proved to be tetraploid in most parts of its distribution in China. In the present study, next generation sequencing (NGS) technology was applied to develop numerous SSR markers for B. alnoides, and 64,376 contig sequences of 106,452 clean reads containing 164,357 candidate SSR loci were obtained. Among the derived SSR repeats, mono-nucleotide was the main type (77.05%), followed by di- (10.18%), tetra- (6.12%), tri- (3.56%), penta- (2.14%) and hexa-nucleotide (0.95%). The short nucleotide sequence repeats accounted for 90.79%. Among the 291 repeat motifs, AG/CT (46.33%) and AT/AT (44.15%) were the most common di-nucleotide repeats, while AAT/ATT (48.98%) was the most common tri-nucleotide repeats. A total of 2549 primer sets were designed from the identified putative SSR regions of which 900 were randomly selected for evaluation of amplification successfulness and detection of polymorphism if amplified successfully. Three hundred and ten polymorphic markers were obtained through testing with 24 individuals from B. alnoides natural forest in Jingxi County, Guangxi, China. The number of alleles (NA) of each marker ranged from 2 to 19 with a mean of 5.14. The observed (HO) and expected (HE) heterozygosities varied from 0.04 to 1.00 and 0.04 to 0.92 with their means being 0.64 and 0.57, respectively. Shannon-Wiener diversity index (I) ranged from 0.10 to 2.68 with a mean of 1.12. Cross-species transferability was further examined for 96 pairs of SSR primers randomly selected, and it was found that 48.96⁻84.38% of the primer pairs could successfully amplify each of six related Betula species. The obtained SSR markers can be used to study population genetics and molecular marker assisted breeding, particularly genome-wide association study of these species in the future.

Harnessing the Potential of Forage Legumes, Alfalfa, Soybean, and Cowpea for Sustainable Agriculture and Global Food Security

Substantial improvements in access to food and increased purchasing power are driving many people toward consuming nutrition-rich foods causing an unprecedented demand for protein food worldwide, which is expected to rise further. Forage legumes form an important source of feed for livestock and have potential to provide a sustainable solution for food and protein security. Currently, alfalfa is a commercially grown source of forage and feed in many countries. However, soybean and cowpea also have the potential to provide quality forage and fodder for animal use. The cultivation of forage legumes is under threat from changing climatic conditions, indicating the need for breeding cultivars that can sustain and acclimatize to the negative effects of climate change. Recent progress in genetic and genomic tools have facilitated the identification of quantitative trait loci and genes/alleles that can aid in developing forage cultivars through genomics-assisted breeding. Furthermore, transgenic technology can be utilized to manipulate the genetic makeup of plants to improve forage digestibility for better animal performance. In this article, we assess the genetic potential of three important legume crops, alfalfa, soybean, and cowpea in supplying quality fodder and feed for livestock. In addition, we examine the impact of climate change on forage quality and discuss efforts made in enhancing the adaptation of the plant to the abiotic stress conditions. Subsequently, we suggest the application of integrative approaches to achieve adequate forage production amid the unpredictable climatic conditions.

Mining and Development of Novel SSR Markers Using Next Generation Sequencing (NGS) Data in Plants

Microsatellites, or simple sequence repeats (SSRs), are one of the most informative and multi-purpose genetic markers exploited in plant functional genomics. However, the discovery of SSRs and development using traditional methods are laborious, time-consuming, and costly. Recently, the availability of high-throughput sequencing technologies has enabled researchers to identify a substantial number of microsatellites at less cost and effort than traditional approaches. Illumina is a noteworthy transcriptome sequencing technology that is currently used in SSR marker development. Although 454 pyrosequencing datasets can be used for SSR development, this type of sequencing is no longer supported. This review aims to present an overview of the next generation sequencing, with a focus on the efficient use of de novo transcriptome sequencing (RNA-Seq) and related tools for mining and development of microsatellites in plants.

Genome-wide identification of markers for selecting higher oil content in oil palm

BACKGROUND

Oil palm (Elaeis guineensis, Jacq.) is the most important source of edible oil. The improvement of oil yield is currently slow in conventional breeding programs due to long generation intervals. Marker-assisted selection (MAS) has the potential to accelerate genetic improvement. To identify DNA markers associated with oil content traits for MAS, we performed quantitative trait loci (QTL) mapping using genotyping by sequencing (GBS) in a breeding population derived from a cross between Deli Dura and Ghana Pisifera, containing 153 F₁ trees.

RESULTS

We constructed a high-density linkage map containing 1357 SNPs and 123 microsatellites. The 16 linkage groups (LGs) spanned 1527 cM, with an average marker space of 1.03 cM. One significant and three suggestive QTL for oil to bunch (O/B) and oil to dry mesocarp (O/DM) were mapped on LG1, LG8, and LG10 in a F₁ breeding population, respectively. These QTL explained 7.6-13.3% of phenotypic variance. DNA markers associated with oil content in these QTL were identified. Trees with beneficial genotypes at two QTL for O/B showed an average O/B of 30.97%, significantly (P < 0.01) higher than that of trees without any beneficial QTL genotypes (average O/B of 28.24%). QTL combinations showed that the higher the number of QTL with beneficial genotypes, the higher the resulting average O/B in the breeding population.

CONCLUSIONS

A linkage map with 1480 DNA markers was constructed and used to identify QTL for oil content traits. Pyramiding the identified QTL with beneficial genotypes associated with oil content traits using DNA markers has the potential to accelerate genetic improvement for oil yield in the breeding population of oil palm.