Application of Genomic Big Data in Plant Breeding:Past, Present, and Future

What plant breeding may (and may not) look like in 2050?

At the turn of 2000 many authors envisioned future plant breeding. Twenty years after, which of those authors' visions became reality or not, and which ones may become so in the years to come. After two decades of debates, climate change is a "certainty," food systems shifted from maximizing farm production to reducing environmental impact, and hopes placed into GMOs are mitigated by their low appreciation by consumers. We revise herein how plant breeding may raise or reduce genetic gains based on the breeder's equation. "Accuracy of Selection" has significantly improved by many experimental-scale field and laboratory implements, but also by vulgarizing statistical models, and integrating DNA markers into selection. Pre-breeding has really promoted the increase of useful "Genetic Variance." Shortening "Recycling Time" has seen great progression, to the point that achieving a denominator equal to "1" is becoming a possibility. Maintaining high "Selection Intensity" remains the biggest challenge, since adding any technology results in a higher cost per progeny, despite the steady reduction in cost per datapoint. Furthermore, the concepts of variety and seed enterprise might change with the advent of cheaper genomic tools to monitor their use and the promotion of participatory or citizen science. The technological and societal changes influence the new generation of plant breeders, moving them further away from field work, emphasizing instead the use of genomic-based selection methods relying on big data. We envisage what skills plant breeders of tomorrow might need to address challenges, and whether their time in the field may dwindle.

ChromaX: a fast and scalable breeding program simulator

SUMMARY

ChromaX is a Python library that enables the simulation of genetic recombination, genomic estimated breeding value calculations, and selection processes. By utilizing GPU processing, it can perform these simulations up to two orders of magnitude faster than existing tools with standard hardware. This offers breeders and scientists new opportunities to simulate genetic gain and optimize breeding schemes.

AVAILABILITY AND IMPLEMENTATION

The documentation is available at https://chromax.readthedocs.io. The code is available at https://github.com/kora-labs/chromax.

Machine Learning-Assisted Approaches in Modernized Plant Breeding Programs

In the face of a growing global population, plant breeding is being used as a sustainable tool for increasing food security. A wide range of high-throughput omics technologies have been developed and used in plant breeding to accelerate crop improvement and develop new varieties with higher yield performance and greater resilience to climate changes, pests, and diseases. With the use of these new advanced technologies, large amounts of data have been generated on the genetic architecture of plants, which can be exploited for manipulating the key characteristics of plants that are important for crop improvement. Therefore, plant breeders have relied on high-performance computing, bioinformatics tools, and artificial intelligence (AI), such as machine-learning (ML) methods, to efficiently analyze this vast amount of complex data. The use of bigdata coupled with ML in plant breeding has the potential to revolutionize the field and increase food security. In this review, some of the challenges of this method along with some of the opportunities it can create will be discussed. In particular, we provide information about the basis of bigdata, AI, ML, and their related sub-groups. In addition, the bases and functions of some learning algorithms that are commonly used in plant breeding, three common data integration strategies for the better integration of different breeding datasets using appropriate learning algorithms, and future prospects for the application of novel algorithms in plant breeding will be discussed. The use of ML algorithms in plant breeding will equip breeders with efficient and effective tools to accelerate the development of new plant varieties and improve the efficiency of the breeding process, which are important for tackling some of the challenges facing agriculture in the era of climate change.

High-precision early warning system for rice cadmium accumulation risk assessment

Rapid global industrialization has resulted in widespread cadmium contamination in agricultural soils and products. A considerable proportion of rice consumers are exposed to Cd levels above the provisional safe intake limit, raising widespread environmental concerns on risk management. Therefore, a generalized approach is urgently needed to enable correct evaluation and early warning of cadmium contaminants in rice products. Combining big data and computer science together, this study developed a system named "SMART Cd Early Warning", which integrated 4 modules including genotype-to-phenotype (G2P) modelling, high-throughput sequencing, G2P prediction and rice Cd contamination risk assessment, for rice cadmium accumulation early warning. This system can rapidly assess the risk of rice cadmium accumulation by genotyping leaves at seeding stage. The parameters including statistical methods, population size, training population-testing population ratio, SNP density were assessed to ensure G2P model exhibited superior performance in terms of prediction precision (up to 0.76 ± 0.003) and computing efficiency (within 2 h). In field trials of cadmium-contaminated farmlands in Wenling and Fuyang city, Zhejiang Province, "SMART Cd Early Warning" exhibited superior capability for identification risk rice varieties, suggesting a potential of "SMART Cd Early-Warning system" in OsGCd risk assessment and early warning in the age of smart.

Plant_SNP_TATA_Z-Tester: A Web Service That Unequivocally Estimates the Impact of Proximal Promoter Mutations on Plant Gene Expression

Synthetic targeted optimization of plant promoters is becoming a part of progress in mainstream postgenomic agriculture along with hybridization of cultivated plants with wild congeners, as well as marker-assisted breeding. Therefore, here, for the first time, we compiled all the experimental data—on mutational effects in plant proximal promoters on gene expression—that we could find in PubMed. Some of these datasets cast doubt on both the existence and the uniqueness of the sought solution, which could unequivocally estimate effects of proximal promoter mutation on gene expression when plants are grown under various environmental conditions during their development. This means that the inverse problem under study is ill-posed. Furthermore, we found experimental data on in vitro interchangeability of plant and human TATA-binding proteins allowing the application of Tikhonov’s regularization, making this problem well-posed. Within these frameworks, we created our Web service Plant_SNP_TATA_Z-tester and then determined the limits of its applicability using those data that cast doubt on both the existence and the uniqueness of the sought solution. We confirmed that the effects (of proximal promoter mutations on gene expression) predicted by Plant_SNP_TATA_Z-tester correlate statistically significantly with all the experimental data under study. Lastly, we exemplified an application of Plant_SNP_TATA_Z-tester to agriculturally valuable mutations in plant promoters.

CRISPR/Cas- and Topical RNAi-Based Technologies for Crop Management and Improvement: Reviewing the Risk Assessment and Challenges Towards a More Sustainable Agriculture

Clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated gene (Cas) system and RNA interference (RNAi)-based non-transgenic approaches are powerful technologies capable of revolutionizing plant research and breeding. In recent years, the use of these modern technologies has been explored in various sectors of agriculture, introducing or improving important agronomic traits in plant crops, such as increased yield, nutritional quality, abiotic- and, mostly, biotic-stress resistance. However, the limitations of each technique, public perception, and regulatory aspects are hindering its wide adoption for the development of new crop varieties or products. In an attempt to reverse these mishaps, scientists have been researching alternatives to increase the specificity, uptake, and stability of the CRISPR and RNAi system components in the target organism, as well as to reduce the chance of toxicity in nontarget organisms to minimize environmental risk, health problems, and regulatory issues. In this review, we discuss several aspects related to risk assessment, toxicity, and advances in the use of CRISPR/Cas and topical RNAi-based technologies in crop management and breeding. The present study also highlights the advantages and possible drawbacks of each technology, provides a brief overview of how to circumvent the off-target occurrence, the strategies to increase on-target specificity, the harm/benefits of association with nanotechnology, the public perception of the available techniques, worldwide regulatory frameworks regarding topical RNAi and CRISPR technologies, and, lastly, presents successful case studies of biotechnological solutions derived from both technologies, raising potential challenges to reach the market and being social and environmentally safe.

Quality Evaluation Indices for Soybean Oil in Relation to Cultivar, Application of N Fertiliser and Seed Inoculation with Bradyrhizobium japonicum

Soybean ranks second in production and consumption of vegetable oils worldwide and these are expected to continue to increase. The suitability of soybean oil for specific uses is determined by the fatty acid composition from which a number of indices and indicators can be calculated. The aim of this study was to evaluate the indices of nutritional and health-promoting fat in seeds of soybean cultivars grown in 2016–2019 under the influence of varying doses of N and inoculation with Bradyrhizobium japonicum. Omega 3 and Omega 6, unsaturated fatty acids (UFA), saturated fatty acids (SFA), polyunsaturated fatty acids (PUFA), index of desirable fatty acids (DFA), sum of hypercholesterolemic fatty acids (OFA), index of atherogenicity (AI), index of thrombogenicity (TI), oleic desaturation ratio (ODR), linoleic desaturation ratio (LDR), calculated oxidizability value (COX) and the hypocholesterolemic/hypercholesterolemic ratio (HH), saturation fat index (S/P) and ALA/LA, OL/(LA+ALA) ratios and the consumer index (CI) were included. Fat quality indices for soybean seeds were strongly determined by weather conditions. Seeds of the cv. Aldana contained higher amounts of Omega 6 and featured more favourable MUFA/PUFA and OL/(LA+ALA) ratios, while the seeds of the cv. Annushka had more favourable CI and higher ODR, COX and S/P indices. No important differences were observed regarding the effect of nitrogen dose and seed inoculation on the formation of the DFA, OFA, HH, AI, TI and CI indices. The value of the S/P index suggests that higher nitrogen rates (60 kg∙ha−1) and the lack of inoculation treatment produce seeds with a more favourable dietary fatty acid balance.

Comparison of Magnoliaceae Plastomes: Adding Neotropical Magnolia to the Discussion

Chloroplast genomes are considered to be highly conserved. Nevertheless, differences in their sequences are an important source of phylogenetically informative data. Chloroplast genomes are increasingly applied in evolutionary studies of angiosperms, including Magnoliaceae. Recent studies have focused on resolving the previously debated classification of the family using a phylogenomic approach and chloroplast genome data. However, most Neotropical clades and recently described species have not yet been included in molecular studies. We performed sequencing, assembly, and annotation of 15 chloroplast genomes from Neotropical Magnoliaceae species. We compared the newly assembled chloroplast genomes with 22 chloroplast genomes from across the family, including representatives from each genus and section. Family-wide, the chloroplast genomes presented a length of about 160 kb. The gene content in all species was constant, with 145 genes. The intergenic regions showed a higher level of nucleotide diversity than the coding regions. Differences were higher among genera than within genera. The phylogenetic analysis in Magnolia showed two main clades and corroborated that the current infrageneric classification does not represent natural groups. Although chloroplast genomes are highly conserved in Magnoliaceae, the high level of diversity of the intergenic regions still resulted in an important source of phylogenetically informative data, even for closely related taxa.

Quantitative trait loci mapping of stem sugar content and stem diameter in sorghum recombinant inbred lines using genotyping-by-sequencing

BACKGROUND

Sweet sorghum is an important crop with sugary stem that can mainly be used for syrup, fodder and bio-fuel. Many sugar content QTLs have been discovered from different sources through breeding worldwide. Most of these QTLs are detected using exotic germplasm as a mapping population. This study aimed to detect and map QTLs for stem sugar content and stem diameter targeting Ethiopian recombinant inbred lines of sorghum using genotyping-by-sequencing.

METHODS AND RESULT

Genotyping-by-sequencing and phenotyping using 139 recombinant inbred lines of sorghum as mapping populations were conducted. A total of 1082 polymorphic and high quality SNP markers that are evenly distributed across the ten linkage groups of sorghum were selected to detect and map the trait of interest. A genetic linkage map using 1082 SNP markers was constructed and several QTLs associated with stem sugar content and stem diameter were identified. Phenotypic variation explained by qBrix4-1 and qBrix2-1 ranged from 6.33 to 14%, respectively. Over two seasons, four QTLs for stem sugar content (qBrix1-1, qBrix2-1, qBrix4-1 and qBrix4-2) and three QTLs for stem diameter (qSD1-1, qSD8-1 and qSD9-1) were detected.

CONCLUSION

QTLs that significantly associated with stem sugar content and stem diameter have been detected and mapped. This will help sorghum breeding program to develop superior sweet sorghum varieties through the use of appropriate crop improvement approaches like marker assisted breeding. This ultimately contributes to the current development plan to considerably improve food, feed and bio-fuel supply in developing countries like Ethiopia.

Transcriptional Regulation of Quinoa Seed Quality: Identification of Novel Candidate Genetic Markers for Increased Protein Content

Quinoa (Chenopodium quinoa Willd.) is a crop that has great potential for increased cultivation in diverse climate regions. The seed protein quality obtained from this crop is high concerning the requirements to meet human nutritional needs, but the seed protein content is relatively low if compared to crops such as grain legumes. Increased seed protein content is desirable for increasing the economic viability of this crop in order for it to be used as a protein crop. In this study, we characterized three genotypes of quinoa with different levels of seed protein content. By performing RNA sequencing of developing seeds, we determined the genotype differences in gene expression and identified genetic polymorphisms that could be associated with increased protein content. Storage nutrient analyses of seeds of three quinoa genotypes (Titicaca, Pasankalla, and Regalona) from different ecoregions grown under controlled climate conditions showed that Pasankalla had the highest protein content (20%) and the lowest starch content (46%). Our seed transcriptome analyses revealed highly differentially expressed transcripts (DETs) in Pasankalla as compared to the other genotypes. These DETs encoded functions in sugar transport, starch and protein synthesis, genes regulating embryo size, and seed transcription factors. We selected 60 genes that encode functions in the central carbon metabolism and transcription factors as potential targets for the development of high-precision markers. Genetic polymorphisms, such as single nucleotide polymorphisms (SNPs) and base insertions and deletions (InDels), were found in 19 of the 60 selected genes, which can be further evaluated for the development of genetic markers for high seed protein content in quinoa. Increased cultivation of quinoa can contribute to a more diversified agriculture and support the plant protein diet shift. The identification of quinoa genotypes with contrasting seed quality can help establish a model system that can be used for the identification of precise breeding targets to improve the seed quality of quinoa. The data presented in this study based on nutrient and transcriptome analyses contribute to an enhanced understanding of the genetic regulation of seed quality traits in quinoa and suggest high-precision candidate markers for such traits.