Machine Learning as an Effective Method for Identifying True Single Nucleotide Polymorphisms in Polyploid Plants

Harnessing the power of machine learning for crop improvement and sustainable production

Crop improvement and production domains encounter large amounts of expanding data with multi-layer complexity that forces researchers to use machine-learning approaches to establish predictive and informative models to understand the sophisticated mechanisms underlying these processes. All machine-learning approaches aim to fit models to target data; nevertheless, it should be noted that a wide range of specialized methods might initially appear confusing. The principal objective of this study is to offer researchers an explicit introduction to some of the essential machine-learning approaches and their applications, comprising the most modern and utilized methods that have gained widespread adoption in crop improvement or similar domains. This article explicitly explains how different machine-learning methods could be applied for given agricultural data, highlights newly emerging techniques for machine-learning users, and lays out technical strategies for agri/crop research practitioners and researchers.

Relationships of the wild peanut species, section Arachis: A resource for botanical classification, crop improvement, and germplasm management

PREMISE

Wild species are strategic sources of valuable traits to be introduced into crops through hybridization. For peanut, the 33 currently described wild species in the section Arachis are particularly important because of their sexual compatibility with the domesticated species, Arachis hypogaea. Although numerous wild accessions are carefully preserved in seed banks, their morphological similarities pose challenges to routine classification.

METHODS

Using a high-density array, we genotyped 272 accessions encompassing all diploid species in section Arachis. Detailed relationships between accessions and species were revealed through phylogenetic analyses and interpreted using the expertise of germplasm collectors and curators.

RESULTS

Two main groups were identified: one with A genome species and the other with B, D, F, G, and K genomes. Species groupings generally showed clear boundaries. Structure within groups was informative, for instance, revealing the history of the proto-domesticate A. stenosperma. However, some groupings suggested multiple sibling species. Others were polyphyletic, indicating the need for taxonomic revision. Annual species were better defined than perennial ones, revealing limitations in applying classical and phylogenetic species concepts to the genus. We suggest new species assignments for several accessions.

CONCLUSIONS

Curated by germplasm collectors and curators, this analysis of species relationships lays the foundation for future species descriptions, classification of unknown accessions, and germplasm use for peanut improvement. It supports the conservation and curation of current germplasm, both critical tasks considering the threats to the genus posed by habitat loss and the current restrictions on new collections and germplasm transfer.

The groundnut improvement network for Africa (GINA) germplasm collection: a unique genetic resource for breeding and gene discovery

Cultivated peanut or groundnut (Arachis hypogaea L.) is a grain legume grown in many developing countries by smallholder farmers for food, feed, and/or income. The speciation of the cultivated species, that involved polyploidization followed by domestication, greatly reduced its variability at the DNA level. Mobilizing peanut diversity is a prerequisite for any breeding program for overcoming the main constraints that plague production and for increasing yield in farmer fields. In this study, the Groundnut Improvement Network for Africa assembled a collection of 1,049 peanut breeding lines, varieties, and landraces from 9 countries in Africa. The collection was genotyped with the Axiom_Arachis2 48K SNP array and 8,229 polymorphic single nucleotide polymorphism (SNP) markers were used to analyze the genetic structure of this collection and quantify the level of genetic diversity in each breeding program. A supervised model was developed using dapc to unambiguously assign 542, 35, and 172 genotypes to the Spanish, Valencia, and Virginia market types, respectively. Distance-based clustering of the collection showed a clear grouping structure according to subspecies and market types, with 73% of the genotypes classified as fastigiata and 27% as hypogaea subspecies. Using STRUCTURE, the global structuration was confirmed and showed that, at a minimum membership of 0.8, 76% of the varieties that were not assigned by dapc were actually admixed. This was particularly the case of most of the genotype of the Valencia subgroup that exhibited admixed genetic heritage. The results also showed that the geographic origin (i.e. East, Southern, and West Africa) did not strongly explain the genetic structure. The gene diversity managed by each breeding program, measured by the expected heterozygosity, ranged from 0.25 to 0.39, with the Niger breeding program having the lowest diversity mainly because only lines that belong to the fastigiata subspecies are used in this program. Finally, we developed a core collection composed of 300 accessions based on breeding traits and genetic diversity. This collection, which is composed of 205 genotypes of fastigiata subspecies (158 Spanish and 47 Valencia) and 95 genotypes of hypogaea subspecies (all Virginia), improves the genetic diversity of each individual breeding program and is, therefore, a unique resource for allele mining and breeding.

Machine learning in photosynthesis: Prospects on sustainable crop development

Improving photosynthesis is a promising avenue to increase food security. Studying photosynthetic traits with the aim to improve efficiency has been one of many strategies to increase crop yield but analyzing large data sets presents an ongoing challenge. Machine learning (ML) represents a ubiquitous tool that can provide a more elaborate data analysis. Here we review the application of ML in various domains of photosynthetic research, as well as in photosynthetic pigment studies. We highlight how correlating hyperspectral data with photosynthetic parameters to improve crop yield could be achieved through various ML algorithms. We also propose strategies to employ ML in promoting photosynthetic pigment research for furthering crop yield.

Demographic history inference and the polyploid continuum

Polyploidy is an important generator of evolutionary novelty across diverse groups in the Tree of Life, including many crops. However, the impact of whole-genome duplication depends on the mode of formation: doubling within a single lineage (autopolyploidy) versus doubling after hybridization between two different lineages (allopolyploidy). Researchers have historically treated these two scenarios as completely separate cases based on patterns of chromosome pairing, but these cases represent ideals on a continuum of chromosomal interactions among duplicated genomes. Understanding the history of polyploid species thus demands quantitative inferences of demographic history and rates of exchange between subgenomes. To meet this need, we developed diffusion models for genetic variation in polyploids with subgenomes that cannot be bioinformatically separated and with potentially variable inheritance patterns, implementing them in the dadi software. We validated our models using forward SLiM simulations and found that our inference approach is able to accurately infer evolutionary parameters (timing, bottleneck size) involved with the formation of auto- and allotetraploids, as well as exchange rates in segmental allotetraploids. We then applied our models to empirical data for allotetraploid shepherd's purse (Capsella bursa-pastoris), finding evidence for allelic exchange between the subgenomes. Taken together, our model provides a foundation for demographic modeling in polyploids using diffusion equations, which will help increase our understanding of the impact of demography and selection in polyploid lineages.

Marker-assisted introgression of wild chromosome segments conferring resistance to fungal foliar diseases into peanut (Arachis hypogaea L.)

INTRODUCTION

Fungal foliar diseases can severely affect the productivity of the peanut crop worldwide. Late leaf spot is the most frequent disease and a major problem of the crop in Brazil and many other tropical countries. Only partial resistance to fungal diseases has been found in cultivated peanut, but high resistances have been described on the secondary gene pool.

METHODS

To overcome the known compatibility barriers for the use of wild species in peanut breeding programs, we used an induced allotetraploid (Arachis stenosperma × A. magna)^4x, as a donor parent, in a successive backcrossing scheme with the high-yielding Brazilian cultivar IAC OL 4. We used microsatellite markers associated with late leaf spot and rust resistance for foreground selection and high-throughput SNP genotyping for background selection.

RESULTS

With these tools, we developed agronomically adapted lines with high cultivated genome recovery, high-yield potential, and wild chromosome segments from both A. stenosperma and A. magna conferring high resistance to late leaf spot and rust. These segments include the four previously identified as having QTLs (quantitative trait loci) for resistance to both diseases, which could be confirmed here, and at least four additional QTLs identified by using mapping populations on four generations.

DISCUSSION

The introgression germplasm developed here will extend the useful genetic diversity of the primary gene pool by providing novel wild resistance genes against these two destructive peanut diseases.

Genome-wide association studies reveal novel loci for resistance to groundnut rosette disease in the African core groundnut collection

KEY MESSAGE

We identified markers associated with GRD resistance after screening an Africa-wide core collection across three seasons in Uganda Groundnut is cultivated in several African countries where it is a major source of food, feed and income. One of the major constraints to groundnut production in Africa is groundnut rosette disease (GRD), which is caused by a complex of three agents: groundnut rosette assistor luteovirus, groundnut rosette umbravirus and its satellite RNA. Despite several years of breeding for GRD resistance, the genetics of the disease is not fully understood. The objective of the current study was to use the African core collection to establish the level of genetic variation in their response to GRD, and to map genomic regions responsible for the observed resistance. The African groundnut core genotypes were screened across two GRD hotspot locations in Uganda (Nakabango and Serere) for 3 seasons. The Area Under Disease Progress Curve combined with 7523 high quality SNPs were analyzed to establish marker-trait associations (MTAs). Genome-Wide Association Studies based on Enriched Compressed Mixed Linear Model detected 32 MTAs at Nakabango: 21 on chromosome A04, 10 on B04 and 1 on B08. Two of the significant markers were localised on the exons of a putative TIR-NBS-LRR disease resistance gene on chromosome A04. Our results suggest the likely involvement of major genes in the resistance to GRD but will need to be further validated with more comprehensive phenotypic and genotypic datasets. The markers identified in the current study will be developed into routine assays and validated for future genomics-assisted selection for GRD resistance in groundnut.

Genome-wide association mapping for LLS resistance in a MAGIC population of groundnut (Arachis hypogaea L.)

The identified 30 functional nucleotide polymorphisms or genic SNP markers would offer essential information for marker-assisted breeding in groundnut. A genome-wide association study (GWAS) on component traits of LLS resistance in an eight-way multiparent advance generation intercross (MAGIC) population of groundnut in the field and in a light chamber (controlled conditions) was performed via an Affymetrix 48 K single-nucleotide polymorphism (SNP) 'Axiom Arachis' array. Multiparental populations with high-density genotyping enable the detection of novel alleles. In total, five quantitative trait loci (QTLs) with marker - log10(p value) scores ranging from 4.25 to 13.77 for the incubation period (IP) and six QTLs with marker - log10(p value) scores ranging from 4.33 to 10.79 for the latent period (LP) were identified across the A- and B-subgenomes. A total of 62 markers‒trait associations (MTAs) were identified across the A- and B-subgenomes. Markers for LLS scores and the area under the disease progression curve (AUDPC) recorded for plants in the light chamber and under field conditions presented - log10 (p value) scores ranging from 4.22 to 27.30. The highest number of MTAs (six) was identified on chromosomes A05, B07 and B09. Out of a total of 73 MTAs, 37 and 36 MTAs were detected in subgenomes A and B, respectively. Taken together, these results suggest that both subgenomes have equal potential genomic regions contributing to LLS resistance. A total of 30 functional nucleotide polymorphisms or genic SNP markers were detected, among which eight genes were found to encode leucine-rich repeat (LRR) receptor-like protein kinases and putative disease resistance proteins. These important SNPs can be used in breeding programmes for the development of cultivars with improved disease resistance.

Innovative Advances in Plant Genotyping

Over the past decade, advances in plant genotyping have been critical in enabling the identification of genetic diversity, in understanding evolution, and in dissecting important traits in both crops and native plants. The widespread popularity of single-nucleotide polymorphisms (SNPs) has prompted significant improvements to SNP-based genotyping, including SNP arrays, genotyping by sequencing, and whole-genome resequencing. More recent approaches, including genotyping structural variants, utilizing pangenomes to capture species-wide genetic diversity and exploiting machine learning to analyze genotypic data sets, are pushing the boundaries of what plant genotyping can offer. In this chapter, we highlight these innovations and discuss how they will accelerate and advance future genotyping efforts.

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.

A first insight into the genetics of maturity trait in Runner × Virginia types peanut background

'Runner' and 'Virginia', the two main market types of Arachis hypogaea subspecies hypogaea, differ in several agricultural and industrial characteristics. One such trait is time to maturation (TTM), contributing to the specific environmental adaptability of each subspecies. However, little is known regarding TTM's genetic and molecular control in peanut in general, and particularly in the Runner/Virginia background. Here, a recombinant inbred line population, originating from a cross between an early-maturing Virginia and a late-maturing Runner type, was used to detect quantitative trait loci (QTL) for maturity. An Arachis SNP-array was used for genotyping, and a genetic map with 1425 SNP loci spanning 24 linkage groups was constructed. Six significant QTLs were identified for the maturity index (MI) trait on chromosomes A04, A08, B02 and B04. Two sets of stable QTLs in the same loci were identified, namely qMIA04a,b and qMIA08_2a,b with 11.5%, 8.1% and 7.3%, 8.2% of phenotypic variation explained respectively in two environments. Interestingly, one consistent QTL, qMIA04a,b, overlapped with the previously reported QTL in a Virginia × Virginia population having the same early-maturing parent ('Harari') in common. The information and materials generated here can promote informed targeting of peanut idiotypes by indirect marker-assisted selection.

Early detection of Alzheimer's disease using single nucleotide polymorphisms analysis based on gradient boosting tree

Alzheimer's disease (AD) is a degenerative disorder that attacks nerve cells in the brain. AD leads to memory loss and cognitive & intellectual impairments that can influence social activities and decision-making. The most common type of human genetic variation is single nucleotide polymorphisms (SNPs). SNPs are beneficial markers of complex gene-disease. Many common and serious diseases, such as AD, have associated SNPs. Detection of SNP biomarkers linked with AD could help in the early prediction and diagnosis of this disease. The main objective of this paper is to predict and diagnose AD based on SNPs biomarkers with high classification accuracy in the early stages. One of the most concerning problems is the high number of features. Thus, the paper proposes a comprehensive framework for early AD detection and detecting the most significant genes based on SNPs analysis. Usage of machine learning (ML) techniques to identify new biomarkers of AD is also suggested. In the proposed system, two feature selection techniques are separately checked: the information gain filter and Boruta wrapper. The two feature selection techniques were used to select the most significant genes related to AD in this system. Filter methods measure the relevance of features by their correlation with dependent variables, while wrapper methods measure the usefulness of a subset of features by training a model on it. Gradient boosting tree (GBT) has been applied on all AD genetic data of neuroimaging initiative phase 1 (ADNI-1) and Whole-Genome Sequencing (WGS) datasets by using two feature selection techniques. In the whole-genome approach ADNI-1, results revealed that the GBT learning algorithm scored an overall accuracy of 99.06% in the case of using Boruta feature selection. Using information gain feature selection, the proposed system achieved an average accuracy of 94.87%. The results show that the proposed system is preferable for the early detection of AD. Also, the results revealed that the Boruta wrapper feature selection is superior to the information gain filter technique.

Identification of a major locus for flowering pattern sheds light on plant architecture diversification in cultivated peanut

A major gene controls flowering pattern in peanut, possibly encoding a TFL1-like. It was subjected to gain/loss events of a deletion and changes in mRNA expression levels, partly explaining the evolution of flowering pattern in Arachis. Flowering pattern (FP) is a major characteristic differentiating the two subspecies of cultivated peanut (Arachis hypogaea L.). Subsp. fastigiata possessing flowers on the mainstem (MSF) and a sequential FP, whereas subsp. hypogaea lacks MSF and exhibits an alternate FP. FP is considered the main contributor to plant adaptability, and evidence indicates that its diversification occurred during the several thousand years of domestication. However, the genetic mechanism that controls FP in peanut is unknown. We investigated the genetics of FP in a recombinant inbred population, derivatives of an A. hypogaea by A. fastigiata cross. Lines segregated 1:1 for FP, indicating a single gene effect. Using Axiom_Arachis2 SNP-array, FP was mapped to a small segment in chromosome B02, wherein a Terminal Flowering 1-like (AhTFL1) gene with a 1492 bp deletion was found in the fastigiata line, leading to a truncated protein. Remapping FP in the RIL population with the AhTFL1 indel as a marker increased the LOD score from 53.3 to 158.8 with no recombination in the RIL population. The same indel was found co-segregating with the phenotype in two independent EMS-mutagenized M2 families, suggesting a hotspot for gene conversion. Also, AhTFL1 was significantly less expressed in the fastigiata line compared to hypogaea and in flowering than non-flowering branches. Sequence analysis of the AhTFL1 in peanut world collections indicated significant conservation, supporting the putative role of AhTFL1 in peanut speciation during domestication and modern cultivation.

Development and Genetic Characterization of Peanut Advanced Backcross Lines That Incorporate Root-Knot Nematode Resistance From Arachis stenosperma

Crop wild species are increasingly important for crop improvement. Peanut (Arachis hypogaea L.) wild relatives comprise a diverse genetic pool that is being used to broaden its narrow genetic base. Peanut is an allotetraploid species extremely susceptible to peanut root-knot nematode (PRKN) Meloidogyne arenaria. Current resistant cultivars rely on a single introgression for PRKN resistance incorporated from the wild relative Arachis cardenasii, which could be overcome as a result of the emergence of virulent nematode populations. Therefore, new sources of resistance may be needed. Near-immunity has been found in the peanut wild relative Arachis stenosperma. The two loci controlling the resistance, present on chromosomes A02 and A09, have been validated in tetraploid lines and have been shown to reduce nematode reproduction by up to 98%. To incorporate these new resistance QTL into cultivated peanut, we used a marker-assisted backcrossing approach, using PRKN A. stenosperma-derived resistant lines as donor parents. Four cycles of backcrossing were completed, and SNP assays linked to the QTL were used for foreground selection. In each backcross generation seed weight, length, and width were measured, and based on a statistical analysis we observed that only one generation of backcrossing was required to recover the elite peanut's seed size. A populating of 271 BC3F1 lines was genome-wide genotyped to characterize the introgressions across the genome. Phenotypic information for leaf spot incidence and domestication traits (seed size, fertility, plant architecture, and flower color) were recorded. Correlations between the wild introgressions in different chromosomes and the phenotypic data allowed us to identify candidate regions controlling these domestication traits. Finally, PRKN resistance was validated in BC3F3 lines. We observed that the QTL in A02 and/or large introgression in A09 are needed for resistance. This present work represents an important step toward the development of new high-yielding and nematode-resistant peanut cultivars.

High-efficiency and high-fidelity ssDNA circularisation via the pairing of five 3′-terminal bases to assist LR-LAMP for the genotyping of single-nucleotide polymorphisms

A high-fidelity ssDNA circularisation via the pairing of five 3′-terminal bases was developed to assist LR-LAMP for genotyping of SNPs.

Bluster or Lustre: Can AI Improve Crops and Plant Health?

In a changing climate where future food security is a growing concern, researchers are exploring new methods and technologies in the effort to meet ambitious crop yield targets. The application of Artificial Intelligence (AI) including Machine Learning (ML) methods in this area has been proposed as a potential mechanism to support this. This review explores current research in the area to convey the state-of-the-art as to how AI/ML have been used to advance research, gain insights, and generally enable progress in this area. We address the question-Can AI improve crops and plant health? We further discriminate the bluster from the lustre by identifying the key challenges that AI has been shown to address, balanced with the potential issues with its usage, and the key requisites for its success. Overall, we hope to raise awareness and, as a result, promote usage, of AI related approaches where they can have appropriate impact to improve practices in agricultural and plant sciences.

Legacy genetics of Arachis cardenasii in the peanut crop shows the profound benefits of international seed exchange

The narrow genetics of most crops is a fundamental vulnerability to food security. This makes wild crop relatives a strategic resource of genetic diversity that can be used for crop improvement and adaptation to new agricultural challenges. Here, we uncover the contribution of one wild species accession, Arachis cardenasii GKP 10017, to the peanut crop (Arachis hypogaea) that was initiated by complex hybridizations in the 1960s and propagated by international seed exchange. However, until this study, the global scale of the dispersal of genetic contributions from this wild accession had been obscured by the multiple germplasm transfers, breeding cycles, and unrecorded genetic mixing between lineages that had occurred over the years. By genetic analysis and pedigree research, we identified A. cardenasii-enhanced, disease-resistant cultivars in Africa, Asia, Oceania, and the Americas. These cultivars provide widespread improved food security and environmental and economic benefits. This study emphasizes the importance of wild species and collaborative networks of international expertise for crop improvement. However, it also highlights the consequences of the implementation of a patchwork of restrictive national laws and sea changes in attitudes regarding germplasm that followed in the wake of the Convention on Biological Diversity. Today, the botanical collections and multiple seed exchanges which enable benefits such as those revealed by this study are drastically reduced. The research reported here underscores the vital importance of ready access to germplasm in ensuring long-term world food security.

Genetic variations analysis for complex brain disease diagnosis using machine learning techniques: opportunities and hurdles

BACKGROUND AND OBJECTIVES

This paper presents an in-depth review of the state-of-the-art genetic variations analysis to discover complex genes associated with the brain's genetic disorders. We first introduce the genetic analysis of complex brain diseases, genetic variation, and DNA microarrays. Then, the review focuses on available machine learning methods used for complex brain disease classification. Therein, we discuss the various datasets, preprocessing, feature selection and extraction, and classification strategies. In particular, we concentrate on studying single nucleotide polymorphisms (SNP) that support the highest resolution for genomic fingerprinting for tracking disease genes. Subsequently, the study provides an overview of the applications for some specific diseases, including autism spectrum disorder, brain cancer, and Alzheimer's disease (AD). The study argues that despite the significant recent developments in the analysis and treatment of genetic disorders, there are considerable challenges to elucidate causative mutations, especially from the viewpoint of implementing genetic analysis in clinical practice. The review finally provides a critical discussion on the applicability of genetic variations analysis for complex brain disease identification highlighting the future challenges.

METHODS

We used a methodology for literature surveys to obtain data from academic databases. Criteria were defined for inclusion and exclusion. The selection of articles was followed by three stages. In addition, the principal methods for machine learning to classify the disease were presented in each stage in more detail.

RESULTS

It was revealed that machine learning based on SNP was widely utilized to solve problems of genetic variation for complex diseases related to genes.

CONCLUSIONS

Despite significant developments in genetic diseases in the past two decades of the diagnosis and treatment, there is still a large percentage in which the causative mutation cannot be determined, and a final genetic diagnosis remains elusive. So, we need to detect the variations of the genes related to brain disorders in the early disease stages.

Spontaneous generation of diversity in Arachis neopolyploids (Arachis ipaënsis × Arachis duranensis)4x replays the early stages of peanut evolution

Polyploidy is considered a driving force in plant evolution and domestication. Although in the genus Arachis, several diploid species were traditionally cultivated for their seeds, only the allotetraploid peanut Arachis hypogaea became the successful, widely spread legume crop. This suggests that polyploidy has given selective advantage for domestication of peanut. Here, we study induced allotetraploid (neopolyploid) lineages obtained from crosses between the peanut's progenitor species, Arachis ipaënsis and Arachis duranensis, at earlier and later generations. We observed plant morphology, seed dimensions, and genome structure using cytogenetics (FISH and GISH) and SNP genotyping. The neopolyploid lineages show more variable fertility and seed morphology than their progenitors and cultivated peanut. They also showed sexual and somatic genome instability, evidenced by changes of number of detectable 45S rDNA sites, and extensive homoeologous recombination indicated by mosaic patterns of chromosomes and changes in dosage of SNP alleles derived from the diploid species. Genome instability was not randomly distributed across the genome: the more syntenic chromosomes, the higher homoeologous recombination. Instability levels are higher than observed on peanut lines, therefore it is likely that more unstable lines tend to perish. We conclude that early stages of the origin and domestication of the allotetraploid peanut involved two genetic bottlenecks: the first, common to most allotetraploids, is composed of the rare hybridization and polyploidization events, followed by sexual reproductive isolation from its wild diploid relatives. Here, we suggest a second bottleneck: the survival of the only very few lineages that had stronger mechanisms for limiting genomic instability.

Homoeologous recombination is recurrent in the nascent synthetic allotetraploid Arachis ipaënsis × Arachis correntina4x and its derivatives

Genome instability in newly synthesized allotetraploids of peanut has breeding implications that have not been fully appreciated. Synthesis of wild species-derived neo-tetraploids offers the opportunity to broaden the gene pool of peanut; however, the dynamics among the newly merged genomes creates predictable and unpredictable variation. Selfed progenies from the neo-tetraploid Arachis ipaënsis × Arachis correntina (A. ipaënsis × A. correntina)4x and F1 hybrids and F2 progenies from crosses between A. hypogaea × [A. ipaënsis × A. correntina]4x were genotyped by the Axiom Arachis 48 K SNP array. Homoeologous recombination between the A. ipaënsis and A. correntina derived subgenomes was observed in the S0 generation. Among the S1 progenies, these recombined segments segregated and new events of homoeologous recombination emerged. The genomic regions undergoing homoeologous recombination segregated mostly disomically in the F2 progenies from A. hypogaea × [A. ipaënsis × A. correntina]4x crosses. New homoeologous recombination events also occurred in the F2 population, mostly found on chromosomes 03, 04, 05, and 06. From the breeding perspective, these phenomena offer both possibilities and perils; recombination between genomes increases genetic diversity, but genome instability could lead to instability of traits or even loss of viability within lineages.

Advances in Cereal Crop Genomics for Resilience under Climate Change

Adapting to climate change, providing sufficient human food and nutritional needs, and securing sufficient energy supplies will call for a radical transformation from the current conventional adaptation approaches to more broad-based and transformative alternatives. This entails diversifying the agricultural system and boosting productivity of major cereal crops through development of climate-resilient cultivars that can sustainably maintain higher yields under climate change conditions, expanding our focus to crop wild relatives, and better exploitation of underutilized crop species. This is facilitated by the recent developments in plant genomics, such as advances in genome sequencing, assembly, and annotation, as well as gene editing technologies, which have increased the availability of high-quality reference genomes for various model and non-model plant species. This has necessitated genomics-assisted breeding of crops, including underutilized species, consequently broadening genetic variation of the available germplasm; improving the discovery of novel alleles controlling important agronomic traits; and enhancing creation of new crop cultivars with improved tolerance to biotic and abiotic stresses and superior nutritive quality. Here, therefore, we summarize these recent developments in plant genomics and their application, with particular reference to cereal crops (including underutilized species). Particularly, we discuss genome sequencing approaches, quantitative trait loci (QTL) mapping and genome-wide association (GWAS) studies, directed mutagenesis, plant non-coding RNAs, precise gene editing technologies such as CRISPR-Cas9, and complementation of crop genotyping by crop phenotyping. We then conclude by providing an outlook that, as we step into the future, high-throughput phenotyping, pan-genomics, transposable elements analysis, and machine learning hold much promise for crop improvements related to climate resilience and nutritional superiority.

Identification of consistent QTL for time to maturation in Virginia-type Peanut (Arachis hypogaea L.)

BACKGROUND

Time-to-maturation (TTM) is an important trait contributing to adaptability, yield and quality in peanut (Arachis hypogaea L). Virginia market-type peanut belongs to the late-maturing A. hypogaea subspecies with considerable variation in TTM within this market type. Consequently, planting and harvesting schedule of peanut cultivars, including Virginia market-type, need to be optimized to maximize yield and grade. Little is known regarding the genetic control of TTM in peanut due to the challenge of phenotyping and limited DNA polymorphism. Here, we investigated the genetic control of TTM within the Virginia market-type peanut using a SNP-based high-density genetic map. A recombinant inbred line (RIL) population, derived from a cross between two Virginia-type cultivars 'Hanoch' and 'Harari' with contrasting TTM (12-15 days on multi-years observations), was phenotyped in the field for 2 years following a randomized complete block design. TTM was estimated by maturity index (MI). Other agronomic traits like harvest index (HI), branching habit (BH) and shelling percentage (SP) were recorded as well.

RESULTS

MI was highly segregated in the population, with 13.3-70.9% and 28.4-80.2% in years 2018 and 2019. The constructed genetic map included 1833 SNP markers distributed on 24 linkage groups, covering a total map distance of 1773.5 cM corresponding to 20 chromosomes on the tetraploid peanut genome with 1.6 cM mean distance between the adjacent markers. Thirty QTL were identified for all measured traits. Among the four QTL regions for MI, two consistent QTL regions (qMIA04a,b and qMIB03a,b) were identified on chromosomes A04 (118680323-125,599,371; 6.9Mbp) and B03 (2839591-4,674,238; 1.8Mbp), with LOD values of 5.33-6.45 and 5-5.35 which explained phenotypic variation of 9.9-11.9% and 9.3-9.9%, respectively. QTL for HI were found to share the same loci as MI on chromosomes B03, B05, and B06, demonstrating the possible pleiotropic effect of HI on TTM. Significant but smaller effects on MI were detected for BH, pod yield and SP.

CONCLUSIONS

This study identified consistent QTL regions conditioning TTM for Virginia market-type peanut. The information and materials generated here can be used to further develop molecular markers to select peanut idiotypes suitable for diverse growth environments.

Machine learning, transcriptome, and genotyping chip analyses provide insights into SNP markers identifying flower color in Platycodon grandiflorus

Bellflower is an edible ornamental gardening plant in Asia. For predicting the flower color in bellflower plants, a transcriptome-wide approach based on machine learning, transcriptome, and genotyping chip analyses was used to identify SNP markers. Six machine learning methods were deployed to explore the classification potential of the selected SNPs as features in two datasets, namely training (60 RNA-Seq samples) and validation (480 Fluidigm chip samples). SNP selection was performed in sequential order. Firstly, 96 SNPs were selected from the transcriptome-wide SNPs using the principal compound analysis (PCA). Then, 9 among 96 SNPs were later identified using the Random forest based feature selection method from the Fluidigm chip dataset. Among six machines, the random forest (RF) model produced higher classification performance than the other models. The 9 SNP marker candidates selected for classifying the flower color classification were verified using the genomic DNA PCR with Sanger sequencing. Our results suggest that this methodology could be used for future selection of breeding traits even though the plant accessions are highly heterogeneous.

Brazilian Kayabi Indian accessions of peanut, Arachis hypogaea (Fabales, Fabaceae): origin, diversity and evolution

Peanut is a crop of the Kayabi tribe, inhabiting the Xingu Indigenous Park, Brazil. Morphological analysis of Xingu accessions showed variation exceeding that described for cultivated peanuts. This raised questions as to the origin of the Xingu accessions: are they derived from different species, or is their diversity a result of different evolutionary and selection processes? To answer these questions, cytogenetic and genotyping analyses were conducted. The karyotypes of Xingu accessions analyzed are very similar to each other, to an A. hypogaea subsp. fastigiata accession and to the wild allotetraploid A. monticola. The accessions share the number and general morphology of the chromosomes; DAPI+ bands; 5S and 45S rDNA loci distribution and a high genomic affinity with A. duranensis and A. ipaënsis genomic probes. However, the number of CMA3+ bands differs from those determined for A. hypogaea and A. monticola, which are also different from each other. SNP genotyping grouped all Arachis allotetraploids into four taxonomic groups: Xingu accessions were closer to A. monticola and A. hypogaea subsp. hypogaea. Our data suggests that the morphological diversity within these accessions is not associated with a different origin and can be attributed to morphological plasticity and different selection by the Indian tribes.

Genotypic Characterization of the U.S. Peanut Core Collection

Cultivated peanut (Arachis hypogaea) is an important oil, food, and feed crop worldwide. The USDA peanut germplasm collection currently contains 8,982 accessions. In the 1990s, 812 accessions were selected as a core collection on the basis of phenotype and country of origin. The present study reports genotyping results for the entire available core collection. Each accession was genotyped with the Arachis_Axiom2 SNP array, yielding 14,430 high-quality, informative SNPs across the collection. Additionally, a subset of 253 accessions was replicated, using between two and five seeds per accession, to assess heterogeneity within these accessions. The genotypic diversity of the core is mostly captured in five genotypic clusters, which have some correspondence with botanical variety and market type. There is little genetic clustering by country of origin, reflecting peanut’s rapid global dispersion in the 18^th and 19^th centuries. A genetic cluster associated with the hypogaea/aequatoriana/peruviana varieties, with accessions coming primarily from Bolivia, Peru, and Ecuador, is consistent with these having been the earliest landraces. The genetics, phenotypic characteristics, and biogeography are all consistent with previous reports of tetraploid peanut originating in Southeast Bolivia. Analysis of the genotype data indicates an early genetic radiation, followed by regional distribution of major genetic classes through South America, and then a global dissemination that retains much of the early genetic diversity in peanut. Comparison of the genotypic data relative to alleles from the diploid progenitors also indicates that subgenome exchanges, both large and small, have been major contributors to the genetic diversity in peanut.

Role of NGS and SNP genotyping methods in sugarcane improvement programs

Sugarcane (Saccharum spp.) is one of the most economically significant crops because of its high sucrose content and it is a promising biomass feedstock for biofuel production. Sugarcane genome sequencing and analysis is a difficult task due to its heterozygosity and polyploidy. Long sequence read technologies, PacBio Single-Molecule Real-Time (SMRT) sequencing, the Illumina TruSeq, and the Oxford Nanopore sequencing could solve the problem of genome assembly. On the applications side, next generation sequencing (NGS) technologies played a major role in the discovery of single nucleotide polymorphism (SNP) and the development of low to high throughput genotyping platforms. The two mainstream high throughput genotyping platforms are the SNP microarray and genotyping by sequencing (GBS). This paper reviews the NGS in sugarcane genomics, genotyping methodologies, and the choice of these methods. Array-based SNP genotyping is robust, provides consistent SNPs, and relatively easier downstream data analysis. The GBS method identifies large scale SNPs across the germplasm. A combination of targeted GBS and array-based genotyping methods should be used to increase the accuracy of genomic selection and marker-assisted breeding.

Genomic diversifications of five Gossypium allopolyploid species and their impact on cotton improvement

Polyploidy is an evolutionary innovation for many animals and all flowering plants, but its impact on selection and domestication remains elusive. Here we analyze genome evolution and diversification for all five allopolyploid cotton species, including economically important Upland and Pima cottons. Although these polyploid genomes are conserved in gene content and synteny, they have diversified by subgenomic transposon exchanges that equilibrate genome size, evolutionary rate heterogeneities and positive selection between homoeologs within and among lineages. These differential evolutionary trajectories are accompanied by gene-family diversification and homoeolog expression divergence among polyploid lineages. Selection and domestication drive parallel gene expression similarities in fibers of two cultivated cottons, involving coexpression networks and N⁶-methyladenosine RNA modifications. Furthermore, polyploidy induces recombination suppression, which correlates with altered epigenetic landscapes and can be overcome by wild introgression. These genomic insights will empower efforts to manipulate genetic recombination and modify epigenetic landscapes and target genes for crop improvement.

Sequencing and genomic diversification of five allopolyploid cotton species provide insights into polyploid genome evolution and epigenetic landscapes for cotton improvement.

Comparison of SNP Calling Pipelines and NGS Platforms to Predict the Genomic Regions Harboring Candidate Genes for Nodulation in Cultivated Peanut

Cultivated peanut (Arachis hypogaea L.) forms root nodules to enable a symbiotic relationship with rhizobia for biological nitrogen fixation. To understand the genetic factors of peanut nodulation, it is fundamental to genetically map and clone the genes involved in nodulation. For genetic mapping, high throughput genotyping with a large number of polymorphic markers is critical. In this study, two sets of sister recombinant inbred lines (RILs), each containing a nodulating (Nod+) and non-nodulating (Nod-) line, and their Nod+ parental lines were extensively genotyped. Several next generation sequencing (NGS) methods including target enrichment sequencing (TES), RNA-sequencing (RNA-seq), genotyping by sequencing (GBS), and the 48K Axiom Arachis2 SNP array, and various analysis pipelines were applied to identify single nucleotide polymorphisms (SNP) among the two sets of RILs and their parents. TES revealed the largest number of homozygous SNPs (15,947) between the original parental lines, followed by the Axiom Arachis2 SNP array (1,887), RNA-seq (1,633), and GBS (312). Among the five SNP analysis pipelines applied, the alignment to A/B genome followed by HAPLOSWEEP revealed the largest number of homozygous SNPs and highest concordance rate (79%) with the array. A total of 222 and 1,200 homozygous SNPs were polymorphic between the Nod+ and Nod− sister RILs and between their parents, respectively. A graphical genotype map of the sister RILs was constructed with these SNPs, which demonstrated the candidate genomic regions harboring genes controlling nodulation across the whole genome. Results of this study mainly provide the pros and cons of NGS and SNP genotyping platforms for genetic mapping in peanut, and also provide potential genetic resources to narrow down the genomic regions controlling peanut nodulation, which would lay the foundation for gene cloning and improvement of nitrogen fixation in peanut.