Structural variants in 3000 rice genomes

A comprehensive benchmark of graph-based genetic variant genotyping algorithms on plant genomes for creating an accurate ensemble pipeline

BACKGROUND

Although sequencing technologies have boosted the measurement of the genomic diversity of plant crops, it remains challenging to accurately genotype millions of genetic variants, especially structural variations, with only short reads. In recent years, many graph-based variation genotyping methods have been developed to address this issue and tested for human genomes. However, their performance in plant genomes remains largely elusive. Furthermore, pipelines integrating the advantages of current genotyping methods might be required, considering the different complexity of plant genomes.

RESULTS

Here we comprehensively evaluate eight such genotypers in different scenarios in terms of variant type and size, sequencing parameters, genomic context, and complexity, as well as graph size, using both simulated and real data sets from representative plant genomes. Our evaluation reveals that there are still great challenges to applying existing methods to plants, such as excessive repeats and variants or high resource consumption. Therefore, we propose a pipeline called Ensemble Variant Genotyper (EVG) that can achieve better genotyping performance in almost all experimental scenarios and comparably higher genotyping recall and precision even using 5× reads. Furthermore, we demonstrate that EVG is more robust with an increasing number of graphed genomes, especially for insertions and deletions.

CONCLUSIONS

Our study will provide new insights into the development and application of graph-based genotyping algorithms. We conclude that EVG provides an accurate, unbiased, and cost-effective way for genotyping both small and large variations and will be potentially used in population-scale genotyping for large, repetitive, and heterozygous plant genomes.

New whole-genome alignment tools are needed for tapping into plant diversity

Genome alignment is one of the most foundational methods for genome sequence studies. With rapid advances in sequencing and assembly technologies, these newly assembled genomes present challenges for alignment tools to meet the increased complexity and scale. Plant genome alignment is technologically challenging because of frequent whole-genome duplications (WGDs) as well as chromosome rearrangements and fractionation, high nucleotide diversity, widespread structural variation, and high transposable element (TE) activity causing large proportions of repeat elements. We summarize classical pairwise and multiple genome alignment (MGA) methods, and highlight techniques that are widely used or are being developed by the plant research community. We also outline the remaining challenges for precise genome alignment and the interpretation of alignment results in plants.

The advent of plant cells in bioreactors

Ever since agriculture started, plants have been bred to obtain better yields, better fruits, or sustainable products under uncertain biotic and abiotic conditions. However, a new way to obtain products from plant cells emerged with the development of recombinant DNA technologies. This led to the possibility of producing exogenous molecules in plants. Furthermore, plant chemodiversity has been the main source of pharmacological molecules, opening a field of plant biotechnology directed to produce high quality plant metabolites. The need for different products by the pharma, cosmetics agriculture and food industry has pushed again to develop new procedures. These include cell production in bioreactors. While plant tissue and cell culture are an established technology, beginning over a hundred years ago, plant cell cultures have shown little impact in biotechnology projects, compared to bacterial, yeasts or animal cells. In this review we address the different types of bioreactors that are currently used for plant cell production and their usage for quality biomolecule production. We make an overview of Nicotiana tabacum, Nicotiana benthamiana, Oryza sativa, Daucus carota, Vitis vinifera and Physcomitrium patens as well-established models for plant cell culture, and some species used to obtain important metabolites, with an insight into the type of bioreactor and production protocols.

Genomic variations combined with epigenetic modifications rewire open chromatin in rice

Cis-regulatory elements (CREs) fine-tune gene transcription in eukaryotes. CREs with sequence variations play vital roles in driving plant or crop domestication. However, how global sequence and structural variations (SVs) are responsible for multilevel changes between indica and japonica rice (Oryza sativa) is still not fully elucidated. To address this, we conducted multiomic studies using MNase hypersensitivity sequencing (MH-seq) in combination with RNA sequencing (RNA-seq), chromatin immunoprecipitation sequencing (ChIP-seq), and bisulfite sequencing (BS-seq) between the japonica rice variety Nipponbare (NIP) and indica rice variety 93-11. We found that differential MNase hypersensitive sites (MHSs) exhibited some distinct intrinsic genomic sequence features between NIP and 93-11. Notably, through MHS-genome-wide association studies (GWAS) integration, we found that key sequence variations may be associated with differences of agronomic traits between NIP and 93-11, which is partly achieved by MHSs harboring CREs. In addition, SV-derived differential MHSs caused by transposable element (TE) insertion, especially by noncommon TEs among rice varieties, were associated with genes with distinct functions, indicating that TE-driven gene neo- or subfunctionalization is mediated by changes of chromatin openness. This study thus provides insights into how sequence and genomic SVs control agronomic traits of NIP and 93-11; it also provides genome-editing targets for molecular breeding aiming at improving favorable agronomic properties.

Accurate prediction of quantitative traits with failed SNP calls in canola and maize

In modern plant breeding, genomic selection is becoming the gold standard to select superior genotypes in large breeding populations that are only partially phenotyped. Many breeding programs commonly rely on single-nucleotide polymorphism (SNP) markers to capture genome-wide data for selection candidates. For this purpose, SNP arrays with moderate to high marker density represent a robust and cost-effective tool to generate reproducible, easy-to-handle, high-throughput genotype data from large-scale breeding populations. However, SNP arrays are prone to technical errors that lead to failed allele calls. To overcome this problem, failed calls are often imputed, based on the assumption that failed SNP calls are purely technical. However, this ignores the biological causes for failed calls-for example: deletions-and there is increasing evidence that gene presence-absence and other kinds of genome structural variants can play a role in phenotypic expression. Because deletions are frequently not in linkage disequilibrium with their flanking SNPs, permutation of missing SNP calls can potentially obscure valuable marker-trait associations. In this study, we analyze published datasets for canola and maize using four parametric and two machine learning models and demonstrate that failed allele calls in genomic prediction are highly predictive for important agronomic traits. We present two statistical pipelines, based on population structure and linkage disequilibrium, that enable the filtering of failed SNP calls that are likely caused by biological reasons. For the population and trait examined, prediction accuracy based on these filtered failed allele calls was competitive to standard SNP-based prediction, underlying the potential value of missing data in genomic prediction approaches. The combination of SNPs with all failed allele calls or the filtered allele calls did not outperform predictions with only SNP-based prediction due to redundancy in genomic relationship estimates.

Evolutionary roles of polyploidization-derived structural variations in the phenotypic diversification of Panax species

Genomic structural variations (SVs) are widespread in plant and animal genomes and play important roles in phenotypic novelty and species adaptation. Frequent whole genome duplications followed by (re)diploidizations have resulted in high diversity of genome architecture among extant species. In this study, we identified abundant genomic SVs in the Panax genus that are hypothesized to have occurred through during the repeated polyploidizations/(re)diploidizations. Our genome-wide comparisons demonstrated that although these polyploidization-derived SVs have evolved at distinct evolutionary stages, a large number of SV-intersecting genes showed enrichment in functionally important pathways related to secondary metabolites, photosynthesis and basic cellular activities. In line with these observations, our metabolic analyses of these Panax species revealed high diversity of primary and secondary metabolites both at the tissue and interspecific levels. In particular, genomic SVs identified at ginsenoside biosynthesis genes, including copy number variation and large fragment deletion, appear to have played important roles in the evolution and diversification of ginsenosides. A further herbivore deterrence experiment demonstrated that, as major triterpenoidal saponins found exclusively in Panax, ginsenosides provide protection against insect herbivores. Our study provides new insights on how polyploidization-derived SVs have contributed to phenotypic novelty and plant adaptation.

Vitis labrusca genome assembly reveals diversification between wild and cultivated grapevine genomes

Wild grapevines are important genetic resources in breeding programs to confer adaptive fitness traits and unique fruit characteristics, but the genetics underlying these traits, and their evolutionary origins, are largely unknown. To determine the factors that contributed to grapevine genome diversification, we performed comprehensive intragenomic and intergenomic analyses with three cultivated European (including the PN40024 reference genome) and two wild North American grapevine genomes, including our newly released Vitis labrusca genome. We found the heterozygosity of the cultivated grapevine genomes was twice as high as the wild grapevine genomes studied. Approximately 30% of V. labrusca and 48% of V. vinifera Chardonnay genes were heterozygous or hemizygous and a considerable number of collinear genes between Chardonnay and V. labrusca had different gene zygosity. Our study revealed evidence that supports gene gain-loss events in parental genomes resulted in the inheritance of hemizygous genes in the Chardonnay genome. Thousands of segmental duplications supplied source material for genome-specific genes, further driving diversification of the genomes studied. We found an enrichment of recently duplicated, adaptive genes in similar functional pathways, but differential retention of environment-specific adaptive genes within each genome. For example, large expansions of NLR genes were discovered in the two wild grapevine genomes studied. Our findings support variation in transposable elements contributed to unique traits in grapevines. Our work revealed gene zygosity, segmental duplications, gene gain-and-loss variations, and transposable element polymorphisms can be key driving forces for grapevine genome diversification.

A High-Continuity Genome Assembly of Chinese Flowering Cabbage (Brassica rapa var. parachinensis) Provides New Insights into Brassica Genome Structure Evolution

Chinese flowering cabbage (Brassica rapa var. parachinensis) is a popular and widely cultivated leaf vegetable crop in Asia. Here, we performed a high quality de novo assembly of the 384 Mb genome of 10 chromosomes of a typical cultivar of Chinese flowering cabbage with an integrated approach using PacBio, Illumina, and Hi-C technology. We modeled 47,598 protein-coding genes in this analysis and annotated 52% (205.9/384) of its genome as repetitive sequences including 17% in DNA transposons and 22% in long terminal retrotransposons (LTRs). Phylogenetic analysis reveals the genome of the Chinese flowering cabbage has a closer evolutionary relationship with the AA diploid progenitor of the allotetraploid species, Brassica juncea. Comparative genomic analysis of Brassica species with different subgenome types (A, B and C) reveals that the pericentromeric regions on chromosome 5 and 6 of the AA genome have been significantly expanded compared to the orthologous genomic regions in the BB and CC genomes, largely driven by LTR-retrotransposon amplification. Furthermore, we identified a large number of structural variations (SVs) within the B. rapa lines that could impact coding genes, suggesting the functional significance of SVs on Brassica genome evolution. Overall, our high-quality genome assembly of the Chinese flowering cabbage provides a valuable genetic resource for deciphering the genome evolution of Brassica species and it can potentially serve as the reference genome guiding the molecular breeding practice of B. rapa crops.

Patterns, mechanisms, and consequences of homoeologous exchange in allopolyploid angiosperms: a genomic and epigenomic perspective

Allopolyploids result from hybridization between different evolutionary lineages coupled with genome doubling. Homoeologous chromosomes (chromosomes with common shared ancestry) may undergo recombination immediately after allopolyploid formation and continue over successive generations. The outcome of this meiotic pairing behavior is dynamic and complex. Homoeologous exchanges (HEs) may lead to the formation of unbalanced gametes, reduced fertility, and selective disadvantage. By contrast, HEs could act as sources of novel evolutionary substrates, shifting the relative dosage of parental gene copies, generating novel phenotypic diversity, and helping the establishment of neo-allopolyploids. However, HE patterns vary among lineages, across generations, and even within individual genomes and chromosomes. The causes and consequences of this variation are not fully understood, though interest in this evolutionary phenomenon has increased in the last decade. Recent technological advances show promise in uncovering the mechanistic basis of HEs. Here, we describe recent observations of the common patterns among allopolyploid angiosperm lineages, underlying genomic and epigenomic features, and consequences of HEs. We identify critical research gaps and discuss future directions with far-reaching implications in understanding allopolyploid evolution and applying them to the development of important phenotypic traits of polyploid crops.

Genetic Diversity and Breeding Signatures for Regional Indica Rice Improvement in Guangdong of Southern China

As the pioneer of the Green Revolution in China, Guangdong province witnessed the improvement and spread of semi-dwarf Xian/Indica rice cultivars and possessed diverse rice germplasm of landrace and cultivars. A total of 517 accessions containing a core germplasm of 479 newly sequenced landraces and modern cultivars were used to reveal breeding signatures and key variations for regional genetic improvement of indica rice from Guangdong. Four subpopulations were identified in the collection, which including Ind IV as a novel subpopulation that not covered by previously released accessions. Modern cultivars of subpopulation Ind II were inferred to have less deleterious variations, especially in yield related genes. About 15 Mb genomic segments were identified as potential breeding signatures by cross-population likelihood method (XP-CLR) of modern cultivars and landraces. The selected regions spanning multiple yield related QTLs (quantitative trait locus) which identified by GWAS (genome-wide association studies) of the same population, and specific variations that fixed in modern cultivars of Ind II were characterized. This study highlights genetic differences between traditional landraces and modern cultivars, which revealed the potential molecular basis of regional genetic improvement for Guangdong indica rice from southern China.

Novel discovery in roles of structural variations and RWP-RK transcription factors in heat tolerance for pearl millet

Global warming adversely affects crop production worldwide. Massive efforts have been undertaken to study mechanisms regulating heat tolerance in plants. However, the roles of structural variations (SVs) in heat stress tolerance remain unclear. In a recent article, Yan et al. (Nat Genet 1-12, 2023) constructed the first pan-genome of pearl millet (Pennisetum glaucum) and identified key SVs linked to genes involved in regulating plant tolerance to heat stress for an important crop with a superior ability to thrive in extremely hot and arid climates. Through multi-omics analyses integrating by pan-genomics, comparative genomics, transcriptomics, population genetics and and molecular biological technologies, they found RWP-RK transcription factors cooperating with endoplasmic reticulum-related genes play key roles in heat tolerance in pearl millet. The results in this paper provided novel insights to advance the understanding of the genetic and genomic basis of heat tolerance and an exceptional resource for molecular breeding to improve heat tolerance in pearl millet and other crops.

A chromosome-level genome assembly of radish (Raphanus sativus L.) reveals insights into genome adaptation and differential bolting regulation

High-quality radish (Raphanus sativus) genome represents a valuable resource for agronomical trait improvements and understanding genome evolution among Brassicaceae species. However, existing radish genome assembly remains fragmentary, which greatly hampered functional genomics research and genome-assisted breeding. Here, using a NAU-LB radish inbred line, we generated a reference genome of 476.32 Mb with a scaffold N50 of 56.88 Mb by incorporating Illumina, PacBio and BioNano optical mapping techniques. Utilizing Hi-C data, 448.12 Mb (94.08%) of the assembled sequences were anchored to nine radish chromosomes with 40 306 protein-coding genes annotated. In total, 249.14 Mb (52.31%) comprised the repetitive sequences, among which long terminal repeats (LTRs, 30.31%) were the most abundant class. Beyond confirming the whole-genome triplication (WGT) event in R. sativus lineage, we found several tandem arrayed genes were involved in stress response process, which may account for the distinctive phenotype of high disease resistance in R. sativus. By comparing against the existing Xin-li-mei radish genome, a total of 2 108 573 SNPs, 7740 large insertions, 7757 deletions and 84 inversions were identified. Interestingly, a 647-bp insertion in the promoter of RsVRN1 gene can be directly bound by the DOF transcription repressor RsCDF3, resulting into its low promoter activity and late-bolting phenotype of NAU-LB cultivar. Importantly, introgression of this 647-bp insertion allele, RsVRN1^In-536 , into early-bolting genotype could contribute to delayed bolting time, indicating that it is a potential genetic resource for radish late-bolting breeding. Together, this genome resource provides valuable information to facilitate comparative genomic analysis and accelerate genome-guided breeding and improvement in radish.

Benchmarking Oxford Nanopore read alignment-based insertion and deletion detection in crop plant genomes

Structural variations (SVs) are larger polymorphisms (> 50 bp in length), which consist of insertions, deletions, inversions, duplications, and translocations. They can have a strong impact on agronomical traits and play an important role in environmental adaptation. The development of long-read sequencing technologies, including Oxford Nanopore, allows for comprehensive SV discovery and characterization even in complex polyploid crop genomes. However, many of the SV discovery pipeline benchmarks do not include complex plant genome datasets. In this study, we benchmarked insertion and deletion detection by popular long-read alignment-based SV detection tools for crop plant genomes. We used real and simulated Oxford Nanopore reads for two crops, allotetraploid Brassica napus (oilseed rape) and diploid Solanum lycopersicum (tomato), and evaluated several read aligners and SV callers across 5×, 10×, and 20× coverages typically used in re-sequencing studies. We further validated our findings using maize and soybean datasets. Our benchmarks provide a useful guide for designing Oxford Nanopore re-sequencing projects and SV discovery pipelines for crop plants.

Pan-genome inversion index reveals evolutionary insights into the subpopulation structure of Asian rice

Understanding and exploiting genetic diversity is a key factor for the productive and stable production of rice. Here, we utilize 73 high-quality genomes that encompass the subpopulation structure of Asian rice (Oryza sativa), plus the genomes of two wild relatives (O. rufipogon and O. punctata), to build a pan-genome inversion index of 1769 non-redundant inversions that span an average of ~29% of the O. sativa cv. Nipponbare reference genome sequence. Using this index, we estimate an inversion rate of ~700 inversions per million years in Asian rice, which is 16 to 50 times higher than previously estimated for plants. Detailed analyses of these inversions show evidence of their effects on gene expression, recombination rate, and linkage disequilibrium. Our study uncovers the prevalence and scale of large inversions (≥100 bp) across the pan-genome of Asian rice and hints at their largely unexplored role in functional biology and crop performance.

Genomic evidence reveals intraspecific divergence of the hot-spring snake (Thermophis baileyi), an endangered reptile endemic to the Qinghai-Tibet plateau

Understanding how and why species evolve requires knowledge on intraspecific divergence. In this study, we examined intraspecific divergence in the endangered hot-spring snake (Thermophis baileyi), an endemic species on the Qinghai-Tibet Plateau (QTP). Whole-genome resequencing of 58 sampled individuals from 15 populations was performed to identify the drivers of intraspecific divergence and explore the potential roles of genes under selection. Our analyses resolved three groups, with major intergroup admixture occurring in regions of group contact. Divergence probably occurred during the Pleistocene as a result of glacial climatic oscillations, Yadong-Gulu rift, and geothermal fields differentiation, while complex gene flow between group pairs reflected a unique intraspecific divergence pattern on the QTP. Intergroup fixed loci involved selected genes functionally related to divergence and local adaptation, especially adaptation to hot spring microenvironments in different geothermal fields. Analysis of structural variants, genetic diversity, inbreeding, and genetic load indicated that the hot-spring snake population has declined to a low level with decreased diversity, which is important for the conservation management of this endangered species. Our study demonstrated that the integration of demographic history, gene flow, genomic divergence genes, and other information is necessary to distinguish the evolutionary processes involved in speciation.

Benchmarking of structural variant detection in the tetraploid potato genome using linked-read sequencing

It has recently been shown that structural variants (SV) can have a higher impact on gene expression variation compared to single nucleotide variants (SNV) in different plant species. Additionally, SV were associated with phenotypic variation in several crops. However, compared to the established SV detection based on short-read sequencing, less approaches were described for linked-read based SV calling. We therefore evaluated the performance of six linked-read SV callers compared to an established short-read SV caller based on simulated linked-reads in tetraploid potato. The objectives of our study were to i) compare the performance of SV callers based on linked-read sequencing to short-read sequencing, ii) examine the influence of SV type, SV length, haplotype incidence (HI), as well as sequencing coverage on the SV calling performance in the tetraploid potato genome, and iii) evaluate the accuracy of detecting insertions by linked-read compared to short-read sequencing. We observed high break point resolutions (BPR) detecting short SV and slightly lower BPR for large SV. Our observations highlighted the importance of short-read signals provided by Manta and LinkedSV to detect short SV. Manta and NAIBR performed well for detecting larger deletions, inversions, and duplications. Detected large SV were weakly influenced by the HI. Furthermore, we illustrated that large insertions can be assembled by Novel-X. Our results suggest the usage of the short-read and linked-read SV callers Manta, NAIBR, LinkedSV, and Novel-X based on at least 90x linked-read sequencing coverage to ensure the detection of a broad range of SV in the tetraploid potato genome.

Pangenomic analysis identifies structural variation associated with heat tolerance in pearl millet

Pearl millet is an important cereal crop worldwide and shows superior heat tolerance. Here, we developed a graph-based pan-genome by assembling ten chromosomal genomes with one existing assembly adapted to different climates worldwide and captured 424,085 genomic structural variations (SVs). Comparative genomics and transcriptomics analyses revealed the expansion of the RWP-RK transcription factor family and the involvement of endoplasmic reticulum (ER)-related genes in heat tolerance. The overexpression of one RWP-RK gene led to enhanced plant heat tolerance and transactivated ER-related genes quickly, supporting the important roles of RWP-RK transcription factors and ER system in heat tolerance. Furthermore, we found that some SVs affected the gene expression associated with heat tolerance and SVs surrounding ER-related genes shaped adaptation to heat tolerance during domestication in the population. Our study provides a comprehensive genomic resource revealing insights into heat tolerance and laying a foundation for generating more robust crops under the changing climate.

Lost genome segments associate with trait diversity during rice domestication

BACKGROUND

DNA mutations of diverse types provide the raw material required for phenotypic variation and evolution. In the case of crop species, previous research aimed to elucidate the changing patterns of repetitive sequences, single-nucleotide polymorphisms (SNPs), and small InDels during domestication to explain morphological evolution and adaptation to different environments. Additionally, structural variations (SVs) encompassing larger stretches of DNA are more likely to alter gene expression levels leading to phenotypic variation affecting plant phenotypes and stress resistance. Previous studies on SVs in rice were hampered by reliance on short-read sequencing limiting the quantity and quality of SV identification, while SV data are currently only available for cultivated rice, with wild rice largely uncharacterized. Here, we generated two genome assemblies for O. rufipogon using long-read sequencing and provide insights on the evolutionary pattern and effect of SVs on morphological traits during rice domestication.

RESULTS

In this study, we identified 318,589 SVs in cultivated and wild rice populations through a comprehensive analysis of 13 high-quality rice genomes and found that wild rice genomes contain 49% of unique SVs and an average of 1.76% of genes were lost during rice domestication. These SVs were further genotyped for 649 rice accessions, their evolutionary pattern during rice domestication and potential association with the diversity of important agronomic traits were examined. Genome-wide association studies between these SVs and nine agronomic traits identified 413 candidate causal variants, which together affect 361 genes. An 824-bp deletion in japonica rice, which encodes a serine carboxypeptidase family protein, is shown to be associated with grain length.

CONCLUSIONS

We provide relatively accurate and complete SV datasets for cultivated and wild rice accessions, especially in TE-rich regions, by comparing long-read sequencing data for 13 representative varieties. The integrated rice SV map and the identified candidate genes and variants represent valuable resources for future genomic research and breeding in rice.

Chromosome-level genome and recombination map of the male buffalo

BACKGROUND

The swamp buffalo (Bubalus bubalis carabanesis) is an economically important livestock supplying milk, meat, leather, and draft power. Several female buffalo genomes have been available, but the lack of high-quality male genomes hinders studies on chromosome evolution, especially Y, as well as meiotic recombination.

RESULTS

Here, a chromosome-level genome with a contig N50 of 72.2 Mb and a fine-scale recombination map of male buffalo were reported. We found that transposable elements (TEs) and structural variants (SVs) may contribute to buffalo evolution by influencing adjacent gene expression. We further found that the pseudoautosomal region (PAR) of the Y chromosome is subject to stronger purification selection. The meiotic recombination map showed that there were 2 obvious recombination hotspots on chromosome 8, and the genes around them were mainly related to tooth development, which may have helped to enhance the adaption of buffalo to inferior feed. Among several genomic features, TE density has the strongest correlation with recombination rates. Moreover, the TE subfamily, SINE/tRNA, is likely to play a role in driving recombination into SVs.

CONCLUSIONS

The male genome and sperm sequencing will facilitate the understanding of the buffalo genomic evolution and functional research.

Spectrum and Density of Gamma and X-ray Induced Mutations in a Non-Model Rice Cultivar

Physical mutagens are a powerful tool used for genetic research and breeding for over eight decades. Yet, when compared to chemical mutagens, data sets on the effect of different mutagens and dosages on the spectrum and density of induced mutations remain lacking. To address this, we investigated the landscape of mutations induced by gamma and X-ray radiation in the most widely cultivated crop species: rice. A mutant population of a tropical upland rice, Oryza sativa L., was generated and propagated via self-fertilization for seven generations. Five dosages ranging from 75 Gy to 600 Gy in both X-ray and gamma-irradiated material were applied. In the process of a forward genetic screens, 11 unique rice mutant lines showing phenotypic variation were selected for mutation analysis via whole-genome sequencing. Thousands of candidate mutations were recovered in each mutant with single base substitutions being the most common, followed by small indels and structural variants. Higher dosages resulted in a higher accumulation of mutations in gamma-irradiated material, but not in X-ray-treated plants. The in vivo role of all annotated rice genes is yet to be directly investigated. The ability to induce a high density of single nucleotide and structural variants through mutagenesis will likely remain an important approach for functional genomics and breeding.

Characteristics and potential functional effects of long insertions in Asian butternuts

Background

Structural variants (SVs) play important roles in adaptation evolution and species diversification. Especially, in plants, many phenotypes of response to the environment were found to be associated with SVs. Despite the prevalence and significance of SVs, long insertions remain poorly detected and studied in all but model species.

Results

We used whole-genome resequencing of paired reads from 80 Asian butternuts to detect long insertions and further analyse their characteristics and potential functional effects. By combining of mapping-based and de novo assembly-based methods, we obtained a multiple related species pangenome representing higher taxonomic groups. We obtained 89,312 distinct contigs totaling 147,773,999 base pair (bp) of new sequences, of which 347 were putative long insertions placed in the reference genome. Most of the putative long insertions appeared in multiple species; in contrast, only 62 putative long insertions appeared in one species, which may be involved in the response to the environment. 65 putative long insertions fell into 61 distinct protein-coding genes involved in plant development, and 105 putative long insertions fell into upstream of 106 distinct protein-coding genes involved in cellular respiration. 3,367 genes were annotated in 2,606 contigs. We propose PLAINS (https://github.com/CMB-BNU/PLAINS.git), a streamlined, comprehensive pipeline for the prediction and analysis of long insertions using whole-genome resequencing.

Conclusions

Our study lays down an important foundation for further whole-genome long insertion studies, allowing the investigation of their effects by experiments.

Prioritized candidate causal haplotype blocks in plant genome-wide association studies

Genome wide association studies (GWAS) can play an essential role in understanding genetic basis of complex traits in plants and animals. Conventional SNP-based linear mixed models (LMM) that marginally test single nucleotide polymorphisms (SNPs) have successfully identified many loci with major and minor effects in many GWAS. In plant, the relatively small population size in GWAS and the high genetic diversity found in many plant species can impede mapping efforts on complex traits. Here we present a novel haplotype-based trait fine-mapping framework, HapFM, to supplement current GWAS methods. HapFM uses genotype data to partition the genome into haplotype blocks, identifies haplotype clusters within each block, and then performs genome-wide haplotype fine-mapping to prioritize the candidate causal haplotype blocks of trait. We benchmarked HapFM, GEMMA, BSLMM, GMMAT, and BLINK in both simulated and real plant GWAS datasets. HapFM consistently resulted in higher mapping power than the other GWAS methods in high polygenicity simulation setting. Moreover, it resulted in smaller mapping intervals, especially in regions of high LD, achieved by prioritizing small candidate causal blocks in the larger haplotype blocks. In the Arabidopsis flowering time (FT10) datasets, HapFM identified four novel loci compared to GEMMA’s results, and the average mapping interval of HapFM was 9.6 times smaller than that of GEMMA. In conclusion, HapFM is tailored for plant GWAS to result in high mapping power on complex traits and improved on mapping resolution to facilitate crop improvement.

Genome-wide association studies (GWAS) are commonly used in human and plant studies to identify genetic variants responsible for the phenotype of interest and provide foundations for studying disease mechanisms and crop improvement. Most GWAS models are developed and optimized using human datasets. However, the difference between human and plant datasets essentially limits their applications in plant studies, especially when mapping complex traits such as drought resistance and yield. In this study, we present a novel GWAS method, HapFM, tailored for plant datasets to overcome the difficulties of many conventional GWAS methods. HapFM resulted in higher statistical power than conventional GWAS methods for mapping complex traits in our simulation and real dataset analyses. In addition, HapFM reduced the mapping interval by prioritizing candidate causal regions in the genome, which benefits the downstream experimental studies. Last but not least, HapFM can incorporate biological annotations to increase statistical power further. Overall, HapFM balances statistical power, result interpretability, and downstream experimental verifiability.

Structural variants in the barley gene pool: precision and sensitivity to detect them using short-read sequencing and their association with gene expression and phenotypic variation

Structural variants (SV) of 23 barley inbreds, detected by the best combination of SV callers based on short-read sequencing, were associated with genome-wide and gene-specific gene expression and, thus, were evaluated to predict agronomic traits. In human genetics, several studies have shown that phenotypic variation is more likely to be caused by structural variants (SV) than by single nucleotide variants. However, accurate while cost-efficient discovery of SV in complex genomes remains challenging. The objectives of our study were to (i) facilitate SV discovery studies by benchmarking SV callers and their combinations with respect to their sensitivity and precision to detect SV in the barley genome, (ii) characterize the occurrence and distribution of SV clusters in the genomes of 23 barley inbreds that are the parents of a unique resource for mapping quantitative traits, the double round robin population, (iii) quantify the association of SV clusters with transcript abundance, and (iv) evaluate the use of SV clusters for the prediction of phenotypic traits. In our computer simulations based on a sequencing coverage of 25x, a sensitivity > 70% and precision > 95% was observed for all combinations of SV types and SV length categories if the best combination of SV callers was used. We observed a significant (P < 0.05) association of gene-associated SV clusters with global gene-specific gene expression. Furthermore, about 9% of all SV clusters that were within 5 kb of a gene were significantly (P < 0.05) associated with the gene expression of the corresponding gene. The prediction ability of SV clusters was higher compared to that of single-nucleotide polymorphisms from an array across the seven studied phenotypic traits. These findings suggest the usefulness of exploiting SV information when fine mapping and cloning the causal genes underlying quantitative traits as well as the high potential of using SV clusters for the prediction of phenotypes in diverse germplasm sets.

Transposable element polymorphisms improve prediction of complex agronomic traits in rice

Transposon insertion polymorphisms can improve prediction of complex agronomic traits in rice compared to using SNPs only, especially when accessions to be predicted are less related to the training set. Transposon insertion polymorphisms (TIPs) are significant sources of genetic variation. Previous work has shown that TIPs can improve detection of causative loci on agronomic traits in rice. Here, we quantify the fraction of variance explained by single nucleotide polymorphisms (SNPs) compared to TIPs, and we explore whether TIPs can improve prediction of traits when compared to using only SNPs. We used eleven traits of agronomic relevance from by five different rice population groups (Aus, Indica, Aromatic, Japonica, and Admixed), 738 accessions in total. We assess prediction by applying data split validation in two scenarios. In the within-population scenario, we predicted performance of improved Indica varieties using the rest of Indica accessions. In the across population scenario, we predicted all Aromatic and Admixed accessions using the rest of populations. In each scenario, Bayes C and a Bayesian reproducible kernel Hilbert space regression were compared. We find that TIPs can explain an important fraction of total genetic variance and that they also improve genomic prediction. In the across population prediction scenario, TIPs outperformed SNPs in nine out of the eleven traits analyzed. In some traits like leaf senescence or grain width, using TIPs increased predictive correlation by 30-50%. Our results evidence, for the first time, that TIPs genotyping can improve prediction on complex agronomic traits in rice, especially when accessions to be predicted are less related to training accessions.

A pan-Zea genome map for enhancing maize improvement

Background

Maize (Zea mays L.) is at the vanguard facing the upcoming breeding challenges. However, both a super pan-genome for the Zea genus and a comprehensive genetic variation map for maize breeding are still lacking.

Results

Here, we construct an approximately 6.71-Gb pan-Zea genome that contains around 4.57-Gb non-B73 reference sequences from fragmented de novo assemblies of 721 pan-Zea individuals. We annotate a total of 58,944 pan-Zea genes and find around 44.34% of them are dispensable in the pan-Zea population. Moreover, 255,821 common structural variations are identified and genotyped in a maize association mapping panel. Further analyses reveal gene presence/absence variants and their potential roles during domestication of maize. Combining genetic analyses with multi-omics data, we demonstrate how structural variants are associated with complex agronomic traits.

Conclusions

Our results highlight the underexplored role of the pan-Zea genome and structural variations to further understand domestication of maize and explore their potential utilization in crop improvement.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13059-022-02742-7.

PerSVade: personalized structural variant detection in any species of interest

Structural variants (SVs) underlie genomic variation but are often overlooked due to difficult detection from short reads. Most algorithms have been tested on humans, and it remains unclear how applicable they are in other organisms. To solve this, we develop perSVade (personalized structural variation detection), a sample-tailored pipeline that provides optimally called SVs and their inferred accuracy, as well as small and copy number variants. PerSVade increases SV calling accuracy on a benchmark of six eukaryotes. We find no universal set of optimal parameters, underscoring the need for sample-specific parameter optimization. PerSVade will facilitate SV detection and study across diverse organisms.

An evolutionary look into the history of lentil reveals unexpected diversity

The characterization and preservation of genetic variation in crops is critical to meeting the challenges of breeding in the face of changing climates and markets. In recent years, the use of single nucleotide polymorphisms (SNPs) has become routine, allowing us to understand the population structure, find divergent lines for crosses, and illuminate the origin of crops. However, the focus on SNPs overlooks other forms of variation, such as copy number variation (CNVs). Lentil is the third most important cold‐season legume and was domesticated in the Fertile Crescent. We genotyped 324 accessions that represent its global diversity, and using both SNPs and CNVs, we dissected the population structure and genetic variation, and identified candidate genes. Eight clusters were detected, most of them located in or near the Fertile Crescent, even though different clusters were present in distinct regions. The cluster from South Asia was particularly differentiated and presented low diversity, contrasting with the clusters from the Mediterranean and the northern temperate. Accessions from North America were mainly assigned to one cluster and were highly diverse, reflecting the efforts of breeding programs to integrate variation. Thirty‐three genes were identified as candidates under selection and among their functions were sporopollenin synthesis in pollen, a component of chlorophyll B reductase that partially determines the antenna size, and two genes related to the import system of chloroplasts. Eleven percent of all lentil genes and 21% of lentil disease resistance genes were affected by CNVs. The gene categories overrepresented in these genes were “enzymes,” “Cell Wall Organization,” and “external stimuli response.” All the genes found in the latter were associated with pathogen response. CNVs provided information about population structure and might have played a role in adaptation. The incorporation of CNVs in diversity studies is needed for a broader understanding of how they evolve and contribute to domestication.

TransposonUltimate: software for transposon classification, annotation and detection

Most genomes harbor a large number of transposons, and they play an important role in evolution and gene regulation. They are also of interest to clinicians as they are involved in several diseases, including cancer and neurodegeneration. Although several methods for transposon identification are available, they are often highly specialised towards specific tasks or classes of transposons, and they lack common standards such as a unified taxonomy scheme and output file format. We present TransposonUltimate, a powerful bundle of three modules for transposon classification, annotation, and detection of transposition events. TransposonUltimate comes as a Conda package under the GPL-3.0 licence, is well documented and it is easy to install through https://github.com/DerKevinRiehl/TransposonUltimate. We benchmark the classification module on the large TransposonDB covering 891,051 sequences to demonstrate that it outperforms the currently best existing solutions. The annotation and detection modules combine sixteen existing softwares, and we illustrate its use by annotating Caenorhabditis elegans, Rhizophagus irregularis and Oryza sativa subs. japonica genomes. Finally, we use the detection module to discover 29 554 transposition events in the genomes of 20 wild type strains of C. elegans. Databases, assemblies, annotations and further findings can be downloaded from (https://doi.org/10.5281/zenodo.5518085).

Advancing designer crops for climate resilience through an integrated genomics approach

Climate change and exponential population growth are exposing an immediate need for developing future crops that are highly resilient and adaptable to changing environments to maintain global food security in the next decade. Rigorous selection from long domestication history has rendered cultivated crops genetically disadvantaged, raising concerns in their ability to adapt to these new challenges and limiting their usefulness in breeding programmes. As a result, future crop improvement efforts must rely on integrating various genomic strategies ranging from high-throughput sequencing to machine learning, in order to exploit germplasm diversity and overcome bottlenecks created by domestication, expansive multi-dimensional phenotypes, arduous breeding processes, complex traits and big data.

Oxford Nanopore and Bionano Genomics technologies evaluation for plant structural variation detection

Background

Structural Variations (SVs) are genomic rearrangements derived from duplication, deletion, insertion, inversion, and translocation events. In the past, SVs detection was limited to cytological approaches, then to Next-Generation Sequencing (NGS) short reads and partitioned assemblies. Nowadays, technologies such as DNA long read sequencing and optical mapping have revolutionized the understanding of SVs in genomes, due to the enhancement of the power of SVs detection.

This study aims to investigate performance of two techniques, 1) long-read sequencing obtained with the MinION device (Oxford Nanopore Technologies) and 2) optical mapping obtained with Saphyr device (Bionano Genomics) to detect and characterize SVs in the genomes of the two ecotypes of Arabidopsis thaliana, Columbia-0 (Col-0) and Landsberg erecta 1 (Ler-1).

Results

We described the SVs detected from the alignment of the best ONT assembly and DLE-1 optical maps of A. thaliana Ler-1 against the public reference genome Col-0 TAIR10.1. After filtering (SV > 1 kb), 1184 and 591 Ler-1 SVs were retained from ONT and Bionano technologies respectively. A total of 948 Ler-1 ONT SVs (80.1%) corresponded to 563 Bionano SVs (95.3%) leading to 563 common locations. The specific locations were scrutinized to assess improvement in SV detection by either technology. The ONT SVs were mostly detected near TE and gene features, and resistance genes seemed particularly impacted.

Conclusions

Structural variations linked to ONT sequencing error were removed and false positives limited, with high quality Bionano SVs being conserved. When compared with the Col-0 TAIR10.1 reference genome, most of the detected SVs discovered by both technologies were found in the same locations. ONT assembly sequence leads to more specific SVs than Bionano one, the latter being more efficient to characterize large SVs. Even if both technologies are complementary approaches, ONT data appears to be more adapted to large scale populations studies, while Bionano performs better in improving assembly and describing specificity of a genome compared to a reference.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08499-4.

Twenty years of rice genomics research: From sequencing and functional genomics to quantitative genomics

Since the completion of the rice genome sequencing project in 2005, we have entered the era of rice genomics, which is still in its ascendancy. Rice genomics studies can be classified into three stages: structural genomics, functional genomics, and quantitative genomics. Structural genomics refers primarily to genome sequencing for the construction of a complete map of rice genome sequence. This is fundamental for rice genetics and molecular biology research. Functional genomics aims to decode the functions of rice genes. Quantitative genomics is large-scale sequence- and statistics-based research to define the quantitative traits and genetic features of rice populations. Rice genomics has been a transformative influence on rice biological research and contributes significantly to rice breeding, making rice a good model plant for studying crop sciences.

Bringing more players into play: Leveraging stress in genome wide association studies

In order to meet the demand of the burgeoning human population as well as to adapt crops to the enhanced abiotic and biotic stress caused by the global climatic change, breeders focus on identifying valuable genes to improve both crop stress tolerance and crop quality. Recently, with the development of next-generation sequencing methods, millions of high quality single-nucleotide polymorphisms (SNPs) have been made available and genome-wide association studies (GWAS) are widely used in crop improvement studies to identify the associations between genetic variants of genomes and relevant crop agronomic traits. Here, we review classic cases of use of GWAS to identify genetic variants associated with valuable traits such as geographic adaptation, crop quality and metabolites. We discuss the power of stress GWAS to identify further associations including those with genes that are not, or only lowly, expressed during optimal growth conditions. Finally, we emphasize recent demonstrations of the efficiency and accuracy of time-resolved dynamic stress GWAS and GWAS based on genomic gene expression and structural variations, which can be applied to resolve more comprehensively the genetic regulation mechanisms of complex traits.

Genomic structural variation in tomato and its role in plant immunity

It is well known that large genomic variations can greatly impact the phenotype of an organism. Structural Variants (SVs) encompass any genomic variation larger than 30 base pairs, and include changes caused by deletions, inversions, duplications, transversions, and other genome modifications. Due to their size and complex nature, until recently, it has been difficult to truly capture these variations. Recent advances in sequencing technology and computational analyses now permit more extensive studies of SVs in plant genomes. In tomato, advances in sequencing technology have allowed researchers to sequence hundreds of genomes from tomatoes, and tomato relatives. These studies have identified SVs related to fruit size and flavor, as well as plant disease response, resistance/susceptibility, and the ability of plants to detect pathogens (immunity). In this review, we discuss the implications for genomic structural variation in plants with a focus on its role in tomato immunity. We also discuss how advances in sequencing technology have led to new discoveries of SVs in more complex genomes, the current evidence for the role of SVs in biotic and abiotic stress responses, and the outlook for genetic modification of SVs to advance plant breeding objectives.

Combined use of Oxford Nanopore and Illumina sequencing yields insights into soybean structural variation biology

BACKGROUND

Structural variants (SVs), including deletions, insertions, duplications, and inversions, are relatively long genomic variations implicated in a diverse range of processes from human disease to ecology and evolution. Given their complex signatures, tendency to occur in repeated regions, and large size, discovering SVs based on short reads is challenging compared to single-nucleotide variants. The increasing availability of long-read technologies has greatly facilitated SV discovery; however, these technologies remain too costly to apply routinely to population-level studies. Here, we combined short-read and long-read sequencing technologies to provide a comprehensive population-scale assessment of structural variation in a panel of Canadian soybean cultivars.

RESULTS

We used Oxford Nanopore long-read sequencing data (~12× mean coverage) for 17 samples to both benchmark SV calls made from Illumina short-read data and predict SVs that were subsequently genotyped in a population of 102 samples using Illumina data. Benchmarking results show that variants discovered using Oxford Nanopore can be accurately genotyped from the Illumina data. We first use the genotyped deletions and insertions for population genetics analyses and show that results are comparable to those based on single-nucleotide variants. We observe that the population frequency and distribution within the genome of deletions and insertions are constrained by the location of genes. Gene Ontology and PFAM domain enrichment analyses also confirm previous reports that genes harboring high-frequency deletions and insertions are enriched for functions in defense response. Finally, we discover polymorphic transposable elements from the deletions and insertions and report evidence of the recent activity of a Stowaway MITE.

CONCLUSIONS

We show that structural variants discovered using Oxford Nanopore data can be genotyped with high accuracy from Illumina data. Our results demonstrate that long-read and short-read sequencing technologies can be efficiently combined to enhance SV analysis in large populations, providing a reusable framework for their study in a wider range of samples and non-model species.

Comparison of structural variants in the whole genome sequences of two Medicago truncatula ecotypes: Jemalong A17 and R108

BACKGROUND

Structural variants (SVs) constitute a large proportion of the genomic variation that results in phenotypic variation in plants. However, they are still a largely unexplored feature in most plant genomes. Here, we present the whole-genome landscape of SVs between two model legume Medicago truncatula ecotypes-Jemalong A17 and R108- that have been extensively used in various legume biology studies.

RESULTS

To catalogue SVs, we first resolved the previously published R108 genome assembly (R108 v1.0) to chromosome-scale using 124 × Hi-C data, resulting in a high-quality genome assembly. The inter-chromosomal reciprocal translocations between chromosomes 4 and 8 were confirmed by performing syntenic analysis between the two genomes. Combined with the Hi-C data, it appears that these translocation events had a significant effect on chromatin organization. Using both whole-genome and short-read alignments, we identified the genomic landscape of SVs between the two genomes, some of which may account for several phenotypic differences, including their differential responses to aluminum toxicity and iron deficiency, and the development of different anthocyanin leaf markings. We also found extensive SVs within the nodule-specific cysteine-rich gene family which encodes antimicrobial peptides essential for terminal bacteroid differentiation during nitrogen-fixing symbiosis.

CONCLUSIONS

Our results provide a near-complete R108 genome assembly and the first genomic landscape of SVs obtained by comparing two M. truncatula ecotypes. This may provide valuable genomic resources for the functional and molecular research of legume biology in the future.

DNA-Based Tools to Certify Authenticity of Rice Varieties—An Overview

Rice (Oryza sativa L.) is one of the most cultivated and consumed crops worldwide. It is mainly produced in Asia but, due to its large genetic pool, it has expanded to several ecosystems, latitudes and climatic conditions. Europe is a rice producing region, especially in the Mediterranean countries, that grow mostly typical japonica varieties. The European consumer interest in rice has increased over the last decades towards more exotic types, often more expensive (e.g., aromatic rice) and Europe is a net importer of this commodity. This has increased food fraud opportunities in the rice supply chain, which may deliver mixtures with lower quality rice, a problem that is now global. The development of tools to clearly identify undesirable mixtures thus became urgent. Among the various tools available, DNA-based markers are considered particularly reliable and stable for discrimination of rice varieties. This review covers aspects ranging from rice diversity and fraud issues to the DNA-based methods used to distinguish varieties and detect unwanted mixtures. Although not exhaustive, the review covers the diversity of strategies and ongoing improvements already tested, highlighting important advantages and disadvantages in terms of costs, reliability, labor-effort and potential scalability for routine fraud detection.

CNVpytor: a tool for copy number variation detection and analysis from read depth and allele imbalance in whole-genome sequencing

BACKGROUND

Detecting copy number variations (CNVs) and copy number alterations (CNAs) based on whole-genome sequencing data is important for personalized genomics and treatment. CNVnator is one of the most popular tools for CNV/CNA discovery and analysis based on read depth.

FINDINGS

Herein, we present an extension of CNVnator developed in Python-CNVpytor. CNVpytor inherits the reimplemented core engine of its predecessor and extends visualization, modularization, performance, and functionality. Additionally, CNVpytor uses B-allele frequency likelihood information from single-nucleotide polymorphisms and small indels data as additional evidence for CNVs/CNAs and as primary information for copy number-neutral losses of heterozygosity.

CONCLUSIONS

CNVpytor is significantly faster than CNVnator-particularly for parsing alignment files (2-20 times faster)-and has (20-50 times) smaller intermediate files. CNV calls can be filtered using several criteria, annotated, and merged over multiple samples. Modular architecture allows it to be used in shared and cloud environments such as Google Colab and Jupyter notebook. Data can be exported into JBrowse, while a lightweight plugin version of CNVpytor for JBrowse enables nearly instant and GUI-assisted analysis of CNVs by any user. CNVpytor release and the source code are available on GitHub at https://github.com/abyzovlab/CNVpytor under the MIT license.

Calling large indels in 1047 Arabidopsis with IndelEnsembler

Large indels greatly impact the observable phenotypes in different organisms including plants and human. Hence, extracting large indels with high precision and sensitivity is important. Here, we developed IndelEnsembler to detect large indels in 1047 Arabidopsis whole-genome sequencing data. IndelEnsembler identified 34 093 deletions, 12 913 tandem duplications and 9773 insertions. Our large indel dataset was more comprehensive and accurate compared with the previous dataset of AthCNV (1). We captured nearly twice of the ground truth deletions and on average 27% more ground truth duplications compared with AthCNV, though our dataset has less number of large indels compared with AthCNV. Our large indels were positively correlated with transposon elements across the Arabidopsis genome. The non-homologous recombination events were the major formation mechanism of deletions in Arabidopsis genome. The Neighbor joining (NJ) tree constructed based on IndelEnsembler's deletions clearly divided the geographic subgroups of 1047 Arabidopsis. More importantly, our large indels represent a previously unassessed source of genetic variation. Approximately 49% of the deletions have low linkage disequilibrium (LD) with surrounding single nucleotide polymorphisms. Some of them could affect trait performance. For instance, using deletion-based genome-wide association study (DEL-GWAS), the accessions containing a 182-bp deletion in AT1G11520 had delayed flowering time and all accessions in north Sweden had the 182-bp deletion. We also found the accessions with 65-bp deletion in the first exon of AT4G00650 (FRI) flowered earlier than those without it. These two deletions cannot be detected in AthCNV and, interestingly, they do not co-occur in any Arabidopsis thaliana accession. By SNP-GWAS, surrounding SNPs of these two deletions do not correlate with flowering time. This example demonstrated that existing large indel datasets miss phenotypic variations and our large indel dataset filled in the gap.

Current status of structural variation studies in plants

Structural variations (SVs) including gene presence/absence variations and copy number variations are a common feature of genomes in plants and, together with single nucleotide polymorphisms and epigenetic differences, are responsible for the heritable phenotypic diversity observed within and between species. Understanding the contribution of SVs to plant phenotypic variation is important for plant breeders to assist in producing improved varieties. The low resolution of early genetic technologies and inefficient methods have previously limited our understanding of SVs in plants. However, with the rapid expansion in genomic technologies, it is possible to assess SVs with an ever-greater resolution and accuracy. Here, we review the current status of SV studies in plants, examine the roles that SVs play in phenotypic traits, compare current technologies and assess future challenges for SV studies.

Metabolomics and complementary techniques to investigate the plant phytochemical cosmos

Covering: up to 2021Plants and their associated microbial communities are known to produce millions of metabolites, a majority of which are still not characterized and are speculated to possess novel bioactive properties. In addition to their role in plant physiology, these metabolites are also relevant as existing and next-generation medicine candidates. Elucidation of the plant metabolite diversity is thus valuable for the successful exploitation of natural resources for humankind. Herein, we present a comprehensive review on recent metabolomics approaches to illuminate molecular networks in plants, including chemical isolation and enzymatic production as well as the modern metabolomics approaches such as stable isotope labeling, ultrahigh-resolution mass spectrometry, metabolome imaging (spatial metabolomics), single-cell analysis, cheminformatics, and computational mass spectrometry. Mass spectrometry-based strategies to characterize plant metabolomes through metabolite identification and annotation are described in detail. We also highlight the use of phytochemical genomics to mine genes associated with specialized metabolites' biosynthesis. Understanding the metabolic diversity through biotechnological advances is fundamental to elucidate the functions of the plant-derived specialized metabolome.

Extensively Current Activity of Transposable Elements in Natural Rice Accessions Revealed by Singleton Insertions

Active transposable elements (TEs) have drawn more attention as they continue to create new insertions and contribute to genetic diversity of the genome. However, only a few have been discovered in rice up to now, and their activities are mostly induced by artificial treatments (e.g., tissue culture, hybridization etc.) rather than under normal growth conditions. To systematically survey the current activity of TEs in natural rice accessions and identify rice accessions carrying highly active TEs, the transposon insertion polymorphisms (TIPs) profile was used to identify singleton insertions, which were unique to a single accession and represented the new insertion of TEs in the genome. As a result, 10,924 high-confidence singletons from 251 TE families were obtained, covering all investigated TE types. The number of singletons varied substantially among different superfamilies/families, perhaps reflecting distinct current activity. Particularly, eight TE families maintained potentially higher activity in 3,000 natural rice accessions. Sixty percent of rice accessions were detected to contain singletons, indicating the extensive activity of TEs in natural rice accessions. Thirty-five TE families exhibited potentially high activity in at least one rice accession, and the majority of them showed variable activity among different rice groups/subgroups. These naturally active TEs would be ideal candidates for elucidating the molecular mechanisms underlying the transposition and activation of TEs, as well as investigating the interactions between TEs and the host genome.

GWAS for genetics of complex quantitative traits: Genome to pangenome and SNPs to SVs and k-mers

The development of improved methods for genome-wide association studies (GWAS) for genetics of quantitative traits has been an active area of research during the last 25 years. This activity initially started with the use of mixed linear model (MLM), which was variously modified. During the last decade, however, with the availability of high throughput next generation sequencing (NGS) technology, development and use of pangenomes and novel markers including structural variations (SVs) and k-mers for GWAS has taken over as a new thrust area of research. Pangenomes and SVs are now available in humans, livestock, and a number of plant species, so that these resources along with k-mers are being used in GWAS for exploring additional genetic variation that was hitherto not available for analysis. These developments have resulted in significant improvement in GWAS methodology for detection of marker-trait associations (MTAs) that are relevant to human healthcare and crop improvement.

Structural Variants Selected during Yak Domestication Inferred from Long-Read Whole-Genome Sequencing

Structural variants (SVs) represent an important genetic resource for both natural and artificial selection. Here we present a chromosome-scale reference genome for domestic yak (Bos grunniens) that has longer contigs and scaffolds (N50 44.72 and 114.39 Mb, respectively) than reported for any other ruminant genome. We further obtained long-read resequencing data for 6 wild and 23 domestic yaks and constructed a genetic SV map of 372,220 SVs that covers the geographic range of the yaks. The majority of the SVs contains repetitive sequences and several are in or near genes. By comparing SVs in domestic and wild yaks, we identified genes that are predominantly related to the nervous system, behavior, immunity, and reproduction and may have been targeted by artificial selection during yak domestication. These findings provide new insights in the domestication of animals living at high altitude and highlight the importance of SVs in animal domestication.

Chasing breeding footprints through structural variations in Cucumis melo and wild relatives

Cucumis melo (melon or muskmelon) is an important crop in the family of the Cucurbitaceae. Melon is cross pollinated and domesticated at several locations throughout the breeding history, resulting in highly diverse genetic structure in the germplasm. Yet, the relations among the groups and cultivars are still incomplete. We shed light on the melonbreeding history, analyzing structural variations ranging from 50 bp up to 100 kb, identified from whole genome sequences of 100 selected melon accessions and wild relatives. Phylogenetic trees based on SV types completely resolve cultivars and wild accessions into two monophyletic groups and clustering of cultivars largely correlates with their geographic origin. Taking into account morphology, we found six mis-categorized cultivars. Unique inversions are more often shared between cultivars, carrying advantageous genes and do not directly originate from wild species. Approximately 60% of the inversion breaks carry a long poly A/T motif, and following observations in other plant species, suggest that inversions in melon likely resulted from meiotic recombination events. We show that resistance genes in the linkage V region are expanded in the cultivar genomes compared to wild relatives. Furthermore, particular agronomic traits such as fruit ripening, fragrance, and stress response are specifically selected for in the melon subspecies. These results represent distinctive footprints of selective breeding that shaped today's melon. The sequences and genomic relations between land races, wild relatives, and cultivars will serve the community to identify genetic diversity, optimize experimental designs, and enhance crop development.

Transposable Elements Contribute to Genome Dynamics and Gene Expression Variation in the Fungal Plant Pathogen Verticillium dahliae

Transposable elements (TEs) are a major source of genetic and regulatory variation in their host genome and are consequently thought to play important roles in evolution. Many fungal and oomycete plant pathogens have evolved dynamic and TE-rich genomic regions containing genes that are implicated in host colonization and adaptation. TEs embedded in these regions have typically been thought to accelerate the evolution of these genomic compartments, but little is known about their dynamics in strains that harbor them. Here, we used whole-genome sequencing data of 42 strains of the fungal plant pathogen Verticillium dahliae to systematically identify polymorphic TEs that may be implicated in genomic as well as in gene expression variation. We identified 2,523 TE polymorphisms and characterize a subset of 8% of the TEs as polymorphic elements that are evolutionary younger, less methylated, and more highly expressed when compared with the remaining 92% of the total TE complement. As expected, the polyrmorphic TEs are enriched in the adaptive genomic regions. Besides, we observed an association of polymorphic TEs with pathogenicity-related genes that localize nearby and that display high expression levels. Collectively, our analyses demonstrate that TE dynamics in V. dahliae contributes to genomic variation, correlates with expression of pathogenicity-related genes, and potentially impacts the evolution of adaptive genomic regions.

Building pan-genome infrastructures for crop plants and their use in association genetics

Pan-genomic studies aim at representing the entire sequence diversity within a species to provide useful resources for evolutionary studies, functional genomics and breeding of cultivated plants. Cost reductions in high-throughput sequencing and advances in sequence assembly algorithms have made it possible to create multiple reference genomes along with a catalogue of all forms of genetic variations in plant species with large and complex or polyploid genomes. In this review, we summarize the current approaches to building pan-genomes as an in silico representation of plant sequence diversity and outline relevant methods for their effective utilization in linking structural with phenotypic variation. We propose as future research avenues (i) transcriptomic and epigenomic studies across multiple reference genomes and (ii) the development of user-friendly and feature-rich pan-genome browsers.

High-quality genome assembly of Huazhan and Tianfeng, the parents of an elite rice hybrid Tian-you-hua-zhan

High-quality rice reference genomes have accelerated the comprehensive identification of genome-wide variations and research on functional genomics and breeding. Tian-you-hua-zhan has been a leading hybrid in China over the past decade. Here, de novo genome assembly strategy optimization for the rice indica lines Huazhan (HZ) and Tianfeng (TF), including sequencing platforms, assembly pipelines and sequence depth, was carried out. The PacBio and Nanopore platforms for long-read sequencing were utilized, with the Canu, wtdbg2, SMARTdenovo, Flye, Canu-wtdbg2, Canu-SMARTdenovo and Canu-Flye assemblers. The combination of PacBio and Canu was optimal, considering the contig N50 length, contig number, assembled genome size and polishing process. The assembled contigs were scaffolded with Hi-C data, resulting in two "golden quality" rice reference genomes, and evaluated using the scaffold N50, BUSCO, and LTR assembly index. Furthermore, 42,625 and 41,815 non-transposable element genes were annotated for HZ and TF, respectively. Based on our assembly of HZ and TF, as well as Zhenshan97, Minghui63, Shuhui498 and 9311, comprehensive variations were identified using Nipponbare as a reference. The de novo assembly strategy for rice we optimized and the "golden quality" rice genomes we produced for HZ and TF will benefit rice genomics and breeding research, especially with respect to uncovering the genomic basis of the elite traits of HZ and TF.

Identification and characterization of inheritable structural variations induced by ion beam radiations in rice

High energy ion beams are effective physical mutagens for mutation induction in plants. Due to their high linear energy transfer (LET) property, they are known to generate single nucleotide variations (SNVs) and insertion/deletions (InDels, <50 bp) as well as structural variations (SVs). However, due to the technical difficulties to identify SVs, studies on ion beam induced SVs by genome sequencing have so far been limited in numbers and inadequate in nature, and knowledge of SVs is scarce with regards to their characteristics. In the present study, we identified and validated SVs in six M₄ plants (designated as Ar_50, Ar_100, C_150, C_200, Ne_50 and Ne_100 according to ion beam types and irradiation doses), two each induced by argon (⁴⁰Ar¹⁸⁺), carbon (¹²C⁶⁺) and neon (²⁰Ne¹⁰⁺) ion beams and performed in depth analyses of their characteristics. In total, 22 SVs were identified and validated, consisting of 11 deletions, 1 duplication, and 4 intra-chromosomal and 6 inter-chromosomal translocations. There were several SVs larger than 1 kbp. The SVs were distributed across the whole genome with an aggregation with SNVs and InDels only in the Ne_50 mutants. An enrichment of a 11-bp wide G-rich DNA motif 'GAAGGWGGRGG' was identified around the SV breakpoints. Three mechanisms might be involved in the SV formation, i.e., the expansion of tandem repeats, transposable element insertion, and non-allelic homologous recombination. Put together, the present study provides a preliminary view of SVs induced by Ar, C and Ne ion beam radiations, and as a pilot study, it contributes to our understanding of how SVs might form after ion beam irradiation in rice.

Machine-learning predicts genomic determinants of meiosis-driven structural variation in a eukaryotic pathogen

Species harbor extensive structural variation underpinning recent adaptive evolution. However, the causality between genomic features and the induction of new rearrangements is poorly established. Here, we analyze a global set of telomere-to-telomere genome assemblies of a fungal pathogen of wheat to establish a nucleotide-level map of structural variation. We show that the recent emergence of pesticide resistance has been disproportionally driven by rearrangements. We use machine learning to train a model on structural variation events based on 30 chromosomal sequence features. We show that base composition and gene density are the major determinants of structural variation. Retrotransposons explain most inversion, indel and duplication events. We apply our model to Arabidopsis thaliana and show that our approach extends to more complex genomes. Finally, we analyze complete genomes of haploid offspring in a four-generation pedigree. Meiotic crossover locations are enriched for new rearrangements consistent with crossovers being mutational hotspots. The model trained on species-wide structural variation accurately predicts the position of >74% of newly generated variants along the pedigree. The predictive power highlights causality between specific sequence features and the induction of chromosomal rearrangements. Our work demonstrates that training sequence-derived models can accurately identify regions of intrinsic DNA instability in eukaryotic genomes.

Pan-genome analysis of 33 genetically diverse rice accessions reveals hidden genomic variations

Structural variations (SVs) and gene copy number variations (gCNVs) have contributed to crop evolution, domestication, and improvement. Here, we assembled 31 high-quality genomes of genetically diverse rice accessions. Coupling with two existing assemblies, we developed pan-genome-scale genomic resources including a graph-based genome, providing access to rice genomic variations. Specifically, we discovered 171,072 SVs and 25,549 gCNVs and used an Oryza glaberrima assembly to infer the derived states of SVs in the Oryza sativa population. Our analyses of SV formation mechanisms, impacts on gene expression, and distributions among subpopulations illustrate the utility of these resources for understanding how SVs and gCNVs shaped rice environmental adaptation and domestication. Our graph-based genome enabled genome-wide association study (GWAS)-based identification of phenotype-associated genetic variations undetectable when using only SNPs and a single reference assembly. Our work provides rich population-scale resources paired with easy-to-access tools to facilitate rice breeding as well as plant functional genomics and evolutionary biology research.