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Summary 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.

Summary Methods based on single nucleotide polymorphism (SNP), copy number variation (CNV) and presence/absence variation (PAV) discovery provide a valuable resource to study gene structure and evolution. However, as a result of these structural variations, a single reference genome is unable to cover the entire gene content of a species. Therefore, pangenomics analysis is needed to ensure that the genomic diversity within a species is fully represented. Brassica napus is one of the most important oilseed crops in the world and exhibits variability in its resistance genes across different cultivars. Here, we characterized resistance gene distribution across 50 B. napus lines. We identified a total of 1749 resistance gene analogs (RGAs), of which 996 are core and 753 are variable, 368 of which are not present in the reference genome (cv. Darmor‐bzh). In addition, a total of 15 318 SNPs were predicted within 1030 of the RGAs. The results showed that core R‐genes harbour more SNPs than variable genes. More nucleotide binding site‐leucine‐rich repeat (NBS‐LRR) genes were located in clusters than as singletons, with variable genes more likely to be found in clusters. We identified 106 RGA candidates linked to blackleg resistance quantitative trait locus (QTL). This study provides a better understanding of resistance genes to target for genomics‐based improvement and improved disease resistance.

There is an increasing awareness that as a result of structural variation, a reference sequence representing a genome of a single individual is unable to capture all of the gene repertoire found in the species. A large number of genes affected by presence/absence and copy number variation suggest that it may contribute to phenotypic and agronomic trait diversity. Here we show by analysis of the Brassica oleracea pangenome that nearly 20% of genes are affected by presence/absence variation. Several genes displaying presence/absence variation are annotated with functions related to major agronomic traits, including disease resistance, flowering time, glucosinolate metabolism and vitamin biosynthesis. Brassica oleracea is a single species that includes diverse crops such as cabbage, broccoli and Brussels sprouts. Here, the authors identify genes not captured in existing B. oleracea reference genomes by the assembly of a pangenome and show variations in gene content that may be related to important agronomic traits

Assessing crop production in the field often requires breeders to wait until the end of the season to collect yield-related measurements, limiting the pace of the breeding cycle. Early prediction of crop performance can reduce this constraint by allowing breeders more time to focus on the highest-performing varieties. Here, we present a multimodal deep learning model for predicting the performance of maize (Zea mays) at an early developmental stage, offering the potential to accelerate crop breeding. We employed multispectral images and eight vegetation indices, collected by an uncrewed aerial vehicle approximately 60 days after sowing, over three consecutive growing cycles (2017, 2018 and 2019). The multimodal deep learning approach was used to integrate field management and genotype information with the multispectral data, providing context to the conditions that the plants experienced during the trial. Model performance was assessed using holdout data, in which the model accurately predicted the yield (RMSE 1.07 t/ha, a relative RMSE of 7.60% of 16 t/ha, and R2 score 0.73) and identified the majority of high-yielding varieties, outperforming previously published models for early yield prediction. The inclusion of vegetation indices was important for model performance, with a normalized difference vegetation index and green with normalized difference vegetation index contributing the most to model performance. The model provides a decision support tool, identifying promising lines early in the field trial.

Summary Brassica oleracea is an important agricultural species encompassing many vegetable crops including cabbage, cauliflower, broccoli and kale; however, it can be susceptible to a variety of fungal diseases such as clubroot, blackleg, leaf spot and downy mildew. Resistance to these diseases is meditated by specific disease resistance genes analogs (RGAs) which are differently distributed across B. oleracea lines. The sequenced reference cultivar does not contain all B. oleracea genes due to gene presence/absence variation between individuals, which makes it necessary to search for RGA candidates in the B. oleracea pangenome. Here we present a comparative analysis of RGA candidates in the pangenome of B. oleracea. We show that the presence of RGA candidates differs between lines and suggests that in B. oleracea, SNPs and presence/absence variation drive RGA diversity using separate mechanisms. We identified 59 RGA candidates linked to Sclerotinia, clubroot, and Fusarium wilt resistance QTL, and these findings have implications for crop breeding in B. oleracea, which may also be applicable in other crops species.

Seafood mislabelling and species substitution, compounded by a convoluted seafood supply chain with significant traceability challenges, hinder efforts towards more sustainable, responsible, and ethical fishing and business practices. We conducted the largest evaluation of the quality and accuracy of labels for 672 seafood products sold in Australia, assessing six seafood groups (i.e., hoki, prawns, sharks and rays, snapper, squid and cuttlefish, and tuna) from fishmongers, restaurants, and supermarkets, including domestically caught and imported products. DNA barcoding revealed 11.8% of seafood tested did not match their label with sharks and rays, and snappers, having the highest mislabelling rate. Moreover, only 25.5% of products were labelled at a species-level, while most labels used vague common names or umbrella terms such as ‘flake’ and ‘snapper’. These poor-quality labels had higher rates of mislabelling than species-specific labels and concealed the sale of threatened or overfished taxa, as well as products with lower nutritional quality, reduced economic value, or potential health risks. Our results highlight Australia’s weak seafood labelling regulations and ambiguous non-mandatory naming conventions, which impede consumer choice for accurately represented, sustainable, and responsibly sourced seafood. We recommend strengthening labelling regulations to mitigate seafood mislabelling and substitution, ultimately improving consumer confidence when purchasing seafood.

Genome-Wide Association Studies are widely used to correlate phenotypic traits with genetic variants. These studies usually compare the genetic variation between two groups to single out certain Single Nucleotide Polymorphisms (SNPs) that are linked to a phenotypic variation in one of the groups. However, it is necessary to have a large enough sample size to find statistically significant correlations. Direct-To-Consumer (DTC) genetic testing can supply additional data: DTC-companies offer the analysis of a large amount of SNPs for an individual at low cost without the need to consult a physician or geneticist. Over 100,000 people have already been genotyped through Direct-To-Consumer genetic testing companies. However, this data is not public for a variety of reasons and thus cannot be used in research. It seems reasonable to create a central open data repository for such data. Here we present the web platform openSNP, an open database which allows participants of Direct-To-Consumer genetic testing to publish their genetic data at no cost along with phenotypic information. Through this crowdsourced effort of collecting genetic and phenotypic information, openSNP has become a resource for a wide area of studies, including Genome-Wide Association Studies. openSNP is hosted at http://www.opensnp.org, and the code is released under MIT-license at http://github.com/gedankenstuecke/snpr.

Recent growth in crop genomic and trait data have opened opportunities for the application of novel approaches to accelerate crop improvement. Machine learning and deep learning are at the forefront of prediction-based data analysis. However, few approaches for genotype to phenotype prediction compare machine learning with deep learning and further interpret the models that support the predictions. This study uses genome wide molecular markers and traits across 1110 soybean individuals to develop accurate prediction models. For 13/14 sets of predictions, XGBoost or random forest outperformed deep learning models in prediction performance. Top ranked SNPs by F-score were identified from XGBoost, and with further investigation found overlap with significantly associated loci identified from GWAS and previous literature. Feature importance rankings were used to reduce marker input by up to 90%, and subsequent models maintained or improved their prediction performance. These findings support interpretable machine learning as an approach for genomic based prediction of traits in soybean and other crops.

Orphan crops are important sources of nutrition in developing regions and many are tolerant to biotic and abiotic stressors; however, modern crop improvement technologies have not been widely applied to orphan crops due to the lack of resources available. There are orphan crop representatives across major crop types and the conservation of genes between these related species can be used in crop improvement. Machine learning (ML) has emerged as a promising tool for crop improvement. Transferring knowledge from major crops to orphan crops and using machine learning to improve accuracy and efficiency can be used to improve orphan crops. Machine learning has emerged as a promising tool for crop improvement. Here, the authors review transferring knowledge from major crops to orphan crops and using machine learning to improve accuracy and efficiency of orphan crops breeding.

Second-generation sequencing (SGS) has advanced the study of crop genomes and has provided insights into diversity and evolution. However, repetitive DNA sequences in crops often lead to incomplete or erroneous assemblies because SGS reads are too short to fully resolve these repeats. To overcome some of these challenges, long-read sequencing and optical mapping have been developed to produce high-quality assemblies for complex genomes. Previously, high error rates, low throughput, and high costs have limited the adoption of long-read sequencing and optical mapping. However, with recent improvements and the development of novel algorithms, the application of these technologies is increasing. We review the development of long-read sequencing and optical mapping, and assess their application in crop genomics for breeding improved crops. Short-read second-generation DNA sequencing has revolutionised our understanding of biology but suffers from significant limitations of scale. Long-read sequencing and optical mapping promise to deliver long-range genomic information, but their adoption has been hampered by low throughput and relatively high error rates. Recent improvements in these long-range technologies have overcome these issues, and open broad applications for genome assembly and the analysis of genome structural variation. These advances will facilitate our understanding of genome structural diversity and heritable agronomic traits, accelerating the development of improved crop varieties to feed the expanding human population.