Copy Number Variation among Resistance Genes Analogues in Brassica napus.

Copy number variations (CNVs) are defined as deletions, duplications and insertions among individuals of a species. There is growing evidence that CNV is a major factor underlining various autoimmune disorders and diseases in humans; however, in plants, especially oilseed crops, the role of CNVs in disease resistance is not well studied. Here, we investigate the genome-wide diversity and genetic properties of CNVs in resistance gene analogues (RGAs) across eight Brassica napus lines. A total of 1137 CNV events (704 deletions and 433 duplications) were detected across 563 RGAs. The results show CNVs are more likely to occur across clustered RGAs compared to singletons. In addition, 112 RGAs were linked to a blackleg resistance QTL, of which 25 were affected by CNV. Overall, we show that the presence and abundance of CNVs differ between lines, suggesting that in B. napus, the distribution of CNVs depends on genetic background. Our findings advance the understanding of CNV as an important type of genomic structural variation in B. napus and provide a resource to support breeding of advanced canola lines.

Pangenomes as a Resource to Accelerate Breeding of Under-Utilised Crop Species.

Pangenomes are a rich resource to examine the genomic variation observed within a species or genera, supporting population genetics studies, with applications for the improvement of crop traits. Major crop species such as maize (Zea mays), rice (Oryza sativa), Brassica (Brassica spp.), and soybean (Glycine max) have had pangenomes constructed and released, and this has led to the discovery of valuable genes associated with disease resistance and yield components. However, pangenome data are not available for many less prominent crop species that are currently under-utilised. Despite many under-utilised species being important food sources in regional populations, the scarcity of genomic data for these species hinders their improvement. Here, we assess several under-utilised crops and review the pangenome approaches that could be used to build resources for their improvement. Many of these under-utilised crops are cultivated in arid or semi-arid environments, suggesting that novel genes related to drought tolerance may be identified and used for introgression into related major crop species. In addition, we discuss how previously collected data could be used to enrich pangenome functional analysis in genome-wide association studies (GWAS) based on studies in major crops. Considering the technological advances in genome sequencing, pangenome references for under-utilised species are becoming more obtainable, offering the opportunity to identify novel genes related to agro-morphological traits in these species.

Skim-Based Genotyping by Sequencing Using a Double Haploid Population to Call SNPs, Infer Gene Conversions, and Improve Genome Assemblies.

Genotyping by sequencing (GBS) is an emerging technology to rapidly call an abundance of single nucleotide polymorphisms (SNPs) using genome sequencing technology. Several different methodologies and approaches have recently been established, most of these relying on a specific preparation of data. Here we describe our GBS pipeline, which uses high coverage reads from two parents and low coverage reads from their double haploid offspring to call SNPs on a large scale. The upside of this approach is the high resolution and scalability of the method.

Machine Learning for Image Analysis: Leaf Disease Segmentation.

Plant phenomics field has seen a great increase in scalability in the last decade mainly due to technological advances in remote sensors and phenotyping platforms. These are capable of screening thousands of plants many times throughout the day, generating massive amounts of data, which require an automated analysis to extract meaningful information. Deep learning is a branch of machine learning that has revolutionized many fields of research. Deep learning models are able to extract autonomously the underlying features within the dataset, providing a multi-level representation of the data. Our intention is to show the feasibility and effectiveness of using deep learning and low-cost technology for automated phenotyping. In this methods chapter, we describe how to train a deep neural network to segment leaf images and extract the pixels related to the disease.

Genotype-Environment mismatch of kelp forests under climate change.

Climate change is increasingly impacting ecosystems globally. Understanding adaptive genetic diversity and whether it will keep pace with projected climatic change is necessary to assess species' vulnerability and design efficient mitigation strategies such as assisted adaptation. Kelp forests are the foundations of temperate reefs globally but are declining in many regions due to climate stress. A lack of knowledge of kelp's adaptive genetic diversity hinders assessment of vulnerability under extant and future climates. Using 4245 single nucleotide polymorphisms (SNPs), we characterized patterns of neutral and putative adaptive genetic diversity for the dominant kelp in the southern hemisphere (Ecklonia radiata) from ~1000 km of coastline off Western Australia. Strong population structure and isolation-by-distance was underpinned by significant signatures of selection related to temperature and light. Gradient forest analysis of temperature-linked SNPs under selection revealed a strong association with mean annual temperature range, suggesting adaptation to local thermal environments. Critically, modelling revealed that predicted climate-mediated temperature changes will probably result in high genomic vulnerability via a mismatch between current and future predicted genotype-environment relationships such that kelp forests off Western Australia will need to significantly adapt to keep pace with projected climate change. Proactive management techniques such as assisted adaptation to boost resilience may be required to secure the future of these kelp forests and the immense ecological and economic values they support.

Plant pangenomics: approaches, applications and advancements.

With the assembly of increasing numbers of plant genomes, it is becoming accepted that a single reference assembly does not reflect the gene diversity of a species. The production of pangenomes, which reflect the structural variation and polymorphisms in genomes, enables in depth comparisons of variation within species or higher taxonomic groups. In this review, we discuss the current and emerging approaches for pangenome assembly, analysis and visualisation. In addition, we consider the potential of pangenomes for applied crop improvement, evolutionary and biodiversity studies. To fully exploit the value of pangenomes it is important to integrate broad information such as phenotypic, environmental, and expression data to gain insights into the role of variable regions within genomes.

Skim-Based Genotyping by Sequencing Using a Double Haploid Population to Call SNPs, Infer Gene Conversions, and Improve Genome Assemblies.

Genotyping by sequencing (GBS) is an emerging technology to rapidly call an abundance of Single Nucleotide Polymorphisms (SNPs) using genome sequencing technology. Several different methodologies and approaches have recently been established, most of these relying on a specific preparation of data. Here we describe our GBS-pipeline, which uses high coverage reads from two parents and low coverage reads from their double haploid offspring to call SNPs on a large scale. The upside of this approach is the high resolution and scalability of the method.

Identification and characterization of more than 4 million intervarietal SNPs across the group 7 chromosomes of bread wheat.

Despite being a major international crop, our understanding of the wheat genome is relatively poor due to its large size and complexity. To gain a greater understanding of wheat genome diversity, we have identified single nucleotide polymorphisms between 16 Australian bread wheat varieties. Whole-genome shotgun Illumina paired read sequence data were mapped to the draft assemblies of chromosomes 7A, 7B and 7D to identify more than 4 million intervarietal SNPs. SNP density varied between the three genomes, with much greater density observed on the A and B genomes than the D genome. This variation may be a result of substantial gene flow from the tetraploid Triticum turgidum, which possesses A and B genomes, during early co-cultivation of tetraploid and hexaploid wheat. In addition, we examined SNP density variation along the chromosome syntenic builds and identified genes in low-density regions which may have been selected during domestication and breeding. This study highlights the impact of evolution and breeding on the bread wheat genome and provides a substantial resource for trait association and crop improvement. All SNP data are publically available on a generic genome browser GBrowse at www.wheatgenome.info.

High-resolution molecular karyotyping uncovers pairing between ancestrally related Brassica chromosomes.

How do chromosomal regions with differing degrees of homology and homeology interact at meiosis? We provide a novel analytical method based on simple genetics principles which can help to answer this important question. This method interrogates high-throughput molecular marker data in order to infer chromosome behavior at meiosis in interspecific hybrids. We validated this method using high-resolution molecular marker karyotyping in two experimental Brassica populations derived from interspecific crosses among B. juncea, B. napus and B. carinata, using a single nucleotide polymorphism chip. This method of analysis successfully identified meiotic interactions between chromosomes sharing different degrees of similarity: full-length homologs; full-length homeologs; large sections of primary homeologs; and small sections of secondary homeologs. This analytical method can be applied to any allopolyploid species or fertile interspecific hybrid in order to detect meiotic associations. This genetic information can then be used to identify which genomic regions share functional homeology (i.e., retain enough similarity to allow pairing and segregation at meiosis). When applied to interspecific hybrids for which reference genome sequences are available, the question of how differing degrees of homology and homeology affect meiotic interactions may finally be resolved.