Graph Pangenomes Track Genetic Variants for Crop Improvement.

Global climate change and the urgency to transform crops require an exhaustive genetic evaluation. The large polyploid genomes of food crops, such as cereals, make it difficult to identify candidate genes with confirmed hereditary. Although genome-wide association studies (GWAS) have been proficient in identifying genetic variants that are associated with complex traits, the resolution of acquired heritability faces several significant bottlenecks such as incomplete detection of structural variants (SV), genetic heterogeneity, and/or locus heterogeneity. Consequently, a biased estimate is generated with respect to agronomically complex traits. The graph pangenomes have resolved this missing heritability and provide significant details in terms of specific loci segregating among individuals and evolving to variations. The graph pangenome approach facilitates crop improvements through genome-linked fast breeding.

Long-read sequencing reveals widespread intragenic structural variants in a recent allopolyploid crop plant.

Genome structural variation (SV) contributes strongly to trait variation in eukaryotic species and may have an even higher functional significance than single-nucleotide polymorphism (SNP). In recent years, there have been a number of studies associating large chromosomal scale SV ranging from hundreds of kilobases all the way up to a few megabases to key agronomic traits in plant genomes. However, there have been little or no efforts towards cataloguing small- (30-10 000 bp) to mid-scale (10 000-30 000 bp) SV and their impact on evolution and adaptation-related traits in plants. This might be attributed to complex and highly duplicated nature of plant genomes, which makes them difficult to assess using high-throughput genome screening methods. Here, we describe how long-read sequencing technologies can overcome this problem, revealing a surprisingly high level of widespread, small- to mid-scale SV in a major allopolyploid crop species, Brassica napus. We found that up to 10% of all genes were affected by small- to mid-scale SV events. Nearly half of these SV events ranged between 100 bp and 1000 bp, which makes them challenging to detect using short-read Illumina sequencing. Examples demonstrating the contribution of such SV towards eco-geographical adaptation and disease resistance in oilseed rape suggest that revisiting complex plant genomes using medium-coverage long-read sequencing might reveal unexpected levels of functional gene variation, with major implications for trait regulation and crop improvement.

Genome-wide patterns of large-size presence/absence variants in sorghum.

The presence/absence variants (PAVs) are a major source of genome structural variation and have profound effects on phenotypic and genomic variation in animals and humans. However, little is understood about PAVs in plant genomes. Our previous resequencing effort on three sorghum (Sorghum bicolour L.) genomes, each 12× coverage, uncovered 5 364 PAVs. Here, we report a detailed characterization of 51 large-size (>30 kb) PAVs. These PAVs spanned a total size of 2.92 Mb of the sorghum genome containing 202 known and predicted genes, including 38 genes annotated to encode cell death and stress response genes. The PAVs varied considerably for repeat sequences and mobile elements with DNA transposons as the major components. The frequency and distribution of these PAVs differed substantially across 96 sorghum inbred lines, and the low- and high frequency PAVs differed in their gene categories. This report shed new light on the occurrence and diversity of PAVs in sorghum genomes. Our research exemplifies a new perspective to explore genome structural variation for genetic improvement in plant breeding.

An SGSGeneloss-Based Method for Constructing a Gene Presence-Absence Table Using Mosdepth.

Presence-absence variants (PAV) are genomic regions present in some individuals of a species, but not others. PAVs have been shown to contribute to genomic diversity, especially in bacteria and plants. These structural variations have been linked to traits and can be used to track a species' evolutionary history. PAVs are usually called by aligning short read sequence data from one or more individuals to a reference genome or pangenome assembly, and then comparing coverage. Regions where reads do not align define absence in that individual, and the regions are classified as PAVs. The method below details how to align sequence reads to a reference and how to use the sequencing-coverage calculator Mosdepth to identify PAVs and construct a PAV table for use in downstream comparative genome analysis.