Background

Existing crop genome studies have tended to rely heavily on reference genomes of single varieties. However, it is realistically difficult for a single standard genome alone to perfectly reflect the genetic diversity among individuals. In particular, the popular Single Nucleotide Polymorphism (SNP) analysis method is useful for identifying micro-variations, but it has limitations in detecting structural variations (SVs) where thousands of base pairs are swapped.

Cucumber, originating in India, has spread around the world, and traits such as fruit length and disease resistance have diversified in response to the climate. Breeders aim to maximize genetic potential and develop superior new varieties. However, the genomic map that identifies the key variations that cause trait changes has long been shrouded in mystery. As the need arises to integrate the genetic information of all cucumber varieties in order to respond agilely to climate change and emerging pests and diseases, the time has come for a massive gene map.

Key Findings

The Vegetable and Flower Research Institute of the Chinese Academy of Agricultural Sciences (CAAS) and Qingdao Agricultural University jointly conducted a study to address this issue, performing precise genetic analysis on 125 cucumber varieties. The research team completed chromosome-level genome assemblies for each variety and successfully constructed a large-scale, graph-based pangenome by organically linking them. The high-confidence SVs identified by this pangenome map totaled 171,892.

Furthermore, the researchers boldly attempted a Genome-Wide Association Study (GWAS) based on SVs, targeting 38 key agricultural traits of cucumber. The analysis revealed that more than 60% of the overall genetic signals were SV-specific association signals that could not be detected by existing SNP analysis methods. This is considered to have maximized the interpretability of genetic diversity in the genetic variation analysis model.

With this genomic map, the research team successfully discovered key genes that have long been a challenge for breeders. A notable achievement is the gene cloning of 'CsCcu', a cucumber black spot resistance gene. This resistance gene was previously in a state of loss due to variation in the single reference genome, but the gene location and sequence were finally restored by precisely tracing back the pangenome graph.

The research team also focused on identifying the causative variations that determine cucumber fruit length. The analysis revealed that a specific Long Terminal Repeat (LTR) transposon inserted in the first exon region of 'CsSPL1', a plant growth regulation gene, acts as a positive regulator that increases cucumber fruit length. Cucumbers with this LTR transposon have long fruits, which explains the preference differences and breeding path divergence between short cucumbers in Eurasia and long cucumbers in East Asia.

Significance and Prospects

The graph pangenome map constructed in this study is attracting attention as an asset that will change the paradigm of crop genome research. It now provides the foundation for molecular design breeding, which can artificially control commercially valuable complex traits such as specific pathogen resistance or fruit length. In the past, conventional breeding methods were passive, waiting for traits to be expressed over several generations after cross-breeding. Pangenome-based molecular design can be defined as an engineering process that precisely targets and assembles target genes.

However, there are still technical hurdles to overcome before these genetic variations can be applied to commercially cultivable common cucumber varieties. In the process of introducing excellent genes from wild species into cultivated species, unexpected trait changes, such as growth reduction or taste changes, are often observed. The precise working mechanism of the multi-gene network that responds to climate change has not yet been fully elucidated. The research team expresses its intention to combine artificial intelligence (AI) technology and integrated biological analysis methods in the future to elucidate this complex genetic association.