A pan-genome data structure induced by pooled sequencing facilitates variant mining in heterogeneous germplasm

Area of Science:

• Genomics
• Plant Breeding
• Bioinformatics

Background:

• Gene banks hold valuable genetic variation, but exploiting heterogeneous germplasm is challenging.
• Molecular breeding techniques like transgenics and genome editing offer new ways to access hidden genetic diversity.

Purpose of the Study:

• To develop a novel data structure for exploiting genetic variation in wild relatives.
• To facilitate allele mining for agronomically important traits in crop species.

Main Methods:

• Whole-genome sequencing of pooled individuals from wild Patellifolia spp. populations.
• Representing the pan-genome as a map of pooled sequencing reads to a reference genome.
• Creating a BLAST database of mapped reads for variant identification.

Main Results:

• Demonstrated querying the data structure by reference genome position or homology to identify sequence variants.
• Successfully cataloged variants in Patellifolia genomic regions with single-copy orthologs in sugar beet.
• The "pooled read archive" can be generated and queried using standard bioinformatics tools.

Conclusions:

• The "pooled read archive" provides an effective method for discovering agronomically important sequence variation.
• This approach enhances the utilization of genetic resources from wild relatives for crop improvement.
• Facilitates the direct exploitation of hidden sequence variation for breeding applications.