Updated resource of 180K soybean SNP genotyping array based on the T2T reference genome
Ji-Hun Hwang1, Sungwoo Lee2, Ju Seok Lee3
1Department of Integrative Biological Sciences and Industry, Sejong University, Seoul, Republic of Korea.
Soybean (Glycine max (L.) Merr.) SNP genotyping data were successfully remapped to updated reference genomes (Wm82.v4 and Wm82.v6). This enhances genomic resources for accelerated crop improvement and advanced soybean breeding programs.
Area of Science:
- Genomics
- Plant Breeding
- Bioinformatics
Background:
- Single nucleotide polymorphism (SNP) genotyping is crucial for crop improvement, but existing soybean data used an outdated reference genome (Wm82.v1).
- This outdated reference genome has assembly gaps and misassemblies, limiting genomic resolution and the utility of valuable SNP data.
- Newer, high-quality reference genomes (Wm82.v4, Wm82.v6) are available but haven't been integrated with existing SNP datasets.
Purpose of the Study:
- To remap existing soybean SNP array data to the latest reference genomes (Wm82.v4 and Wm82.v6).
- To create an integrated genomic resource for soybean research and crop improvement.
- To leverage past genotyping investments with current genomic assembly standards.
Main Methods:
- Extracted flanking sequences from SNP markers in the Wm82.v1 reference genome.
- Performed sequence-based alignment of flanking regions to the Wm82.v4 and Wm82.v6 reference genomes.
- Filtered out markers with mapping failures, allele mismatches, low identity, or multiple mappings.
Main Results:
- Successfully remapped 175,202 SNP markers to Wm82.v4 and 175,763 markers to Wm82.v6.
- Remapped genotype data for 927 soybean accessions, including Korean and USDA-GRIN collections.
- Generated a high-resolution SNP dataset integrated with advanced soybean reference genomes.
Conclusions:
- The remapped SNP data provide a valuable, updated genomic resource for the soybean research community.
- This resource facilitates enhanced crop improvement through high-resolution genomic studies.
- It bridges the gap between historical genotyping data and state-of-the-art reference assemblies.
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