Development of an inclusive 580K SNP array and its application for genomic selection and genome-wide association studies in rice Article Swipe
Kyu‐Won Kim
,
Bhagwat Nawade
,
Jungrye Nam
,
Sang‐Ho Chu
,
Jungmin Ha
,
Yong‐Jin Park
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3389/fpls.2022.1036177
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3389/fpls.2022.1036177
Rice is a globally cultivated crop and is primarily a staple food source for more than half of the world’s population. Various single-nucleotide polymorphism (SNP) arrays have been developed and utilized as standard genotyping methods for rice breeding research. Considering the importance of SNP arrays with more inclusive genetic information for GWAS and genomic selection, we integrated SNPs from eight different data resources: resequencing data from the Korean World Rice Collection (KRICE) of 475 accessions, 3,000 rice genome project (3 K-RGP) data, 700 K high-density rice array, Affymetrix 44 K SNP array, QTARO, Reactome, and plastid and GMO information. The collected SNPs were filtered and selected based on the breeder’s interest, covering all key traits or research areas to develop an integrated array system representing inclusive genomic polymorphisms. A total of 581,006 high-quality SNPs were synthesized with an average distance of 200 bp between adjacent SNPs, generating a 580 K Axiom Rice Genotyping Chip (580 K _ KNU chip). Further validation of this array on 4,720 genotypes revealed robust and highly efficient genotyping. This has also been demonstrated in genome-wide association studies (GWAS) and genomic selection (GS) of three traits: clum length, heading date, and panicle length. Several SNPs significantly associated with cut-off, −log 10 p -value >7.0, were detected in GWAS, and the GS predictabilities for the three traits were more than 0.5, in both rrBLUP and convolutional neural network (CNN) models. The Axiom 580 K Genotyping array will provide a cost-effective genotyping platform and accelerate rice GWAS and GS studies.
Related Topics
Concepts
Biology
Genomic selection
Genetics
Selection (genetic algorithm)
SNP
Genome
Genome-wide association study
SNP array
Computational biology
Genetic association
Evolutionary biology
Single-nucleotide polymorphism
Genotype
Computer science
Gene
Artificial intelligence
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fpls.2022.1036177
- OA Status
- gold
- Cited By
- 24
- References
- 67
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4307271543
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4307271543Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3389/fpls.2022.1036177Digital Object Identifier
- Title
-
Development of an inclusive 580K SNP array and its application for genomic selection and genome-wide association studies in riceWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-10-24Full publication date if available
- Authors
-
Kyu‐Won Kim, Bhagwat Nawade, Jungrye Nam, Sang‐Ho Chu, Jungmin Ha, Yong‐Jin ParkList of authors in order
- Landing page
-
https://doi.org/10.3389/fpls.2022.1036177Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.3389/fpls.2022.1036177Direct OA link when available
- Concepts
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Biology, Genomic selection, Genetics, Selection (genetic algorithm), SNP, Genome, Genome-wide association study, SNP array, Computational biology, Genetic association, Evolutionary biology, Single-nucleotide polymorphism, Genotype, Computer science, Gene, Artificial intelligenceTop concepts (fields/topics) attached by OpenAlex
- Cited by
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24Total citation count in OpenAlex
- Citations by year (recent)
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2025: 9, 2024: 11, 2023: 4Per-year citation counts (last 5 years)
- References (count)
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67Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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