Extreme trait GWAS (Et-GWAS): Unraveling rare variants in the 3,000 rice genome
Identifying high-impact, rare genetic variants associated with specific traits is crucial for crop improvement. The 3,010 rice genome (3K RG) dataset offers a valuable resource for discovering genomic regions with potential applications in crop breeding. We used Extreme Trait GWAS (Et-GWAS), employi...
| Autores principales: | , , , , , , , , , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
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Life Science Alliance
2024
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/163737 |
| _version_ | 1855524534020472832 |
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| author | Gnanapragasam, Niranjani Prasanth, Vinukonda Vishnu Sundaram, Krishna Tesman Kumar, Ajay Pahi, Bandana Gurjar, Anoop Venkateshwarlu, Challa Kalia, Sanjay Kumar, Arvind Dixit, Shalabh Kohli, Ajay Singh, Uma Maheshwer Singh, Vikas Kumar Sinha, Pallavi |
| author_browse | Dixit, Shalabh Gnanapragasam, Niranjani Gurjar, Anoop Kalia, Sanjay Kohli, Ajay Kumar, Ajay Kumar, Arvind Pahi, Bandana Prasanth, Vinukonda Vishnu Singh, Uma Maheshwer Singh, Vikas Kumar Sinha, Pallavi Sundaram, Krishna Tesman Venkateshwarlu, Challa |
| author_facet | Gnanapragasam, Niranjani Prasanth, Vinukonda Vishnu Sundaram, Krishna Tesman Kumar, Ajay Pahi, Bandana Gurjar, Anoop Venkateshwarlu, Challa Kalia, Sanjay Kumar, Arvind Dixit, Shalabh Kohli, Ajay Singh, Uma Maheshwer Singh, Vikas Kumar Sinha, Pallavi |
| author_sort | Gnanapragasam, Niranjani |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Identifying high-impact, rare genetic variants associated with specific traits is crucial for crop improvement. The 3,010 rice genome (3K RG) dataset offers a valuable resource for discovering genomic regions with potential applications in crop breeding. We used Extreme Trait GWAS (Et-GWAS), employing bulk pooling and allele frequency measurement to efficiently extract rare variants from the 3K RG. This innovative approach facilitates the detection of associations between genetic variants and target traits, concentrating and quantifying rare alleles. In our study, on grain yield under drought stress, Et-GWAS successfully identified five key genes (OsPP2C11, OsK5.2, OsIRO2, OsPEX1, and OsPWA1) known for enhancing yield under drought. In addition, we examined the overlap of our results with previously reported qDTY-QTLs and observed that OsUCH1 and OsUCH2 genes were located within qDTY2.2. We compared Et-GWAS with conventional GWAS, finding it effectively capturing most candidate genes associated with the target trait. Validation with resistant starch showed similar results. To enhance user-friendliness, we developed a GUI for Et-GWAS; https://et-gwas.shinyapps.io/Et-GWAS/. |
| format | Journal Article |
| id | CGSpace163737 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Life Science Alliance |
| publisherStr | Life Science Alliance |
| record_format | dspace |
| spelling | CGSpace1637372025-11-12T04:57:19Z Extreme trait GWAS (Et-GWAS): Unraveling rare variants in the 3,000 rice genome Gnanapragasam, Niranjani Prasanth, Vinukonda Vishnu Sundaram, Krishna Tesman Kumar, Ajay Pahi, Bandana Gurjar, Anoop Venkateshwarlu, Challa Kalia, Sanjay Kumar, Arvind Dixit, Shalabh Kohli, Ajay Singh, Uma Maheshwer Singh, Vikas Kumar Sinha, Pallavi crops germplasm crop improvement innovation systems yields genotypes drought tolerance Identifying high-impact, rare genetic variants associated with specific traits is crucial for crop improvement. The 3,010 rice genome (3K RG) dataset offers a valuable resource for discovering genomic regions with potential applications in crop breeding. We used Extreme Trait GWAS (Et-GWAS), employing bulk pooling and allele frequency measurement to efficiently extract rare variants from the 3K RG. This innovative approach facilitates the detection of associations between genetic variants and target traits, concentrating and quantifying rare alleles. In our study, on grain yield under drought stress, Et-GWAS successfully identified five key genes (OsPP2C11, OsK5.2, OsIRO2, OsPEX1, and OsPWA1) known for enhancing yield under drought. In addition, we examined the overlap of our results with previously reported qDTY-QTLs and observed that OsUCH1 and OsUCH2 genes were located within qDTY2.2. We compared Et-GWAS with conventional GWAS, finding it effectively capturing most candidate genes associated with the target trait. Validation with resistant starch showed similar results. To enhance user-friendliness, we developed a GUI for Et-GWAS; https://et-gwas.shinyapps.io/Et-GWAS/. 2024-03 2024-12-18T17:04:11Z 2024-12-18T17:04:11Z Journal Article https://hdl.handle.net/10568/163737 en Open Access application/pdf Life Science Alliance Gnanapragasam, Niranjani, Vinukonda Vishnu Prasanth, Krishna Tesman Sundaram, Ajay Kumar, Bandana Pahi, Anoop Gurjar, Challa Venkateshwarlu et al. "Extreme trait GWAS (Et-GWAS): Unraveling rare variants in the 3,000 rice genome." Life Science Alliance 7, no. 3 (2024). |
| spellingShingle | crops germplasm crop improvement innovation systems yields genotypes drought tolerance Gnanapragasam, Niranjani Prasanth, Vinukonda Vishnu Sundaram, Krishna Tesman Kumar, Ajay Pahi, Bandana Gurjar, Anoop Venkateshwarlu, Challa Kalia, Sanjay Kumar, Arvind Dixit, Shalabh Kohli, Ajay Singh, Uma Maheshwer Singh, Vikas Kumar Sinha, Pallavi Extreme trait GWAS (Et-GWAS): Unraveling rare variants in the 3,000 rice genome |
| title | Extreme trait GWAS (Et-GWAS): Unraveling rare variants in the 3,000 rice genome |
| title_full | Extreme trait GWAS (Et-GWAS): Unraveling rare variants in the 3,000 rice genome |
| title_fullStr | Extreme trait GWAS (Et-GWAS): Unraveling rare variants in the 3,000 rice genome |
| title_full_unstemmed | Extreme trait GWAS (Et-GWAS): Unraveling rare variants in the 3,000 rice genome |
| title_short | Extreme trait GWAS (Et-GWAS): Unraveling rare variants in the 3,000 rice genome |
| title_sort | extreme trait gwas et gwas unraveling rare variants in the 3 000 rice genome |
| topic | crops germplasm crop improvement innovation systems yields genotypes drought tolerance |
| url | https://hdl.handle.net/10568/163737 |
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