- Ivana Kaňovská, Jana Biová and Mária Škrabišová
- Department of Biochemistry, Faculty of Science, Palacký University in Olomouc, Slechtitel ů 27, Olomouc 77900, Czech Republic
- 2024
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Crop breeding advancement is hindered by the imperfection of methods to reveal genes underlying key traits. Genome-wide Association Study (GWAS) is one such method, identifying genomic regions linked to phenotypes. Post-GWAS analyses predict candidate genes and assist in causative mutation (CM) recognition. Here, we assess post-GWAS approaches, address limitations in omics data integration and stress the importance of evaluating associated variants within a broader context of publicly available datasets. Recent advances in bioinformatics tools and genomic strategies for CM identification and allelic variation exploration are reviewed. We discuss the role of markers and marker panel development for more precise breeding. Finally, we highlight the perspectives and challenges of GWAS-based CM prediction for complex quantitative traits.
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article_postGWAS_Kanovska - Kanovska et al. 2024 Current Opinion in Plant Biology
Kanovska et al. 2024 Current Opinion in Plant Biology -
Vollmann Johann, Rittler Leopold, Hahn Volker, Yao Xindong, Đorđević Vuk, Martin Pachner, Willmar Leiser, Christine Riedel, Raluca Rezi, Claude-Alain Bétrix, Jerzy Nawracała, Inna Temchenko, Li-Juan Qiu, 2024. Soybean flowering in the north: Combination of Chinese and European genetics could support better adaptation of soybean to northern latitudes. Legume Hub. https://www.legumehub.eu
New perspectives of post-GWAS analyses: From markers to causal genes for more precise crop breeding
Posted: 28.01.2025
Crop breeding advancement is hindered by the imperfection of methods to reveal genes underlying key traits. Genome-wide Association Study (GWAS) is one such method, identifying genomic regions linked to phenotypes. Post-GWAS analyses predict candidate genes and assist in causative mutation (CM) recognition. Here, we assess post-GWAS approaches, address limitations in omics data integration and stress the importance of evaluating associated variants within a broader context of publicly available datasets. Recent advances in bioinformatics tools and genomic strategies for CM identification and allelic variation exploration are reviewed. We discuss the role of markers and marker panel development for more precise breeding. Finally, we highlight the perspectives and challenges of GWAS-based CM prediction for complex quantitative traits.
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Acknowledgement: Legume Generation (Boosting innovation in breeding for the next generation of legume crops for Europe) has received funding from the European Union through Horizon Europe under grant agreement No 101081329 and co-funding from UK Research and Innovation (UKRI) from the UK government’s Horizon Europe funding guarantee. It also receives support from the governments of Switzerland and New Zealand.
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