{"title":"New perspectives of post-GWAS analyses: From markers to causal genes for more precise crop breeding","authors":"Ivana Kaňovská, Jana Biová, Mária Škrabišová","doi":"10.1016/j.pbi.2024.102658","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":11003,"journal":{"name":"Current opinion in plant biology","volume":"82 ","pages":"Article 102658"},"PeriodicalIF":8.3000,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current opinion in plant biology","FirstCategoryId":"99","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1369526624001493","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PLANT SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
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.
由于揭示关键性状基因的方法不完善，农作物育种的进展受到阻碍。全基因组关联研究（GWAS）就是这样一种方法，它能确定与表型相关的基因组区域。全基因组关联研究（GWAS）后分析可预测候选基因，并帮助识别致病突变（CM）。在此，我们将评估后GWAS方法，解决omics数据整合的局限性，并强调在更广泛的公开数据集背景下评估相关变异的重要性。我们回顾了用于 CM 鉴定和等位基因变异探索的生物信息学工具和基因组策略的最新进展。我们还讨论了标记的作用以及为实现更精确育种而进行的标记组开发。最后，我们强调了基于 GWAS 的复杂数量性状 CM 预测的前景和挑战。
期刊介绍：
Current Opinion in Plant Biology builds on Elsevier's reputation for excellence in scientific publishing and long-standing commitment to communicating high quality reproducible research. It is part of the Current Opinion and Research (CO+RE) suite of journals. All CO+RE journals leverage the Current Opinion legacy - of editorial excellence, high-impact, and global reach - to ensure they are a widely read resource that is integral to scientists' workflow.
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