| DOI | Trouver le DOI : https://doi.org/10.1504/IJDMB.2021.116881 |
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| Auteur | Rechercher : Dagasso, Gabrielle; Rechercher : Yan, N.A.; Rechercher : Wang, Lipu; Rechercher : Li, Longhai; Rechercher : Kutcher, Randy; Rechercher : Zhang, Wentao1; Rechercher : Jin, Lingling |
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| Affiliation | - Conseil national de recherches du Canada. Développement des cultures et des ressources aquatiques
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| Format | Texte, Article |
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| Sujet | genome-wide association studies; machine learning; population structure analysis; cross-validation; LASSO; fusarium head blight |
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| Résumé | Genome-Wide Association Studies (GWAS) has demonstrated its power in discovering genetic variations to particular traits related to agronomically important features in crops. The typical output of a GWAS program includes a series of Single Nucleotide Polymorphisms (SNPs) and their significance. Currently, there is no standard way to compare results across different programs or to select the most ‘significant’ results uniformly and consistently. To obtain a comprehensive and accurate set of SNPs associated with a trait of interest, we present a novel automated pipeline that leverages machine learning for GWAS discoveries. The pipeline first performs population structure analysis, then executes multiple GWAS software and combines their results into a single SNP set. After that, it selects SNPs from the set with high individual and/or joint effects with the Least Absolute Shrinkage and Selection Operator analysis. Finally, the predictivity of the model is assessed using cross-validation. |
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| Date de publication | 2021 |
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| Maison d’édition | Inderscience |
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| Dans | |
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| Langue | anglais |
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| Publications évaluées par des pairs | Oui |
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| Exporter la notice | Exporter en format RIS |
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| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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| Identificateur de l’enregistrement | 45ec777b-155c-4950-961f-c86d97b9bb25 |
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| Enregistrement créé | 2023-01-23 |
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| Enregistrement modifié | 2023-01-23 |
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