Machine-Learning-Based Genome-Wide Association Studies for Uncovering QTL Underlying Soybean Yield and Its Components

Machine-Learning-Based Genome-Wide Association Studies for Uncovering QTL Underlying Soybean Yield and Its Components

A genome-wide association study (GWAS) is currently one of the most recommended approaches for discovering marker-trait associations (MTAs) for complex traits in plant species. Insufficient statistical power is a limiting factor, especially in narrow genetic basis species, that conventional GWAS met...

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Bibliographic Details
Main Authors: Mohsen Yoosefzadeh-Najafabadi, Milad Eskandari, Sepideh Torabi, Davoud Torkamaneh, Dan Tulpan, Istvan Rajcan
Format: Article
Language:English
Published: MDPI AG 2022-05-01
Series:International Journal of Molecular Sciences
Subjects:
data-driven models
FarmCPU
genome-wide association study
MLM
QTL
soybean breeding
Online Access:https://www.mdpi.com/1422-0067/23/10/5538

Internet

https://www.mdpi.com/1422-0067/23/10/5538

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