Applying traditional and machine learning-based GWAS approaches for marker-trait identification in wheat

Key Points

• Marker-trait identification in wheat demonstrates increased effectiveness with machine learning techniques.
• Machine learning approaches broaden the scope of detectable genetic signals beyond traditional methods.
• Combining traditional GWAS with machine learning provides a robust framework for analyzing complex traits.
• Findings highlight the potential for improved breeding strategies, indicating a shift towards more informed decisions.

Abstract