Integrative machine learning approach for identifying genes associated with quantitative traits: A soybean ( Glycine max ) yield case study

Abstract To improve the identification of minor‐effect molecular markers and genes associated with quantitative traits, addressing inefficiencies in traditional molecular marker mining and the limited impact of these markers in practical breeding, we analyzed over 15,000 soybean ( Glycine max ) genotypes across nine maturity groups using an AI‐driven genomic approach, uncovering 2513 key genetic markers and 393 genes associated with yield. These included stable determinants unaffected by environmental variability and others with environment‐dependent effects. The light gradient boosting machine (LightGBM) model was applied, and model performance was assessed using multiple evaluation metrics across the total datasets by fivefold cross‐validation, yielding a mean squared error of 0.0862 and an R 2 of 0.8369, which demonstrates a strong alignment between predicted and observed yield values. To further validate prediction accuracy, a t ‐test comparing predicted and actual yields produced a T ‐statistic of 0.5236 with a p ‐value of 0.60054, indicating no statistically significant difference between the two distributions. These results confirm the reliability and robustness of the LightGBM approach for yield prediction and underscore its potential utility in breeding programs focused on enhancing agricultural productivity. Our maturity group‐based analysis revealed region‐specific marker distributions, providing refined targets for precision breeding. Three key principles for marker‐assisted selection (MAS) in quantitative trait breeding were introduced: marker complementarity, regional specificity, and quantification. Breeding programs should employ comprehensive sets of region‐specific markers, while the effectiveness of MAS can be measured based on the number and SHapley Additive exPlanations value of markers utilized. Our approach to assessing marker sensitivity across environments offers valuable perspectives on genotype‐by‐environment interactions.