Machine learning models outperform deep learning models, provide interpretation and facilitate feature selection for soybean trait prediction

• Published in: BMC plant biology Vol. 22; no. 1; pp. 180 - 8
• Main Authors: Gill, Mitchell, Anderson, Robyn, Hu, Haifei, Bennamoun, Mohammed, Petereit, Jakob, Valliyodan, Babu, Nguyen, Henry T, Batley, Jacqueline, Bayer, Philipp E, Edwards, David
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 08-04-2022; BioMed Central; BMC
• Subjects: Accuracy; Algorithms; Analysis; Classification; Crop improvement; Data analysis; Datasets; Deep Learning; Feature selection; Genetic aspects; Genomes; Genomic selection; Genomics; Genotype; Genotype & phenotype; Genotypes; Glycine max - genetics; Growth; Interpretable models; Learning algorithms; Machine Learning; Markers; Methods; Neural networks; Performance prediction; Phenotype; Phenotypes; Prediction models; Seeds; Soybean; Soybeans; XGBoost; United States; Feature selection; Interpretable models; Soybean; Genomic selection; XGBoost; Machine learning
• ISSN: 1471-2229; 1471-2229
• DOI: 10.1186/s12870-022-03559-z

Recent growth in crop genomic and trait data have opened opportunities for the application of novel approaches to accelerate crop improvement. Machine learning and deep learning are at the forefront of prediction-based data analysis. However, few approaches for genotype to phenotype prediction compare machine learning with deep learning and further interpret the models that support the predictions. This study uses genome wide molecular markers and traits across 1110 soybean individuals to develop accurate prediction models. For 13/14 sets of predictions, XGBoost or random forest outperformed deep learning models in prediction performance. Top ranked SNPs by F-score were identified from XGBoost, and with further investigation found overlap with significantly associated loci identified from GWAS and previous literature. Feature importance rankings were used to reduce marker input by up to 90%, and subsequent models maintained or improved their prediction performance. These findings support interpretable machine learning as an approach for genomic based prediction of traits in soybean and other crops.