Additional file 2 of Genomic prediction of blood biomarkers of metabolic disorders in Holstein cattle using parametric and nonparametric models
Creators: name: Mota, Lucio F. M.
name: Giannuzzi, Diana
name: Pegolo, Sara
name: Sturaro, Enrico
name: Gianola, Daniel
name: Negrini, Riccardo
name: Trevisi, Erminio
name: Ajmone Marsan, Paolo
name: Cecchinato, Alessio
Publisher: name: figshare
Year: 2024
Subjects: subject: Genetics
subject: FOS: Biological sciences
subject: Biological Sciences not elsewhere classified
subject: Information Systems not elsewhere classified
subject: Mathematical Sciences not elsewhere classified
subject: Cancer

description: Additional file 2: Table S1. Descriptive statistics for blood metabolites related to energy, liver function/hepatic damage, oxidative stress, inflammation/innate immunity, and minerals (n = 1353). Table S2. Estimates of variance components, heritability ( $${{\text{h}}}^{2}$$ h 2 ), and batch incidence ( $${{\text{h}}}_{{\text{batch}}}^{2}$$ h batch 2 ) for blood metabolites related to energy-related metabolites, liver function/hepatic damage, and oxidative stress metabolites. Table S3. Estimates of variance components, heritability ( $${{\text{h}}}^{2}$$ h 2 ), and batch incidence ( $${{\text{h}}}_{{\text{batch}}}^{2}$$ h batch 2 ) for blood metabolites related to inflammation/innate immunity and minerals. Table S4. Estimates of variance components considering non-additive effects, heritability ( $${{\text{h}}}^{2}$$ h 2 ), batch incidence ( $${{\text{h}}}_{{\text{batch}}}^{2}$$ h batch 2 ), dominance ( $${{\text{d}}}^{2}$$ d 2 ), and additive-by-additive epistasis ( $${{\text{ep}}}_{{\text{aa}}}^{2})$$ ep aa 2 ) , for blood metabolites. Table S5. Prediction metrics with standard errors, obtained from genomic BLUP (GBLUP), BayesB, elastic net (ENET), gradient boosting machine (GBM), and stacking ensemble (Stack) for energy-related metabolites for tenfold cross-validation. Table S6. Prediction metrics with standard errors, obtained from genomic BLUP (GBLUP), BayesB, elastic net (ENET), gradient boosting machine (GBM), and stacking ensemble (Stack) for liver function/hepatic damage for tenfold cross-validation. Table S7. Prediction metrics with standard errors, obtained from genomic BLUP (GBLUP), BayesB, elastic net (ENET), gradient boosting machine (GBM), and stacking ensemble (Stack) for oxidative stress metabolites for tenfold cross-validation. Table S8. Prediction metrics with standard errors, obtained from genomic BLUP (GBLUP), BayesB, elastic net (ENET), gradient boosting machine (GBM), and stacking ensemble (Stack) for inflammation/innate immunity for tenfold cross-validation. Table S9. Prediction metrics with standard errors, obtained from genomic BLUP (GBLUP), BayesB, elastic net (ENET), gradient boosting machine (GBM), and stacking ensemble (Stack) for blood minerals for tenfold cross-validation. Table S10. Prediction metrics with standard errors, obtained from genomic BLUP (GBLUP), BayesB, elastic net (ENET), gradient boosting machine (GBM), and stacking ensemble (Stack) for energy-related metabolites for batch-out cross-validation. Table S11. Prediction metrics with standard errors, obtained from genomic BLUP (GBLUP), BayesB, elastic net (ENET), gradient boosting machine (GBM), and stacking ensemble (Stack) for liver function/hepatic damage for batch-out cross-validation. Table S12. Prediction metrics with standard errors, obtained from genomic BLUP (GBLUP), BayesB, elastic net (ENET), gradient boosting machine (GBM), and stacking ensemble (Stack) for oxidative stress metabolites for batch-out cross-validation. Table S13. Prediction metrics with standard errors, obtained from genomic BLUP (GBLUP), BayesB, elastic net (ENET), gradient boosting machine (GBM), and stacking ensemble (Stack) for inflammation/innate immunity for batch-out cross-validation. Table S14. Prediction metrics with standard errors, obtained from genomic BLUP (GBLUP), BayesB, elastic net (ENET), gradient boosting machine (GBM), and stacking ensemble (Stack) for blood minerals for batch-out cross-validation. Table S15. Prediction fit parameters including standard errors, considering the top 1500 SNP markers ranked by a GBM, obtained from genomic BLUP (GBLUP), Bayesian B (BayesB), elastic net (ENET), gradient boosting machine (GBM) and stacking ensemble (Stack) for energy-related metabolites. Table S16. Prediction fit parameters including standard errors, considering the top 1500 SNPs ranked by a GBM, obtained from genomic BLUP (GBLUP), Bayesian B (BayesB), elastic net (ENET), gradient boosting machine (GBM) and stacking ensemble (Stack) for liver function/hepatic damage. Table S17. Prediction fit parameters including standard errors, considering the top 1500 SNPs ranked by a GBM, obtained from genomic BLUP (GBLUP), Bayesian B (BayesB), elastic net (ENET), gradient boosting machine (GBM) and stacking ensemble (Stack) for oxidative stress metabolites. Table S18. Prediction fit parameters including standard errors, considering the top 1500 SNPs ranked by a GBM, obtained from genomic BLUP (GBLUP), Bayesian B (BayesB), elastic net (ENET), gradient boosting machine (GBM) and stacking ensemble (Stack) for inflammation/innate immunity. Table S19. Prediction fit parameters including standard errors, considering the top 1500 SNPs ranked by a GBM, obtained from genomic BLUP (GBLUP), Bayesian B (BayesB), elastic net (ENET), gradient boosting machine (GBM) and stacking ensemble (Stack) for minerals in blood.