MOESM2 of Genetic and genomic basis of antibody response to porcine reproductive and respiratory syndrome (PRRS) in gilts and sows Nick Serão Robert Kemp Benny Mote Philip Willson John Harding Stephen Bishop Graham Plastow Jack Dekkers 10.6084/m9.figshare.c.3636122_D2.v1 https://springernature.figshare.com/articles/journal_contribution/MOESM2_of_Genetic_and_genomic_basis_of_antibody_response_to_porcine_reproductive_and_respiratory_syndrome_PRRS_in_gilts_and_sows/4440623 Additional file 2 Figures S1, S2, S3, S4, S5, S6 and S7. Genomic prediction accuracies across genomic prediction methods, and SNP, and sample-to-positive (S/P) datasets for Fold 1, 2, 3, 4, 5, 6 and 7 (Figures S1, S2, S3, S4, S5, S6 and S7, respectively) of the seven-fold cross-validation using the gilt acclimation dataset. Results when using S/P datasets from S/P0% to S/P100% used for training are shown in panels designated by 0 % to 100 %, respectively. Within each column, color-coded bars represent genomic prediction accuracies for each method across SNP datasets. SNP datasets SNPAll, SNPMHC, SNP130, SNPSSC7, and SNPRest are represented by All, MHC, 130, SSC7, and Rest, respectively. 2016-07-14 05:00:00 Disease resistance Disease resilience Genomic prediction Genomic selection PRRSV Vaccination