MOESM2 of Genetic and genomic basis of antibody response to porcine reproductive and respiratory syndrome (PRRS) in gilts and sows
Nick SerĂŁo
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 0Â % to 100Â %, 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