Additional file 4: Table S4. Bias of the GEBV measured by regression slope using various marker sets for the index trait affected by different rare QTN sets, averaged over 10 replicates
Additional file 1: Figure S1. Reliabilities of genomic predictions in different scenarios for fat an...
BACKGROUND: Sequence data can potentially increase the reliability of genomic predictions, because s...
Additional file 4: Table S4. Description of other significant QTL regions detected in within-breed (...
Additional file 3: Table S3. Reliabilities of genomic prediction using various marker sets for the i...
Additional file 1: Table S1. Number of rare and low-frequency variants (RLFV) selected for inclusion...
Additional file 5: Table S5. Proportion of rare or low-frequency variants (RLFV)a segregating both i...
Additional file 6: Table S6. The additive genetic variances explained in the models for one replicat...
Additional file 2: Table S2. Characteristics for each simulation scenario across 10 replicates
Additional file 4: Table S4. The Akaike information criterion (AIC) for different models compared to...
Additional file 5: Table S1. Posterior standard deviations of genomic correlations between breeds. A...
Additional file 4: Figure S4. Heritabilities of QTL (h2 QTL) according to number of QTL markers for ...
Additional file 9: Figure S4. Relationship between bias of genomic predictions and changes in predic...
BACKGROUND: Availability of whole-genome sequence data for a large number of cattle and efficient im...
Additional file 7: Figure S3. Relationship between bias of genomic predictions and changes in predic...
Additional file 2: Table S2. Number of variants in different MAF classes, imputation accuracy for di...
Additional file 1: Figure S1. Reliabilities of genomic predictions in different scenarios for fat an...
BACKGROUND: Sequence data can potentially increase the reliability of genomic predictions, because s...
Additional file 4: Table S4. Description of other significant QTL regions detected in within-breed (...
Additional file 3: Table S3. Reliabilities of genomic prediction using various marker sets for the i...
Additional file 1: Table S1. Number of rare and low-frequency variants (RLFV) selected for inclusion...
Additional file 5: Table S5. Proportion of rare or low-frequency variants (RLFV)a segregating both i...
Additional file 6: Table S6. The additive genetic variances explained in the models for one replicat...
Additional file 2: Table S2. Characteristics for each simulation scenario across 10 replicates
Additional file 4: Table S4. The Akaike information criterion (AIC) for different models compared to...
Additional file 5: Table S1. Posterior standard deviations of genomic correlations between breeds. A...
Additional file 4: Figure S4. Heritabilities of QTL (h2 QTL) according to number of QTL markers for ...
Additional file 9: Figure S4. Relationship between bias of genomic predictions and changes in predic...
BACKGROUND: Availability of whole-genome sequence data for a large number of cattle and efficient im...
Additional file 7: Figure S3. Relationship between bias of genomic predictions and changes in predic...
Additional file 2: Table S2. Number of variants in different MAF classes, imputation accuracy for di...
Additional file 1: Figure S1. Reliabilities of genomic predictions in different scenarios for fat an...
BACKGROUND: Sequence data can potentially increase the reliability of genomic predictions, because s...
Additional file 4: Table S4. Description of other significant QTL regions detected in within-breed (...
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