Controlling Coancestry and Thereby Future Inbreeding by Optimum-Contribution Selection Using Alternative Genomic-Relationship Matrices

We tested the consequences of using alternative genomic relationship matrices to predict genomic breeding values (GEBVs) and control of coancestry in optimum contribution selection, where the relationship matrix used to calculate GEBVs was not necessarily the same as that used to control coancestry. A stochastic simulation study was carried out to investigate genetic gain and true genomic inbreeding in breeding schemes that applied genomic optimum contribution selection (GOCS) with different genomic relationship matrices. Three genomic-relationship matrices were used to predict the GEBVs based on three information sources: markers (G(M)), QTL (G(Q)), and markers and QTL (G(A)). Strictly, G(Q) is not possible to implement in practice since we do not know the quantitative trait loci (QTL) positions, but more and more information is becoming available especially about the largest QTL. Two genomic-relationship matrices were used to control coancestry: G(M) and G(A). Three genetic architectures were simulated: with 7702, 1000, and 500 QTLs together with 54,218 markers. Selection was for a single trait with heritability 0.2. All selection candidates were phenotyped and genotyped before selection. With 7702 QTL, there were no significant differences in rates of genetic gain at the same rate of true inbreeding using different genomic relationship matrices in GOCS. However, as the number of QTLs was reduced to 1000, prediction of GEBVs using a genomic relationship matrix constructed based on G(Q) and control of coancestry using G(M) realized 29.7% higher genetic gain than using G(M) for both prediction and control of coancestry. Forty-three percent of this increased rate of genetic gain was due to increased accuracies of GEBVs. These findings indicate that with large numbers of QTL, it is not critical what information, i.e., markers or QTL, is used to construct genomic-relationship matrices. However, it becomes critical with small numbers of QTL. This highlights the importance of using genomic-relationship matrices that focus on QTL regions for GEBV estimation when the number of QTL is small in GOCS. Relationships used to control coancestry are preferably based on marker data.