Shrinkage estimation of the genomic relationship matrix can improve genomic estimated breeding values in the training set.
We evaluated several methods for computing shrinkage estimates of the genomic relationship matrix and demonstrated their potential to enhance the reliability of genomic estimated breeding values of training set individuals. In genomic prediction in plant breeding, the training set constitutes a large fraction of the total number of genotypes assayed and is itself subject to selection. The objective of our study was to investigate whether genomic estimated breeding values (GEBVs) of individuals in the training set can be enhanced by shrinkage estimation of the genomic relationship matrix. We simulated two different population types: a diversity panel of unrelated individuals and a biparental family of doubled haploid lines. For different training set sizes (50, 100, 200), number of markers (50, 100, 200, 500, 2,500) and heritabilities (0.25, 0.5, 0.75), shrinkage coefficients were computed by four different methods. Two of these methods are novel and based on measures of LD, the other two were previously described in the literature, one of which was extended by us. Our results showed that shrinkage estimation of the genomic relationship matrix can significantly improve the reliability of the GEBVs of training set individuals, especially for a low number of markers. We demonstrate that the number of markers is the primary determinant of the optimum shrinkage coefficient maximizing the reliability and we recommend methods eligible for routine usage in practical applications.
University of Hohenheim, Stuttgart, Germany, Dominik_Mueller@uni-hohenheim.de.
Dominik
D
Müller
Frank
F
Technow
Albrecht E
AE
Melchinger
D001947:Breeding; D003198:Computer Simulation; D018745:Genome, Plant; D015810:Linkage Disequilibrium; D008957:Models, Genetic; D015233:Models, Statistical; D010944:Plants / Q000235:genetics*; D040641:Quantitative Trait Loci; D015203:Reproducibility of Results; D003313:Zea mays / Q000235:genetics