Two methods to efficiently approximate theoretical genomic reliabilities are presented. The first relies on the direct inverse of the left hand side (LHS) of mixed model equations. It uses the genomic relationship matrix for a small subset of individuals with the highest genomic relationship with the individual of interest. The second is a ridge-regression method using the direct inverse of LHS for a small subset of SNP. The performance of the methods was evaluated for the North American genomic data set, consisting of 228,168 genotyped individuals. The ridge-regression method gives very high correlations between theoretical and estimated reliabilities for subsets of 5k SNP and above. It is easily applicable to large data sets. Both methods lead to some biases in the mean and SD of reliabilities but these can be corrected by pegging to theoretical values or values from validation studies.
Proceedings of the World Congress on Genetics Applied to Livestock Production, Volume Genetic Improvement Programs: Selection using molecular information (Posters), , 485, 2014
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