Abstract
The snp_blup_rel program computes model reliabilities for genomic breeding values. The program assumes a single trait SNP-BLUP model where the breeding value can include a residual polygenic (RPG) effect. The reliability calculation requires elements of the inverse of the mixed model equations (MME). The calculation has three steps: 1) MME calculation, 2) MME coefficient matrix inversion, and 3) reliability computation. When needed, the inverted matrix can be saved after step 2. Step 3 can be used separately to new genotypes which do not contribute information to Step 2. When an RPG effect is included, an approximate method based on Monte Carlo sampling is applied. This reduces the MME matrix size and allows including many genotyped individuals. The program is written in Fortran 90/95, and uses LAPACK subroutines which enable multi-threaded parallel computing. The program is efficient in terms of computing time and memory requirements, and can be used to analyze even large genomic data. Thus, the program can be used in calculating model reliabilities for large national genomic evaluations.
Highlights
The dairy cattle evaluation community is increasingly relying on estimated breeding values (EBV) based on genomic information
Calculation of genomic reliability for individual EBV by genomic best linear unbiased prediction (GBLUP) requires inverting the coefficient matrix of the mixed model equations (MME) that include the inverse of the genomic relationship matrix
The MME matrix size of single nucleotide polymorphisms (SNP)-BLUP is bounded by the number of SNP markers
Summary
Snp_blup_rel: software for calculating individual animal SNP-BLUP model reliabilities. The snp_blup_rel program computes model reliabilities for genomic breeding values. The program assumes a single trait SNP-BLUP model where the breeding value can include a residual polygenic (RPG) effect. The reliability calculation requires elements of the inverse of the mixed model equations (MME). The calculation has three steps: 1) MME calculation, 2) MME coefficient matrix inversion, and 3) reliability computation. When an RPG effect is included, an approximate method based on Monte Carlo sampling is applied. This reduces the MME matrix size and allows including many genotyped individuals. The program is efficient in terms of computing time and memory requirements, and can be used to analyze even large genomic data. The program can be used in calculating model reliabilities for large national genomic evaluations
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