Computational strategies for the preconditioned conjugate gradient method applied to ssSNPBLUP, with an application to a multivariate maternal model

摘要: The single-step single nucleotide polymorphism best linear unbiased prediction (ssSNPBLUP) is one of the evaluations that enable a simultaneous analysis phenotypic and pedigree information genotyped non-genotyped animals with large number genotypes. aim this study was to develop illustrate several computational strategies efficiently solve different ssSNPBLUP systems genotypes on current computers. developed were based simplified computations some terms preconditioner, splitting coefficient matrix into multiple parts perform its multiplication by vector more efficiently. Some matrices computed explicitly stored in memory (e.g. inverse relationship matrix), or using compressed form Plink 1 binary for genotype permit use efficient parallel procedures while limiting required amount memory. tested bivariate genetic evaluation livability calves Netherlands Flemish region Belgium. There 29,885,286 pedigree, 25,184,654 calf records, 131,189 animals. system around 18 GB Random Access Memory 12 h be solved most performing implementation. Based our proposed approaches results, we showed provides feasible approach time requirements estimate genomic breeding values