Jeremie Vandenplas1, Herwin Eding2, Maarten Bosmans3, Mario P L Calus4. 1. Animal Breeding and Genomics, Wageningen UR, P.O. 338, 6700 AH, Wageningen, The Netherlands. jeremie.vandenplas@wur.nl. 2. CRV BV, Wassenaarweg, 20, 6843 NW, Arnhem, The Netherlands. 3. VORtech Scientific Software Engineers, Westlandseweg 40d, 2624 AD, Delft, The Netherlands. 4. Animal Breeding and Genomics, Wageningen UR, P.O. 338, 6700 AH, Wageningen, The Netherlands.
Abstract
BACKGROUND: The single-step single nucleotide polymorphism best linear unbiased prediction (ssSNPBLUP) is one of the single-step evaluations that enable a simultaneous analysis of phenotypic and pedigree information of genotyped and non-genotyped animals with a large number of genotypes. The aim of this study was to develop and illustrate several computational strategies to efficiently solve different ssSNPBLUP systems with a large number of genotypes on current computers. RESULTS: The different developed strategies were based on simplified computations of some terms of the preconditioner, and on splitting the coefficient matrix of the different ssSNPBLUP systems into multiple parts to perform its multiplication by a vector more efficiently. Some matrices were computed explicitly and stored in memory (e.g. the inverse of the pedigree relationship matrix), or were stored using a compressed form (e.g. the Plink 1 binary form for the genotype matrix), to permit the use of efficient parallel procedures while limiting the required amount of memory. The developed strategies were tested on a bivariate genetic evaluation for livability of calves for the Netherlands and the Flemish region in Belgium. There were 29,885,286 animals in the pedigree, 25,184,654 calf records, and 131,189 genotyped animals. The ssSNPBLUP system required around 18 GB Random Access Memory and 12 h to be solved with the most performing implementation. CONCLUSIONS: Based on our proposed approaches and results, we showed that ssSNPBLUP provides a feasible approach in terms of memory and time requirements to estimate genomic breeding values using current computers.
BACKGROUND: The single-step single nucleotide polymorphism best linear unbiased prediction (ssSNPBLUP) is one of the single-step evaluations that enable a simultaneous analysis of phenotypic and pedigree information of genotyped and non-genotyped animals with a large number of genotypes. The aim of this study was to develop and illustrate several computational strategies to efficiently solve different ssSNPBLUP systems with a large number of genotypes on current computers. RESULTS: The different developed strategies were based on simplified computations of some terms of the preconditioner, and on splitting the coefficient matrix of the different ssSNPBLUP systems into multiple parts to perform its multiplication by a vector more efficiently. Some matrices were computed explicitly and stored in memory (e.g. the inverse of the pedigree relationship matrix), or were stored using a compressed form (e.g. the Plink 1 binary form for the genotype matrix), to permit the use of efficient parallel procedures while limiting the required amount of memory. The developed strategies were tested on a bivariate genetic evaluation for livability of calves for the Netherlands and the Flemish region in Belgium. There were 29,885,286 animals in the pedigree, 25,184,654 calf records, and 131,189 genotyped animals. The ssSNPBLUP system required around 18 GB Random Access Memory and 12 h to be solved with the most performing implementation. CONCLUSIONS: Based on our proposed approaches and results, we showed that ssSNPBLUP provides a feasible approach in terms of memory and time requirements to estimate genomic breeding values using current computers.
Authors: Christopher C Chang; Carson C Chow; Laurent Cam Tellier; Shashaank Vattikuti; Shaun M Purcell; James J Lee Journal: Gigascience Date: 2015-02-25 Impact factor: 6.524
Authors: Jeremie Vandenplas; Mario P L Calus; Herwin Eding; Mathijs van Pelt; Rob Bergsma; Cornelis Vuik Journal: Genet Sel Evol Date: 2021-04-09 Impact factor: 4.297
Authors: Renzo Bonifazi; Mario P L Calus; Jan Ten Napel; Roel F Veerkamp; Alexis Michenet; Simone Savoia; Andrew Cromie; Jérémie Vandenplas Journal: Genet Sel Evol Date: 2022-09-04 Impact factor: 5.100