Animal Breeding and Genomics, Wageningen UR, P.O. 338, 6700 AH, Wageningen, The Netherlands.
CRV BV, Wassenaarweg, 20, 6843 NW, Arnhem, The Netherlands.
Genet Sel Evol. 2020 May 13;52(1):24. doi: 10.1186/s12711-020-00543-9.
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.
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.
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.
单步单核苷酸多态性最佳线性无偏预测（ssSNPBLUP）是一种单步评估方法之一，可同时分析大量基因型的表型和系谱信息。本研究的目的是开发和说明几种计算策略，以便在当前计算机上高效解决具有大量基因型的不同 ssSNPBLUP 系统。
不同开发的策略基于简化预条件器某些项的计算，并将不同 ssSNPBLUP 系统的系数矩阵拆分为多个部分，以便更有效地对其与向量相乘。一些矩阵被显式计算并存储在内存中（例如系谱关系矩阵的逆），或者以压缩形式存储（例如基因型矩阵的 Plink 1 二进制形式），以允许使用高效的并行过程，同时限制所需的内存量。所开发的策略在荷兰和比利时佛兰德斯地区小牛生存力的双变量遗传评估中进行了测试。系谱中有 29,885,286 只动物，有 25,184,654 只小牛记录，有 131,189 只基因型动物。ssSNPBLUP 系统需要大约 18GB 随机存取存储器，并需要 12 小时才能用最有效的实现来解决。
基于我们提出的方法和结果，我们表明，ssSNPBLUP 在使用当前计算机估计基因组育种值方面，在内存和时间需求方面提供了一种可行的方法。
