Taskinen Matti, Mäntysaari Esa A, Strandén Ismo
Natural Resources Institute Finland (Luke), Myllytie 1, Jokioinen, Finland.
Genet Sel Evol. 2017 Mar 30;49(1):36. doi: 10.1186/s12711-017-0310-9.
Single-step genomic best linear unbiased prediction (BLUP) evaluation combines relationship information from pedigree and genomic marker data. The inclusion of the genomic information into mixed model equations requires the inverse of the combined relationship matrix [Formula: see text], which has a dense matrix block for genotyped animals.
To avoid inversion of dense matrices, single-step genomic BLUP can be transformed to single-step single nucleotide polymorphism BLUP (SNP-BLUP) which have observed and imputed marker coefficients. Simple block LDL type decompositions of the single-step relationship matrix [Formula: see text] were derived to obtain different types of linearly equivalent single-step genomic mixed model equations with different sets of reparametrized random effects. For non-genotyped animals, the imputed marker coefficient terms in the single-step SNP-BLUP were calculated on-the-fly during the iterative solution using sparse matrix decompositions without storing the imputed genotypes. Residual polygenic effects were added to genotyped animals and transmitted to non-genotyped animals using relationship coefficients that are similar to imputed genotypes. The relationships were further orthogonalized to improve convergence of iterative methods.
All presented single-step SNP-BLUP models can be solved efficiently using iterative methods that rely on iteration on data and sparse matrix approaches. The efficiency, accuracy and iteration convergence of the derived mixed model equations were tested with a small dataset that included 73,579 animals of which 2885 were genotyped with 37,526 SNPs.
Inversion of the large and dense genomic relationship matrix was avoided in single-step evaluation by using fully orthogonalized single-step SNP-BLUP formulations. The number of iterations until convergence was smaller in single-step SNP-BLUP formulations than in the original single-step GBLUP when heritability was low, but increased above that of the original single-step when heritability was high.
单步基因组最佳线性无偏预测(BLUP)评估结合了系谱和基因组标记数据中的亲缘关系信息。将基因组信息纳入混合模型方程需要对组合的亲缘关系矩阵求逆[公式:见正文],对于已分型动物,该矩阵具有一个密集的矩阵块。
为避免对密集矩阵求逆,单步基因组BLUP可转换为单步单核苷酸多态性BLUP(SNP - BLUP),其具有观测和推算的标记系数。推导了单步亲缘关系矩阵[公式:见正文]的简单块LDL型分解,以获得不同类型的线性等价单步基因组混合模型方程,这些方程具有不同的重新参数化随机效应集。对于未分型动物,在迭代求解过程中,使用稀疏矩阵分解即时计算单步SNP - BLUP中的推算标记系数项,而不存储推算的基因型。将残余多基因效应添加到已分型动物中,并使用与推算基因型相似的亲缘系数传递给未分型动物。进一步对亲缘关系进行正交化处理以提高迭代方法的收敛性。
所有提出的单步SNP - BLUP模型都可以使用依赖于数据迭代和稀疏矩阵方法的迭代方法有效地求解。使用一个包含73579只动物的小型数据集测试了推导的混合模型方程的效率、准确性和迭代收敛性,其中2885只动物进行了37526个SNP的基因分型。
通过使用完全正交化的单步SNP - BLUP公式,在单步评估中避免了对大型密集基因组亲缘关系矩阵的求逆。当遗传力较低时,单步SNP - BLUP公式收敛所需的迭代次数比原始单步GBLUP少,但当遗传力较高时,其迭代次数增加到超过原始单步的次数。