A relationship matrix including full pedigree and genomic information
A relationship matrix including full pedigree and genomic information
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DOI：
10.3168/jds.2009-2061
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发表时间：
2009-09-01
影响因子：
3.5
通讯作者：
Misztal, I.
中科院分区：
文献类型：
--
作者：
Legarra, A.;Aguilar, I.;Misztal, I.
高密度分子标记正被用于部分群体的遗传评估。这需要一个两步程序，其中根据完整记录和谱系数据计算伪数据（例如，子代产量偏差），然后用于基因组评估。这导致了偏见和信息的丢失。将基因组信息并入完整遗传评估的一种方式是通过修改分子关系矩阵。一个天真的提议是用基因组关系矩阵代替基因分型动物的关系。然而，这会导致不一致性，因为基因组关系矩阵包括关于祖先和后代之间关系的信息。换句话说，假设基因组信息不存在，使用基因分型和未分型个体之间的谱系协方差会导致不一致。建议通过选择指数（e）将未基因分型动物的遗传值与基因分型动物的遗传值相结合。例如，在一个实施例中，系谱信息），然后对后者使用基因组关系矩阵。这导致基因分型和未基因分型遗传值的联合分布，具有谱系-基因组关系矩阵H。在这个矩阵中，基因组信息被传递到所有未分型个体之间的协方差。该矩阵是（半）正定的建设，这是不是这种情况下的朴素的方法。数值例子和替代表达式进行了讨论。矩阵H适用于将向量乘以矩阵的数据算法的迭代，例如预处理的共轭梯度。
Dense molecular markers are being used in genetic evaluation for parts of the population. This requires a two-step procedure where pseudo-data (for instance, daughter yield deviations) are computed from full records and pedigree data and later used for genomic evaluation. This results in bias and loss of information. One way to incorporate the genomic information into a full genetic evaluation is by modifying the numerator relationship matrix. A naive proposal is to substitute the relationships of genotyped animals with the genomic relationship matrix. However, this results in incoherencies because the genomic relationship matrix includes information on relationships among ancestors and descendants. In other words, using the pedigree-derived covariance between genotyped and ungenotyped individuals, with the pretense that genomic information does not exist, leads to inconsistencies. It is proposed to condition the genetic value of ungenotyped animals on the genetic value of genotyped animals via the selection index (e. g., pedigree information), and then use the genomic relationship matrix for the latter. This results in a joint distribution of genotyped and ungenotyped genetic values, with a pedigree-genomic relationship matrix H. In this matrix, genomic information is transmitted to the covariances among all ungenotyped individuals. The matrix is (semi) positive definite by construction, which is not the case for the naive approach. Numerical examples and alternative expressions are discussed. Matrix H is suitable for iteration on data algorithms that multiply a vector times a matrix, such as preconditioned conjugated gradients.