Fikse Freddy
Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, 75007 Uppsala, Sweden.
Genet Sel Evol. 2009 Sep 28;41(1):42. doi: 10.1186/1297-9686-41-42.
Genetic evaluation models often include genetic groups to account for unequal genetic level of animals with unknown parentage. The definition of phantom parent groups usually includes a time component (e.g. years). Combining several time periods to ensure sufficiently large groups may create problems since all phantom parents in a group are considered contemporaries.
To avoid the downside of such distinct classification, a fuzzy logic approach is suggested. A phantom parent can be assigned to several genetic groups, with proportions between zero and one that sum to one. Rules were presented for assigning coefficients to the inverse of the relationship matrix for fuzzy-classified genetic groups. This approach was illustrated with simulated data from ten generations of mass selection. Observations and pedigree records were randomly deleted. Phantom parent groups were defined on the basis of gender and generation number. In one scenario, uncertainty about generation of birth was simulated for some animals with unknown parents. In the distinct classification, one of the two possible generations of birth was randomly chosen to assign phantom parents to genetic groups for animals with simulated uncertainty, whereas the phantom parents were assigned to both possible genetic groups in the fuzzy classification.
The empirical prediction error variance (PEV) was somewhat lower for fuzzy-classified genetic groups. The ranking of animals with unknown parents was more correct and less variable across replicates in comparison with distinct genetic groups. In another scenario, each phantom parent was assigned to three groups, one pertaining to its gender, and two pertaining to the first and last generation, with proportion depending on the (true) generation of birth. Due to the lower number of groups, the empirical PEV of breeding values was smaller when genetic groups were fuzzy-classified.
Fuzzy-classification provides the potential to describe the genetic level of unknown parents in a more parsimonious and structured manner, and thereby increases the precision of predicted breeding values.
遗传评估模型通常包含遗传群体，以考虑无系谱记录动物的不同遗传水平。虚拟亲本群体的定义通常包含一个时间成分（如年份）。将多个时间段合并以确保群体足够大可能会产生问题，因为一个群体中的所有虚拟亲本都被视为同代个体。
为避免这种明确分类的弊端，建议采用模糊逻辑方法。一个虚拟亲本可以被分配到几个遗传群体中，比例在0到1之间且总和为1。给出了为模糊分类的遗传群体的关系矩阵的逆矩阵分配系数的规则。用十代群体选择的模拟数据说明了这种方法。随机删除观测值和系谱记录。根据性别和世代数定义虚拟亲本群体。在一种情况下，对一些无系谱记录的动物模拟出生世代的不确定性。在明确分类中，对于有模拟不确定性的动物，随机选择两个可能的出生世代之一将虚拟亲本分配到遗传群体中，而在模糊分类中，虚拟亲本被分配到两个可能的遗传群体中。
模糊分类的遗传群体的经验预测误差方差（PEV）略低。与明确的遗传群体相比，无系谱记录动物的排名在各重复中更准确且变异性更小。在另一种情况下，每个虚拟亲本被分配到三个群体，一个与其性别相关，另外两个与第一代和最后一代相关，比例取决于（真实的）出生世代。由于群体数量较少，当遗传群体进行模糊分类时，育种值的经验PEV较小。
模糊分类提供了以更简约和结构化的方式描述无系谱记录亲本遗传水平的潜力，从而提高了预测育种值的精度。
