Génétique Animale et Biologie Intégrative, UMR1313, Institut national de la recherche agronomique, Jouy-en-Josas Cedex, France.
J Anim Breed Genet. 2011 Jun;128(3):163-73. doi: 10.1111/j.1439-0388.2010.00899.x. Epub 2011 Mar 28.
In real data, inbreeding is usually underestimated because of missing pedigree information. A method adapted to the dairy cattle situation is presented to approximate inbreeding when the stored population pedigree is incomplete. Missing parents in incomplete pedigrees were given a dummy identification and assigned to groups (up to nine for a given birth date of progeny). These groups were linked to contemporary reference groups with known parents. An explicit model considered that polygenic breeding values in a censored group were centred on a function of the average breeding value in the corresponding reference group and deviated independently. Inbreeding coefficients were obtained progressively over birth dates starting from founders. For each date considered, the parameters pertaining to its groups were computed using the parameters already obtained from groups belonging to the previous dates. The updating algorithms were given in detail. An indirect method was implemented to expedite mass computations of the relationship coefficients involved (MIM). MIM was compared to Van Raden's (VR) method using simulated populations with 20 overlapping generations and different rates of missing sires and dams. In the situation of random matings, the average inbreeding coefficients by date obtained by MIM were close to true values, whereas they were strongly underestimated by VR. In the situation of assortative matings, MIM gave average inbreeding coefficients moderately underestimated, whereas those of VR's method were still strongly underestimated. The main conclusion of this study adapted to the situation of dairy cattle with incomplete pedigrees was that corrections for inbreeding and coancestry coefficients are more efficient with an explicit appropriate genetic model than without.
在实际数据中，由于缺乏系谱信息，近交通常被低估。本文提出了一种适用于奶牛情况的方法，用于在存储的群体系谱不完整时估算近交。不完整系谱中缺失的父母被赋予一个虚拟身份，并被分配到组中（对于给定的后代出生日期，最多有九个组）。这些组与具有已知父母的当代参考组相关联。一个显式模型认为，在一个被屏蔽的组中，多基因育种值以对应参考组中平均育种值的函数为中心，并独立偏离。近交系数从创始人开始，根据出生日期逐步获得。对于考虑的每个日期，使用已经从属于前一个日期的组获得的参数来计算与其组相关的参数。详细给出了更新算法。实现了一种间接方法来加速涉及的关系系数的大规模计算（MIM）。使用具有 20 个重叠世代和不同缺失 sire 和 dam 率的模拟群体，比较了 MIM 与 Van Raden（VR）方法。在随机交配的情况下，MIM 获得的按日期计算的平均近交系数接近真实值，而 VR 则严重低估。在选配交配的情况下，MIM 给出的平均近交系数被适度低估，而 VR 方法的则仍然被严重低估。本研究的主要结论是，对于不完整系谱的奶牛情况，与没有适当遗传模型的情况相比，通过显式适当的遗传模型对近交和共同祖先系数进行校正更为有效。
