GenPhySE, Université de Toulouse, INRAE, ENVT, F-31326, Castanet Tolosan, France,
GenPhySE, Université de Toulouse, INRAE, ENVT, F-31326, Castanet Tolosan, France.
G3 (Bethesda). 2020 Aug 5;10(8):2829-2841. doi: 10.1534/g3.120.401376.
We investigated the effectiveness of mate allocation strategies accounting for non-additive genetic effects to improve crossbred performance in a two-way crossbreeding scheme. We did this by computer simulation of 10 generations of evaluation and selection. QTL effects were simulated as correlated across purebreds and crossbreds, and (positive) heterosis was simulated as directional dominance. The purebred-crossbred correlation was 0.30 or 0.68 depending on the genetic variance component used. Dominance and additive marker effects were estimated simultaneously for purebreds and crossbreds by multiple trait genomic BLUP. Four scenarios that differ in the sources of information (only purebred data, or purebred and crossbred data) and mate allocation strategies (mating at random, minimizing expected future inbreeding, or maximizing the expected total genetic value of crossbred animals) were evaluated under different cases of genetic variance components. Selecting purebred animals for purebred performance yielded a response of 0.2 genetic standard deviations of the trait "crossbred performance" per generation, whereas selecting purebred animals for crossbred performance doubled the genetic response. Mate allocation strategy to maximize the expected total genetic value of crossbred descendants resulted in a slight increase (0.8%, 4% and 0.5% depending on the genetic variance components) of the crossbred performance. Purebred populations increased homozygosity, but the heterozygosity of the crossbreds remained constant. When purebred-crossbred genetic correlation is low, selecting purebred animals for crossbred performance using crossbred information is a more efficient strategy to exploit heterosis and increase performance at the crossbred commercial level, whereas mate allocation did not improve crossbred performance.
我们研究了考虑非加性遗传效应的交配策略在双向杂交方案中提高杂交种性能的有效性。我们通过 10 代评估和选择的计算机模拟来实现这一点。QTL 效应在纯种和杂交种之间模拟为相关的，（正）杂种优势模拟为定向显性。纯系-杂交系相关系数取决于所用遗传方差分量，为 0.30 或 0.68。通过多性状基因组 BLUP 同时估计纯种和杂交种的显性和加性标记效应。在不同遗传方差分量的情况下，评估了在信息来源（仅纯种数据或纯种和杂交种数据）和交配策略（随机交配、最小化预期未来近交或最大化杂交种动物预期总遗传值）方面存在差异的四个方案。选择纯种动物以获得纯种性能，每代可使“杂交种性能”性状的遗传标准差增加 0.2 个，而选择纯种动物以获得杂交种性能可使遗传响应增加一倍。最大化杂交后代预期总遗传值的交配策略导致杂交种性能略有增加（取决于遗传方差分量，分别为 0.8%、4%和 0.5%）。纯种群体增加了纯合度，但杂交种的杂合度保持不变。当纯系-杂交系遗传相关系数较低时，使用杂交种信息选择纯种动物以获得杂交种性能是利用杂种优势和提高杂交商业水平性能的更有效策略，而交配策略并未提高杂交种性能。
