Plant Breeding, TUM School of Life Sciences, Technical University of Munich, 85354, Freising, Germany.
Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, 70599, Stuttgart, Germany.
Theor Appl Genet. 2023 Oct 31;136(11):236. doi: 10.1007/s00122-023-04447-2.
Mating designs determine the realized additive genetic variance in a population sample. Deflated or inflated variances can lead to reduced or overly optimistic assessment of future selection gains. The additive genetic variance [Formula: see text] inherent to a breeding population is a major determinant of short- and long-term genetic gain. When estimated from experimental data, it is not only the additive variances at individual loci (QTL) but also covariances between QTL pairs that contribute to estimates of [Formula: see text]. Thus, estimates of [Formula: see text] depend on the genetic structure of the data source and vary between population samples. Here, we provide a theoretical framework for calculating the expectation and variance of [Formula: see text] from genotypic data of a given population sample. In addition, we simulated breeding populations derived from different numbers of parents (P = 2, 4, 8, 16) and crossed according to three different mating designs (disjoint, factorial and half-diallel crosses). We calculated the variance of [Formula: see text] and of the parameter b reflecting the covariance component in [Formula: see text] standardized by the genic variance. Our results show that mating designs resulting in large biparental families derived from few disjoint crosses carry a high risk of generating progenies exhibiting strong covariances between QTL pairs on different chromosomes. We discuss the consequences of the resulting deflated or inflated [Formula: see text] estimates for phenotypic and genome-based selection as well as for applying the usefulness criterion in selection. We show that already one round of recombination can effectively break negative and positive covariances between QTL pairs induced by the mating design. We suggest to obtain reliable estimates of [Formula: see text] and its components in a population sample by applying statistical methods differing in their treatment of QTL covariances.
交配设计决定了群体样本中实现的加性遗传方差。收缩或膨胀的方差会导致对未来选择增益的评估降低或过于乐观。育种群体中固有的加性遗传方差[Formula: see text]是短期和长期遗传增益的主要决定因素。当从实验数据中估计时，不仅是个体基因座（QTL）的加性方差，而且是 QTL 对之间的协方差也有助于[Formula: see text]的估计。因此，[Formula: see text]的估计取决于数据源的遗传结构，并且在群体样本之间有所不同。在这里，我们提供了一个从给定群体样本的基因型数据计算[Formula: see text]的期望和方差的理论框架。此外，我们根据三种不同的交配设计（不相交、因子和半双列杂交）模拟了来自不同亲本数量（P = 2、4、8、16）的育种群体。我们计算了[Formula: see text]的方差和参数 b，该参数反映了[Formula: see text]中协方差分量的标准化，由基因方差表示。我们的结果表明，来自少数不相交杂交的大双亲家族的交配设计存在很大的风险，会导致不同染色体上 QTL 对之间产生强烈的协方差。我们讨论了由此产生的收缩或膨胀[Formula: see text]估计对表型和基于基因组的选择以及应用选择的有用性标准的影响。我们表明，仅一轮重组就可以有效地打破交配设计引起的 QTL 对之间的负协方差和正协方差。我们建议通过应用在处理 QTL 协方差方面有所不同的统计方法来获得群体样本中可靠的[Formula: see text]及其分量的估计。
