KERMIT, Department of Data Analysis and Mathematical Modelling, Ghent University, B-9000, Ghent, Belgium.
IDLab, Department of Information Technology, Ghent University - imec, B-9052, Ghent, Belgium.
Theor Appl Genet. 2021 Dec;134(12):3845-3861. doi: 10.1007/s00122-021-03932-w. Epub 2021 Aug 13.
The deep scoping method incorporates the use of a gene bank together with different population layers to reintroduce genetic variation into the breeding population, thus maximizing the long-term genetic gain without reducing the short-term genetic gain or increasing the total financial cost. Genomic prediction is often combined with truncation selection to identify superior parental individuals that can pass on favorable quantitative trait locus (QTL) alleles to their offspring. However, truncation selection reduces genetic variation within the breeding population, causing a premature convergence to a sub-optimal genetic value. In order to also increase genetic gain in the long term, different methods have been proposed that better preserve genetic variation. However, when the genetic variation of the breeding population has already been reduced as a result of prior intensive selection, even those methods will not be able to avert such premature convergence. Pre-breeding provides a solution for this problem by reintroducing genetic variation into the breeding population. Unfortunately, as pre-breeding often relies on a separate breeding population to increase the genetic value of wild specimens before introducing them in the elite population, it comes with an increased financial cost. In this paper, on the basis of a simulation study, we propose a new method that reintroduces genetic variation in the breeding population on a continuous basis without the need for a separate pre-breeding program or a larger population size. This way, we are able to introduce favorable QTL alleles into an elite population and maximize the genetic gain in the short as well as in the long term without increasing the financial cost.
深度扩库方法将基因库与不同的群体层结合使用,将遗传变异重新引入到育种群体中,从而在不降低短期遗传增益或增加总财务成本的情况下最大化长期遗传增益。基因组预测通常与截断选择相结合,以识别能够将有利的数量性状基因座 (QTL) 等位基因传递给后代的优良亲本个体。然而,截断选择会降低育种群体内的遗传变异,导致过早收敛到次优的遗传值。为了在长期内也能增加遗传增益,已经提出了不同的方法来更好地保留遗传变异。然而,如果由于先前的密集选择已经降低了育种群体的遗传变异,即使使用这些方法也无法避免这种过早收敛。预繁殖通过将遗传变异重新引入育种群体来解决这个问题。不幸的是,由于预繁殖通常依赖于一个单独的繁殖群体来提高野生标本的遗传值,然后再将它们引入精英群体,因此会增加财务成本。在本文中,我们基于模拟研究提出了一种新方法,该方法可以在不需要单独的预繁殖计划或更大的群体规模的情况下,持续地将遗传变异重新引入到育种群体中。这样,我们就能够将有利的 QTL 等位基因引入精英群体,并在不增加财务成本的情况下最大化短期和长期的遗传增益。