Optimization of selection contribution and mate allocations in monoecious tree breeding populations

Abstract

BackgroundThe combination of optimized contribution dynamic selection and various mating schemes was investigated over seven generations for a typical tree breeding scenario. The allocation of mates was optimized using a simulated annealing algorithm for various object functions including random mating (RM), positive assortative mating (PAM) and minimization of pair-wise coancestry between mates (MCM) all combined with minimization of variance in family size and coancestry. The present study considered two levels of heritability (0.05 and 0.25), two restrictions on relatedness (group coancestry; 1 and 2%) and two maximum permissible numbers of crosses in each generation (100 and 400). The infinitesimal genetic model was used to simulate the genetic architecture of the trait that was the subject of selection. A framework of the long term genetic contribution of ancestors was used to examine the impacts of the mating schemes on population parameters.ResultsMCM schemes produced on average, an increased rate of genetic gain in the breeding population, although the difference between schemes was small but significant after seven generations (up to 7.1% more than obtained with RM). In addition, MCM reduced the level of inbreeding by as much as 37% compared with RM, although the rate of inbreeding was similar after three generations of selection. PAM schemes yielded levels of genetic gain similar to those produced by RM, but the increase in the level of inbreeding was substantial (up to 43%).ConclusionThe main reason why MCM schemes yielded higher genetic gains was the improvement in managing the long term genetic contribution of founders in the population; this was achieved by connecting unrelated families. In addition, the accumulation of inbreeding was reduced by MCM schemes since the variance in long term genetic contributions of founders was smaller than in the other schemes. Consequently, by combining an MCM scheme with an algorithm that optimizes contributions of the selected individuals, a higher long term response is obtained while reducing the risk within the breeding program.

Strategies of preserving genetic diversity while maximizing genetic response from implementing genomic selection in pulse breeding programs

“…Two main reasons that cause loss of genetic variation in the breeding populations include the loss or fixation of favourable quantitative trait loci (QTL) alleles and the increased relatedness between selected individuals, which reduces trait variation and, in turn, reduces long-term genetic response (Falconer and Mackay 1996 ; Jannink 2010 ; Li et al 2008 ). Several strategies of preserving genetic variation have been applied in livestock and plant breeding, including minimizing the average coancestry (Cervantes et al 2016 ; Hallander and Waldmann 2009 ; Meuwissen 1997 ; Villanueva et al 2006 ), minimizing inbreeding coefficients (Brisbane and Gibson 1995 ; Li et al 2008 ; Lin et al 2017 ; Meuwissen 1997 ), avoiding the selection of closely related individuals (Lindgren and Mullin 1997 ), and reducing the loss of favourable QTL alleles (Vanavermaete et al 2020 ). In recent years, the genomic relationship matrix has been used to control inbreeding levels and maximizing long-term genetic gain (De Beukelaer et al 2017 ; Lin et al 2017 ; Pryce et al 2012 ; Santantonio and Robbins 2020 ; Sonesson et al 2012 ).…”

Performance of Seven Tree Breeding Strategies Under Conditions of Inbreeding Depression

“…For instance, selection in SBPM, NUC, or even SELFP could be enhanced by applying optimum contribution selection and minimum coancestry mating designs, which could optimize the balance between accumulated inbreeding and genetic gains ( e.g. , Stoehr et al 2008 ; Hallander and Waldmann 2009 ). Such improvements offer prospects for still better performance in terms of inbreeding control or purging than those investigated in this study.…”

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“…In a study simulating a plant breeding program, it was verified the superiority on genetic and inbreeding trend of the scenario that minimized the coancestry of the progeny when compared to the scenarios of assortative or random mating [40]. The authors attributed this superiority to the maintenance of genetic variabili-ty throughout the generations, measured from the long-term contribution of the ancestors and the higher connectivity between unrelated families of the reproductive pairs.…”