Multi-objective optimized genomic breeding strategies for sustainable food improvement

Abstract

The purpose of breeding programs is to obtain sustainable gains in multiple traits while controlling the loss of genetic variation. The decisions at each breeding cycle involve multiple, usually competing, objectives; these complex decisions can be supported by the insights that are gained by applying multi-objective optimization principles to breeding. The discussion in this manuscript includes the definition of several multi-objective optimized breeding approaches within the phenotypic or genomic breeding frameworks and the comparison of these approaches with the standard multi-trait breeding schemes such as tandem selection, independent culling and index selection. Proposed methods are demonstrated with two empirical data sets and simulations. In addition, we have described several graphical tools that can aid breeders in arriving at a compromise decision. The results show that the proposed methodology is a viable approach to answer several real breeding problems. In simulations, the newly proposed methods resulted in gains larger than the methods previously proposed including index selection: Compared to the best alternative breeding strategy, the gains from multi-objective optimized parental proportions approaches were about 20–30% higher at the end of long-term simulations of breeding cycles. In addition, the flexibility of the multi-objective optimized breeding strategies were displayed with methods and examples covering non-dominated selection, assignment of optimal parental proportions, using genomewide marker effects in producing optimal mating designs, and finally in selection of training populations for genomic prediction.

Improving short and long term genetic gain by accounting for within family variance in optimal cross selection

“…Accounting for within cross variance to measure the expected gain of a 437 cross in optimal cross selection was already suggested in Shepherd and Kinghorn (1998). More recently, 438 Akdemir and Sánchez (2016) and Akdemir et al (2018) accounted for within cross variance considering 439 linkage equilibrium between QTLs. Akdemir and Sánchez (2016) also observed that accounting for 440 within cross variance during cross selection yielded higher long term mean performance with a penalty 441 at short term mean progeny performance.…”

“…Optimal contribution 62 selection aims at identifying the optimal contributions ( ) of candidate parents to the next generation 63 obtained by random mating, in order to maximize the expected genetic value in the progeny ( ) under 64 a certain constraint on inbreeding ( ) (Wray and Goddard 1994;Meuwissen 1997;Woolliams et al 65 2015). Optimal cross selection, further referred as OCS, is an extension of the optimal contribution 66 selection to deliver a crossing plan that maximizes under the constraint by considering additional 67 constraints on the allocation of mates in crosses (Kinghorn et al 2009;Kinghorn 2011;Akdemir and 68 Sánchez 2016;Akdemir et al 2018). In the era of genomic selection, the expected 69 genetic value in progeny ( ) to be maximized is defined as the mean of parental GEBV ( ) weighted 70 by parental contributions , i.e.…”

“…pairing of candidates, differential evolutionary algorithms have been suggested (Storn 73 and Price 1997;Kinghorn et al 2009;Kinghorn 2011). Using the concept of optimal contribution 74 selection for mating decisions is common in animal breeding (Woolliams et al 2015) and is increasingly 75 adopted in plant breeding (Akdemir and Sánchez 2016;De Beukelaer et al 2017;Lin et al 2017;76 Gorjanc et al 2018;Akdemir et al 2018). 77…”

“…However, in a climate change context and with rapidly evolving societal demands for sustainable 522 agricultural practices, environments and breeders objectives will likely change over time. In a multi-523 trait context, the multi-objective optimization framework proposed in Akdemir et al (2018) can be 524 adapted to UCPC based optimal cross selection. The upcoming but yet unknown breeding objectives 525 make the necessity to manage genetic diversity even more important than highlighted in this study.…”

Multi-Trait Improvement by Predicting Genetic Correlations in Breeding Crosses

“…While they 101 applied this equation to the case of parental contribution (treated as a quantitative trait) correlated 102 with an agronomic trait of interest, the theory could be generalized to two or more traits in the 103 traditional sense. Additionally, Akdemir et al (2019) applied a multi-objective optimized 104 7 breeding strategy in simulations to select parent combinations and improve two unfavorably 105 correlated traits. This approach solves the multiple objective optimization problem of 106 maximizing the genetic gain of two or more traits while constraining inbreeding.…”