Efficient Breeding by Genomic Mating

Abstract

Selection in breeding programs can be done by using phenotypes (phenotypic selection), pedigree relationship (breeding value selection) or molecular markers (marker assisted selection or genomic selection). All these methods are based on truncation selection, focusing on the best performance of parents before mating. In this article we proposed an approach to breeding, named genomic mating, which focuses on mating instead of truncation selection. Genomic mating uses information in a similar fashion to genomic selection but includes information on complementation of parents to be mated. Following the efficiency frontier surface, genomic mating uses concepts of estimated breeding values, risk (usefulness) and coefficient of ancestry to optimize mating between parents. We used a genetic algorithm to find solutions to this optimization problem and the results from our simulations comparing genomic selection, phenotypic selection and the mating approach indicate that current approach for breeding complex traits is more favorable than phenotypic and genomic selection. Genomic mating is similar to genomic selection in terms of estimating marker effects, but in genomic mating the genetic information and the estimated marker effects are used to decide which genotypes should be crossed to obtain the next breeding population.

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.…”

“…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. 442 Short term economic returns condition the ability of a breeder to target long term genetic gain.…”

“…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…”

“…In 80 previous works, the genetic gain ( ) and constraint ( ) have been defined at the level of the progeny 81 before within family selection. Exceptions are represented by the work of Shepherd and Kinghorn (1998) and Akdemir et al (2016; who added a term to accounting for within cross variance 83 assuming LE between QTLs. However, to our knowledge no previous study allowed for linkage 84 disequilibrium (LD) between QTLs.…”

Usefulness Criterion and post-selection Parental Contributions in Multi-parental Crosses: Application to Polygenic Trait Introgression

“…Applying UCPC within 516 the context of mating design optimization would enable to account for parental 517 complementarity through the use of progeny variation, i.e. within cross variance, as proposed 518 by Shepherd and Kinghorn (1998), Akdemir and Sánchez (2016) and Müller et al (2018).…”