Moving Beyond Managing Realized Genomic Relationship in Long-Term Genomic Selection

Abstract

Long-term genomic selection (GS) requires strategies that balance genetic gain with population diversity, to sustain progress for traits under selection, and to keep diversity for future breeding. In a simulation model for a recurrent selection scheme, we provide the first head-to-head comparison of two such existing strategies: genomic optimal contributions selection (GOCS), which limits realized genomic relationship among selection candidates, and weighted genomic selection (WGS), which upscales rare allele effects in GS. Compared to GS, both methods provide the same higher long-term genetic gain and a similar lower inbreeding rate, despite some inherent limitations. GOCS does not control the inbreeding rate component linked to trait selection, and, therefore, does not strike the optimal balance between genetic gain and inbreeding. This makes it less effective throughout the breeding scheme, and particularly so at the beginning, where genetic gain and diversity may not be competing. For WGS, truncation selection proved suboptimal to manage rare allele frequencies among the selection candidates. To overcome these limitations, we introduce two new set selection methods that maximize a weighted index balancing genetic gain with controlling expected heterozygosity (IND-HE) or maintaining rare alleles (IND-RA), and show that these outperform GOCS and WGS in a nearly identical way. While requiring further testing, we believe that the inherent benefits of the IND-HE and IND-RA methods will transfer from our simulation framework to many practical breeding settings, and are therefore a major step forward toward efficient long-term genomic selection.

Impact of Mislabeling on Genomic Selection in Cassava Breeding

“…That is, the result is driven by the fact that mislabeling reduces the negative impact that GS has on genetic variance. Thus, it seems likely that under optimal use of GS, in which genomic data can be used to maximize selection gain while minimizing inbreeding or loss of genetic diversity (Goddard, 2009;Jannink, 2010;Yabe et al, 2016;De Beukelaer et al, 2017;Lin et al, 2017), the impact of mislabeling would be greater than observed here. When using such methods, the mislabeling might not only reduce gain but also disrupt efforts at genetic diversity maintenance (e.g., affect estimates of favorable allele frequencies used to weight rare favorable alleles, or estimates of the relationships among selected individuals used to minimize those relationships).…”

A hybrid optimal contribution approach to drive short-term gains while maintaining long-term sustainability in a modern plant breeding program

“…With proper constraints, optimal contribution can either drive means for short-term gains while exhausting genetic variability, or achieve modest gains while maintaining genetic variation for long-term sustainability. Use of optimal contributions is widespread in animal breeding applications, and has recently been adopted for a few plant breeding applications (Lin et al 2017; De Beukelaer et al 2017; Cowling et al 2017). Gorjanc et al (2018) show that incorporating optimal contributions with mate selection can further increase genetic gain for the two-part rapid-cycle plant breeding program of Gaynor et al (2017).…”

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

“…(1 − ) ( ) + ( ), where is the weight balancing the expected gain ( ) and constraint 507 ( ) (De Beukelaer et al 2017). However, the appropriate choice of is difficult and is not explicit 508 either in terms of expected diversity nor expected 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…”