Island-Model Genomic Selection for Long-Term Genetic Improvement of Autogamous Crops

Abstract

Acceleration of genetic improvement of autogamous crops such as wheat and rice is necessary to increase cereal production in response to the global food crisis. Population and pedigree methods of breeding, which are based on inbred line selection, are used commonly in the genetic improvement of autogamous crops. These methods, however, produce a few novel combinations of genes in a breeding population. Recurrent selection promotes recombination among genes and produces novel combinations of genes in a breeding population, but it requires inaccurate single-plant evaluation for selection. Genomic selection (GS), which can predict genetic potential of individuals based on their marker genotype, might have high reliability of single-plant evaluation and might be effective in recurrent selection. To evaluate the efficiency of recurrent selection with GS, we conducted simulations using real marker genotype data of rice cultivars. Additionally, we introduced the concept of an “island model” inspired by evolutionary algorithms that might be useful to maintain genetic variation through the breeding process. We conducted GS simulations using real marker genotype data of rice cultivars to evaluate the efficiency of recurrent selection and the island model in an autogamous species. Results demonstrated the importance of producing novel combinations of genes through recurrent selection. An initial population derived from admixture of multiple bi-parental crosses showed larger genetic gains than a population derived from a single bi-parental cross in whole cycles, suggesting the importance of genetic variation in an initial population. The island-model GS better maintained genetic improvement in later generations than the other GS methods, suggesting that the island-model GS can utilize genetic variation in breeding and can retain alleles with small effects in the breeding population. The island-model GS will become a new breeding method that enhances the potential of genomic selection in autogamous crops, especially bringing long-term improvement.

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).…”

Potential of Genomic Selection in Mass Selection Breeding of an Allogamous Crop: An Empirical Study to Increase Yield of Common Buckwheat

“…Rutkoski et al ( 2015 ) showed that GS decreased genetic variance and increased mean inbreeding more rapidly than did PS even with the same level of genetic improvement. The decrease in genetic variation in a breeding population would decrease GS prediction accuracy and prevent long-term genetic improvement (Jannink, 2010 ; Yabe et al, 2016 ). Thus, maintaining genetic variation in a breeding population would be necessary to attain long-term improvement in GS.…”

Persistency of Prediction Accuracy and Genetic Gain in Synthetic Populations Under Recurrent Genomic Selection

“…Others assumed selection and were therefore able to evaluate potential genetic gain using GS (Muir 2007; Sonesson and Meuwissen 2009; Jannink 2010; Bastiaansen et al 2012; Yabe et al 2013, 2016; Liu et al 2015). However, these studies generally considered fairly large effective population sizes $N_e\ge 100,$ which are unrealistic for synthetics in plant breeding.…”