Genomic selection with rapid cyclingcycling: Current insights and future prospects

Abstract:

Enhancing the rate of genetic gain in plant breeding program is critical to address global food security in the face of climate change and a growing population. Rapid cycling genomic selection offers a powerful breeding strategy to reduce the breeding cycle and obtain rapid genetic gains in plant breeding programs. In this paper, we discuss theoretical and empirical approaches to deploy and optimize rapid cycling genomic selection in crop improvement programs. We highlight major advantages and challenges associated with rapid cycling genomic selection and provide example to overcome these issues. Finally, we discuss the trends and general conclusion on this breeding strategy and provide recommendations for future discussions and continued adoption in plant breeding programs.

Keywords:
Recurrent Genomic selection; plant breeding; food security; genetic gain

INTRODUCTION

Plant breeding is likely to play an important role to sustain global food security and nutritional quality in the face of climate change and a growing world population, which is projected to reach 9.5 billion by 2050 (He and Li 2020, Qaim 2020). However, to keep pace with the ever-growing population and to counter the increased impact of climate change (Wheeler and von Braun 2013, Hummel et al. 2018), global crop production must be doubled by 2050, which current yield trends are insufficient to achieve in this timeframe (Godfray et al. 2010). Therefore, enhancing genetic gain to double production is crucial to sustain global food security and nutrional quality. Genomic prediction proposed proposed almost 20 years ago has revolutionized design and implementation of plant and animal breeding programs (Meuwissen et al. 2001, Hickey et al. 2017), and has become a promising tool for plant breeding programs to accelerate the rate of genetic gain in crops (Bernardo and Yu 2007, Hickey et al. 2017, Voss-Fels et al. 2018, Cobb et al. 2019, Xu et al. 2020). The widespread adoption of genomic selection in plant breeding programs has been driven by the technological developments in genotyping, whole-genome sequencing, and statistical computing. These technological improvements have enabled plant breeding programs to obtain cheap and abundant single nucleotide polymorphism (SNP) markers for prediction. Therefore, we believe the continued adoption of genomic selection in breeding programs is critical to meet future food security.

Genomic selection is a form of marker-assisted selection (MAS) that uses all available molecular markers to predict the overall genomic merit of an individual to make selections (Meuwissen et al. 2001, Bernardo and Yu 2007, Goddard and Hayes 2007). Compared to MAS, in genomic selection all marker effects are estimated simultaneously, avoiding the two-step procedure of determining which markers are “significant,” followed by estimation of their effects using multiple linear regression (Hospital et al. 1997, Lorenz et al. 2012), making it more appealing for genetically complex traits. Initially, a training population is first established, of individuals wich are phenotyped for the target trait(s) and genotyped with DNA markers across the genome. The training set is used to train a statistical model for associations between molecular markers and traits of interest to derive a prediction equation, which predicts the effect of each marker on the trait, with all marker effects fitted simultaneously (Voss-Fels et al. 2018). This prediction equation is then used to estimate the genetic value of selection candidates using only their genotypic information. The goal is to make a prediction with high enough accuracy to allow parent selection on a testing population based on those predictions, known as genomic estimated breeding values (GEBVs) (Lorenz et al. 2012). Genomic selection can be used in place of phenotyping to drastically restructure the breeding program design (Heffner et al. 2009) and increase genetic gain per unit of time compared to phenotypic selection without increasing costs (Crossa et al. 2017). Therefore, an appropriate breeding strategy using genomic selection can reduce the breeding cyle, increase the accuracy of estimated breeding values, and improve the selection accuracy (Crossa et al. 2021).

THE BREEDER’S EQUATIONS - SHORTENING THE BREEDING CYCLE TO ACCELERATE GENETIC GAIN

A useful framework that is commonly used to quantify the effectiveness of a breeding strategy on genetic gain per unit time is the breeder’s equation (Lush 1937). This equation models the expected change in a trait in response to selection and can be approximated in four key parameters written as:

$R=\frac{i{\sigma }_{G}h}{L}$

Where R is the change in trait mean per year or referred to as response to selection, i is the selection intensity, σ _G is the amount of genetic variation in the population, h represents the accuracy of selection or heritability, and L is the breeding cycle time (or generation interval). Response to selection can be increased by either increasing the numerator or by decreasing the breeding cycle time in the denominator. In terms, of the four components of the breeders equation, genomic selection can be used to effectively address each of the four components in the breeder’s equation (Hickey et al. 2017). However, while heritability (accuracy), selection intensity, additive genetic variance can increase genetic gain, the breeding cycle time is the most affordable and powerful of these components to increase genetic gain (Cobb et al. 2019). For example, livestock breeding programs using genomic selection has enabled to shorten the generation interval, thus incrasing the rates of genetic gain (Kasinathan et al. 2015, Hickey et al. 2017). In plant breeding programs, the breeding cycle involves identifying elite breeding material through performance history or evaluation and reusing it to be used as parents for the next breedin cycle. In an excellent review by Cobb et al. (2019) the authors point out that accelerating the breeding cycles in a breeding program can be the most efficient way to increase the rate of genetic gain, but it has been underutilized in many breeding programs, which primariliy focus on other components of the breeders equation such as heritability, selection intensity, and additive genetic variance. The authors also point out that these components can provide rapid genetic gain initially in a breeding program but are subject to rapidly diminishing returns on investments and can ultimately cost more than shortening the breeding cycle in the long term.

Therefore, an effective breeding strategy that can shorten the breeding cycle is one of the most efficient ways to increase the rate of genetic gain in plant breeding programs. As such, deploying genomic selection in a breeding program would enhance the rate of genetic gain crucial for future food security. Herein, we review breeding schemes that utilize genomic selection to increase genetic gains in plant breeding programs to accelerate the breeding cycle. Current status, advantages of challenges, and trends in these breeding schemes are highlighted and discussed. We refer to the reader to other focused review and research articles for more information on the general topic of genomic selection (Voss-Fels et al. 2018, Xu et al. 2020, Crossa et al. 2021), training population optimization (Hickey et al. 2014, Akdemir et al. 2015, Isidro et al. 2015, Berro et al. 2019), and model selection (Heslot et al. 2012, Crossa et al. 2017).

CURRENT STATUS OF RAPID CYCLE GENOMIC SELECTION

The development of efficient breeding strategies to inocoprorate genomic selection in plant breeding programs is an active research field and can drastically change a breeding programs. A breeding scheme utilizing genomic selection will depend on the crop species and several subcomponents including the breeding program, integrated breeding platforms, and off-season screening (Xu et al. 2020). To date several breeding schemes to shorten the generation interval have been used (Massman et al. 2013, Beyene et al. 2015, Vivek et al. 2017, Sleper and Bernardo 2018, Beyene et al. 2019) to improve yield and other traits. This population advancement strategy to reduce the breeding cycle has been denoted as rapid cycling genomic selection and rapid recurrent genomic selection (Figure 1). Many empirical studies have shown rapid cycling genomic selection can improve the rate of genetic gain per year when used appropiately. For example, rapid cycle genomic selection was first reported by Massman et al. (2013), whose results demonstrated actual genetic gains achieved through genomic selection in a bi-parental maize populations. This study evaluated testcrosses of 233 inbeds from distinct heterotic groups between B73 and Mo17 under well-watered conditions and advanced using genomic selection and marker-assisted recurrent selection (MARS). The authors reported superior gains with genomic selection for stover yield, as well as stover and grain yield indices by 14 to 50% over MARS. For example, Beyene et al. (2015) showed the application of genomic selection to improve tropical maize populations for grain yield under drought. Using three rapid cycles of recombination in a year, they reported an average gain per cycle of 0.086 Mg ha^-1 across eight populations and hybrids derived from cycle three produced 7.3%(0.176 ton ha^-1) higher grain yield than those developed through the conventional pedigree method. Similarly, Vivek et al. (2017) reported rapid genetic gains for drought stress using genomic selection was higher than with phenotypic selection in two bi-parental maize populations. Rapid cycle genomic selection was also applied in a multi-parental topical maize population (Zhang et al. 2017). In thist study the authors reported realized grain yield from cycle 1 to cyle 4 reached 0.100 tons ha^-1 yr^-1 over a 4.5-yr breeding period from the initial cross to the last cycle with a minimal loss of genetic diversity during the last cycle of genomic selection. Recently, Das et al. (2020) used rapid cycle genomic selection to improve drought and waterlogging tolerance on two multi-parent yellow synthetic maize populations in Asia. This study showed that realized genetic gains after two cycles of rapid cycle recombination of genomic selection under drought stress were 0.110 and 0.135 tons ha⁻¹ yr⁻¹, and 0.038 and 0.113 tons ha⁻¹ yr⁻¹ under waterlogging in two populations, respectively. Furthermore, genomic selection for drough and waterlogging did not result in any yield penalty under optimal moisture conditions and a genetic diversity analysis of the parents showed an increase from parents to the the first cycle (C₁) and narrowed slightly with the next two cycles of rapid cycling.

Figure 1
General breeding scheme for recurrent genome selection or rapid cycling genome selection scheme using “n” cycle in a crop breeding program.

While the previous studies have evaluated recurrent genomic selection schemes, few breeding programs have yet to adopt them. In a recent study Bernardo (2021) attributed this to a tradeoff between higher genetic gains and the time needed to develop a maize hybrid. He pointed out that most studies using recurrent genomic selection lead to delay in the first-year of phenotyping for line developed from the improved populations. In this study, he described the two-cycle genomic selection as a compromise the one-cycle genomic selection with similar time and costs. The results from this study found that the two-cycle genomic selection can increase genetic gains without increasing the cost or time in a maize breeding program. Furthermore, the largest gains from two-cycle genomic selection were 124-178% of the largest gains from phenotypic selection and 112 to 135% of the larged gains from one-cyelc genomic selection. These results demonstrates that incorporating a single recombination step can lead to increase gains and that advantage of leveraging cost-efficient genotyping over more costly phenotyping.

The breeding cycle can also be shortened by parental selection in a rapid-cycle recurrent genomic selection program with the aim to improve the breeding value of a given population by decoupling the conventional parent selection selection from elite breeding populations (Cobb et al. 2019). Similar approaches have been demonstrated for traditional phenotypic recurrent selection programs based (Frey et al. 1988, Hallauer et al. 2010). Heffner et al. (2009) mentioned of the possibility for future elite and parental lines which could be selected based on their GEBVs rather than on their phenotypic performance and in the past few years breeding schemes using this concept have been previously described in several crops to date (Bernardo and Yu 2007, Bernardo 2009, 2010). Furthermore, more recently also, simulations have become a powerful tool to explore the design of breeding program and breeding strategies. For example, Gaynor et al. (2017) proposed a two-part strategy as an extension of the aformentioned studies with the aim to maximize the potential of genomic selection in a breeding program. This strategy proposes to reorganization of a traditional plant breeding program for inbred or hybrid crops into a population improvement component that develops improved germplasm and the second component to identify new inbred varieties as parents or hybrids (Gaynor et al. 2017, Hickey et al. 2017). The first component uses rapid recurrent genomic selection with the goal to minimize breeding cycle time to maximize genetic gain per year. While the product development component aims at developing inbred liens for release as inbred varieties or hybrid parents. Using stochastic simulations to compare several breeding strategies with and without genomic selection including two with the proposed two-part breeding strategy found that programs using the two-part strategy generated a rate of genetic gain more than 2.5 times that of a conventional program and between 1.46 time more than a standard genomic selection breeding strategy. The authors modeled all breeding program scenarios using a similar cost to compare all breeding strategies. Therefore, the results indicate that the two-part strategy is a cost-effective strategy to implement genomic selection in plant breeding programs. Ultimately, adoption of this breeding strategy would require a breeding program to run on solely genomic selection and would require a large costly training populations to achieve acceptable prediction accuracy, which may not be feasible in smaller breeding programs for some years (Cobb et al. 2019). While some commercial breeding programs may be leading the way on this strategy to date few public breeding programs have reported any empirical results for the two-part strategy.

ADVANTAGES AND CHALLENGES OF RAPID CYCLING METHODS

In a conventional breeding program rapid cycling genomic selection can shorten the breeding cycle and reduce the cost of phenotyping, while increasing the rate of genetic gain. However, Gorjanc et al. (2017) pointed some important factors to consider to ensure large genetics gains when considering rapid cycling genomic selection: number of cycles (C₀, C₁ … C_n) used, size of the population, number of parents, accuracy of genomic prediction, maintenance of genetic diversity, and costs. While the author was referring to the two-part strategy approach proposed by (Gaynor et al. 2017) these factors should be considered in many of the rapid cycling schemes mentioned in this review. In the two-part strategy. Increasing the number of cycles in a rapid cycling genomic selection scheme can increase the costs required to genotype many selection candidate as well as other operating costs. Managing the size of the training population and the number of parents will also need to be balanced to achieve an acceptable prediction accuracy for a breeder to make selection on. Increasing the number of cycles can also reduce the amount of genetic diversity and can ultimately reduce long term genetic gains in a breeding program. For example, in the simulation performed by Gaynor et al. (2017) he found that all breeding programs lose genetic variance over time. However, the rate of loss different breeding the breeding programa and strategy used. While the breeding strategies using genomic selection without reducing the breeding cycle showed a large drop in initial genetiv variance relative to the conventional approach. The two-part strategy led to a larger initial decrease followed by a more rapid decrease in genetic variance. Thus, (Gorjanc et al. 2018) proposed an optimal cross selection strategy to reduce the loss of genetic diversity and reduce the drop of genomic prediction accuracy with rapid cycling. Furthermore, in an optimal breeding pipeline the genetic diversity should not be affected after cycles of GS in an optimal breeding pipeline, as mentioned by Dias et al. (2020) study. On the other hand, the increase in population size at the expense of performing several parental crosses and fewer recombination cycles comprises the selection performed under more narrow selection intensity. It tends to deliver greater genetic gains as well, despite the increase in overall genotyping costs. Therefore a major challenge in the rapid cycling genomic slection method will be to balance the long-term the genetic gains, maintain the genetic diversity, and handle increasing genotyping costs, pointed out by Gorjanc et al. (2018)

GENERAL CONSIDERATIONS FOR THE FUTURE

Rapid cycling genomic selection has tremendous potential to increase the genetic gains in plant breeding programs. However, these methods have not been adopted due to genotyping costs that are still considerably high for several public-sector plant breeding programs or smaller breeding program without the resources of capacity to undertake a large genotyping effort. More effort is needed to evaluate rapid cycling in different crop species and model breeding program in terms of resource allocation to redesign current breeding programs to integrate genomic selection. For example, Muleta et al. (2019) simulated the Chibas sorghum breeding program in Haiti in order to evaluate an optimal way to deploy genomic selection. Efforts like this and that of CGIAR centers like International Maize and Wheat Improvement Center (CIMMYT) to utilize rapid cyling genomic selection in Africa (Beyene et al. 2015) and Asia (Vivek et al. 2017) will help increase the rate of genetic gain in maize. In additional to deployment of rapid cycling genomic selection, modern tools such as high-thoughput phenotyping and machine learning or deep learning algorithms will optimize the breeding strategy.

CONCLUSIONS

Reducing the breeding cycle will likely increase the rate of genetic gain in plant breeding programs. However, methods to reduce the breeding cycle have not been adopted as routinely as other components of the breeder’s equation to increase the rate of genetic gain per year. Plant breeding programs continue to struggle to adopt genomic selection and re-design breeding programs to fully integrate genomic selection and alter breeding pipelines all together. While the incorporation of simulation tools will allow for more computational based decision making within breeding programs to deploy genomic selection, most plant public sector breeding programs in both developed and developing coutries may still find bottlenecks in the cost of genotyping or managing large training populations. Continues investment to modernize plant breeding programs is required to adopt modern technologies in order to maximize the rate of genetic gain. Rapid cycling genomic selection offers an effective breeding strategy to achive rapid genetic gains to critically meet future food security in the face of climate change.

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Publication Dates

• Publication in this collection
  29 Oct 2021
• Date of issue
  2021

History

• Received
  25 Aug 2021
• Accepted
  31 Aug 2021
• Published
  20 Sept 2021