Introduction

Rice (Oryza sativa L.) is one of the world’s major staple crops feeding more than 3.5 billion people (Global Rice Science Partnership 2013). It is believed that by 2050 the global population will be approximately 10 billion (United Nations 2019) and much of this population increase will occur in the regions of Africa and Southern Asia, which are highly dependent on rice. As such, rice will be crucial to ensuring equitable food security for the foreseeable future (Peng et al. 2004; Godfray 2014; Li et al. 2018). Challenges posed by climate change as well as increasing consumer demand further highlight the importance of rice to global food security (Silvern and Young 2013). While agricultural intensification using modernized management practices (Garnett et al. 2013) can help boost productivity, the importance of rice genetic improvement in the context of these management systems is also an important driver of sustainable productivity (Guimaraes 2009; Atlin et al. 2017). The rate at which this genetic improvement occurs is often referred to as genetic gain and in order to deliver improved varieties to the farmers of the twenty-first century, the rate of genetic gain in rice must accelerate relative to twentieth century levels (Atlin et al. 2017).

With the acceleration of genoty** technologies through the early twenty-first century and the subsequent maturation of genomic selection-based breeding strategies, there has been a renewed interest in the application of quantitative genetics to plant breeding programs (Cobb et al. 2019b; Bernardo 2020). To this end the irrigated rice breeding program at the International Rice Research Institute (IRRI) has spent significant effort to develop a modernized approach to rice breeding to substantially and sustainably increase response to selection (Collard et al. 2019). In addition to implementing accelerated single seed descent strategies (Collard et al. 2017), another major pillar of IRRI’s effort to transform rice breeding is the deep characterization of the elite genetic base from which new products are derived. While the characterization and dissection of rice genetic diversity in public germplasm collections has advanced considerably (Li et al. 2014; McCouch et al. 2016; Sun et al. 2017), to be fully leveraged for varietal improvement, it needs to be paired with an equally in-depth characterization of the elite genetic diversity residing in breeding programs across the world.

The irrigated rice breeding program at IRRI has been a source of elite breeding germplasm for decades (Peng and Khush 2003; Mackill and Khush 2018; Collard et al. 2019). This genetic diversity has been utilized in combination with landraces and local varieties to contribute substantially to the yield improvement achieved in Asia to date. The breeding strategies used to achieve this post-Green Revolution yield improvement however, frequently varied according to funding priorities, available technology, and evolution of scientific thinking (see Fig. 1). IRRI’s early breeding effort culminated in the development of IR8, the first widely-adopted semi-dwarf variety of the Green Revolution (Chandler 1982; Peng et al. 1999; Peng and Khush 2003). Though this variety was high yielding, it lacked acceptable cooking and eating quality and therefore was quickly superseded by other varieties that excelled in both grain yield and marketability (Khush 2001). During this time, a focus on improved disease resistance and continued efforts to increase genetic variation led to many new varieties introgressed with genetics from wild species (Brar and Khush 2002, 2018) that were created using strategies such as backcrossing, top crossing, and pedigree breeding methods. IR 36, for instance, resulted from the combination of 13 landraces from six different countries (Khush 2005). This variety displayed good grain quality, early maturity, tolerance to abiotic stresses, and resistance to multiple pests and disease (Peng and Khush 2003). Further advances in grain quality (soft gel consistency, translucent and long slender grains, intermediate amylose content and intermediate gelatinization temperature) were made with the release of IR 64 which resulted from combining extant improved lines with 19 traditional varieties (Mackill and Khush 2018), but which was still heavily based on IR8. A renewed focus on yield improvement in the late 1980s and 1990s sparked the development of an ideotype breeding strategy known as the new plant type (NPT, Fig. 1) (Cassman 1994; Peng et al. 2004; Yadi et al. 2021). With the advent of the molecular marker technologies during the same period, this was quickly followed up by selection strategies based on marker-assisted backcrossing to introduce major genes for biotic or abiotic stress tolerance to produce enhanced versions of existing varieties. This effort was recently coupled with an enhanced focus on bio-fortification in order to couple high yield with high nutritional value. However, post-Green Revolution breeding for quality and disease resistance, while successful, has not brought about the realized genetic gain for yield that is needed to meet the projected demand. More recent approaches aim to integrate principles of quantitative genetics into the breeding strategy by focusing the molecular breeding strategy on well-known high-value haplotypes and using a genomics-enabled rapid recurrent selection strategy to improve quantitative traits mainly through accelerated breeding cycles (Fig. 1).

Fig. 1
figure 1

Evolution of main techniques and breeding targets across decades for the irrigated breeding program at IRRI. Changes in breeding objectives in the decade are indicated in the boxes. Important IRRI rice varieties for each decade are shown in the ellipses

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The objective of this study was twofold: (i) estimate gains in breeding value for yield over the entire history of IRRI’s breeding program for irrigated systems, and (ii) identify and characterize a panel of elite lines from among the available germplasm that balances high breeding value for yield with sufficient genetic variance to preserve long term gain from selection. To this end, we gathered historical data from 102 yield trials in the IRRI phenotypic database (Breeding4Results 2021) spanning the years from 2012 to 2016 and combined it with pedigree data from the International Rice Information System (McLaren et al. 2005; Collard et al. 2019) to estimate breeding values for grain yield for all extant or recently extant lines. These trials included most of the existing advanced material of the breeding program as well as many replicated observations of IRRI released varieties, allowing us to estimate the rate of genetic gain over five decades. The same data was then used to identify high yielding lines from the breeding program to form the IRRI irrigated elite core panel (ECP). Seventy-two lines were ultimately chosen to comprise the elite panel and were subjected to extensive genetic and phenotypic characterization to assess suitability for short-cycle recurrent selection.

Results

Estimation of the Genetic Gain for Grain Yield

Genetic gain for grain yield was estimated as a function of change in breeding value over time. Breeding values for 15,286 lines evaluated in 102 trials conducted between 2012 and 2016 were estimated using a two-stage mixed model analysis (Table 1, Additional file 1: Table S1). The majority of these lines were advanced lines from the breeding program that never achieved varietal status and released varieties from different decades. Eighty percent of the lines originated from crosses that were made after 2009 (Fig. 2A). As expected, the reliability of the breeding values of older lines (generated before 2000) were higher compared to more recent material, with an average value of 0.43 (σ = 0.23) and 0.1 (σ = 0.17), respectively. Breeding values for grain yield ranged from 2.12 to 6.27 t·ha−1. The genetic trend as measured by this analysis of the IRRI irrigated rice breeding program since its initiation in 1960 to 2014 is presented in Fig. 2B. Over this period the linearized genetic trend was estimated to be 8.75 kg·ha−1 year−1 (0.23%). Despite the smaller sample size for the earlier historical periods, an upward trend from 1960 to 1980 is apparent followed by a period of variability in the average breeding value which eventually plateaus around 4.38 t·ha−1 after 2008. In order to interrogate the drivers of this genetic trend further, the equivalent complete generation (EqG, see “Materials and Methods” section) for each line was calculated as an estimate of the number of effective breeding cycles that had taken place prior to the crossing event. EqG is a key indicator of the rate of introduction of new material and the extent at which improved material is recycled into the breeding program. A similar trend to breeding values was also observed for EqG for the same period. Values had exceeded two by the end of the sixties to reach its maximum average value of six in the eighties (Fig. 2C). This was followed by a marked decrease to an average value of four in the nineties the average maximal values after 2000 never exceeded six equivalent generations. In addition, a large variance in EqG was found across lines from the most recent decade with values ranging from 1 to 7.56 highlighting the extensive use of non-improved material in combination with more elite lines.

Table 1 Summary of yield trials used to estimate breeding values for grain yield
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Fig. 2
figure 2

Time trend of important dataset characteristics used to assess genetic gain. A Evolution of the number of lines evaluated for this analysis according to the year the cross was initially made. The color gradient indicates the average reliability associated with the breeding values for yield. B Time trend in average breeding value for grain yield across years. The error bars represent the standard error associated with the mean. The blue line represents the linear regression of the breeding value for grain yield on the year. C Evolution of the average equivalent complete generation (EqG) of the lines through time. The error bars represent the standard error associated with the mean

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Eighty-six released varieties included in the dataset were analyzed separately to better characterize the long-term trend in breeding values for yield and its relationship with EqG (Fig. 3). This includes material dating from the Green Revolution and post-Green revolution eras (IR8, IR36), mega-variety (IR64) and more recent high performing releases (IRRI 154, IRRI 156). Altogether, these lines covered a large period from 1962 to 2006. In this period genetic gain for grain yield was estimated based on released varieties to be 17.36 kg·ha−1 year−1 (0.46%; Fig. 3A). When regressing breeding values on EqG we observed significant correlation and estimated the rate of genetic gain per cycle to be 185 kg·ha−1 cycle−1 (4.95%; Fig. 3B).

Fig. 3
figure 3

Trend in breeding values for grain yield on 86 IRRI released varieties. A Evolution of breeding value according to the year the cross was initially made. B Evolution of breeding value according to equivalent complete generation (EqG) as the proxy of realised breeding cycles. The color gradient for the dots represent the reliability of the breeding values from low (blue) to high (red). The blue lines represent the linear regression of the breeding value on the year or EqG. The associated equations are also displayed

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Retrospective Analysis of Crosses

The pattern of parental selection among crosses made by IRRI’s irrigated breeding program was analyzed over a period of thirty years (1985–2014) to assess the evolution of the crossing strategy and its relationship to EqG. During this period, 13,190 crosses were made. The number of crosses and the proportion of the type of cross varied substantially from one year to the next (Fig. 4A). However, the total number of crosses has been on a downward trend. During this period, most crosses were single crosses (71.9%) or three-way crosses (24.9%) and a small proportion were backcrosses (2.7%), complex crosses (0.4%) or double crosses (0.1%). The proportion of single crosses varied from 42.9% in 2007 to a high of 99.6% in 1989. To further dissect the impact of parental selection and mating design on EqG, crosses from this period were classified as elite by elite (41.4%), elite by non-elite (34.2%) and non-elite by non-elite (24.4%) based on the EqG of the parents. Since an EqG of 4 represented lines from the most advanced available breeding cycle in 1985, any line with an EqG of four or greater was considered elite and any line with an EqG of less than four was considered non-elite. Similar to cross type, the three classes of cross varied substantially from one year to the next (Fig. 4B). Notably, from 1991 to 1997 the proportion of non-elite by non-elite crosses increased dramatically with up to 82% of the crosses falling in this category for that period. This corresponds to a decrease in EqG for the same period and is likely a function of the introduction of new material into the breeding program to achieve the objectives of the NPT initiative (Additional file 2: Fig. S1). During the period 1985–2014, 6,228 unique lines were used as parents and most of them were used only once (65.4%). On the other hand, a few lines were heavily used as parents with 90 lines being used more than 40 times each during the 29-year period. As expected, this list included well-known IRRI varieties such as IR36, IR64, IR72, IRRI 104, IRRI 105, IRRI 118 IRRI 123 and IRRI 154 but also included traditional varieties like Kalimonch, Basmati 370, Shen Nung 89–366 and MD-2 used as donors of alleles with particular value. Interestingly, some of the most used parental lines were crossed during several periods with sometimes more than 20 years between their first and last use (IR64, IR72, IRRI 104, and IRRI 105). This prominent reuse of old material serves to lengthen effective breeding cycles despite advancing the pedigree and is likely one of the primary limitations on the historical rate of genetic gain for grain yield.

Fig. 4
figure 4

Evolution of the crossing strategy between 1985 and 2014 for IRRI’s irrigated breeding program. The cross type (A) and cross status (B) are represented. The cross status has been defined based on the equivalent complete generation (EqG) of the two parents (see “Materials and Methods” section)

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Defining the Elite Core Panel (ECP)

The best performing lines in terms of breeding value for yield were selected and filtered based on the reliability of the breeding value estimate and their relatedness to other lines in the dataset based on pedigree (see “Materials and Methods” section). The final ECP was composed of 72 lines falling within the top 2% of breeding values, ranging between 4.93 and 6.01 t·ha−1 with a mean value of 5.27 t·ha−1 (Fig. 5). Most of the selected lines were of medium duration with breeding values for flowering time (days to 50% heading) averaging at 90 days. The average EqG was 5.7 with 90% of the lines having an EqG greater than four. The majority of the lines were developed after 2000 with 37.5% in 2010 and onward (Fig. 5). Interestingly one line (IR05N341) has a significantly lower EqG of 2.44, but has a breeding value of 5158 kg·ha−1 and ranks as 45/72 among ECP breeding values. IR05N341 is an NPT inbred with a number of introduced lines in its pedigree, namely SHEN-NUNG 89–366, KETAN LUMBU, GUNDIL KUNING, and JHUM PADDY 7.

Fig. 5
figure 5

Distribution of 72 elite core panel (ECP) lines among the complete data set. The ECP lines are in red and the rest in grey. Four parameters are represented: breeding values (BV) for days to flowering and grain yield, the equivalent complete generation metric (EqG) and the year the cross was initially made a given line

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Genetic Characterization of the ECP

In order to quantify the genetic variation available to breeding in the ECP, the panel was genotyped with an amplicon panel of genome-wide markers specifically chosen to be informative among elite indica lines known as the 1k-RiCA (see “Materials and Methods” section). Using publicly available sequence data, the genome-wide SNPs assayed on the ECP lines were compared to the ** of ECP with ** the Elite Core Panel

The contemporary program has moved to a much more intensive recurrent selection strategy based on quantitative genetics principles to drive genetic gain for yield in the context of a disease resistant and high grain quality genetic background. This approach is a natural progression building on previous eras where the focus was on the identification and integration of genetic variation for yield potential traits (Peng et al. 2008). With that, it becomes necessary to systematically evaluate the existing genotypes in the program and select a number of high performing lines to form the basis of a gene pool upon which selection for high breeding value can, in combination with other innovations, drive improved rates of genetic gain (Xu et al. 2017). The 72 lines selected based on breeding value to be part of the ECP essentially represent the initial founders of the recurrent selection program moving forward. While the phenotypic value of this panel should be quickly eclipsed by successive generations of breeding, every new cohort represents an admixture of allelic variance of the panel. Thus, having clearly maintained seed sources for the original lines offers several distinct advantages, including as an elite source of genetic variation to be evaluated alongside the contemporary cohorts for new traits of interest. Such a panel is also helpful for validating trait markers for high-value haplotypes which reduce the occurrence of type I and type II errors when genoty** the progeny (Platten et al. 2019; Cobb et al. 2019a). Once sequenced, the panel also becomes a powerful resource for determining identity-by-descent (IBD) information among progeny cohorts and potentially reducing the need for routine use of high-density markers through the development of a breeding program specific imputation framework (Browning and Browning 2012; Nyine et al. 2019; Wang et al. 2020).

Genetic Diversity Captured by the Elite Core Panel

A natural concern to limiting the breeding program to crosses among such a small number of lines is the reduction in genetic variation that may occur due both to selection and genetic drift. As the ECP lines were selected based on the pedigree-estimated breeding values, the genetic characterization of the panel is a necessary next step to demonstrate its utility as a resource for breeding in a population improvement program (Warburton et al. 2005; Wen et al. 2012). While the panel itself was selected based on breeding value for yield, the mean flowering time compared to the entire dataset has not changed. This is largely a function of including flowering time as a covariate in the model, which factored out confounding effects due to the positive correlation between yield and flowering in rice.

We used the 3K reference genome panel (Wang et al. 2018) to better understand how well the ECP sampled the genetic space within rice genetic diversity at large. Unsurprisingly, it falls within the ** amplicon-based SNP assay for genetics and breeding applications in rice. Rice 12:55. https://doi.org/10.1186/s12284-019-0311-0 " href="/article/10.1186/s12284-021-00533-5#ref-CR4" id="ref-link-section-d192778788e3355">2019). This level of Ne is similar to what has been calculated in other rice breeding programs. For example, Grenier et al. (2015) showed a Ne in the range of 23–57 in four breeding populations of rice derived through recurrent selection programs. Morais Júnior et al. (2017) observed slightly higher values (40–60) in an irrigated rice breeding population using pedigree data. Values of Ne associate positively with additive genetic variation and the ability of a population to respond to selection for the trait under consideration (Falconer and Mackay 1996). Depletion in variability in population is proportional to Ne, and the time required to deplete the variability or fix one or other alternative allele in a population is a function of Ne, allele frequency (p) in the population, and the selection intensity (Walsh 2003). The theoretical limits of selection response as given by Robertson (1960) postulate that the total response to selection is equal to 2Ne times the initial gain in the first generation assuming genes with additive effects and relatively low selection intensity. This is to say that the Ne of any given generation is equal to the number of effective cycles before half the genetic variability is eroded by selection or drift. Therefore, holding the unlikely assumption that no new introductions were to be made into the program moving forward, the ECP could theoretically support at least 22 breeding cycles before half of the genetic variance is eroded. This could be further extended by the implementation of marker-optimized mating designs such as optimum contribution selection (Akdemir and Sánchez 2016; Akdemir et al. 2019) and targeted pre-breeding activities.

Expected Performance of the ECP Across Target Environments

Many traits in rice are governed by a number of high value, large effect alleles that affect patterns of phenotypic variance across environments (Wei et al. 2021). Some of these alleles (particularly disease and grain quality loci) are extremely valuable and deserve proactive management of their frequencies (Cobb et al. 2019a). Understanding these allele frequencies among the ECP lines is therefore essential for setting breeding strategy. As might be expected, the ECP displayed a wide range of frequencies for major pathogens and pests related genes. A few were essentially fixed for the positive allele; many of these represent indica/japonica differences, where the indica allele is favorable, such as Pi25 or Pid2. The value of these genes has already been captured by the breeding program, thus further improvement of these traits must rely on other genetic variation. Other genes are absent, such as xa13, Xa23, the rice yellow mottle virus resistance genes, among others. The lack of these genes in elite material necessitates some pre-breeding effort to introduce them to the elite pool before their value can be leveraged (Cobb et al. 2019a). Between these two extremes are the genes that can actually be selected in existing breeding material, and so are those contributing to variation in the elite pool. These include genes such as Xa7, Xa21 or TBV1 which although present are very rare and only available from particular lines, thus delaying their full deployment as the program generally cannot risk bottleneck a cohort through just a few lines. A few genes are at appreciable frequencies (but not fixed), and so represent diversity that is easily selectable in the existing breeding populations; these include Pita, Ptr, Pii, BPH32, sweet13, xa5 and TSV1.

Given the importance of bacterial blight and blast resistance specifically to germplasm exchange across Asia and Africa we decided to challenge the ECP lines against several common isolates of both pathogens to check the effectiveness of the gene combinations present in the ECP against blast and BLB disease in these specific genetic backgrounds. It has been reported by Shanti et al. (2010) that a four-gene combination (Xa4, xa5, xa13 and Xa21) is the most effective combination conferring broad-spectrum resistance for bacterial blight. While 30% of the ECP lines contained one or more bacterial blight resistance alleles, none of the ECP lines contained this specific combination as xa13 in particular is at a frequency of zero. However, it is clear the gene combinations that are present offer resistance to all tested isolates except PXO 99. PXO 99 is a common Xanthamonas isolate in the Philippines (Tu et al. 2000) indicating that while the ECP lines are directly useful in many geographies, a targeted backcrossing and MAS approach is needed to increase the frequency of high value alleles currently at low frequency among the breeding progeny of the ECP. Likewise, the blast resistance genes present in most of ECP lines also effectively controlled the manifestation of disease among the ECP lines for the five strains tested, likely due to the high frequency and broad spectrum resistance offered by the Pi-ta locus (Jia et al. 2016).

It is not unexpected that a subset of the breeding germplasm selected based on breeding value for yield would require pre-breeding/backcrossing and MAS to fully address the complexity of trait targets in the product concept. In order to avoid further erosion of the genetic variance (particularly in the form of selective sweeps around low frequency loci) some strategic cautions are warranted. Product development is the primary goal of a breeding program, however when product development is strongly emphasized, it is tempting for breeders to overuse specific high-value lines in a crossing block, at the expense of gene pool management. While every cross may not bring together the complete package necessary for a product release, specific crosses generated with the intention of creating progressively improved allelic combinations (and slowly increase in frequency of major genes) can generate useful lines that can be recycled into the crossing block as parents. Likewise, the use of haplotype-matched backcrossing donors of key genes can be a powerful tool for introducing novel alleles that are at zero or low frequency into the elite gene pool while preserving the availability of genetic variance immediately around them for recombination and the improvement of quantitative traits.

Managing the deployment of single genes within the breeding program due to limitations imposed by their frequency within the ECP is not the only consideration future breeding efforts based on this germplasm resource must consider. Since quantitative traits are not governed by single genes, location main effects and genotype by environment interactions must be routinely accounted for in phenotypic analysis strategies to factor out their strong influence on phenotypic outcomes. Because the primary source of data for determining the breeding values for yield that were used to identify the ECP were trials conducted in the Philippines, an understanding of yield performance in other target environments was also necessary. In order to evaluate the performance of the ECP lines relative to local and IRRI checks in relevant geographies outside the Philippines, six breeding trials were conducted in India, Kenya, and Tanzania. BLUP values centered on the mean performance of each location indicate strong performance of the ECP lines relative to the highest yielding local checks in each location. The performance relative to IRRI varieties and the local checks is a strong indication that the observed genetic variance is manifested as phenotypic variance within each location, indicating that crossing and selection among high performing lines within each breeding zone is likely to result in genetic gain for yield. Further analysis of genotype x environment interactions and the genetic correlations between target environments is warranted to help identify a global testing strategy that best leverages limited public resources available to the IRRI breeding program.

Conclusion

Achieving short- and medium-term genetic gains for yield is a key target for almost every breeding program. In the case of the IRRI’s breeding program for irrigated systems, the rate of genetic gain for grain yield was estimated at 17.36 kg·ha−1 year−1 (0.46%) for released varieties. This rate of gain appears to be largely limited by long cycle times and the re-introduction of old material or landraces into the elite pool. This observation highlights the need to optimize the breeding strategy for quantitative traits by using quantitative genetics principles to get closer to the annual 1.5% gain in grain yield needed to cover the expected increase in rice consumption. The elite core panel identified and characterized in this study is a key component of this optimization. Indeed, recurrent selection with short cycles based on elite-by-elite crosses implemented at IRRI to deliver a higher rate of genetic gain for grain yield requires careful management of the genetic diversity, which starts with a comprehensive characterization of the most elite germplasm.

Materials and Methods

Historical Yield Data

Experimental Studies and Pedigree Information

All the yield data from trials conducted by the irrigated breeding program during the period 2012–2016 across multiple locations were retrieved from the IRRI database. From these trials, the following phenotypic information was extracted: plant height, number of days to flowering, grain yield and number of hills per plot. The phenotypic information extracted were filtered based on the following quality criterion: presence of a randomized experimental design, percentage of missing data for grain yield and flowering time lower than 15%, and harvested area greater than 2 m2. We considered an environment as the combination of location, year and season. The environments considered varied in experimental designs according to either a row-column, alpha-lattice, augmented randomized complete block, or ordinary randomized complete block design (RCBD). A total of 102 studies were conducted in 23 environments with a total of 17,216 lines from which 15,286 were sorted out as irrigated rice lines data after filtering (Additional file 1: Table S1). All the studies were conducted in the Philippines, 51 studies having been planted during the wet season and another 51 studies during the dry season. The pedigree information for the selected lines was extracted from the IRRI genealogy management system (McLaren et al. 2005) database using custom scripts. The date of the initial cross was also retrieved for all the breeding lines whose crossing year information was available in the database (16,317 lines). The pedigree information was also used to compute equivalent complete generations (EqG) (Boichard et al. 1997; Gutiérrez et al. 2008; Leroy et al. 2013) for each line. EqG for a given line was calculated as follow:

$$EqG=\sum _{i = 1}^{n}\left(\frac{1}{{2}^{{g}_{i}}}\right)$$

where gi represents the number of generations between the line and its ancestor i (one for the parents, two for the grandparents, etc.).

Estimating Breeding Values for Lines

A two-stage mixed model analysis (Piepho et al. 2008; Smith and Cullis 2018) using grain yield data as response variable was used to estimate the breeding values of each line. The two-stage mixed model analysis was adopted to account for specific experimental design layouts across the environments (Damesa et al. 2017). In the first stage, each trial or environment (combination of location, year and season) was analyzed separately and best linear unbiased predictors (BLUPs) were extracted per environment using the following baseline mixed-model:

$$y_{ij} = \mu + g_{i} + \cdots + \varepsilon_{i}$$
(1)

where yij represents grain yield for ith observation, μ is the overall mean, gi is the random effect of ith genotype with iid gi ∼ N(0, Iσ2g) where σ2g is genetic variance and εij is the residual error with iid εij ∼ N(0, Rσ2ε). To account for heterogeneous error variance caused by differences in the numbers of hills harvested from plot to plot and from trial to trial, the diagonal of R was set to h/hmax where, h is the number of hills harvested and hmax is the maximum number of hills harvested in the environment. The … in the model denotes the blocking factors and a covariate for missing hills which were conditional to the trial. These terms were included in the model because they were identified as improving model fit during analyses of individual trials. Blocking factors were considered random if they had more than five levels. The possible blocking factors were modelled to determine which factors led to the lowest Bayesian information criterion (Spilke et al. 2010; Piepho et al. 2015). For trials that followed a row-column design, the possible factors were row and column, for those following a partially replicated design, the possible factors were row, column, replicate, and block, for those following a RCBD or augmented RCBD, the possible factor was replicate, for those following an alpha-lattice design the possible factors were replicate, block nested within replicate, row, and column. The model with lowest Bayesian information criterion was selected and used to extract BLUP of each line and their prediction error variances (PEV) were obtained for each environment. Reliabilities of the BLUPs were estimated according to \(r^{2} = 1 - \frac{PEV}{{\sigma_{g}^{2} }}\). The process for BLUP estimation per environment was repeated for days to flowering.

In the second stage model, the BLUPs obtained from the first stage model were de-regressed by dividing by the reliability as described in Garrick et al. (2009), and used as response variable in the second stage pedigree-based mixed model analysis. The de-regressed BLUPs for yield within each environment were modeled according to Bates et al. (2014). The model used is as follows:

$$y_{ij} = \mu + g_{i} + e_{j} + \varepsilon_{ijk}$$
(2)

where yij is the de-regressed BLUP of each line in environment j, μ is the overall mean, gi is a random effect of line i with gi ∼ N(0, Aσ2g) where σ2g is the genetic variance and A is the additive genetic relationship matrix based on pedigree, ej is a fixed effect of the environment j, εij is the residual error with εij ∼ N(0, Rσ2ε) where R is a matrix proportional to the residual error covariance matrix and σ2ε is the error variance. To account for heterogeneous error variance, the diagonal of R was 1/r2. In the above model yield was adjusted using days to flowering as covariate in the model. The R packages lme4 (Bates et al. 2015) and pedigreemm (Bates et al. 2014) were used to implement the models.

Assessment of Rate of Genetic Gain

Genetic gain was assessed using breeding values following the procedure reviewed by Garrick (2010). Briefly, for each year, the breeding values obtained were regressed on the year when the cross was made to get the genetic gain trends.

Retrospective Analysis of Crosses

The pedigree data of breeding lines developed by the IRRI’s irrigated breeding program was obtained from the genealogy management system. All crosses made were retrieved from the database and filtered based on the availability of the following information: whether or not it belonged to the irrigated program and the year when the cross was made. After those filters, only crosses made in 1985 and onward were available. This is related to the absence of clear boundaries between breeding programs before that date. Indeed, even though lots of material has been produced since IRRI inception in 1960, the information of the breeding program was not recorded in the database before 1985 making it difficult to extract relevant information. For each cross the EqGs of the F1 and of the parental lines were computed based on pedigree information. Each cross was then classified based on EqG of the parental lines. Parental lines with an EqG lower than four (mean EqG value of parents used in 1985) were classified as non-elite material and the remaining part as elite material.

Formulation of the Elite Core Panel

In order to identify the best breeding genotypes (hereafter called elite lines) the lines were filtered based on high ranking for their grain yield breeding values. Genetic relationship for the genotypes was measured by calculating the coefficient of parentage (CoP) based on the pedigree information using R package pedigreemm (Bates et al. 2014). Among the 15,286 evaluated lines, 1192 had reliability for the breeding value greater than or equal to 0.4. Then ten percent of these lines were selected to represent elite material. In total, 119 elite lines were selected among which only 80 elite lines were retained based on seed availability. Further based on seed-viability and intellectual property restrictions, some of the lines were removed and 72 elite lines were forwarded for the formulation of the elite core panel (ECP). The 72 ECP lines are reported in Additional file 1: Table S1.

Genetic Characterization of the ECP

Genetic diversity of ECP and its relationship with the 3000 rice genomes (Li et al. 2014) was assessed. Effective population size (Ne) of ECP was calculated using SNP data. Further, frequency of major trait genes was estimated in the ECP.

DNA Extraction, Genoty** and SNP Filtering

Leaf samples were obtained from the 72 ECP lines and 10 IRRI varieties (IR 6, IR 64, IR 68, IR 72, IR 74, IRRI 115, IRRI 116, IRRI 146, IRRI 151, IRRI 164) at vegetative growth stage (28–35 days old) plants. DNA was isolated and purified according to the modified Cetyltrimmethyl Ammonium Bromide (CTAB) protocol (Aboul-Maaty and Oraby 2019). Genoty** was done using the 1k-RiCA assay (Arbelaez et al. 2019). The 1k-RiCA SNPs were filtered in TASSEL v5.0 (Bradbury et al. 2007) using the following criteria: individuals with more than 15% of heterozygous loci where removed, markers with more than 15% of missing values and a minor allele frequency below 0.05 were removed. After filtering, 703 markers on 76 lines including 66 ECP lines were retained for downstream genetic analyses (Additional file 1: Table S2).

To enable a better characterization of key genes related to biotic stresses, resequencing data was generated on ECP lines. DNA was extracted from mature leaf tissue using the QIAgen DNeasy Plant maxi kit. Sequencing was performed on total genomic DNA on an Illumina Sequel II system (Macrogen, Korea) or a HiSeq 2000 system (Corteva, Hyderabad). Resequencing data was filtered and trimmed for low-quality base calls using standard pipelines, and mapped to the MSU7 build of the Nipponbare genome. Based on sequencing data quality, four lines were discarded. Base calls at specified informative positions were generated using SAMtools (Li et al. 2009) and analysed to generate a call for the allele present at each of 37 specified high-value genes related to abiotic stresses but only 33 had sufficient data to be called. The information related to the 37 genes is available in Additional file 1: Table S3.

Diversity of ECP and Its Relationship with 3K-RG, and Favorable Frequency Estimation

The relationship of ECP lines with the indica subpopulation of the 3K-RG was assessed using principal component analysis (PCA). First, the physical position coordinates of the 703 filtered 1k-RiCA SNPs were used to extract the filtered set of markers from the 3K-RG using the rice SNP-Seek database (Mansueto et al. 2017). Out of 703 filtered markers, 625 markers were common between the two data sets and used for downstream analysis. Modern varieties coming from IRRI are known to be within the ** method. Three leaves per plant were inoculated. Evaluation of resistance was done at 14 days after inoculation (DAI) by measuring the lesion starting from the point of inoculation to the end with visible symptoms. For a given isolate, the genotypes with less than four measurements (out of six) were removed from the analysis. The average lesion length was computed for all the ECP lines and the checks. The final dataset had 2.9% missing data and is available in Additional file 1: Table S6.

Field Based Evaluation and Data Analysis

Phenotypic characterization of ECP for grain yield was conducted in Kenya, Tanzania, India and the Philippines. Different experimental designs such as randomized complete block, alpha-lattice and partially replicated designs were used across these environments (Additional file 1: Table S7). Plot-level yields were normalized based on plot size and all the experimental data is stored in the breeding for results (B4R) data management system.

Grain yield (ton/ha) and days to 50% flowering time (days) from all the ECP trials were used in this study. The best linear unbiased predictor (BLUP) values were calculated using the predict function from the R package asreml in which the entry was used as a random effect in all the trials. For the trials that followed a partially replicated design, the possible factors were row, column, replicate, and block, for those following a randomized complete block design, the possible factor was replicate, for those following an alpha-lattice design the possible factors were replicate, block nested within replicate, row, and column. The BLUP values were used to rank the performance of the accessions along with the IRRI and local checks grown in each location. The three IRRI checks used in this study were IR 64, IR 72 and IRRI 154, which are high yielding varieties released in the years 1985, 1988 (Peng and Khush 2003) and 2010, respectively. The local checks are specific to the regions and are selected by the partners conducting the trial. BASMATI 370 and IRRI 215 in Kenya; SUPA, SUPA BC and TXD 306 in Tanzania; MTU 1010, IGKV-R1, CG-Deobhog, CR Dhan-304, CR Dhan-307, ARIZE 6444 Gold, SWARNA, IRRI 216 in India were the local check entries in the trials. For the Philippines, the global checks also served as the local checks.