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Reciprocal Recurrent Genomic Selection Is Impacted by Genotype-by-Environment Interactions.

ABSTRACT: Reciprocal recurrent genomic selection is a breeding strategy aimed at improving the hybrid performance of two base populations. It promises to significantly advance hybrid breeding in wheat. Against this backdrop, the main objective of this study was to empirically investigate the potential and limitations of reciprocal recurrent genomic selection. Genome-wide predictive equations were developed using genomic and phenotypic data from a comprehensive population of 1,604 single crosses between 120 female and 15 male wheat lines. Twenty superior female lines were selected for initiation of the reciprocal recurrent genomic selection program. Focusing on the female pool, one cycle was performed with genomic selection steps at the F₂ (60 out of 629 plants) and the F₅ stage (49 out of 382 plants). Selection gain for grain yield was evaluated at six locations. Analyses of the phenotypic data showed pronounced genotype-by-environment interactions with two environments that formed an outgroup compared to the environments used for the genome-wide prediction equations. Removing these two environments for further analysis resulted in a selection gain of 1.0 dt ha^-1 compared to the hybrids of the original 20 parental lines. This underscores the potential of reciprocal recurrent genomic selection to promote hybrid wheat breeding, but also highlights the need to develop robust genome-wide predictive equations.

SUBMITTER: Rembe M

PROVIDER: S-EPMC8498042 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Reciprocal Recurrent Genomic Selection Is Impacted by Genotype-by-Environment Interactions.

Rembe Maximilian M Reif Jochen Christoph JC Ebmeyer Erhard E Thorwarth Patrick P Korzun Viktor V Schacht Johannes J Boeven Philipp H G PHG Varenne Pierrick P Kazman Ebrahim E Philipp Norman N Kollers Sonja S Pfeiffer Nina N Longin C Friedrich H CFH Hartwig Niklas N Gils Mario M Zhao Yusheng Y

Frontiers in plant science 20210924

Reciprocal recurrent genomic selection is a breeding strategy aimed at improving the hybrid performance of two base populations. It promises to significantly advance hybrid breeding in wheat. Against this backdrop, the main objective of this study was to empirically investigate the potential and limitations of reciprocal recurrent genomic selection. Genome-wide predictive equations were developed using genomic and phenotypic data from a comprehensive population of 1,604 single crosses between 12 ...[more]

PMID: 34630453

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Project description:BackgroundFamily-based selective breeding programs typically employ both between-family and within-family selection in aquaculture. However, these programs may exhibit a reduced genetic gain in the presence of a genotype by environment interactions (G × E) when employing biosecurity-based breeding schemes (BS), compared to non-biosecurity-based breeding schemes (NBS). Fortunately, genomic selection shows promise in improving genetic gain by taking within-family variance into account. Stochastic simulation was employed to evaluate genetic gain and G × E trends in BS for improving the body weight of L. vannamei, considering selective genotyping strategies for test group (TG) at a commercial farm environment (CE), the number individuals of the selection group (SG) genotyped at nucleus breeding center (NE), and varying levels of G × E.ResultsThe loss of genetic gain in BS ranged from 9.4 to 38.9% in pedigree-based selection and was more pronounced when G × E was stronger, as quantified by a lower genetic correlation for body weight between NE and CE. Genomic selection, particularly with selective genotyping of TG individuals with extreme performance, effectively offset the loss of genetic gain. With a genetic correlation of 0.8, genotyping 20 SG individuals in each candidate family achieved 93.2% of the genetic gain observed for NBS. However, when the genetic correlation fell below 0.5, the number of genotyped SG individuals per family had to be increased to 50 or more. Genetic gain improved by on average 9.4% when the number of genotyped SG individuals rose from 20 to 50, but the increase in genetic gain averaged only 2.4% when expanding from 50 to 80 individuals genotyped. In addition, the genetic correlation decreased by on average 0.13 over 30 generations of selection when performing BS and the genetic correlation fluctuated across generations.ConclusionsGenomic selection can effectively compensate for the loss of genetic gain in BS due to G × E. However, the number of genotyped SG individuals and the level of G × E significantly affected the extra genetic gain from genomic selection. A family-based BS selective breeding program should monitor the level of G × E and genotyping 50 SG individuals per candidate family to minimize the loss of genetic gain due to G × E, unless the level of G × E is confirmed to be low.

Project description:IntroductionGenomic selection (GS) experiments in forest trees have largely reported estimates of predictive abilities from cross-validation among individuals in the same breeding generation. In such conditions, no effects of recombination, selection, drift, and environmental changes are accounted for. Here, we assessed the effectively realized predictive ability (RPA) for volume growth at harvest age by GS across generations in an operational reciprocal recurrent selection (RRS) program of hybrid Eucalyptus.MethodsGenomic best linear unbiased prediction with additive (GBLUP_G), additive plus dominance (GBLUP_G+D), and additive single-step (HBLUP) models were trained with different combinations of growth data of hybrids and pure species individuals (N = 17,462) of the G1 generation, 1,944 of which were genotyped with ~16,000 SNPs from SNP arrays. The hybrid G2 progeny trial (HPT267) was the GS target, with 1,400 selection candidates, 197 of which were genotyped still at the seedling stage, and genomically predicted for their breeding and genotypic values at the operational harvest age (6 years). Seedlings were then grown to harvest and measured, and their pedigree-based breeding and genotypic values were compared to their originally predicted genomic counterparts.ResultsGenomic RPAs ≥0.80 were obtained as the genetic relatedness between G1 and G2 increased, especially when the direct parents of selection candidates were used in training. GBLUP_G+D reached RPAs ≥0.70 only when hybrid or pure species data of G1 were included in training. HBLUP was only marginally better than GBLUP. Correlations ≥0.80 were obtained between pedigree and genomic individual ranks. Rank coincidence of the top 2.5% selections was the highest for GBLUP_G (45% to 60%) compared to GBLUP_G+D. To advance the pure species RRS populations, GS models were best when trained on pure species than hybrid data, and HBLUP yielded ~20% higher predictive abilities than GBLUP, but was not better than ABLUP for ungenotyped trees.DiscussionWe demonstrate that genomic data effectively enable accurate ranking of eucalypt hybrid seedlings for their yet-to-be observed volume growth at harvest age. Our results support a two-stage GS approach involving family selection by average genomic breeding value, followed by within-top-families individual GS, significantly increasing selection intensity, optimizing genotyping costs, and accelerating RRS breeding.

Project description:Key messageGenomic prediction of GCA effects based on model training with full-sib rather than half-sib families yields higher short- and long-term selection gain in reciprocal recurrent genomic selection for hybrid breeding, if SCA effects are important. Reciprocal recurrent genomic selection (RRGS) is a powerful tool for ensuring sustainable selection progress in hybrid breeding. For training the statistical model, one can use half-sib (HS) or full-sib (FS) families produced by inter-population crosses of candidates from the two parent populations. Our objective was to compare HS-RRGS and FS-RRGS for the cumulative selection gain ([Formula: see text]), the genetic, GCA and SCA variances ([Formula: see text],[Formula: see text], [Formula: see text]) of the hybrid population, and prediction accuracy ([Formula: see text]) for GCA effects across cycles. Using SNP data from maize and wheat, we simulated RRGS programs over 10 cycles, each consisting of four sub-cycles with genomic selection of [Formula: see text] out of 950 candidates in each parent population. Scenarios differed for heritability [Formula: see text] and the proportion [Formula: see text] of traits, training set (TS) size ([Formula: see text]), and maize vs. wheat. Curves of [Formula: see text] over selection cycles showed no crossing of both methods. If [Formula: see text] was high, [Formula: see text] was generally higher for FS-RRGS than HS-RRGS due to higher [Formula: see text]. In contrast, HS-RRGS was superior or on par with FS-RRGS, if [Formula: see text] or [Formula: see text] and [Formula: see text] were low. [Formula: see text] showed a steeper increase and higher selection limit for scenarios with low [Formula: see text], high [Formula: see text] and large [Formula: see text]. [Formula: see text] and even more so [Formula: see text] decreased rapidly over cycles for both methods due to the high selection intensity and the role of the Bulmer effect for reducing [Formula: see text]. Since the TS for FS-RRGS can additionally be used for hybrid prediction, we recommend this method for achieving simultaneously the two major goals in hybrid breeding: population improvement and cultivar development.

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Reciprocal Recurrent Genomic Selection Is Impacted by Genotype-by-Environment Interactions.

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Reciprocal Recurrent Genomic Selection Is Impacted by Genotype-by-Environment Interactions.

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