Genotype × environment interactions for grain iron and zinc content in rice

Naik, S M; Raman, Anitha; Nagamallika, Minnuru; Venkateshwarlu, Challa; Singh, Suresh Prasad; Kumar, Santosh; Singh, Satish Kumar; Ahmed, T.; Das, Surojit; K, Krishna Prasad; Izhar, Tajwar; Mandal, N.P.; Singh, N. K.; Yadav, Shailesh; Reinke, Russell; Swamy, B. P. Mallikarjuna; Virk, P. S.; Kumar, Arvind

doi:10.1002/jsfa.10454

Cited by 25 publications

(17 citation statements)

References 59 publications

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“…In this study, the genomic heritabilities spanned a wide range from 0.14 to 0.62 (Table 2), which enabled the evaluation of the performance of MT models under contrasting levels of genomic heritability. The genomic heritability of Zn was the lowest (h 2 = 0.14, Table 2), which contradicts several previous studies that reported moderate to high heritability of Zn (Norton et al, 2010;Pinson et al, 2015;Naik et al, 2020). The poor heritability estimate of Zn in this study could be due to potential environmental effects.…”

Section: Factors Affecting the Observed Prediction Accuracies: Trait ...contrasting

confidence: 99%

Multi-Trait Genomic Prediction Models Enhance the Predictive Ability of Grain Trace Elements in Rice

et al. 2022

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Multi-trait (MT) genomic prediction models enable breeders to save phenotyping resources and increase the prediction accuracy of unobserved target traits by exploiting available information from non-target or auxiliary traits. Our study evaluated different MT models using 250 rice accessions from Asian countries genotyped and phenotyped for grain content of zinc (Zn), iron (Fe), copper (Cu), manganese (Mn), and cadmium (Cd). The predictive performance of MT models compared to a traditional single trait (ST) model was assessed by 1) applying different cross-validation strategies (CV1, CV2, and CV3) inferring varied phenotyping patterns and budgets; 2) accounting for local epistatic effects along with the main additive effect in MT models; and 3) using a selective marker panel composed of trait-associated SNPs in MT models. MT models were not statistically significantly (p < 0.05) superior to ST model under CV1, where no phenotypic information was available for the accessions in the test set. After including phenotypes from auxiliary traits in both training and test sets (MT-CV2) or simply in the test set (MT-CV3), MT models significantly (p < 0.05) outperformed ST model for all the traits. The highest increases in the predictive ability of MT models relative to ST models were 11.1% (Mn), 11.5 (Cd), 33.3% (Fe), 95.2% (Cu) and 126% (Zn). Accounting for the local epistatic effects using a haplotype-based model further improved the predictive ability of MT models by 4.6% (Cu), 3.8% (Zn), and 3.5% (Cd) relative to MT models with only additive effects. The predictive ability of the haplotype-based model was not improved after optimizing the marker panel by only considering the markers associated with the traits. This study first assessed the local epistatic effects and marker optimization strategies in the MT genomic prediction framework and then illustrated the power of the MT model in predicting trace element traits in rice for the effective use of genetic resources to improve the nutritional quality of rice grain.

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Section: Factors Affecting the Observed Prediction Accuracies: Trait ...contrasting

confidence: 99%

Multi-Trait Genomic Prediction Models Enhance the Predictive Ability of Grain Trace Elements in Rice

et al. 2022

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“…In irrigated method, the GCA effect of SPY and grain Fe and Zn contents were observed in contrasting manner. Negative correlation and negative GCA effect was observed between grain yield and Fe & Zn content under irrigated and aerobic method indicates the need for Fe and Zn content improvement of parental lines in line with grain yield 19 .…”

Section: Discussionmentioning

confidence: 95%

Grain Fe and Zn content, heterosis, combining ability and its association with grain yield in irrigated and aerobic rice

Anusha

Rao

Jaldhani

et al. 2021

Sci Rep

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Genetic improvement of rice for grain micronutrients, viz., iron (Fe) and zinc (Zn) content is one of the important breeding objectives, in addition to yield improvement under the irrigated and aerobic ecosystems. In view of developing genetic resources for aerobic conditions, line (L) × tester (T) analysis was conducted with four restorers, four CMS lines and 16 hybrids. Both hybrids and parental lines were evaluated in irrigated and aerobic field conditions for grain yield, grain Fe and Zn content. General Combining Ability (GCA) effects of parents and Specific Combining Ability (SCA) effects of hybrids were observed to be contrasting for the micronutrient content in both the growing environments. The grain Fe and Zn content for parental lines were negatively correlated with grain yield in both the contrasting growing conditions. However, hybrids exhibited positive correlation for grain Fe and Zn with grain yield under limited water conditions. The magnitude of SCA mean squares was much higher than GCA mean squares implying preponderance of dominance gene action and also role of complementary non-allelic gene(s) interaction of parents and suitability of hybrids to the aerobic system. The testers HHZ12-SAL8-Y1-SAL1 (T1) and HHZ17-Y16-Y3-Y2 (T2) were identified as good combiners for grain Zn content under irrigated and aerobic conditions respectively.

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“…Although various studies on maize and spring wheat have proven the effectiveness of the GP-based approach for kernel zinc concentration, to our knowledge no study applying GP to rice for grain zinc concentration has yet been reported. Grain zinc concentration is a complex trait greatly influenced by soil and other associated factors ( Jin et al 2013 ; Hindu et al 2018 ; Velu et al 2018 ; Naik et al 2020 ), so there are great hopes that GP will simplify the process of breeding rice for nutritional quality. On average, the PA for ZN in a single environment was low (0.26 and 0.24, for 2017 and 2018, respectively).…”

Section: Discussionmentioning

confidence: 99%

Impact of early genomic prediction for recurrent selection in an upland rice synthetic population

Baertschi

Cao

Bartholome

et al. 2021

G3 Genes|Genomes|Genetics

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Population breeding through recurrent selection is based on the repetition of evaluation and recombination among best-selected individuals. In this type of breeding strategy, early evaluation of selection candidates combined with genomic prediction could substantially shorten the breeding cycle length, thus increasing the rate of genetic gain. The objective of the present study was to optimize early genomic prediction in an upland rice (Oryza sativa L.) synthetic population improved through recurrent selection via shuttle breeding in two sites. To this end, we used genomic prediction on 334 S0 genotypes evaluated with early generation progeny testing (S0:2 and S0:3) across two sites. Four traits were measured (plant height, days to flowering, grain yield and grain zinc concentration) and the predictive ability was assessed for the target site. For days to flowering and plant height, which correlate well among sites (0.51–0.62), an increase of up to 0.4 in predictive ability was observed when the model was trained using the two sites. For grain zinc concentration, adding the phenotype of the predicted lines in the non-target site to the model improved the predictive ability (0.51 with two-site and 0.31 with single-site model), while for grain yield the gain was less (0.42 with two-site and 0.35 with single-site calibration). Through these results, we found a good opportunity to optimize the genomic recurrent selection scheme and maximize the use of resources by performing early progeny testing in two sites for traits with best expression and/or relevance in each specific environment.

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Genotype × environment interactions for grain iron and zinc content in rice

Cited by 25 publications

References 59 publications

Multi-Trait Genomic Prediction Models Enhance the Predictive Ability of Grain Trace Elements in Rice

Multi-Trait Genomic Prediction Models Enhance the Predictive Ability of Grain Trace Elements in Rice

Grain Fe and Zn content, heterosis, combining ability and its association with grain yield in irrigated and aerobic rice

Impact of early genomic prediction for recurrent selection in an upland rice synthetic population

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