Rootstock-Mediated Genetic Variance in Cadmium Uptake by Juvenile Cacao (Theobroma cacao L.) Genotypes, and Its Effect on Growth and Physiology

Fernández-Paz, J.; Cortés, Andrés J.; Hernández-Varela, Camila A.; Mejía-de-Tafur, Maria Sara; Rodríguez-Medina, Caren; Baligar, V. C.

doi:10.3389/fpls.2021.777842

Cited by 30 publications

(19 citation statements)

References 87 publications

(116 reference statements)

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“…Our results suggest that these four genes played an essential role in the resistance of kiwifruit to Psa ( Figure 3 D). It will be interesting to investigate genome environment associations (GEAs) to identify adaptive variations between green and non-green flesh kiwifruit [ 33 , 34 , 35 ].…”

Section: Resultsmentioning

confidence: 99%

Genome-Wide Identification of the LHC Gene Family in Kiwifruit and Regulatory Role of AcLhcb3.1/3.2 for Chlorophyll a Content

Luo

Abid

et al. 2022

IJMS

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Light-harvesting chlorophyll a/b-binding (LHC) protein is a superfamily that plays a vital role in photosynthesis. However, the reported knowledge of LHCs in kiwifruit is inadequate and poorly understood. In this study, we identified 42 and 45 LHC genes in Actinidia chinensis (Ac) and A. eriantha (Ae) genomes. Phylogenetic analysis showed that the kiwifruit LHCs of both species were grouped into four subfamilies (Lhc, Lil, PsbS, and FCII). Expression profiles and qRT-PCR results revealed expression levels of LHC genes closely related to the light, temperature fluctuations, color changes during fruit ripening, and kiwifruit responses to Pseudomonas syringae pv. actinidiae (Psa). Subcellular localization analysis showed that AcLhcb1.5/3.1/3.2 were localized in the chloroplast while transient overexpression of AcLhcb3.1/3.2 in tobacco leaves confirmed a significantly increased content of chlorophyll a. Our findings provide evidence of the characters and evolution patterns of kiwifruit LHCs genes in kiwifruit and verify the AcLhcb3.1/3.2 genes controlling the chlorophyll a content.

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Section: Resultsmentioning

confidence: 99%

Genome-Wide Identification of the LHC Gene Family in Kiwifruit and Regulatory Role of AcLhcb3.1/3.2 for Chlorophyll a Content

Luo

Abid

et al. 2022

IJMS

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“…This was expected due to their overlapping genetic and physiological mechanisms ( Sasaki et al, 2012 ; Cu et al, 2020 ). For example, transporter gene families like zinc-iron permease (ZIP), natural resistance-associated macrophage proteins (NRAMPs), and heavy metal transporting, ATPases (HMAs) have been associated with uptake and translocation of several trace elements in plants ( Fernández-Paz et al, 2021 ; Vanderschueren et al, 2021 ; Zhang et al, 2022 ). As a result, borrowing information from correlated traits overall improved the prediction accuracy of the MT models.…”

Section: Discussionmentioning

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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“…The variability in the G matrix (from highly related to completely unrelated trees) enabled the use of the G -BLUP predictor. Hence, we relied on this G relationship matrix to compute the genetic-based G -BLUP model, following Arenas et al (2021) and Fernandez-Paz et al (2021) , as in:…”

Section: Methodsmentioning

confidence: 99%

Inheritance of Yield Components and Morphological Traits in Avocado cv. Hass From “Criollo” “Elite Trees” via Half-Sib Seedling Rootstocks

et al. 2022

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Grafting induces precocity and maintains clonal integrity in fruit tree crops. However, the complex rootstock × scion interaction often precludes understanding how the tree phenotype is shaped, limiting the potential to select optimum rootstocks. Therefore, it is necessary to assess (1) how seedling progenies inherit trait variation from elite ‘plus trees’, and (2) whether such family superiority may be transferred after grafting to the clonal scion. To bridge this gap, we quantified additive genetic parameters (i.e., narrow sense heritability—h2, and genetic-estimated breeding values—GEBVs) across landraces, “criollo”, “plus trees” of the super-food fruit tree crop avocado (Persea americanaMill.), and their open-pollinated (OP) half-sib seedling families. Specifically, we used a genomic best linear unbiased prediction (G-BLUP) model to merge phenotypic characterization of 17 morpho-agronomic traits with genetic screening of 13 highly polymorphic SSR markers in a diverse panel of 104 avocado “criollo” “plus trees.” Estimated additive genetic parameters were validated at a 5-year-old common garden trial (i.e., provenance test), in which 22 OP half-sib seedlings from 82 elite “plus trees” served as rootstocks for the cv. Hass clone. Heritability (h2) scores in the “criollo” “plus trees” ranged from 0.28 to 0.51. The highesth2values were observed for ribbed petiole and adaxial veins with 0.47 (CI 95%0.2–0.8) and 0.51 (CI 0.2–0.8), respectively. Theh2scores for the agronomic traits ranged from 0.34 (CI 0.2–0.6) to 0.39 (CI 0.2–0.6) for seed weight, fruit weight, and total volume, respectively. When inspecting yield variation across 5-year-old grafted avocado cv. Hass trees with elite OP half-sib seedling rootstocks, the traits total number of fruits and fruits’ weight, respectively, exhibitedh2scores of 0.36 (± 0.23) and 0.11 (± 0.09). Our results indicate that elite “criollo” “plus trees” may serve as promissory donors of seedling rootstocks for avocado cv. Hass orchards due to the inheritance of their outstanding trait values. This reinforces the feasibility to leverage natural variation from “plus trees”viaOP half-sib seedling rootstock families. By jointly estimating half-sib family effects and rootstock-mediated heritability, this study promises boosting seedling rootstock breeding programs, while better discerning the consequences of grafting in fruit tree crops.

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Rootstock-Mediated Genetic Variance in Cadmium Uptake by Juvenile Cacao (Theobroma cacao L.) Genotypes, and Its Effect on Growth and Physiology

Cited by 30 publications

References 87 publications

Genome-Wide Identification of the LHC Gene Family in Kiwifruit and Regulatory Role of AcLhcb3.1/3.2 for Chlorophyll a Content

Genome-Wide Identification of the LHC Gene Family in Kiwifruit and Regulatory Role of AcLhcb3.1/3.2 for Chlorophyll a Content

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

Inheritance of Yield Components and Morphological Traits in Avocado cv. Hass From “Criollo” “Elite Trees” via Half-Sib Seedling Rootstocks

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