Enviromics in breeding: applications and perspectives on envirotypic-assisted selection

Resende, Rafael Tassinari; Piepho, Hans‐Peter; Rosa, Guilherme J. M.; Silva‐Junior, Orzenil B.; Silva, Fabyano F.; Resende, Marcos Deon Vilela de; Grattapaglia, Dário

doi:10.1007/s00122-020-03684-z

Cited by 119 publications

(121 citation statements)

References 91 publications

Supporting

Mentioning

121

Contrasting

Order By: Relevance

“…Neyhart et al (2021) assessed the genome-wide predictions in the unobserved environments for both between and within breeding generations. Resende et al (2020) recently proposed the geospatial (geographic information system) genotype-environment interaction (GIS-GEI) method within an enviromics framework. This framework involves the joint analysis of MET data accounting for phenotypic, genotypic and envirotypic sources of information.…”

Section: Discussionmentioning

confidence: 99%

Projecting results of zoned multi-environment trials to new locations using environmental covariates with random coefficient models: accuracy and precision

2021

Self Cite

View full text Add to dashboard Cite

Key message We propose the utilisation of environmental covariates in random coefficient models to predict the genotype performances in new locations. Abstract Multi-environment trials (MET) are conducted to assess the performance of a set of genotypes in a target population of environments. From a grower’s perspective, MET results must provide high accuracy and precision for predictions of genotype performance in new locations, i.e. the grower’s locations, which hardly ever coincide with the locations at which the trials were conducted. Linear mixed modelling can provide predictions for new locations. Moreover, the precision of the predictions is of primary concern and should be assessed. Besides, the precision can be improved when auxiliary information is available to characterize the targeted locations. Thus, in this study, we demonstrate the benefit of using environmental information (covariates) for predicting genotype performance in some new locations for Swedish winter wheat official trials. Swedish MET locations can be stratified into zones, allowing borrowing information between zones when best linear unbiased prediction (BLUP) is used. To account for correlations between zones, as well as for intercepts and slopes for the regression on covariates, we fitted random coefficient (RC) models. The results showed that the RC model with appropriate covariate scaling and model for covariate terms improved the precision of predictions of genotypic performance for new locations. The prediction accuracy of the RC model was competitive compared to the model without covariates. The RC model reduced the standard errors of predictions for individual genotypes and standard errors of predictions of genotype differences in new locations by 30–38% and 12–40%, respectively.

show abstract

Section: Discussionmentioning

confidence: 99%

Projecting results of zoned multi-environment trials to new locations using environmental covariates with random coefficient models: accuracy and precision

2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…A so far unexplored yet promising alternative would be to calibrate Genomic Prediction ( Crossa et al, 2017 ; Grattapaglia et al, 2018 ) and Machine Learning ( Gianola et al, 2011 ; Libbrecht and Noble, 2015 ; Schrider and Kern, 2018 ) models using high-throughput genotyping ( Cortés et al, 2020b ) of phenotyped ungrafted avocado trees spanning all three races, to predict rootstocks’ own unobserved phenotypes. Interpolating these predictions and quantitative genetic parameters across the rich ecological continuum of the northern Andean mountains ( Madriñán et al, 2013 ; Valencia et al, 2020 ), within a multi-climate ( Costa-Neto et al, 2020 ) “enviromic prediction” paradigm ( Resende et al, 2020 ), will be key to target optimum genotype x environment arrangements for yield ( Galeano et al, 2012 ; Blair et al, 2013 ) and quality ( Wu et al, 2020 ) components, as well as in the face of abiotic ( Cortés et al, 2020a ) and biotic ( Naidoo et al, 2019 ) stresses imposed by climate change.…”

Section: Discussionmentioning

confidence: 99%

Inheritance of Rootstock Effects in Avocado (Persea americana Mill.) cv. Hass

et al. 2020

View full text Add to dashboard Cite

Grafting is typically utilized to merge adapted seedling rootstocks with highly productive clonal scions. This process implies the interaction of multiple genomes to produce a unique tree phenotype. However, the interconnection of both genotypes obscures individual contributions to phenotypic variation (rootstock-mediated heritability), hampering tree breeding. Therefore, our goal was to quantify the inheritance of seedling rootstock effects on scion traits using avocado (Persea americana Mill.) cv. Hass as a model fruit tree. We characterized 240 diverse rootstocks from 8 avocado cv. Hass orchards with similar management in three regions of the province of Antioquia, northwest Andes of Colombia, using 13 microsatellite markers simple sequence repeats (SSRs). Parallel to this, we recorded 20 phenotypic traits (including morphological, biomass/reproductive, and fruit yield and quality traits) in the scions for 3 years (2015–2017). Relatedness among rootstocks was inferred through the genetic markers and inputted in a “genetic prediction” model to calculate narrow-sense heritabilities (h2) on scion traits. We used three different randomization tests to highlight traits with consistently significant heritability estimates. This strategy allowed us to capture five traits with significant heritability values that ranged from 0.33 to 0.45 and model fits (r) that oscillated between 0.58 and 0.73 across orchards. The results showed significance in the rootstock effects for four complex harvest and quality traits (i.e., total number of fruits, number of fruits with exportation quality, and number of fruits discarded because of low weight or thrips damage), whereas the only morphological trait that had a significant heritability value was overall trunk height (an emergent property of the rootstock–scion interaction). These findings suggest the inheritance of rootstock effects, beyond root phenotype, on a surprisingly wide spectrum of scion traits in “Hass” avocado. They also reinforce the utility of polymorphic SSRs for relatedness reconstruction and genetic prediction of complex traits. This research is, up to date, the most cohesive evidence of narrow-sense inheritance of rootstock effects in a tropical fruit tree crop. Ultimately, our work highlights the importance of considering the rootstock–scion interaction to broaden the genetic basis of fruit tree breeding programs while enhancing our understanding of the consequences of grafting.

show abstract

“…FIGURE 1 | Trans-disciplinary approaches (arrows) such as predictive breeding (GP) and machine learning (ML) promise supporting genome-wide marker-assisted (MAS) pre-breeding and breeding strategies for the selection of (A) "plus trees" in the wild, key (B) intra-and (C) inter-specific parental combinations, and (D) elite offspring from those parents. GP and ML should go beyond breeding and feedback (E) germplasm utilization and environmental niche classification (Cortés et al, 2013) and enviromics (Costa-Neto et al, 2020;Resende et al, 2020). Genomic-assisted characterizations, such as Genome-Wide Association Studies-GWAS (Neale and Savolainen, 2004), Genome-Environment Associations-GEA (Rellstab et al, 2015;Cortés and Blair, 2018;López-Hernández and Cortés, 2019) and Genome-Wide Selection Scans-GWSS (Zahn and Purnell, 2016), must also start considering more thoroughly (F) novel sources of local adaptation, (G) genetic-guided infusions and assisted gene flow (AGF), as well an overall systems genetics thinking (Ingvarsson et al, 2016;Myburg et al, 2019).…”

Section: Predictive Breeding Promises Boosting Forest Tree Genetic Immentioning

confidence: 99%

Modern Strategies to Assess and Breed Forest Tree Adaptation to Changing Climate

2020

View full text Add to dashboard Cite

Studying the genetics of adaptation to new environments in ecologically and industrially important tree species is currently a major research line in the fields of plant science and genetic improvement for tolerance to abiotic stress. Specifically, exploring the genomic basis of local adaptation is imperative for assessing the conditions under which trees will successfully adapt in situ to global climate change. However, this knowledge has scarcely been used in conservation and forest tree improvement because woody perennials face major research limitations such as their outcrossing reproductive systems, long juvenile phase, and huge genome sizes. Therefore, in this review we discuss predictive genomic approaches that promise increasing adaptive selection accuracy and shortening generation intervals. They may also assist the detection of novel allelic variants from tree germplasm, and disclose the genomic potential of adaptation to different environments. For instance, natural populations of tree species invite using tools from the population genomics field to study the signatures of local adaptation. Conventional genetic markers and whole genome sequencing both help identifying genes and markers that diverge between local populations more than expected under neutrality, and that exhibit unique signatures of diversity indicative of "selective sweeps." Ultimately, these efforts inform the conservation and breeding status capable of pivoting forest health, ecosystem services, and sustainable production. Key long-term perspectives include understanding how trees' phylogeographic history may affect the adaptive relevant genetic variation available for adaptation to environmental change. Encouraging "big data" approaches (machine learning-ML) capable of comprehensively merging heterogeneous genomic and ecological datasets is becoming imperative, too.

show abstract

Enviromics in breeding: applications and perspectives on envirotypic-assisted selection

Abstract: We propose the application of Enviromics to breeding practice, by which the similarity among sites assessed on an "omics" scale of environmental attributes drives the prediction of unobserved genotypes.

Cited by 119 publications

References 91 publications

Projecting results of zoned multi-environment trials to new locations using environmental covariates with random coefficient models: accuracy and precision

Projecting results of zoned multi-environment trials to new locations using environmental covariates with random coefficient models: accuracy and precision

Inheritance of Rootstock Effects in Avocado (Persea americana Mill.) cv. Hass

Modern Strategies to Assess and Breed Forest Tree Adaptation to Changing Climate

Contact Info

Product

Resources

About