Genomic‐assisted breeding for climate‐smart coffee

Ferrão, Maria Amélia Gava; Fonseca, Aymbiré Francisco Almeida da; Volpi, P. S.; Souza, Lucimara Cruz de; Comério, Marcone; Filho, A. C. Verdin; Riva-Souza, Elaine Manelli; Muñoz, Patricio; Ferrão, R. G.; Ferrão, Luís Felipe Ventorim

doi:10.1002/tpg2.20321

Cited by 9 publications

(3 citation statements)

References 105 publications

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“…Specifically, the heritability estimates varied between (0.1–0.74) [ Alkimim et al. (2020) and Alemayehu, 2019 ], (0.30–0.55) [ Gokavi et al., 2023 and Weldemichael et al., 2017 ], (0.30–0.51) [ Chrigui et al., 2020 and Ferrão et al., 2023 ], and (0.09–0.61) Alkimim et al., 2021 and Ferrão et al., 2023 ] for the YL, NF, LM, and Cer, respectively. Although the Pearson correlation between YL and the disease resistance traits were not statistically significant, a significant and positive genetic correlation (0.52) was obtained between YL and NF.…”

Section: Discussionmentioning

confidence: 99%

Enhancing genomic prediction with Stacking Ensemble Learning in Arabica Coffee

Nascimento,

Azevedo

et al. 2024

Front. Plant Sci.

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Coffee Breeding programs have traditionally relied on observing plant characteristics over years, a slow and costly process. Genomic selection (GS) offers a DNA-based alternative for faster selection of superior cultivars. Stacking Ensemble Learning (SEL) combines multiple models for potentially even more accurate selection. This study explores SEL potential in coffee breeding, aiming to improve prediction accuracy for important traits [yield (YL), total number of the fruits (NF), leaf miner infestation (LM), and cercosporiosis incidence (Cer)] in Coffea Arabica. We analyzed data from 195 individuals genotyped for 21,211 single-nucleotide polymorphism (SNP) markers. To comprehensively assess model performance, we employed a cross-validation (CV) scheme. Genomic Best Linear Unbiased Prediction (GBLUP), multivariate adaptive regression splines (MARS), Quantile Random Forest (QRF), and Random Forest (RF) served as base learners. For the meta-learner within the SEL framework, various options were explored, including Ridge Regression, RF, GBLUP, and Single Average. The SEL method was able to predict the predictive ability (PA) of important traits in Coffea Arabica. SEL presented higher PA compared with those obtained for all base learner methods. The gains in PA in relation to GBLUP were 87.44% (the ratio between the PA obtained from best Stacking model and the GBLUP), 37.83%, 199.82%, and 14.59% for YL, NF, LM and Cer, respectively. Overall, SEL presents a promising approach for GS. By combining predictions from multiple models, SEL can potentially enhance the PA of GS for complex traits.

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Section: Discussionmentioning

confidence: 99%

Enhancing genomic prediction with Stacking Ensemble Learning in Arabica Coffee

Nascimento,

Azevedo

et al. 2024

Front. Plant Sci.

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“…The study identified key candidate genes like CaTIFY4b and FRIGIDA that hold potential for enhancing drought tolerance in chickpea. In another study, Ferrão et al (2023) integrated GWAS and diallel analyses to investigate the genetic basis of key agronomic traits evaluated under drought environments in coffee. Molecular markers with major effects associated with disease resistance and post-harvest traits were identified, while yield and plant architecture displayed a polygenic background.…”

Section: Drought Tolerancementioning

confidence: 99%

Exploring the genomics of abiotic stress tolerance and crop resilience to climate change

Varshney,

Barmukh,

Bentley

et al. 2024

The Plant Genome

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Abiotic stresses have a detrimental impact on crop production globally. The escalating frequency and intensity of these stresses, driven by rapid changes in climatic conditions, pose significant challenges to agriculture. This situation is worsened by the burgeoning human population that is projected to heighten the demand for food in the coming years, emphasizing the need for further agricultural innovations. Addressing these challenges and steering agriculture toward sustainability requires concerted research efforts to mitigate the adverse effects of climate change-induced abiotic stresses. One of the most logical and cost-effective strategies on a global scale is the development and utilization of crop varieties endowed with increased tolerance to different abiotic stresses.Conventional breeding has played a key role in developing crops resilient to abiotic stress; however, recent advancements in genomics technologies have expedited these efforts. The development and public availability of multiple crop genomes, coupled with improvements in genome assembly quality and the emergence of pangenome and super-pangenome, signify a substantial leap forward. Highquality reference genomes, whole-genome resequencing, and pangenome approaches have enabled the mapping of allelic variants, discovery of candidate genes, development of molecular markers, and the introgression of traits related to abiotic stress tolerance. This wealth of genomic data, complemented by other omics datasets such as transcriptomics, proteomics, metabolomics, and phenomics, has effectively bridged the genotype-phenotype gap (Varshney, Bohra et al., 2021). This integration is crucial for gene mapping and marker-assistedThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…Genomics-assisted breeding, at present, is employed to genetically improve plant species like Coffea arabica [153], sorghum [154], Virginia-type peanuts [155], Triticum sp. [156][157][158], pulses (pea, cowpea, faba-bean and lentil) [152], Cajanus cajan [159,160], Cicer arietinum, Arachis hypogaea [160], Avena Sativa [161], O. sativa [162], and many others.…”

Section: Gblupmentioning

confidence: 99%

Integrated Genomic Selection for Accelerating Breeding Programs of Climate-Smart Cereals

Sinha,

Maurya,

Abdi

et al. 2023

Genes

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Rapidly rising population and climate changes are two critical issues that require immediate action to achieve sustainable development goals. The rising population is posing increased demand for food, thereby pushing for an acceleration in agricultural production. Furthermore, increased anthropogenic activities have resulted in environmental pollution such as water pollution and soil degradation as well as alterations in the composition and concentration of environmental gases. These changes are affecting not only biodiversity loss but also affecting the physio-biochemical processes of crop plants, resulting in a stress-induced decline in crop yield. To overcome such problems and ensure the supply of food material, consistent efforts are being made to develop strategies and techniques to increase crop yield and to enhance tolerance toward climate-induced stress. Plant breeding evolved after domestication and initially remained dependent on phenotype-based selection for crop improvement. But it has grown through cytological and biochemical methods, and the newer contemporary methods are based on DNA-marker-based strategies that help in the selection of agronomically useful traits. These are now supported by high-end molecular biology tools like PCR, high-throughput genotyping and phenotyping, data from crop morpho-physiology, statistical tools, bioinformatics, and machine learning. After establishing its worth in animal breeding, genomic selection (GS), an improved variant of marker-assisted selection (MAS), has made its way into crop-breeding programs as a powerful selection tool. To develop novel breeding programs as well as innovative marker-based models for genetic evaluation, GS makes use of molecular genetic markers. GS can amend complex traits like yield as well as shorten the breeding period, making it advantageous over pedigree breeding and marker-assisted selection (MAS). It reduces the time and resources that are required for plant breeding while allowing for an increased genetic gain of complex attributes. It has been taken to new heights by integrating innovative and advanced technologies such as speed breeding, machine learning, and environmental/weather data to further harness the GS potential, an approach known as integrated genomic selection (IGS). This review highlights the IGS strategies, procedures, integrated approaches, and associated emerging issues, with a special emphasis on cereal crops. In this domain, efforts have been taken to highlight the potential of this cutting-edge innovation to develop climate-smart crops that can endure abiotic stresses with the motive of keeping production and quality at par with the global food demand.

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Genomic‐assisted breeding for climate‐smart coffee

Cited by 9 publications

References 105 publications

Enhancing genomic prediction with Stacking Ensemble Learning in Arabica Coffee

Enhancing genomic prediction with Stacking Ensemble Learning in Arabica Coffee

Exploring the genomics of abiotic stress tolerance and crop resilience to climate change

Integrated Genomic Selection for Accelerating Breeding Programs of Climate-Smart Cereals

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