Integrating molecular markers into metabolic models improves genomic selection for Arabidopsis growth

Tong, Hao; Küken, Anika; Nikoloski, Zoran

doi:10.1038/s41467-020-16279-5

Cited by 36 publications

(57 citation statements)

References 49 publications

(62 reference statements)

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“…Hence, a high output and quick methods of efficiency are important factors to be taken into account. These should also possess high polymorphism and should have codominance inheritance for homo and hetero zygotes in segregating offspring and should be cost effective [93][94][95].…”

Section: Molecular Marker Assisted Selectionmentioning

confidence: 99%

Gene Pyramiding for Sustainable Crop Improvement against Biotic and Abiotic Stresses

Dormatey¹,

Sun²,

Ali³

et al. 2020

Preprint

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Sustainable agricultural production is endangered by several ecological factors such as drought, extreme temperatures, excessive salts, parasitic ailments, and insect pest infestation. These challenging environmental factors may have adverse effects on future agriculture production in many countries. In modern agriculture, conventional crop breeding techniques alone are inadequate for achieving the increasing population’s food demand on a sustainable basis. The advancement of molecular genetics and related technologies are promising tools for the selection of new crop species. Gene pyramiding through marker assisted selection (MAS) and other techniques have accelerated the development of durable resistant/tolerant lines with high accuracy in the shortest possible period of time for agricultural sustainability. Gene stacking has not been fully utilized for biotic stress resistance development and quality improvement in most of the major cultivated crops. This review emphasizes on gene pyramiding techniques that are being successfully deployed in modern agriculture for improving crop tolerance to abiotic and biotic stresses for sustainable crop improvement.

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Section: Molecular Marker Assisted Selectionmentioning

confidence: 99%

Gene Pyramiding for Sustainable Crop Improvement against Biotic and Abiotic Stresses

Dormatey¹,

Sun²,

Ali³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Compared with the previous selection methods that based on pedigree information and progeny testing, GS possesses the natural advantages that the phenotype and the genomic breeding values data can be obtained as soon as the descendant arrives, which dramatically accelerates the breeding process. A large number of researches have proved that GS facilitates the rapid selection of superior genotypes and accelerates genetic gain by shortening the breeding cycles (Bouquet & Juga, 2013; Crossa et al., 2017; Hayes et al., 2009; Nakaya & Isobe, 2012; Tong et al., 2020). After nearly two decades of studies on the GS methods, the prediction accuracy measured by the correlation between phenotype data and the predicted genomic breeding values has improved dramatically.…”

Section: Introductionmentioning

confidence: 99%

Application of ensemble learning to genomic selection in chinese simmental beef cattle

Liang

Miao

Wang

et al. 2020

J Animal Breeding Genetics

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Genomic selection (GS) involves estimating genome estimate breeding values (GEBVs) using molecular markers spanning the whole-genome (Meuwissen et al., 2001), which is not limited to traits determined by a few major genes (Montesinos-López et al., 2019). Compared with the previous selection methods that based on pedigree information and progeny testing, GS possesses the natural advantages that the phenotype and the genomic breeding values data can be obtained as soon as the descendant arrives, which dramatically accelerates the breeding process. A large number of researches have proved that GS facilitates the rapid selection of superior genotypes and accelerates genetic gain by shortening the breeding cy-

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“…Using expression compendia based on multiple experiments poses an interesting alternative, since genes with similar expression patterns are more likely functionally related, hence more likely involved in the same biological process(es) (Kourmpetis et al, 2011). Alternatives are to define phenotype associated genomic regions based on differential gene expression levels (Fang et al, 2017) or metabolite levels and metabolic fluxes (Tong et al, 2020), or to construct haplotypes in genic regions based on their ontology information (Gao et al, 2018). The GP requiring genomics inferred relationship matrices (GRM), e.g., GBLUP and its variants, can make use of information derived from these sources to construct a population variance-covariance structure (Zhang et al, 2010(Zhang et al, , 2011Fragomeni et al, 2017).…”

Section: Exploiting Biological Knowledge To Improve Genomic Predictionmentioning

confidence: 99%

Prior Biological Knowledge Improves Genomic Prediction of Growth-Related Traits in Arabidopsis thaliana

et al. 2021

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Prediction of growth-related complex traits is highly important for crop breeding. Photosynthesis efficiency and biomass are direct indicators of overall plant performance and therefore even minor improvements in these traits can result in significant breeding gains. Crop breeding for complex traits has been revolutionized by technological developments in genomics and phenomics. Capitalizing on the growing availability of genomics data, genome-wide marker-based prediction models allow for efficient selection of the best parents for the next generation without the need for phenotypic information. Until now such models mostly predict the phenotype directly from the genotype and fail to make use of relevant biological knowledge. It is an open question to what extent the use of such biological knowledge is beneficial for improving genomic prediction accuracy and reliability. In this study, we explored the use of publicly available biological information for genomic prediction of photosynthetic light use efficiency (ΦPSII) and projected leaf area (PLA) in Arabidopsis thaliana. To explore the use of various types of knowledge, we mapped genomic polymorphisms to Gene Ontology (GO) terms and transcriptomics-based gene clusters, and applied these in a Genomic Feature Best Linear Unbiased Predictor (GFBLUP) model, which is an extension to the traditional Genomic BLUP (GBLUP) benchmark. Our results suggest that incorporation of prior biological knowledge can improve genomic prediction accuracy for both ΦPSII and PLA. The improvement achieved depends on the trait, type of knowledge and trait heritability. Moreover, transcriptomics offers complementary evidence to the Gene Ontology for improvement when used to define functional groups of genes. In conclusion, prior knowledge about trait-specific groups of genes can be directly translated into improved genomic prediction.

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Integrating molecular markers into metabolic models improves genomic selection for Arabidopsis growth

Cited by 36 publications

References 49 publications

Gene Pyramiding for Sustainable Crop Improvement against Biotic and Abiotic Stresses

Gene Pyramiding for Sustainable Crop Improvement against Biotic and Abiotic Stresses

Application of ensemble learning to genomic selection in chinese simmental beef cattle

Prior Biological Knowledge Improves Genomic Prediction of Growth-Related Traits in Arabidopsis thaliana

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