Molecular basis of trait correlations

Chen, Yongsheng; Lübberstedt, Thomas

doi:10.1016/j.tplants.2010.05.004

Cited by 142 publications

(108 citation statements)

References 82 publications

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“…Notably, such traits in the dissection approach typically are not measurable via traditional phenotyping approaches. As a further step toward biological insights from such image-derived parameters, we calculated genetic correlations between traits, such as might be considered for selection of desired phenotypic trait combinations in breeding programs (Chen and Lübberstedt, 2010;Stackpole et al, 2011;Porth et al, 2013). The identification of a concerted negative genetic correlation of an indicator of water content/drought tolerance (NIR signal; Figure 2D) with plant height ( Figure 6B) appears to be highly advantageous for breeding strategies: Breeding for higher drought tolerance could simultaneously select lower plant height and vice versa.…”

Section: Discussionmentioning

confidence: 99%

“…decisions concerning the design and selection of breeding schemes (Holland et al, 2003;Chen and Lübberstedt, 2010). It has been speculated that the dynamic change of heritability over time for a population is a consequence of changes in the magnitude of G and E effects (Visscher et al, 2008).…”

Section: Change Of Heritability and Trait-trait Genetic And Phenotypimentioning

confidence: 99%

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Dissecting the Phenotypic Components of Crop Plant Growth and Drought Responses Based on High-Throughput Image Analysis

et al. 2014

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Significantly improved crop varieties are urgently needed to feed the rapidly growing human population under changing climates. While genome sequence information and excellent genomic tools are in place for major crop species, the systematic quantification of phenotypic traits or components thereof in a high-throughput fashion remains an enormous challenge. In order to help bridge the genotype to phenotype gap, we developed a comprehensive framework for highthroughput phenotype data analysis in plants, which enables the extraction of an extensive list of phenotypic traits from nondestructive plant imaging over time. As a proof of concept, we investigated the phenotypic components of the drought responses of 18 different barley (Hordeum vulgare) cultivars during vegetative growth. We analyzed dynamic properties of trait expression over growth time based on 54 representative phenotypic features. The data are highly valuable to understand plant development and to further quantify growth and crop performance features. We tested various growth models to predict plant biomass accumulation and identified several relevant parameters that support biological interpretation of plant growth and stress tolerance. These image-based traits and model-derived parameters are promising for subsequent genetic mapping to uncover the genetic basis of complex agronomic traits. Taken together, we anticipate that the analytical framework and analysis results presented here will be useful to advance our views of phenotypic trait components underlying plant development and their responses to environmental cues.

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Section: Discussionmentioning

confidence: 99%

Section: Change Of Heritability and Trait-trait Genetic And Phenotypimentioning

confidence: 99%

Dissecting the Phenotypic Components of Crop Plant Growth and Drought Responses Based on High-Throughput Image Analysis

et al. 2014

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“…Moreover, root allocation tradeoffs may limit certain RSA combinations, such as between root growth in the topsoil versus deep soil horizons (3,10,47,48), and these tradeoffs may have a genetic basis. Thus, to understand the full architectural possibilities for crop improvement, it is imperative to identify the precise nucleotide polymorphisms that underlie quantitative differences in central RSA traits (49). We used 3D imaging and digital phenotyping to detect regions of the rice genome that control the growth of RSA.…”

Section: Discussionmentioning

confidence: 99%

3D phenotyping and quantitative trait locus mapping identify core regions of the rice genome controlling root architecture

Topp

Iyer‐Pascuzzi

Anderson

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

275

255

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Identification of genes that control root system architecture in crop plants requires innovations that enable high-throughput and accurate measurements of root system architecture through time. We demonstrate the ability of a semiautomated 3D in vivo imaging and digital phenotyping pipeline to interrogate the quantitative genetic basis of root system growth in a rice biparental mapping population, Bala × Azucena. We phenotyped >1,400 3D root models and >57,000 2D images for a suite of 25 traits that quantified the distribution, shape, extent of exploration, and the intrinsic size of root networks at days 12, 14, and 16 of growth in a gellan gum medium. From these data we identified 89 quantitative trait loci, some of which correspond to those found previously in soil-grown plants, and provide evidence for genetic tradeoffs in root growth allocations, such as between the extent and thoroughness of exploration. We also developed a multivariate method for generating and mapping central root architecture phenotypes and used it to identify five major quantitative trait loci (r 2 = 24-37%), two of which were not identified by our univariate analysis. Our imaging and analytical platform provides a means to identify genes with high potential for improving root traits and agronomic qualities of crops.Oryza sativa | QTL | three-dimensional | live root imaging | multivariate analysis R oot systems are high-value targets for crop improvement because of their potential to boost or stabilize yields in saline, dry, and acid soils, improve disease resistance, and reduce the need for unsustainable fertilizers (1-7). Root system architecture (RSA) describes the spatial organization of root systems, which is critical for root function in challenging environments (1-10). Modern genomics could allow us to leverage both natural and engineered variation to breed more efficient crops, but the lack of parallel advances in plant phenomics is widely considered to be a primary hindrance to developing "next-generation" agriculture (3,11,12). Root imaging and analysis have been particularly intractable: Decades of phenotyping efforts have failed to identify genes controlling quantitative RSA traits in crop species. Several factors confound RSA gene identification, including polygenic inheritance of root traits, soil opacity, and a complex 3D morphology that can be influenced heavily by the environment. Most phenotyping efforts have relied on small numbers of basic measurements to extrapolate system-wide traits. For example, given the length and mass of a few sample roots and the excavated root system mass, one can estimate the total root length, volume, and average root width of the entire root system (13,14). Other common measurements involve measuring the root surface exposed on a soil core or pressed against a transparent surface to estimate root coverage at a certain soil horizon. In these cases, the choices of sample roots and phenotyping standards, the size and shape of the container, and the limitations of 2D descriptions of 3D struct...

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“…These traits, which showed moderate to high correlations, could be a guide to breeding programs and these germplasm sets could be used to improve certain traits and design efficient breeding programs. Trait correlations are very useful, as breeders could use them to ameliorate simultaneously correlated traits by reducing undesired side effects when the focus is only on one of the correlated traits (Chen and Lübberstedt, 2010).…”

Section: Correlation Of Qualitative Traitsmentioning

confidence: 99%

Phenotypic and molecular characterization of apple (Malus × domestica Borkh) genetic resources in Greece

Ganopoulos¹,

Tourvas

Xanthopoulou

et al. 2018

Sci. agric. (Piracicaba, Braz.)

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ABSTRACT:The phenotypic and genetic analysis of apple (Malus × domestica Borkh) genotypes is essential for breeding species. Information on the morphology and genetic structure of apple offers significant help for germplasm maintenance and selection of suitable material to breed superior cultivars. This study shows the results of an investigation on the morphology and the genetic diversity for 19 apple cultivars, which are preserved in an ex situ collection in Naoussa, Central Macedonia, Greece. Information was recorded over a 5-year period for 47 traits describing plant morphology and phenotype, as well as leaf and fruit quality. Data were analyzed using the principal component analysis (PCA) method. The Euclidean distance metric and the Ward's agglomeration method were used in an unsupervised hierarchical cluster analysis of all cultivars. The cultivars were grouped into four main clusters, suggesting that the characterized apple collection has a high potential for specific breeding goals. Furthermore, the cultivars were genotyped using seven microsatellite primers. Moderate levels of polymorphism were detected, and 38 distinctive alleles (5.4 alleles per primer pair) were identified. Both multivariate clustering approach (phenotypic data) and the genetic distance clustering approach (genetic data) grouped the apple cultivars according to their type. Hence, these data could be used for protection or patenting processes of existing or new apple cultivars carried out by the EU-Community Plant Variety Office.

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Molecular basis of trait correlations

Cited by 142 publications

References 82 publications

Dissecting the Phenotypic Components of Crop Plant Growth and Drought Responses Based on High-Throughput Image Analysis

Dissecting the Phenotypic Components of Crop Plant Growth and Drought Responses Based on High-Throughput Image Analysis

3D phenotyping and quantitative trait locus mapping identify core regions of the rice genome controlling root architecture

Phenotypic and molecular characterization of apple (Malus × domestica Borkh) genetic resources in Greece

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