Next-generation phenotyping: requirements and strategies for enhancing our understanding of genotype–phenotype relationships and its relevance to crop improvement

By J. Cobb, G. DeClerck, A. Greenberg, R. Clark, Susan McCouch. Theoretical and Applied Genetics, 2013

595 citations recorded by the source corpus.

More accurate and precise phenotyping strategies are necessary to empower high-resolution linkage mapping and genome-wide association studies and for training genomic selection models in plant improvement. Within this framework, the objective of modern phenotyping is to increase the accuracy, precision and throughput of phenotypic estimation at all levels of biological organization while reducing costs and minimizing labor through automation, remote sensing, improved data integration and experimental design. Much like the efforts to optimize genotyping during the 1980s and 1990s, designing effective phenotyping initiatives today requires multi-faceted collaborations between biologists, computer scientists, statisticians and engineers. Robust phenotyping systems are needed to characterize the full suite of genetic factors that contribute to quantitative phenotypic variation across cells, organs and tissues, developmental stages, years, environments, species and research programs. Next-generation phenotyping generates significantly more data than previously and requires novel data management, access and storage systems, increased use of ontologies to facilitate data integration, and new statistical tools for enhancing experimental design and extracting biologically meaningful signal from environmental and experimental noise. To ensure relevance, the implementation of efficient and informative phenotyping experiments also requires familiarity with diverse germplasm resources, population structures, and target populations of environments. Today, phenotyping is quickly emerging as the major operational bottleneck limiting the power of genetic analysis and genomic prediction. The challenge for the next generation of quantitative geneticists and plant breeders is not only to understand the genetic basis of complex trait variation, but also to use that knowledge to efficiently synthesize twenty-first century crop varieties.

Subfields: Genetics, Plant Science

View Paper: https://www.semanticscholar.org/paper/3a81113cb14a4b2d437cc9e3e51134889eea9561

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Citations in the corpus

Cell Membrane Stability and Association Mapping for Drought and Heat Tolerance in a Worldwide Wheat Collection (2017, background): “Identifying QTLs associated with CMS using association mapping requires large populations to increase the recombination frequency and the frequency of rare alleles [19].”
The Vulnerability of Plant Genetic Resources Conserved Ex Situ (2017, background): “Next-generation phenotyping of genetic resources has emerged to generate high-quality phenotypic and environmental information through computer image analysis (Cobb et al., 2013; Afonnikov et al., 2016).”
Unmanned Aerial Vehicle Remote Sensing for Field-Based Crop Phenotyping: Current Status and Perspectives (2017, background): “…and biochemical characteristics and traits that are influenced by the genetic information of the crop and environmental factors, and it can be divided into different levels, such as groups, individuals, organs, tissues and cells (Cobb et al., 2013; Yang et al., 2013; Araus and Cairns, 2014).”
Plant phenotyping: from bean weighing to image analysis (2015, background): “These in turn affect the metabolites and ions and act on the development of the plant leading to observable differences in crop physiology and morphology. plant breeding [14,15] or precision agriculture [16,17].”
Translating High-Throughput Phenotyping into Genetic Gain (2018, background): “Obtaining precise and accurate estimates of genetic value (selection accuracy) is a fundamental goal of breeders and for that precision phenotyping is a fundamental issue [16].”
Proximal Remote Sensing Buggies and Potential Applications for Field-Based Phenotyping (2014, background): “The limited availability of field-based high-throughput phenotyping methods has impeded progress in crop genetic improvement [8,9], though recent reviews [9–12] have highlighted the opportunities now available through sensor technology and the digital age.”
Image-Based Phenotyping of the Mature Arabidopsis Shoot System (2014, background): “The interested reader is referred to [11] for an extended list of frameworks for next-generation phenotyping.”
Correcting for spatial heterogeneity in plant breeding experiments with P-splines (2018, unknown): “This requires a spatio-temporal perspective (Fahlgren et al., 2015; Cobb et al., 2013) for the analysis of field trials.”
Genetic Improvement of Pearl Millet in India (2013, background): “Advances in phenomics, together with genomics and bioinformatics, present exciting new opportunities to understand plant development and adaptation [29].”
Crop Phenomics: Current Status and Perspectives (2019, background): “Although genomic data has a major role in crop genetic improvements and breeding programs, with the advent of the era of omics, considerable gain can only be achieved by tightly coupling genomic discovery to plant phenomics (Cobb et al., 2013; Bolger et al., 2017).”
A review of breeding objectives, genomic resources, and marker-assisted methods in common bean (Phaseolus vulgaris L.) (2019, methodology): “This bottleneck has led to some new HTP approaches, unlocking prospects for non-destructive field and lab-based phenotyping (Cobb et al. 2013; D’Agostino and Tripodi 2017; Varshney et al. 2018).”