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

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          Abstract

          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.

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          Most cited references135

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          NIH Image to ImageJ: 25 years of image analysis.

          For the past 25 years NIH Image and ImageJ software have been pioneers as open tools for the analysis of scientific images. We discuss the origins, challenges and solutions of these two programs, and how their history can serve to advise and inform other software projects.
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            Marker-assisted selection: an approach for precision plant breeding in the twenty-first century.

            DNA markers have enormous potential to improve the efficiency and precision of conventional plant breeding via marker-assisted selection (MAS). The large number of quantitative trait loci (QTLs) mapping studies for diverse crops species have provided an abundance of DNA marker-trait associations. In this review, we present an overview of the advantages of MAS and its most widely used applications in plant breeding, providing examples from cereal crops. We also consider reasons why MAS has had only a small impact on plant breeding so far and suggest ways in which the potential of MAS can be realized. Finally, we discuss reasons why the greater adoption of MAS in the future is inevitable, although the extent of its use will depend on available resources, especially for orphan crops, and may be delayed in less-developed countries. Achieving a substantial impact on crop improvement by MAS represents the great challenge for agricultural scientists in the next few decades.
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              GS3, a major QTL for grain length and weight and minor QTL for grain width and thickness in rice, encodes a putative transmembrane protein.

              The GS3 locus located in the pericentromeric region of rice chromosome 3 has been frequently identified as a major QTL for both grain weight (a yield trait) and grain length (a quality trait) in the literature. Near isogenic lines of GS3 were developed by successive crossing and backcrossing Minghui 63 (large grain) with Chuan 7 (small grain), using Minghui 63 as the recurrent parent. Analysis of a random subpopulation of 201 individuals from the BC3F2 progeny confirmed that the GS3 locus explained 80-90% of the variation for grain weight and length in this population. In addition, this locus was resolved as a minor QTL for grain width and thickness. Using 1,384 individuals with recessive phenotype (large grain) from a total of 5,740 BC3F2 plants and 11 molecular markers based on sequence information, GS3 was mapped to a DNA fragment approximately 7.9 kb in length. A full-length cDNA corresponding to the target region was identified, which provided complete sequence information for the GS3 candidate. This gene consists of five exons and encodes 232 amino acids with a putative PEBP-like domain, a transmembrane region, a putative TNFR/NGFR family cysteine-rich domain and a VWFC module. Comparative sequencing analysis identified a nonsense mutation, shared among all the large-grain varieties tested in comparison with the small grain varieties, in the second exon of the putative GS3 gene. This mutation causes a 178-aa truncation in the C-terminus of the predicted protein, suggesting that GS3 may function as a negative regulator for grain size. Cloning of such a gene provided the opportunity for fully characterizing the regulatory mechanism and related processes during grain development.
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                Author and article information

                Contributors
                srm4@cornell.edu
                Journal
                Theor Appl Genet
                Theor. Appl. Genet
                TAG. Theoretical and Applied Genetics. Theoretische Und Angewandte Genetik
                Springer-Verlag (Berlin/Heidelberg )
                0040-5752
                1432-2242
                8 March 2013
                8 March 2013
                April 2013
                : 126
                : 4
                : 867-887
                Affiliations
                [ ]Department of Plant Breeding and Genetics, Cornell University, Ithaca, NY 14853 USA
                [ ]United States Department of Agriculture-Agricultural Research Service, Robert W. Holley Center for Agriculture and Health, Ithaca, NY 14853 USA
                [ ]Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY 14853 USA
                [ ]Department of Biological and Environmental Engineering, Cornell University, Ithaca, NY 14853 USA
                Author notes

                Communicated by R. Varshney.

                Article
                2066
                10.1007/s00122-013-2066-0
                3607725
                23471459
                5122064d-252a-4033-a587-b73513cca286
                © The Author(s) 2013

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.

                History
                : 1 November 2012
                : 8 February 2013
                Categories
                Review
                Custom metadata
                © Springer-Verlag Berlin Heidelberg 2013

                Genetics
                Genetics

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