Literature DB >> 16290213

Genomics-assisted breeding for crop improvement.

Rajeev K Varshney1, Andreas Graner, Mark E Sorrells.   

Abstract

Genomics research is generating new tools, such as functional molecular markers and informatics, as well as new knowledge about statistics and inheritance phenomena that could increase the efficiency and precision of crop improvement. In particular, the elucidation of the fundamental mechanisms of heterosis and epigenetics, and their manipulation, has great potential. Eventually, knowledge of the relative values of alleles at all loci segregating in a population could allow the breeder to design a genotype in silico and to practice whole genome selection. High costs currently limit the implementation of genomics-assisted crop improvement, particularly for inbreeding and/or minor crops. Nevertheless, marker-assisted breeding and selection will gradually evolve into 'genomics-assisted breeding' for crop improvement.

Mesh:

Year:  2005        PMID: 16290213     DOI: 10.1016/j.tplants.2005.10.004

Source DB:  PubMed          Journal:  Trends Plant Sci        ISSN: 1360-1385            Impact factor:   18.313


  128 in total

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2.  QTL analysis of soybean seed weight across multi-genetic backgrounds and environments.

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3.  Identification of QTLs for seed and pod traits in soybean and analysis for additive effects and epistatic effects of QTLs among multiple environments.

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6.  Challenges for effective marker-assisted selection in plants.

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7.  Isolation of Ty1-copia retrotransposon in myrtle genome and development of S-SAP molecular marker.

Authors:  Pasqualina Woodrow; Giovanni Pontecorvo; Loredana F Ciarmiello
Journal:  Mol Biol Rep       Date:  2011-07-02       Impact factor: 2.316

Review 8.  Genomics and bioinformatics resources for crop improvement.

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Journal:  Plant Cell Physiol       Date:  2010-03-05       Impact factor: 4.927

9.  Proteome characterization of cassava (Manihot esculenta Crantz) somatic embryos, plantlets and tuberous roots.

Authors:  Kaimian Li; Wenli Zhu; Kang Zeng; Zhenwen Zhang; Jianqiu Ye; Wenjun Ou; Samrina Rehman; Bruria Heuer; Songbi Chen
Journal:  Proteome Sci       Date:  2010-02-27       Impact factor: 2.480

10.  UPIC: Perl scripts to determine the number of SSR markers to run.

Authors:  Renee S Arias; Linda L Ballard; Brian E Scheffler
Journal:  Bioinformation       Date:  2009-04-21
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