Maize Breeding and Genomics: An Historical Overview and Perspectives

  • Michael Lee


The 1909 publication of G. H. Shull was the dawn of the modern era of maize breeding. Since that time maize has been unique as a major crop of the world and a very important species for basic research. Most of the knowledge from that research has been either worthless or inaccessible to maize breeding programs and actual improvement of maize. Recently, this relationship has changed and the connections between basic research and breeding have improved. The previous 10-15 years have been especially eventful with the advent of transgenic maize and DNA sequence information for maize germplasm. There is much to learn about the maize genome. Only a small fraction of the 30,000 to 60,000 genes and other sequences have functions assigned to them on the basis of direct experimentation. The advent of the genomics era of maize breeding, even at such an early stage, has clearly underlined our abilities to empirically assess all of the promising genetic options and questions that are raised by new sources of information. Field-based phenotyping and the prioritization of phenotyping have become much more important in the genomics era of maize breeding. The gains and pains of the initial decades of maize breeding have been well documented for some traits and production environments. But, with the exception of improved resistance to biotic and abiotic stress as a basis for genetic gains in grain yield, very little fundamental information and few validated mechanisms have been identified from that era. The next era of maize breeding should be fundamentally different in the sense that the strategies will rely more on a scientific method; consequently, some of the reasons for success or failure will be known and considered for devising more efficient methods of maize improvement.


Maize Genome Transgenic Maize Maize Breeding Quality Protein Maize Matrix Attachment Region 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer 2007

Authors and Affiliations

  • Michael Lee
    • 1
  1. 1.Iowa State UniversityAmesIowa USA

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