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Award Abstract #1202795

NSF Postdoctoral Fellowship in Biology FY 2012

NSF Org: IOS
Division Of Integrative Organismal Sys
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Initial Amendment Date: May 30, 2012
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Latest Amendment Date: July 16, 2014
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Award Number: 1202795
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Award Instrument: Fellowship
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Program Manager: Anne Sylvester
IOS Division Of Integrative Organismal Sys
BIO Direct For Biological Sciences
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Start Date: June 1, 2012
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End Date: May 31, 2015 (Estimated)
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Awarded Amount to Date: $138,000.00
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Investigator(s): Jason Peiffer (Principal Investigator)
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Sponsor: Peiffer, Jason A

Raleigh, NC 27606-
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NSF Program(s): NPGI PostDoc Rsrch Fellowship
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Program Reference Code(s): 7577, 9109, 9179, BIOT
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Program Element Code(s): 8105

ABSTRACT

This action funds a National Plant Genome Initiative Postdoctoral Research Fellowship for FY 2012. The fellowship supports a research and training plan in a host laboratory for the Fellow to focus their studies in plant genomics with an emphasis on quantitative genetics, modern breeding approaches, and bioinformatics. The title of the research and training plan for this fellowship to Jason A. Peiffer is "Combinatorial Optimization of Molecular Plant Breeding Strategies". The host institution for the fellowship is North Carolina State University and the sponsoring scientists are Drs. Trudy F. C. Mackay and Eric A. Stone.

Phenomics and high-throughput sequencing have drastically increased the information available to crop geneticists and breeders. From this information, linkage and association mapping of alleles and genomic prediction of breeding values have enhanced our knowledge of genetic architecture. New analytical approaches are needed to efficiently use this architectural knowledge in the optimization of mapping efforts and to better inform breeding practices. Drawing techniques from the disciplines of combinatorial optimization, statistical mechanics, and information geometry, this project will optimize the selection of mapping and genomic prediction populations for multi-environment trials. These techniques will incorporate prior genetic knowledge in an adaptive sequential manner to better explore the fitness landscape of potential genotypes. By implementing related techniques, scientists will be able to use this understanding of a population's orientation on the landscape to direct cross-pollination and selection decisions that better exploit genetic diversity and allow breeders to more rapidly converge on a desired phenotype for a given crop application and environment.

Training objectives include computational science, applied mathematics, evolutionary biology, and genetics. Broader impacts include capacity-building and advanced training for students from the United States to engage in interdisciplinary research in plant improvement and associated sciences such as physiology, quantitative genetics, and computational biology. Programs and source code facilitating execution of these approaches will be made publicly available.

 

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