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A novel genetic framework for studying response to artificial selection

Published online by Cambridge University Press:  16 March 2011

Randall J. Wisser ,
Peter J. Balint-Kurti  and
James B. Holland
Randall J. Wisser*
Affiliation:
Department of Plant and Soil Sciences, University of Delaware, Newark, DE, USA
Peter J. Balint-Kurti
Affiliation:
Department of Plant Pathology, North Carolina State University, Raleigh, NC, USA United States Department of Agriculture-Agricultural Research Service, Plant Science Research Unit, Raleigh, NC, USA
James B. Holland
Affiliation:
United States Department of Agriculture-Agricultural Research Service, Plant Science Research Unit, Raleigh, NC, USA Department of Crop Science, North Carolina State University, Raleigh, NC, USA
*
*Corresponding author. E-mail: rjw@udel.edu
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Abstract

Response to selection is fundamental to plant breeding. To gain insight into the genetic basis of response to selection, we propose a new experimental genetic framework allowing for the identification of trait-specific genomic loci underlying population improvement and the characterization of allelic frequency responses at those loci. This is achieved by employing a sampling scheme for recurrently selected populations that allows for the simultaneous application of genetic association mapping and analysis of allelic frequency change across generations of selection. The combined method unites advantages of the two approaches, permitting the estimation of trait-specific allelic effects by association mapping and the detection of rare favourable alleles by their significant enrichment over generations of selection. Our aim is to develop a framework applicable for many crop species in order to gain a broader and deeper understanding of the genetic architecture of response to artificial selection.

Keywords

adaptationassociation mappingplant breeding, quantitative traitselection
Type
Research Article
Information
Plant Genetic Resources , Volume 9 , Issue 2 , July 2011 , pp. 281 - 283
Copyright
Copyright © NIAB 2011

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