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ARS Home » Southeast Area » Raleigh, North Carolina » Plant Science Research » Research » Publications at this Location » Publication #257202
Title: A novel genetic framework for studying response to artificial selection

Author
item WISSER, RANDALL - University Of Delaware
item Balint-Kurti, Peter
item Holland, Jim - Jim

Submitted to: Plant Genetic Resources
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/29/2010
Publication Date: 3/16/2011
Citation: Wisser, R., Balint Kurti, P.J., Holland, J.B. 2011. A novel genetic framework for studying response to artificial selection. Plant Genetic Resources. 9:281-283.

Interpretive Summary: Association mapping works by identifying significant correlations between allelic and trait variation within diverse populations. In some cases, very high-resolution mapping can be achieved, but potentially important alleles may not be detected if they are quite rare. Analysis of allele frequency change in selected populations across generations of selection can identify alleles that are rare in the starting population but that increase in frequency due to selection. A method is proposed that combines the advantages of the two approaches, permitting detection of rare favorable alleles by their significant enrichment over selection cycles and also the estimation of their effects by association analysis.

Technical 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 to simultaneously map loci controlling specific traits associated with population improvement and characterize the allele frequency response at those loci. This is achieved by employing a sampling scheme for recurrently selected populations that allows for the simultaneous application of association mapping and analysis of allele frequency change across generations of selection. The combined method combines advantages of the two approaches, permitting detection of rare favorable alleles by their significant enrichment over selection cycles and also the estimation of allelic phenotypic effects by association analysis. Our aim is to develop a framework for the analysis of quantitative traits applicable to many crop species in order to gain a broader and deeper understanding of the genetic architecture of response to artificial selection.