Hostname: page-component-586b7cd67f-rdxmf Total loading time: 0 Render date: 2024-11-25T21:14:36.088Z Has data issue: false hasContentIssue false

A novel genetic framework for studying response to artificial selection

Published online by Cambridge University Press:  16 March 2011

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: [email protected]

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.

Type
Research Article
Copyright
Copyright © NIAB 2011

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Breseghello, F and Sorrells, ME (2006) Association analysis as a strategy for improvement of quantitative traits in plants. Crop Science 46: 13231330.CrossRefGoogle Scholar
Brown, AHD (1971) Isozyme variation under selection in Zea mays. Nature 232: 570571.CrossRefGoogle ScholarPubMed
Brown, AHD and Allard, RW (1971) Effect of reciprocal recurrent selection for yield on isozyme polymorphisms in maize (Zea mays L.). Crop Science 11: 888893.CrossRefGoogle Scholar
Brown, PJ, Rooney, WL, Franks, C and Kresovich, S (2008) Efficient mapping of plant height quantitative trait loci in a sorghum association population with introgressed dwarfing genes. Genetics 180: 629637.CrossRefGoogle Scholar
Coque, M and Gallais, A (2006) Genomic regions involved in response to grain yield selection at high and low nitrogen fertilization in maize. Theoretical and Applied Genetics 112: 12051220.CrossRefGoogle ScholarPubMed
De Koeyer, DL, Phillips, RL and Stuthman, DD (2001) Allelic shifts and quantitative trait loci in a recurrent selection population of oat. Crop Science 41: 12281234.CrossRefGoogle Scholar
Harjes, CE, Rocheford, TR, Bai, L, Brutnell, TP, Kandianis, CB, Sowinski, SG, Stapleton, AE, Vallabhaneni, R, Williams, M, Wurtzel, ET, Yan, JB and Buckler, ES (2008) Natural genetic variation in lycopene epsilon cyclase tapped for maize biofortification. Science 319: 330333.CrossRefGoogle ScholarPubMed
Labate, JA, Lamkey, KR, Lee, M and Woodman, WL (1999) Temporal changes in allele frequencies in two reciprocally selected maize populations. Theoretical and Applied Genetics 99: 11661178.CrossRefGoogle Scholar
Laurie, CC, Chasalow, SD, LeDeaux, JR, McCarroll, R, Bush, D, Hauge, B, Lai, CQ, Clark, D, Rocheford, TR and Dudley, JW (2004) The genetic architecture of response to long-term artificial selection for oil concentration in the maize kernel. Genetics 168: 21412155.CrossRefGoogle ScholarPubMed
Remington, DL, Thornsberry, JM, Matsuoka, Y, Wilson, LM, Whitt, SR, Doeblay, J, Kresovich, S, Goodman, MM and Buckler, ES (2001) Structure of linkage disequilibrium and phenotypic associations in the maize genome. Proceedings of the National Academy of Sciences USA 98: 1147911484.CrossRefGoogle ScholarPubMed
Stuber, CW and Moll, RH (1972) Frequency changes in isozyme alleles in a selection experiment for grain yield in maize (Zea mays L.). Crop Science 12: 337340.CrossRefGoogle Scholar
Wisser, RJ, Murray, SC, Kolkman, JM, Ceballos, H and Nelson, RJ (2008) Selection mapping of loci for quantitative disease resistance in a diverse maize population. Genetics 180: 583599.CrossRefGoogle Scholar
Yu, JM, Pressoir, G, Briggs, WH, Bi, IV, Yamasaki, M, Doebley, JF, McMullen, MD, Gaut, BS, Nielsen, DM, Holland, JB, Kresovich, S and Buckler, ES (2006) A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nature Genetics 38: 203208.CrossRefGoogle ScholarPubMed
Zhu, C, Gore, M, Buckler, ES and Yu, J (2008) Status and prospects of association mapping in plants. The Plant Genome 1: 520.CrossRefGoogle Scholar