Crop Science Grow Your Career with CSSA
HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
 QUICK SEARCH:   [advanced]


     


This Article
Right arrow Figures Only
Right arrow Full Text Free
Right arrow Full Text (PDF) Free
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Related articles in Crop Science
Right arrow Similar articles in this journal
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via ISI Web of Science (16)
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Podlich, D. W.
Right arrow Articles by Cooper, M.
Right arrow Search for Related Content
PubMed
Right arrow Articles by Podlich, D. W.
Right arrow Articles by Cooper, M.
Agricola
Right arrow Articles by Podlich, D. W.
Right arrow Articles by Cooper, M.
Related Collections
Right arrow Cell Biology & Molecular Genetics
Right arrow Crop Genetics
Published in Crop Sci. 44:1560-1571 (2004).
© 2004 Crop Science Society of America
677 S. Segoe Rd., Madison, WI 53711 USA

CROP BREEDING, GENETICS & CYTOLOGY

Mapping As You Go

An Effective Approach for Marker-Assisted Selection of Complex Traits

Dean W. Podlich*, Christopher R. Winkler and Mark Cooper

Pioneer Hi-Bred International, 7250 NW 62nd Ave., P.O. Box 552, Johnston, IA 50131-0552

* Corresponding author (dean.podlich{at}pioneer.com).

The advent of high throughput molecular technologies has led to an expectation that breeding programs will use marker–trait associations to conduct marker-assisted selection (MAS) for traits. Many challenges exist with this molecular breeding approach for so-called complex traits. A major restriction to date has been the limited ability to detect and quantify marker–trait relationships, especially for traits influenced by the effects of gene-by-gene and gene-by-environment interactions. A further complication has been that estimates of quantitative trait loci (QTL) effects are biased by the necessity of working with a limited set of genotypes in a limited set of environments, and hence the applications of these estimates are not as effective as expected when used more broadly within a breeding program. The approach considered in this paper, referred to as the Mapping As You Go (MAYG) approach, continually revises estimates of QTL allele effects by remapping new elite germplasm generated over cycles of selection, thus ensuring that QTL estimates remain relevant to the current set of germplasm in the breeding program. Mapping As You Go is a mapping-MAS strategy that explicitly recognizes that alleles of QTL for complex traits can have different values as the current breeding material changes with time. Simulation was used to investigate the effectiveness of the MAYG approach applied to complex traits. The results indicated that greater levels of response were achieved and these responses were less variable when estimates were revised frequently compared with situations where estimates were revised infrequently or not at all.

Abbreviations: MAS, marker-assisted selection • MAYG, Mapping As You Go • MET, multienvironment trial • MSO, Mapping Start Only • QTL, quantitative trait loci


Related articles in Crop Science:

THIS ISSUE IN CROP SCIENCE

Crop Science 2004 44: 1507-1510. [Full Text]  



This article has been cited by other articles:


Home page
Plant Physiol.Home page
N. C. Collins, F. Tardieu, and R. Tuberosa
Quantitative Trait Loci and Crop Performance under Abiotic Stress: Where Do We Stand?
Plant Physiology, June 1, 2008; 147(2): 469 - 486.
[Full Text] [PDF]


Home page
Crop Sci.Home page
Y. Xu and J. H. Crouch
Marker-Assisted Selection in Plant Breeding: From Publications to Practice
Crop Sci., March 19, 2008; 48(2): 391 - 407.
[Abstract] [Full Text] [PDF]


Home page
Crop Sci.Home page
R. Tuberosa, S. Salvi, S. Giuliani, M. C. Sanguineti, M. Bellotti, S. Conti, and P. Landi
Genome-wide Approaches to Investigate and Improve Maize Response to Drought
Crop Sci., December 18, 2007; 47(Supplement_3): S-120 - S-141.
[Abstract] [Full Text] [PDF]


Home page
GeneticsHome page
M. P. Boer, D. Wright, L. Feng, D. W. Podlich, L. Luo, M. Cooper, and F. A. van Eeuwijk
A Mixed-Model Quantitative Trait Loci (QTL) Analysis for Multiple-Environment Trial Data Using Environmental Covariables for QTL-by-Environment Interactions, With an Example in Maize
Genetics, November 1, 2007; 177(3): 1801 - 1813.
[Abstract] [Full Text] [PDF]


Home page
GeneticsHome page
E. Esch, J. M. Szymaniak, H. Yates, W. P. Pawlowski, and E. S. Buckler
Using Crossover Breakpoints in Recombinant Inbred Lines to Identify Quantitative Trait Loci Controlling the Global Recombination Frequency
Genetics, November 1, 2007; 177(3): 1851 - 1858.
[Abstract] [Full Text] [PDF]




HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
The SCI Journals Agronomy Journal Vadose Zone Journal
Journal of Plant Registrations Soil Science Society of America Journal
Journal of Natural Resources
and Life Sciences Education
Journal of
Environmental Quality
Copyright © 2004 by the Crop Science Society of America.