HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Figures Only Full Text Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Alert me to new issues of the journal Download to citation manager Google Scholar Articles by Bernardo, R. PubMed Articles by Bernardo, R. Agricola Articles by Bernardo, R. Related Collections Marker-assisted selection Crop Genetics

REVIEW & INTERPRETATION

Molecular Markers and Selection for Complex Traits in Plants: Learning from the Last 20 Years

Dep. of Agronomy and Plant Genetics, Univ. of Minnesota, 411 Borlaug Hall, 1991 Upper Buford Cir., Saint Paul, MN 55108

^* Corresponding author (bernardo{at}umn.edu).

In the mid-1980s, the development of abundant molecular markers, appropriate statistical procedures, and user-friendly computer software that implemented these statistical procedures permitted the detection of molecular markers associated with quantitative trait loci (QTL) for complex traits. Marker-assisted selection was then proposed as a means of exploiting markers linked to QTL to develop improved cultivars. But while thousands of marker-trait associations have been reported for many traits in different plant species, far fewer examples of successfully exploiting mapped QTL have been reported in the literature. Key lessons learned from applying markers in plant breeding include the following: (i) the purpose of detecting QTL should be clearly defined before embarking on QTL mapping; (ii) procedures for marker-based selection depend on the number of QTL; (iii) estimates of QTL effects for complex traits are often inconsistent; and (iv) gain per unit cost and time rather than gain per cycle should be considered. Future applications for complex traits will likely focus on predictive methodologies for marker-based selection before phenotyping and for marker-based selection without QTL mapping. These applications will take advantage of cheaper costs of genotyping than of phenotyping.

Abbreviations: BLUP, Best linear unbiased prediction • QTL, quantitative trait loci • RFLP, restriction fragment length polymorphisms • SNP, single nucleotide polymorphisms