marker-assisted selection in wheat - ictsd

Chapter 8 – Marker-assisted selection in maize 121predicted gains and extrapolations acrosspopulations are more reliable. Also, phenotypicanalyses of many traits in a sweetcornbreeding programme are extremely expensivebecause they involve processing largevolumes of grain; therefore, MAS would berelatively inexpensive and effective undersuch circumstances. However, subsequentdevelopments in technology, refinementsin analytical methods and improvementsin experimental designs have been assembledinto a process that has shown promisefor some reference populations of dentmaize (Ragot et al., 2000; Johnson, 2004;Crosbie et al., 2006) as improvement ingrain yield from MAS often exceeded thatfrom non-MAS approaches. Presumably,such results will lead to the developmentof new and superior inbred lines and commercialhybrids in a cost-effective manner.While the impact of such MAS has not yetbeen fully realized in the maize crop, themethods have been employed to variousdegrees by programmes in the private sectorthat have the necessary infrastructure.The potential for MAS to contribute toimprovements in the maize crop shouldincrease in parallel with our understandingof the relationships among genomes, theenvironment and phenotypes. Candidatetransgenes will be developed on a regularbasis and their contributions to maizeimprovement will be realized in the mostefficient manner with MAS. Likewise, theidentification of candidate native genes andtheir gene products and functions, andof other DNA sequences (e.g. miRNA,matrix attachment and regulatory regions),will improve the power of methods such asassociation mapping and genome scans toassess their genotypic value in the contextof defined reference populations of significanceto maize breeding (Thornsberryet al., 2001; Rafalski, 2002; Niebur et al.,2004; Varshney, Graner and Sorrels, 2005).Beyond its use in MARS schemes, thisinformation might make it reasonable toreconsider ideas such as methods for predictinghybrid performance that may havebeen limited by the amount and type ofinformation and by the design of the experimentwhen they were initially evaluated(Bernardo, 1994).methodology and design ofbreeding programmes supportedby MASAs expected, private sector maize programmesfocus entirely on inbred-hybridbreeding schemes intended to develop eliteinbred lines that enable the profitable productionof commercial F 1 hybrids. To alarge extent, MAS breeding programmesuse the same designs and methods knownto maize breeders for decades and genericdescriptions of these have been published(Hallauer and Miranda, 1981; Sprague andDudley, 1988; Bernardo, 2002). When MASis included in the breeding programme, thesignificant differences are, of course, theavailability of genotypic data at differentstages of selection and some knowledgeof the relationships between the genotypicand phenotypic data sets for the referencepopulation(s) in the target environment(s).In contrast to conventional breedingschemes, the methods and design ofinfrastructure needed to support MAShave been the areas of greatest change.In order to utilize MAS, companieshad to make significant investments toassemble or modify various aspects ofinfrastructure such as methods to detectDNA polymorphism, manage information,or analyse and track samples, softwareto relate genotype with phenotype, andoff-season or continuous nurseries. Thesecomponents had to be integrated with each