marker-assisted selection in wheat - ictsd

344Marker-assisted selection – Current status and future perspectives in crops, livestock, forestry and fishpower is expected to be lower than that forcrosses between clonal lines. The powerfor detecting the QTL depends on allelefrequencies, the probability of sampling aninformative parent and family size.Factors influencing the power of detectingQTLDue to the large family sizes that can beobtained in many fish species, differentmating designs using full-sib groups can becarried out for outbred populations. Forexample, full factorial designs may be usedin which many males and females are matedto one another, and hierarchical designsmay be applied in which each male is matedwith multiple females, or each female withmultiple males. For a given size of experiment,factorial and hierarchical designs havepotentially a lower probability of samplinga heterozygous parent (because fewer siresand or dams are sampled overall), comparedwith the full-sib design in whicheach family has potentially two informativeparents. For this reason, factorial and hierarchicaldesigns can potentially give lowerpower compared with the simple full-sibdesign (Muranty, 1996; Martinez, Hill andKnott, 2002).The optimum number of full-sib familiessampled in the QTL mapping populationdepends on the intrinsic power of theexperiment (i.e. size of the QTL effect andsize of the population). As expected, largefamily sizes are needed for detecting QTLof small effects (Martinez, Hill and Knott,2002). When the QTL explains 10 percentof the phenotypic variance, the optimumfamily size appears to be 50 individuals perfamily for a reasonably-sized QTL mappingexperiment in outbred populations(Figure 4). Further increases in the numberof individuals per family provide only amodest increase in power. Further, the sameresults used simulation models showingdominance and additive effects under thevariance components method for mappingQTL (Martinez et al., 2006a).Methods of analysisThe method of choice when analysing datafrom outbred populations is the variancecomponent method, in which QTL effectsare included as random effects with a covarianceproportional to the probability thatrelatives (e.g. full-sibs) share alleles identicalby descent conditional on marker data (Xuand Atchley, 1995). This model is similarto the one used more generally for geneticevaluation of candidate fish for selection,but includes the random QTL effect.A considerable proportion of the geneticvariance for growth-related traits in fishpopulations has been explained by dominance(Rye and Mao, 1998; Pante, Gjerdeand McMillan, 2001; Pante et al., 2002).When mapping QTL using the randommodel, it is assumed that only additiveeffects are of importance and thereforeonly matrices of additive relationships conditionalon marker data are fitted in theresidual effect maximum likelihood procedure(George, Visscher and Haley, 2000;Pong-Wong et al., 2001). However, thelarge family sizes in fish enable hypothesesfor different modes of inheritance atthe QTL to be tested using the withinfamilyvariance. While some authors havespeculated that including dominance in themodel will increase the power of detectingQTL (Liu, Jansen and Lin, 2002), others(Martinez, 2003; Martinez, 2006a) haveshown that power to detect QTL was comparablebetween models including or notincluding dominance. This was particularlythe case for the larger family sizes simulatedand it was concluded that for mostscenarios, the additive model was quite