marker-assisted selection in wheat - ictsd

Chapter 10 – Strategies, limitations and opportunities for marker-assisted selection in livestock 175Alternatively, QTL effects could bemodelled as random effects, with eachindividual having a different QTL effect.Co-variances are based on the probabilityof QTL alleles being identical by descentrather than on numerator relationshipsas in the usual animal model with polygeniceffects. With full knowledge aboutsegregation, this would effectively fit allfounder alleles as different effects. Therandom QTL model was first describedby Fernando and Grossman (1989), wherefor each animal both the paternal and thematernal allele were fitted. Without loss ofinformation, these effects can be collapsedinto one genotypic effect for each animal(Pong-Wong et al., 2001). The randomQTL model makes no assumptions aboutnumber of alleles at a QTL and it automaticallyaccommodates possible interactioneffects of QTL with genetic background(families or lines). Therefore, the randomQTL model is less reliant on assumptionsabout homogeneity of QTL effects. Therandom QTL model is a natural extensionto the usual mixed model and seems thereforea logical way to incorporate genotypeinformation into an overall genetic evaluationsystem. These models result in EBVsfor QTL effects along with a polygenicEBV. The total EBV is the simple sum ofthese estimates. One of the main computationallimitations of this method, however,is the large number of equations that mustbe solved, which increases by two peranimal for each QTL that is fitted. Thus,the number of QTL regions that can beincorporated is limited.Genetic evaluation using direct markersWhen the genotype of an actual functionalmutation is available, no pedigree informationis needed to predict the genotypiceffect, as QTL genotypes are measureddirectly. When there is only a small numberof alleles, the number of specific genotypesis limited. In genetic evaluation, it wouldseem appropriate to treat the genotypeeffect as a fixed effect, i.e. the assumptionis that genotype differences are the samein different families and herds or flocks.Such assumptions might be reasonable for abi-allelic QTL model in a relatively homogeneouspopulation. Alternatively, randomQTL models could be used with differenteffects for different founder alleles, or evenQTL by environment interactions. In bothfixed and random QTL models, genotypeprobabilities can be derived for individualswith missing genotypes.Genetic evaluation using LE markersWhen the genotype test is not for the geneitself, but for a linked marker, QTL probabilitiesderived from marker genotypeswill be affected by the recombination ratebetween marker and QTL and by theextent of LD between the QTL and markeracross the population. If LD betweenthe QTL and a linked marker only existswithin families, marker effects or, at a minimum,marker-QTL linkage phase must bedetermined separately for each family. Thisrequires marker genotypes and phenotypeson family members. If linkage betweenthe marker and QTL is loose, phenotypicrecords must be from close relatives ofthe selection candidate because associationswill erode quickly through recombination.With progeny data, marker-QTL effectsor linkage phases can be determined basedon simple statistical tests that contrast themean phenotype of progeny that inheritedalternate marker alleles from the commonparent. A more comprehensive approach isbased on Fernando and Grossman’s (1989)random QTL model, where marker informationfrom complex pedigrees can be used