marker-assisted selection in wheat - ictsd

Chapter 10 – Strategies, limitations and opportunities for marker-assisted selection in livestock 177the amount of information that is availableto estimate their effects. This will be importantif some of the genotype or haplotypeeffects cannot be estimated with substantialaccuracy because the number of individualswith that genotype or haplotype is limited.Haplotype effects could also be fitted asrandom, but more development is neededin this area.Whole genome approach for geneticevaluation using high-density LD markersWith more and more QTL being discovered,the polygenic component will slowly bereplaced by multiple QTL effects, the inheritanceof each being followed by markerbrackets or more generally by informationon haplotypes. Nejati-Javaremi, Smith andGibson (1997) presented the concept of thetotal allelic relationship, where the co-variancebetween two individuals was derivedfrom allelic identity by descent, or by state(based on molecular marker information),with each location weighted by the varianceexplained by that region. This approachcontrasts with the average relationshipsderived from pedigree that are used inthe numerator relationship matrix. Nejati-Javaremi, Smith and Gibson (1997) showedthat using total allelic relationship resultedin a higher selection response than pedigreebased relationships, because it moreaccurately accounts for the variation in theadditive genetic relationships between individuals.Therefore, the gain of followinginheritance at specific genome locationscontributes to more accurate genetic evaluation,and is able to deal more specificallywith within and between loci interactionsand with specific modes of inheritance atdifferent QTL.When large-scale marker genotypingbecomes cheap and available to breedersat low cost, this approach could even beused for non-detected QTL and geneticevaluation could be based on a “wholegenome approach” (Meuwissen, Hayesand Goddard, 2001). In this approach,marker haplotypes are fitted as independentrandom effects for each, e.g. 1 cM region ofthe genome. In the work by Meuwissen,Hayes and Goddard (2001), variances associatedwith each haplotype were eitherassumed to be equal for each chromosomalregion or estimated from the datausing Bayesian procedures with alternateprior distributions. In essence, this procedureestimates breeding values for eachhaplotype, and EBVs of individuals arecomputed by simply summing EBVs forthe haplotypes that they contain.Using this procedure, Meuwissen,Hayes and Goddard (2001) demonstratedthrough simulation, that for populationswith an effective population size of 100 anda spacing of 1 or 2 cM between informativemarkers across the genome, sufficient LDwas present to predict genetic values withsubstantial accuracy for several generationsbased on associations of marker haplotypeswith phenotype on as few as 500 individuals.It should be noted that, in the approachproposed by these authors, no polygeniceffect is included since all regions of thegenome are included in the model. It may,however, be useful to include a polygeniceffect because LD between markers andQTL will not be complete for all regions.In addition, this model assumes that haplotypeeffects are independent within andacross regions. Incorporating IBD probabilitiesto model co-variances betweenhaplotypes within a region as in Meuwissenand Goddard (2000), and by incorporatingco-variances between adjacent regionscaused by LD between regions, could leadto further improvements but would alsolead to increasing computational demands.