Mapeamento de QTLs em populações com pedigrees complexos - Silva, Maria Imaculada de Sousa (2008)
Nowadays, many kinds of molecular markers have been used to map loci that controls expression of quantitative traits (QTL). The usual objective is to identify position and effects of QTL, conditioning on a QTL number. Most common QTL designs consider simple pedigree (F2 or backcross populations from a single cross) and biallelic models with fixed QTL effects. This work aimed to implement a bayesian analysis to jointly map number and effects of multiallelic QTL in a mixed model framework. A two-generation complex pedigree with its respective genetic and phenotypic values was simulated. The full pedigree, marker data and phenotypic values were then taken as known observables. A Markov Chain Monte Carlo sampling procedure was turned on to GENERATEsamples of the joint posterior, using Gibbs Sampling when possible and Metropolis Hastings sampling when needed. A Reversible Jump (RJ) procedure was implemented. This RJ allowed models with different numbers of QTL to be sampled. Unknown parameters in the models were overall mean and error variance, QTL positions, additive and dominance variance components, allelic additive effects and intra-locus interactions (dominance effects) for each QTL locus. These parameters were conditioned on the number of QTLs in the model, thus implying models with different dimensions. Three simulations were carried out. The first small example with five founders and six individuals per cross has shown that RJ allows inference on QTL number, but a less precise inference on QTL position and effects. Analysis conditioning on correct number of QTLs (three) did not improve inference on QTL position and effects when compared to previous analysis. The second example had more individuals per cross and the third one had a more saturated map. Both examples lead to posterior distributions that would allow better inference, but results are not conclusive. The whole methodology seems promising, but faster and more efficient programs are needed to allow analysing larger pedigrees.