A Bayesian Approach to Map Multiple QTL in Pedigreed Plant Breeding Populations
The availability of molecular genetic markers enables the dissection of a quantitative trait into quantitative trait loci (QTL), i.e., chromosomal regions that show strong association with the observed phenotypic trait variance. In plants, the first QTL experiments were targeted to a single mapping population that was derived from crossing two extreme, often fully-inbred, individuals. This simple design allowed regression and Maximum Likelihood methods for data analysis. However, the success of the identified QTL was hampered by several factors. That is, only QTL segregating between the two parental individuals could be detected, the action of QTL was often dependent on the genetic background, and the frequency of the favorable QTL allele was often already fixed in ongoing breeding program populations. These factors stimulated the broadening of the design to multiple connected populations. I will analyze data from such designs via a Bayesian approach that utilizes known pedigree structures. This approach has been implemented into the software package FlexQTL (www.flexqtl.nl) and employs Markov chain Monte Carlo algorithms to obtain samples from the joint posterior distribution. Our methods are currently applied to phenotypic and marker data on apples in the European Union project HiDRAS (www.hidras.unimi.it). In this project 27 mapping populations with their known ancestors and a set of additional apple cultivars were scored for 83 markers on 17 linkage groups, and a large number of quality and resistance traits. The first results of these analyses will be presented.