1 research outputs found
On the informativeness of dominant and co-dominant genetic markers for Bayesian supervised clustering
We study the accuracy of Bayesian supervised method used to cluster
individuals into genetically homogeneous groups on the basis of dominant or
codominant molecular markers. We provide a formula relating an error criterion
the number of loci used and the number of clusters. This formula is exact and
holds for arbitrary number of clusters and markers. Our work suggests that
dominant markers studies can achieve an accuracy similar to that of codominant
markers studies if the number of markers used in the former is about 1.7 times
larger than in the latter