Clustering methods applied to allele sharing data

Here we focus on using clustering methods to disentangle the interacting factors that lead to the presentation of complex diseases. Relative pairs are placed in discrete subgroups, or classes, based upon their pattern of allele sharing at a sequence of markers and on concomitant risk factors. The relationship between the locus information and the affectation status of the relative pairs within each subgroup then can be assessed. Cluster analysis (CLA) and latent class analysis (LCA) were applied to sibling allele sharing data from GAW11 simulated data, and to an existing Alzheimer's disease (AD) dataset. Both methods were able to identify markers linked to all 3 disease loci in the GAW11 data. LCA and CLA also replicated regions of chromosomes identified in an analysis of the AD data using affected-sib-pair methods. These analyses indicate that classification tools may be useful for detecting susceptibility genes for complex traits.