Mutation region detection for closely related individuals without a known pedigree using high-density genotype data

The fundamental problem in linkage analysis is to identify regions whose allele is shared by all or almost all affected members but by none or few unaffected members. Almost all the existing methods for linkage analysis are for families with clearly given pedigrees. Little work has been done for the case where the sampled individuals are closely related, but their pedigree is not known. This situation occurs very often when the individuals share a common ancestor at least six generations ago. Solving this case will tremendously extend the use of linkage analysis for finding genes that cause genetic diseases. In this paper, we propose a mathematical model (the shared center problem) for inferring the allele-sharing status of a given set of individuals using a database of confirmed haplotypes as reference. We show the NP-completeness of the shared center problem and present a ratio-2 polynomial-time approximation algorithm. We then convert the approximation algorithm into a heuristic algorithm for the shared center problem. Based on this heuristic, we finally design a heuristic algorithm for mutation region detection. We further implement the algorithms to obtain a software package. Our experimental data shows that the software works very well. The package is available at http://www.cs.cityu.edu.hk/~lwang/software/LDWP/index.html for non-commercial use.