Relationship estimation in affected sib pair analysis of late-onset diseases.

Abstract

In linkage studies, errors in pedigree structure will often be uncovered through Mendelian inconsistencies. In affected sib pair analysis of diseases with late onset, however, such mistakes will usually go undetected since parental genotypes are commonly not known. Cases of nonpaternity, unrecorded adoption or accidental sample swap in the laboratory will then not be noticed. Typically, such relationship errors lead to a decrease in power for linkage. In this paper, a method is presented which allows verification of the relationship between stated sibs using their marker genotypes. The method is likelihood-based and incorporates a Bayesian approach to compute posterior relationship probabilities. It is shown that sibs, half-sibs and unrelated individuals can be distinguished from each other quite reliably using numbers of markers that should be available in most sib pair studies. It is demonstrated that elimination of false sib pairs increases the power to detect linkage in affected sib pair studies. The gain in power may be large if relationship errors occur quite frequently; the gain will be only moderate if relationship errors are very infrequent. Software for relationship estimation is provided.