On a randomization procedure in linkage analysis.

Although much theoretical work has been undertaken to derive thresholds for statistical significance in genetic linkage studies, real data are often complicated by many factors, such as missing individuals or uninformative markers, which make the validity of these theoretical results questionable. Many simulation-based methods have been proposed in the literature to determine empirically the statistical significance of the observed test statistics. However, these methods either are not generally applicable to complex pedigree structures or are too time-consuming. In this article, we propose a computationally efficient simulation procedure that is applicable to arbitrary pedigree structures. This procedure can be combined with statistical tests, to assess the statistical significance for genetic linkage between a locus and a qualitative or quantitative trait. Furthermore, the genomewide significance level can be appropriately controlled when many linked markers are studied in a genomewide scan. Simulated data and a diabetes data set are analyzed to demonstrate the usefulness of this novel simulation method.