Conditional Two-Locus NPL-Analysis: Theory and Applications

Lars Ängquist

Centre for Mathematical Sciences
Mathematical Statistics
Lund Institute of Technology,
Lund University,

ISSN 1403-6342
Type-2 diabetes is a serious, genetically influenced disease for which no fully effective treatments are available (Gelder Ehm et al., 2000). The molecular basis of type 2 diabetes is unknown, to a great extent because of the substantial locus heterogeneity that is associated with diabetes risk (Gelder Ehm et al., 2000) and Parker et al., 2001). However, studies indicate that a genetic component exists. Type 2 diabetes is a complex disorder and therefore it is assumed to depend on the actions and interactions of multiple genetic and environmental factors (see for example Cox et al.,1999). Simultaneous consideration of susceptibility from multiple regions may improve the possibility to find genes that are involved in the mechanism behind a complex disorder.
In this work conditional two-locus NPL-analyses will be performed. That means that one uses one-locus family scores from interesting regions (markers) to weight the results from other regions and finally, as a result, get the conditional two-locus NPL-score. This aims to find two correlated regions in linkage with the disease. Theory that describes how to calculate p-values under the null hypothesis of no linkage, which will make it possible to draw conclusions about any possible significance of the results, will be described and applied to the present data set. Aspects of robustness will be discussed.
The data set consists of 2606 individuals belonging to 337 families originating from Sweden and Finland.