The largest part of this thesis is devoted to newly developed statistical methods for age at onset linkage analysis. We used frailty models in which random effects were introduced to model the dependence between outcomes of relatives due to sharing of marker alleles Identical By Descent. From the retrospective likelihood of the marker data conditional on the phenotypes, we derived score tests for genetic linkage analysis. The score statistics appear to be classical Non-Parametric Linkage statistics weighted by functions of the age at onset (or age at censoring) of the family members. These tests are based on allele-sharing, they can be applied to families ascertained through their phenotypes, and they do not require specification of genetic models or penetrance functions. Further, they can incorporate both affected and unaffected family members. In fact, the age at disease onset of the affecteds and the age at censoring of the unaffecteds are considered by this approach. Finally, with respect to the likelihood-ratio tests proposed in the literature the derived score tests are computationally faster, locally most powerful, and robust. For all these reasons, the proposed weighted NPL statistics provide a practical solution for mapping genes for complex diseases with variable age at onset.UBL - phd migration 201
