Abstract
With the unveiling of the human genome, the speed at which new genes are discovered has dramatically increased. Consequently disease gene identification is rapidly changing, moving from the cloning of unknown genes towards the mutation analysis of already cloned suitable candidate genes. In the quest to resolve the genetic basis underlying several monogenic syndromes of multiple congenital anomalies (MCA) we have used systematic phenotype analysis in combination with analysis of gene-specific information available through the internet. MCA are the most common cause of infant mortality in Western society. Identifying the molecular causes of the monogenic forms is particularly important because of their high risk of recurrence. Identification of the genetic aetiology of MCA can broaden our knowledge of embryological development, provide diagnostic or counselling tools, and, in future, eventually prevent congenital abnormalities. Molecular analysis of human defects can also provide greater insights in human evolution. Four main strategies for identifying human monogenic disease genes (Chapter 1.3) have been developed: functional cloning, positional cloning, positional candidate and position-independent candidate gene analysis. Here we present different genetic approaches in the search for MCA genes (Chapters 2-4 and Appendices 1-5). In each case we attempted to use a candidate gene analysis as the preferred method. The 'in silico' candidate gene approach has given positive results in some instances and failed to identify candidate genes in others. The failures are mainly due to incomplete information in the databases at the time of the research. Undoubtedly the 'in silico' candidate gene approach will become more and more important for disease gene discovery. The potential of a candidate gene approach is very large and we would advocate this approach as an optimal starting point for the search of a gene causing MCA, especially when it is possible to integrate the position-independent candidate strategy with positional information.
