The identification of genes predisposing to human diseases is of paramount importance for understanding the molecular basis of the disease and individually different drug response, and will establish new routes to diagnosis and therapeutic advances of immense medical benefit. A key step common to all strategies for disease gene identification is the systematic analysis of candidate gene sequences to identify specific sequence variations associated with disease or any other phenotype of pharmaceutical relevance. In this article, current concepts and approaches to haplotype-based candidate gene analysis are reviewed. Moreover, a comprehensive summary of recent studies and data on the amount, nature, pattern and structure of genetic variation in candidate genes is given. These data demonstrate altogether remarkable gene sequence and haplotype diversity. Numerous individually different forms of a gene may exist. This presents challenges to the traditional views of the concept of ‘a’ gene with far-reaching implications on the functional analysis of candidate gene variation, on the establishment of ‘sequence’-‘structure’-‘function’ and complex haplotype / genotype-phenotype relationships, on the identification, evaluation and prioritization of drug targets and the concept of a ‘personalized medicine’ in general. Moreover, present and future approaches to the identification of candidate and disease genes will be addressed. These include whole genome-based approaches such as integrative genomics as well as functional genomics-based approaches to analyze and model complex biological and medical processes. The analysis of whole complex systems in particular will provide the basis to make ‘maximally informed’ guesses on candidate genes and address complex variability patterns in genes as well as complex genotype-phenotype relationships comprehensively at an advanced level.
Keywords: candidate genes, genetic variation, functional analysis, haplotypes, genotype-phenotype relationships, pharmacogenomics, drug targets, functional genomics, systems biology
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