Genetic studies based on association from candidate genes to genomewide association studies

Aside from the fact of familial aggregation, the earliest evidence of a genetic component to autoimmunity was the discovery of the HLA associations with these disorders. This is the classic example of 'candidate gene' association studies, in which a genetic variant is studied because it is located in a gene of obvious functional interest. The candidate gene approach has been widely applied in recent years for a variety of reasons. First, it is hypothesis driven, and thus conforms to traditional investigator initiated approaches to scientific investigation. Second, it generally requires a relatively small number of genotypes to be performed and thus is not overwhelmingly costly. Thirdly, advances in understanding the molecular basis of both the adaptive and innate immune response have presented a wealth of provocative candidate genes to study. Of course, linkage results may also drive the selection of candidate genes. In this case, the positional mapping information provides the underlying 'hypothesis' that a risk gene exists in a particular genetic region. Indeed, the end game of gene identification nearly always takes the form of a candidate gene association study, regardless of what method is used for the mapping.

Because of the rapid advances in genotyping technology, it is now possible to pursue a broad 'discovery driven' approach to genetic association studies. This may involve a focus on functional polymorphisms in a large number, or all, of the known genes. This approach is currently being taken by Celera Diagnostics, and applied in combination with knowledge of linkage data, led to the discovery of the PTPN22 association with rheumatoid arthritis [7, 40] (see below). Chip technology is now available to pursue a variant of this approach [41]. Alternatively, very comprehensive genome-wide association studies are fast becoming a reality, with the simultaneous testing of hundreds of thousands of SNPs across the genome using various platforms [42, 43]. The computational and statistical issues raised by these studies are substantial [44, 45], and in general will result in the production of hundreds or thousands of positive findings that will require confirmation in follow up studies. Nevertheless, early results suggest that genome-wide association is likely to be a very powerful means of identifying common susceptibility genes for autoimmunity and other complex disorders [46].

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