Genetic studies in complex disease

Abstract

Since the introduction of user-friendly and high-throughput techniques for performing genetic studies in complex diseases, a large number of (published and unpublished) studies have been carried out in an attempt to find polymorphisms or variants of putative genes and establish their relationships with myocardial infarction. However, as none of the single nucleotide polymorphisms (SNPs) of genes encoding proteins involved in atherothrombosis has yet been consistently associated with an increased or decreased risk of myocardial infarction or stroke, it is not surprising that doubts are beginning to arise as to whether the main reason for this lack of success is methodological [1]. Are we using an inadequate tool? Association studies are appealingly quick and cheap, have successfully identified traditional risk factors and, Rosendaal points out [2], have been extensively used in genetic research—but extensively is not the same as successfully. On the other hand, although linkage studies have been successfully used in the study of monogenic diseases, their value in identifying new culprit genes associated with myocardial infarction has not yet been proved. Rosendaal makes a very good point that should not be forgotten: it is not a question of deciding between association and linkage studies, but between ‘proof of concept’ studies and ‘fishing expeditions’. We know quite a lot about the pathophysiology of myocardial infarction, and have identified a number of factors that are almost certainly involved, such as inflammation, thrombosis and lipid metabolisms. However, the proteins involved in these mechanisms are encoded by thousands of genes, and we still do not know enough about other mechanisms involving an equally large number of genes. The result is that, although there are many pathophysiological candidates, we need to identify the most important and complete our understanding of them. Positional candidates are welcome. Linkage studies can help us to focus on the most likely regions and thus enable us to concentrate on what our knowledge indicates as being the most likely candidates; the epidemiological relevance of such candidates can then be confirmed (or not) by association studies. Linkage and association approaches are therefore not mutually exclusive but complementary, and have been recently integrated with other approaches such as gene expression profiling (transcriptome analysis) and proteomics. By studying the tissues or cells (atherosclerotic plaques, circulating blood cells) directly involved in both normal and diseased states, as well as at different time points during the course of disease, we can identify new candidates deserving further investigation in functional and epidemiological studies; furthermore, the availability of new databases correlating gene function and known SNPs could lead to a major breakthrough. In our opinion, we are now beginning to come out of the fog. Rather than being simply a means of finding genes, genetic studies are much more complex and require the integration of different methodologies and approaches, in the same way that the development of a new drug does not depend on Phase III trials alone but has to go through many other stages before reaching the clinical arena.