The same story plays out for the toxicity and efficacy of drugs, where a number of examples of a role for genetic variation in pharmacologic response have been identified. One example is the relationship of genetic variation in the enzyme thiopurine methyltransferase with the efficacy and toxicity of the thiopurine drugs (Weinshilboum, 2001). Individuals with reduced-activity variants of this enzyme are at elevated risk of drug-induced toxicity from standard treatment regimens. These same dosages have reduced impact on disease or efficacy in other individuals with hyperactive variants of this enzyme, as the drug is rapidly inactivated in these individuals. Other well-characterized examples of clinically relevant variation include the genes in the CYP3A family and CYP2D6, genes encoding proteins with roles in the activation and detoxification or inactivation of many drugs. Several recent reviews and commentaries include discussions of the current state of application of pharmacogenetics in the clinical laboratory and medical practice (Wolf et al., 2000; Kalow, 2001a; 2001b; McLeod and Evans, 2001; Shi et al., 2001; Zanger et al., 2001; Ingelman-Sundberg and Evans, 2001). A common aspect of the examples described is the involvement of a single gene in the activation and/or detoxification of the drug. A second feature is the existence of a relatively small number of common variants at each locus, each with a significant impact on the level of enzyme activity. Thus it is relatively straightforward to relate genotype to phenotype or individual response.
Progress in the study of the role of genetics in the incidence of complex diseases and traits, often characterized by complex patterns of inheritance (sometimes referred to as non-Mendelian inheritance) and gene-gene interaction, has been more limited. The same is true for diseases resulting from gene-environment (or exposure) interaction. In the successes described above, although variation in different genes may be associated with disease, the aberrant functioning of one gene was sufficient to place an individual at high risk of disease. This is not the situation for the more complex diseases and most of the disease burden in the population, where disease risk for each individual is defined by the combination of alleles inherited from parents at several different genes. The picture becomes even more complex when it involves variation in a number of genes and the level of exposure to disease-causing agents. In this context, the environmental component or exposure can be controlled (prescribed drug) or uncontrolled (pollution) or a lifestyle factor (smoking, diet). For the more simple Mendelian traits, individuals in a family can usually be divided into high risk and low risk on the basis of genetic variation at a single locus. For complex diseases, an almost continuous gradient of individual risk will exist within a family, and especially within a population. The individual risk will reflect genotypes at multiple loci and the exposure. In these cases, some individuals in the population having the "at-risk" genotype will not exhibit disease because they have not been exposed to levels of a drug or toxic agent above the threshold necessary to induce disease. This is the situation for the thiopurine toxicity described above. Most individuals with reduced enzyme activity in the population are not exposed to thiopurines and thus exhibit no readily obvious consequence of the genetic variation. For individuals with reduced enzyme activity, the toxicity is reduced to normal levels, but the efficacy of the drug is nearly normal when the dosage is reduced (Weinshilboum, 2001). The existence of individuals with disease but with neither obvious genetic risks nor exposure to compounds illustrates the difficulty of identifying both the exposure and genetic factors. Nonsmokers have lung cancer, just at a much lower rate than smokers, and some heavy, long-term smokers are disease free.
Diseases with complex patterns of inheritance, and where genetic variation comes into play after an exposure, account for the vast majority of the disease burden in the population. Examples of both the strategies and the problems involved in identifying genes with roles in these complex diseases can be appreciated from reading recent papers describing efforts to identify genes associated with risk for prostate cancer (Nwosu et al., 2001), type 2 diabetes, (Cox et al., 2001), and asthma (Xu et al., 2001). Cox (2001) outlines the challenges in moving from the identification of a chromosomal region associated with an elevated risk of disease via linkage mapping to the definitive estimate of risk associated with specific variants in specific genes. It should be noted that "common" refers to the relatively high incidence of the disease in the population and "complex" describes the pattern of inheritance of the genetic factors; neither term relates to the clinical characteristics of a disease. It should be apparent that progress is being made in understanding cellular biology and disease processes. However, even for the simpler situations, we have much to learn about the relationship of genetic variation to disease susceptibility.
The research products and information from the HGP are the reagents and tools for tackling these problems. High-density genetic maps are critical for identifying the chromosomal regions, and ultimately the genes, involved in susceptibility to diseases with complex patterns of inheritance and diseases resulting from the interaction of genetic variation and exposure. Continued development of new approaches to experimental design and data analysis—and in some instances entirely new paradigms for experimental approaches and data analysis—will be required to address these complex and data-intensive problems (Ponder, 2001; Risch, 2000). These are the problems that must be addressed and conquered if the much-discussed potential development of individualized disease treatment regimes is to become a reality for the common diseases affecting the population.
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