Goals of complex disease trait mapping

The main aims of these studies are:-

1 to understand physiological and pathological mechanisms and pathways leading to disease through the identification of genetic factors underlying these processes. It is hoped that the resulting new knowledge will lead to new treatments and disease prevention strategies.

2 to identify increased genetic susceptibility within individuals where this risk has been shown to be reversible and amenable to reduction. This includes genetic redefinition of disease subtypes, with the goal of explaining differential response to treatments and predicting drug responses or side-effects.

3 to better elucidate the role of environmental risk factors. It is now possible to measure genetic factors with considerably greater precision and validity and at lower cost than for most environmental exposures. Therefore, the identification of novel genetic variants and hence pathways that influence disease risk may suggest which environmental exposures are important in determining disease risk. An important element of this is the use of genetic data in an experimental framework (equivalent to a randomized comparison) to extend observational data by means of ''Mendelian randomisation" design (Keavney, 2002; Davey Smith and Ebrahim, 2003). This approach controls for most sources of confounding influences on genetic associations and so can be utilised to identify small to moderate environmental effects and to test hypotheses about causal pathways amenable to intervention. The approach does not control for population stratification but this can be minimized through study design or within the analysis (Pritchard et al., 2000; Devlin et al., 2004).

There has been general over-optimism regarding the immediate benefits of improved knowledge of the genetic basis of complex diseases. Public health benefits will not be realized until gene function, and related biochemical and physiological pathways, are understood and molecular targets for new drug discovery identified. The growing realization that new genomic knowledge is the start and not the end of the process is now leading to more emphasis on integrated ''systems biology" approaches.

Since the greatest public health benefit is expected to follow from the first aim, it will be important to identify and focus on key pathways. Single genes exerting large phenotypic effects, such as cell cycle and mismatch repair genes in colorectal cancer, are likely to be closest to the key, rate-limiting steps in the disease process. From this follows the need to give some priority to identifying moderate-to-large rather than average genetic effects, since these are likely to be concentrated in important pathophysiological pathways. In addition, the complexity of common late-onset disorders suggests that identifying genes with the largest effects, which contribute most to the extremes of the disease or trait distribution, may be the most robust approach, similar in principle to the methods successfully applied to monogenic disorders. For example, the identification of genetic variants accounting for rare monogenic forms of common disease (e.g. breast and colon cancer, Alzheimer disease) has made a substantial contribution towards elucidating disease mechanisms and holds out the potential to lead to therapeutic progress (see Chapter 10). Similarly, knowledge of the genetic basis of a rare Mendelian disorder in which there is a substantially increased risk of a complex disease has provided clues to the etiology of the complex disease. It is less clear whether the identification of variants with small effects on disease risk will have a similar impact.

A counter-argument has been that this approach is important to the families with these generally rare conditions but is of little public health significance. Population attributable fractions (PAF) or risks (which estimate the overall contribution of a risk factor to a disease in a specific population) are often quoted for common variants with small effects in support of their ''public health importance." However, unlike prevalent and modifiable environmental exposures with adverse health effects, PAF values for genetic variants have less clear meaning or public health utility. This is particularly true in complex multifactorial disease in which PAFs typically sum to much greater than 100%, due to interactions among environmental and/or genetic factors. PAFs for genetic variants may retain meaning in terms of the potential for the reduction in drug side-effects, if genetic testing could identify all those genetically predisposed to a certain adverse effect and prescriptions avoided in those found to carry that variant.

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