Recognizing that all traits are the result of an interaction between genes and environment, I offer a set of criteria for nevertheless making sense of our practice of singling out certain traits as genetic ones, in effect making a distinction between causes and mere conditions. The central criterion is that a trait is genetic if it is genetic differences that make the differences in that trait variable in a given population. A second criterion requires that genetic traits be individuated in (...) a way that matches what some genetic factors cause specifically. Clarifying our causal and classificatory language here can help us to avoid confusions of both theoretical and practical significance. (shrink)

The paper argues for a pragmatic account of genetic explanation. This is to say that when a disease or other trait is termed genetic, the reasons for singling out genes as causes over other, also necessary, genetic and nongenetic conditions are not wholly theoretical but include pragmatic dimensions. Whether the explanation is the presence of a trait in an individual or differences in a trait among individuals, genetic explanations are context-dependent in three ways: they are relative to a causal background (...) of genetic and nongenetic factors; they are relative to a population; and they are relative to the present state of knowledge. Criteria like causal priority, nonstandardness, and causal efficacy that purport to distinguish objectively between genetic causes and nongenetic conditions either incorporate pragmatic elements or fail for other reasons. When the pragmatic dimensions of genetic explanations are recognized, we come to understand the current phenomenon of geneticization to be a reflection of increased technological capacities to manipulate genes in the laboratory, and potentially the clinic, rather than theoretical progress in understanding how diseases and other traits arise. This calls into question the value of the search for theoretical definitions of designations like genetic disease or genetic susceptibility as directives for action. (shrink)

Explanations in the life sciences frequently involve presenting a model of the mechanism taken to be responsible for a given phenomenon. Such explanations depart in numerous ways from nomological explanations commonly presented in philosophy of science. This paper focuses on three sorts of differences. First, scientists who develop mechanistic explanations are not limited to linguistic representations and logical inference; they frequently employ diagrams to characterize mechanisms and simulations to reason about them. Thus, the epistemic resources for presenting mechanistic explanations are (...) considerably richer than those suggested by a nomological framework. Second, the fact that mechanisms involve organized systems of component parts and operations provides direction to both the discovery and testing of mechanistic explanations. Finally, models of mechanisms are developed for specific exemplars and are not represented in terms of universally quantified statements. Generalization involves investigating both the similarity of new exemplars to those already studied and the variations between them. (shrink)

Modern medicine has produced many successful theories concerning the causes of diseases. For example, we know that tuberculosis is caused by the bacterium Mycobacterium tuberculosis, and that scurvy is caused by a deficiency of vitamin C. This chapter discusses the nature of medical theories from the perspective of the philosophy, history, and psychology of science. I will review prominent philosophical accounts of what constitutes a scientific theory, and develop a new account of medical theories as representations of mechanisms that explain (...) disease. An account of the nature of medical theories should illuminate many aspects of the development and application of medical knowledge. Most importantly, it should contribute to understanding of medical explanation, both at the general level of causes of diseases and at the individual level of diagnosis of particular cases of a disease. Medical researchers seek to explain the causes of diseases such as tuberculosis, while physicians seek to identify diseases that explain symptoms such as fever. A medical theory such as the bacterial theory of tuberculosis provides good explanations at both the general and individual levels. The primary aim of this chapter is to show how these explanations work. A secondary aim is to show how an account of medical theories can shed light on other aspects of medical research and practice, including the nature of medical discovery, the process of evaluation of competing medical theories, and the ways in which effective treatments of disease depend on the development of good mechanistic theories about diseases. (shrink)

Complex diseases involve both a genetic component and a response to environmental factors or lifestyle changes. Recently, genome-wide association studies (GWAS) have succeeded in identifying hundreds of polymorphisms that are statistically associated with complex diseases. However, the association is usually weak and none of the associated allelic forms is either necessary or sufficient for the disease occurrence. We argue that this promotes a network view, centred on functional redundancy. We adapted reliability theory to the concerned sub-network, modelled as a parallel (...) array of functional modules. In our model, as long as one module remains active, the function correlated with the respective disease is ensured and disease does not occur. Genetic factors reduce the initial number of available modules while environment, contingent surroundings, personal history, epigenetics, and some intrinsic stochasticity influence their persistence time. This model reproduces age-specific incidence curves and explains the influence of environmental changes. It offers a new paradigm, according to which disease occurs due to a lack of functional elements, depending on many idiosyncratic factors. Genetic risk assessed from GWAS is only a statistical notion with no direct interpretation at the individual level. However, genomic profiling could be useful at population level in devising models to guide decisions in health care policy. (shrink)