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Implications for Health and Wellness

Richard C. Strohman


The trouble with the extended theory of the gene is that genetic elements, while critical, are only one aspect of biological regulation. They cannot, in themselves, specify details of organismal phenotype, including complex diseases like sporadic cancer and cardiovascular diseases. To be sure, there are cases in which genes may be said to “cause” attributes of an organism, but these are rare; in the realm of human diseases they account for about 2% of our total disease load.1 For the most part, complex attributes … phenotypes of organisms … are not caused by genes even though genes are the ultimate agents used to create phenotypes. But if genes don't determine us, if our disease causality cannot be located in genetic agents alone, if developmental processes characterized by high fidelity adherence to species form cannot be reduced to genetic programs, if the source of evolutionary change is not traced solely to random genetic mutation, then what does determine us? Where is disease causality located, where and what is the nature of programmed growth and development in living organisms, and what is the creative source of new morphology and function acting as substrates for natural selection? In short, if the program for life is not in the genes … and organisms are clearly programmed …, then where is the program?

The short answer is that the program is in no one place; it is distributed at many levels of the organism, and all levels are open to environmental signals. Controls may be found distributed in gene circuits, in metabolic networks, in cytoskeletal structures, in membrane units, in extracellular

matrix elements, and finally in the cell as a whole and in networks of cells at the various levels of organization above the cell. These levels of control each have their own rules, and all levels are interactive with one another and, in the case of cells and organisms, with the world around them. The major new idea here is that these levels of control are not reducibly connected; it is not possible, for example, to reduce common cancer to rules that govern DNA,2 just as it is not possible to reduce intelligence simply to the laws governing ion fluxes in brain neurons. DNA is involved in the phenotype “cancer” or “intelligence,” but the cause of both lies elsewhere at higher levels of organization, including the level of the cell as a whole and the level of cell-cell networking.

This short answer is already extremely complex compared to the idea of reducibility, that ultimate control is in the gene. Part of the current maturing of biology is the surrender of simple “storybook” explanations for how life works and the acceptance that life is beginning to appear more like a complex adaptive system than like a gene machine. It is not the purpose of this chapter to treat in detail the various levels of complexity in cells and organisms, and in any case that would be beyond the reach of the author. What is attempted here is an effort to define the deficiencies evident in genetic reductionism and the problems presented by these deficiencies to medical thinking and to concepts of wellness.

Holism and Epigenesis:
Alternatives to Reductionism in Biology

Reductionism is being questioned at many levels.3,4 We are hearing of concerns about the lack of relationship between genomic and morphological complexity of cells and organisms. We hear questions concerning whether genomic databases provide the information necessary to define function at higher levels.5,6 We are becoming aware of theories of development that do not rely so heavily on genetic mutation as the source of new morphology and action but that instead emphasize the presence of robust generic processes of cells and organisms that generate new phenotypes.7 And we are learning of theories of evolution distinctly different from standard brand neo-Darwinism.8 These are not special creation theories but scientific theories that truly aim to incorporate developmental processes into a new and more complex theory of evolution. Finally, we are beginning to see a view of complex human disease that is not reductionist in nature and does not rely on causal explanations rooted in gene mutation but rather sees disease as a function of

organismal or at least organ- and tissue-level dysfunction.9,10 In short, we are bearing witness to the reemergence of the organism as a legitimate focus of research in biology and of the genome as an embodied, experience-contingent entity. What used to be referred to as the book of life written in the concrete of DNA is now being referred to as the flexible genome. Genes alone are vitally important; they are necessary but not sufficient to determine function or dysfunction in cells and organisms (the exceptions are the rare monogenic diseases discussed here).

If we are seeing a shift away from formal reductionism that identifies genes and genetic programs as the causes of complex diseases, it is also a shift toward an emphasis on higher levels of analysis and in many ways involves a turn toward physiological levels analysis informed perhaps by complex adaptive systems theory.7,8 While this shift is just beginning to be appreciated in the basic research community, where it and the accompanying revolution will certainly take some time to complete itself, the implications for medicine and for the further evolution of our concepts of health and wellness require immediate attention.11–13 This is so for many reasons, not the least of which is that medical research continues to be dominated by molecular/genetic analysis and by a reductionist program that resists any tendency toward hierarchical analysis in which the gene appears not as sole causal agent but merely as an important part of the overall complex biological system. Genetic analysis does contribute uniquely to rare monogenic diseases (see the following discussion) but cannot extend the notion of unique genetic cause to complex diseases (common cancer and heart diseases for example) or to regulatory levels above the gene where the issues of health and wellness and their relationship to the world are most likely to be joined. This is a major problem (discussed in the following) since the stated goal of one of our most ambitious and expensive scientific projects, the Human Genome Project (HGP), is to map all complex human diseases to “Mendelian” genes.

Biological Complexity and Concepts of Wellness

Why is it that molecular/genetic reductionism does not address issues of wellness and health? First, molecular and genetic focus is almost exclusively on the diagnosis and cure of disease symptoms. This focus provides a valuable contribution because some diseases are truly genetic in the strict sense and because the design of many drugs is seen to depend more and more on a molecular understanding. However, the strategies (causal pathways) for health and wellness are profoundly different from

those of genetic diseases, as will be explained here. Second, real genetic diseases are rare and account for less than 2% of the disease load in the economically advanced sectors of the postindustrial world.1,14 Common diseases like most cancer and cardiovascular diseases that account for over 70% of premature morbidity and mortality are not genetic in the strict Mendelian sense. Nevertheless, the vast majority of our research budget is assigned to genetic-related problems.15 This 70% represents multifactorial diseases involving many genes whose interactions with one another and with their encoded proteins define an open network sensitive to environmental signals.1,15 The problem here is that, while the HGP will be able to provide a detailed genetic map for complex polygenic diseases, it cannot provide the instructions for reading these maps. Therefore, insights into the vast majority of complex human diseases and into their prevention are not to be expected from the HGP as such. Third, therefore, multifactorial diseases and states of health and wellness are to be seen as emergent features of these interactive informational networks. They are not reducible solely to the actions of single or even multiple genetic agents or to the actions of their encoded proteins. Fourth, while the economics of managed care forces an emphasis on disease prevention and on the superficial aspects of wellness, there is no theoretical insight into the concepts of wellness and health from fundamental research in experimental biology centered in a reductionistic genetics. Concepts of health and wellness are characteristics of whole organisms and of processes that are time and place dependent—dynamic processes open to environmental signals and contextualized by an individual's life experience. This is simply to say that in the matter of complex disease, it is necessary to go beyond the (genetic) information given, just as in the matter of a mental process, it is necessary to go beyond information in the neural cells and structures associated with that process. In complex adaptive systems like organisms and brains, even a complete description of initial conditions (even if that were possible) cannot provide a sufficient basis for predicting the outcome of the system.8

For all the previously stated reasons, we now find ourselves in a most critical situation in health care. Driven by economic requirements of managed care, we are coming to recognize the importance of disease prevention combined with health promotion/maintenance, and that is a giant step forward. The direction here is to extend the period of healthy life without necessarily increasing life expectancy. That is, there is increasing evidence that populations in economically advanced countries are rapidly approaching a maximum life expectancy and that further

investments in this direction will be expensive without necessarily being productive. As reviewed in this chapter, even the elimination of cancer and heart disease is expected to provide only a marginal increase in life expectancy in the population as a whole. Real improvement, both in quality of life and in lowering the cost of health care, is seen increasingly to come from the effort to bring more of our population into the realm of a maximum life expectancy now enjoyed mostly by the more affluent sectors. There is overwhelming evidence that increases in life expectancy have come in the past through holistic measures and not from applications of medical technology (see the following discussion). At the same time, the community of fundamental biological researchers, as exemplified by current directions within the National Institutes of Health (NIH) and the HGP, continue to emphasize a molecular/genetic approach to disease, an approach that theoretically could marginally extend the lives of the affluent few and of the fewer with rare monogenic diseases but that says very little about substantially extending the lives of the many who, because of socioeconomic reasons, do not possess the environments necessary for a long and relatively inexpensive-to-maintain healthy life. One of the arguments made here is that our national research portfolio needs to be balanced with a larger effort dedicated to understanding the processes by which the organism integrates its world of experience into a phenotype of health and well-being. Knowledge of this integration process at fundamental levels of cells and organisms cannot fail to support the further development of health and wellness concepts now seeking recognition and resources in our health care system.

This is not in any way meant to discredit or undermine research in gene/molecular-based biology, which continues to be essential. Indeed molecular genetic research as defined by newer epigenetic approaches is essential to our understanding of the pathways from environment to genome and to the changes in patterns of gene expression that take place during exposure to disease-related stress. The argument here is for balancing our national research effort with a commitment to inquiry into the more complex issues of living organisms and of their interactions with the world in which they live.


The Medical-Epidemiological Background

Substantial evidence from diverse studies now points to the possibility that most human diseases in the Western world are manageable and that

we are reaching a limiting plateau in our attempts to extend life. In addition, there is mounting evidence that disease management and longer life expectancy are more related to the presence of an environment appropriate to the conserved human genome than they are to the total medical intervention effort. Life span is a species constant,16,17 and in the United States we appear now to be rapidly approaching a maximum life expectancy of age 85.18 Even the elimination of the most serious premature killers—cancer and cardiovascular diseases—is predicted to provide a mere two to three years of additional life for the population at large.19,20 Increases in life expectancy coming from molecular genetic approaches are not expected since monogenic diseases remain stable at only 2% of total diseases and since the afflictions of older people are seen to be multifactorial, polygenic, and therefore ultimately beyond the reach of applied molecular genetics. That is to say, progeroid syndromes have a genetic basis fundamentally different from the simple monogenic diseases afflicting mostly younger people. In younger people, but not in the older, the power of modern molecular biology is seen as sufficient to provide, in theory, a successful genetic analysis and even therapy based on a linear (single gene → single disease) format. Attempts by gene cloners, armed with advanced statistical devices, to redefine common polygenic diseases in terms of genetic tendency 21 and attempts by behavioral scholars of various backgrounds to apply monogenic “software” to the reality of polygenic human traits 22 all appear to discount the warnings coming from cell and molecular embryological studies 23,24 that genetic approaches alone are not sufficient to yield a satisfactory picture of complex phenotypes. These, as well as other studies discussed later in more detail, include examples of nongenetic but nevertheless cellular responses to developmental environments in which the genotype is constrained by local circumstances.

The Biomedical Paradigm
and the Problem of Informational Redundancy

The major assumption of modern biomedical research is that unique genes have unique effects. This assumption is essential in the following areas:

Medical genetics, which seeks isomorphic mapping of human diseases to Mendelian genes 25

Molecular biology, which seeks to identify unique, genetically based mechanisms driving cellular processes 26


Developmental biology, which presupposes (1) the presence of genetic programs, (2) additivity of gene effects, and (3) the ability to map complex developmental stages to additive programmatic sequences in DNA 27

These assumptions and presuppositions, now experiencing major problems, are also the major features of the HGP. The HGP has become the centerpiece of the biomedical paradigm and has distilled a simplistic guide for future research and application. This guide is summarized as follows:

  1. All major noninfectious diseases are caused by defective genes.
  2. Diagnosis and therapy are available through genetic analysis alone.
  3. Aging and other complex human behavior is genetic, and all may be mapped to Mendelian factors.

As Brenner 28 and Wilkins 27 have pointed out, however, the uniqueness assumption of genetic determinism,

Unique Genes → Unique Effects,

is undermined by an emerging body of evidence showing functional informational redundancy in cell regulation. Here the focus is on redundant genes that more than one gene may specify any given function.29 In this case the reductionistic plan to associate genetic causality with complex phenotype is brought into question since the major research approach, saturation mutagenesis, depends completely on the uniqueness equation. This approach to understanding disease will generate a map or network of factors that interact to provide a useful background for a complex phenotype. However, as argued here, ultimate behavior is encoded not in DNA but rather in the environmentally interactive cellular epigenetic network, which includes the genome.

Levels of Biological Regulation

It is important here to distinguish three modes of gene activity that are operative in determining complex phenotype in organisms. The first is monogenic, which specifies a one gene → one trait pathway. This path


Figure 5.1. Genetic and epigenetic regulation.

[Full Size]
way is often influenced by environment or by other genes, but in some cases, when mutation involves a specific DNA sequence, environment is seen to be irrelevant. Diseases like sickle cell anemia and Duchenne muscular dystrophy come to mind as prime examples of monogenic diseases. The second pathway is polygenic, which refers to the fact that phenotype is determined by many genes acting together.

The third path is epigenetic, which may involve both single-gene and multigene interaction. Epigenesis implies a level of complexity beyond gene-gene interaction and extends to interaction between genes, between genes and gene products (proteins), and between all of these and environmental signals, including, of course, the individual organismal experience. But in addition, epigenetic pathways are usually thought by developmental biologists to involve progressive states of organization, each succeeding state depending on the prior state. Epigenetic pathway therefore implies great complexity of interaction as well as the production of entire states of organization arising from that interaction (see Figure 5.1). Finally, an epigenetic change in a cell, in a strict sense, is heritable; initial cellular responses not restricted to genomic alterations, usually called phenotypic or physiological adaptations, may persist over time and become stable so that change is transmitted to daughter cells during mitosis.

The heritable aspect of epigenetic change is an obvious aspect of differentiation where many different cell types, all with identical genomic sequences, maintain their differences over many generations. Of course, secondary changes in DNA may also contribute to the stabilization of cellular change, but these changes are not programmed by genes; they are rather programmed into DNA by regulatory events about which we now know quite a bit. Changes in methylation pattern, in DNA-binding proteins, or chromatin structure are examples of inherited secondary changes in DNA. These epigenetic changes result in altered transcriptional patterns and therefore in altered patterns of behavior at all levels

of the organism, from cellular to integrated psychophysiological action.30 Epigenesis has been given a modern definition as follows:

Classical genetics has revealed the mechanisms for the transmission of genes from generation to generation, but the strategy of the genes in unfolding the developmental programme remains obscure. Epigenetics comprises the study of the mechanisms that impart temporal and spatial control on the activity of all those genes required for the development of a complex organism from the zygote to the adult.31

As such, the definition establishes the basis for a level of organizational control above the genome, a level that is now well established in fact, but it is a level of complexity that continues to evade decisive theoretical insight. That is, epigenetic regulation is already extending and stretching the limits of our ability to draw the limits of interactional networks that are at work in governing a major phenotype like a complex disease. For example, the mechanisms of DNA marking (e.g., methylation) may be elucidated, but what is missing is any understanding of the question, “Where and how are these mechanisms deployed in cells … what are the rules, the boundary conditions for such deployment?” These questions are being addressed,10 but currently we have no consensus in biology that is necessary for a major new direction to be implemented. Courage and vision may be required on the part of our research leadership if we are to progress. Meanwhile we expect that a full description of a genetic network will come complete with a set of rules for its operation as an open system. But the rules do not come with the network diagram; they have to be discovered by human ingenuity. The differences between a genetic and an epigenetic informational system are depicted in Figure 5.1.

We have wrongly extended the theory of the gene to another area altogether; we have been lulled into reasoning that if the gene theory works at one level, from DNA to protein, it must work at all higher levels as well. We have thus extended the theory of the gene to the realm of gene management. But gene management is an entirely different process involving interactive cellular processes that display a complexity that may be described only as transcalculational, a mathematical term for “mind-boggling.” This interactive complexity is epigenetic in nature; it involves open networks of genes, proteins, and environmental signals that may turn out to be coextensive with the cell itself. It is as if the cell has interposed between its genome and its behavior a second informational

system able to integrate environmental and genetic information into its dynamical process and able to generate from this integration responses that are functional, or adaptive.

Genetic pathways specify organismal function only in rare cases, as in monogenic diseases like sickle cell anemia or muscular dystrophy, where mutation produces dysfunction in a protein of crucial importance. In these cases the cell (mostly but not always) has no compensatory mechanism, and environmental influences are nil; redundant information at either the genetic or the epigenetic level appears to be absent, and the mutant gene becomes the disease. But this rare event has such a powerful effect in making real the critical issues of disease and health that it has commanded our attention in other areas of our lives. Common diseases like cancer and cardiovascular problems that account for over 70% of premature morbidity and mortality are not the effects of single genes.

Epigenetic networks have been described as cellular neural networks and, given their great complexity and openness to environmental signals, most probably utilize a (nonlinear) logic and set of rules quite different from the comparatively linear rules needed for completing the genetic sequence of events. This comparison also emphasizes feedback from epigenetic networks to the genome, feedback that includes changing the patterns of gene expression. This change in pattern of gene expression is accomplished by enzymatic changes in chromosome structure and by “marking” sections of DNA chemically without changing the genetic code in any way. What is changed is the accessibility of genes to expression pathways. But the decisions to mark or not to mark are in the epigenetic and not the genetic pathway. These details of epigenetic biology, as defined by Jablonka and Holliday,30,31 are well known and are thoroughly covered in the literature. We can see at once that failure to include epigenetic processes and their rules in predicting outcomes and basing outcome analysis only on information in DNA will lead to the anomalies that are now being seen. Thus, information for cellular integration and response is encoded not only in DNA, and there are no genetic programs for this process; rather integration and response come out of the dynamics of the interactive system itself. The system response includes the genome but is not reducible to it. The cell is starting to look more like a complex adaptive system rather than a factory floor of robotic gene machines, and that is well and good.

In what follows, whenever I refer to polygenic traits or diseases, I assume,

along with mainstream biology, strong environmental interaction.

For my purposes, therefore, polygenic and epigenetic are synonymous. The basic assumption is that complex disease states, at a cellular level, involve heritable changes that may include gene mutation but that also include persistent cytoplasmic changes. In addition, it must be clear what classical developmental biologists mean when they discuss complex phenotypes in terms of genotypes. What is usually meant is that all complex traits (e.g., intelligence, aggressiveness, and cancer) have some genetic basis. But this basis is so polygenic (interactive and epigenetic)—it may extend to the entire genome—that there is little in the way of practical meaning given to “genetic basis.” For example, there is a genetic basis for speaking French, but the meaning of this does not go beyond the idea that there is a genetic basis for being human. In order to speak any language, we need to have something called a human genome (of which there are as many different kinds as there are humans) consisting of about 100,000 genes. But while these genes are necessary for speaking French, they are not sufficient. We also need the appropriate environment, the appropriate body, and the appropriate experience, all of which provide information not contained in the genome. Unfortunately, most behavioral and medical geneticists continue to believe that even the most complex human behavior can be reduced to genetic circuits. We now turn to examples where predictions and diagnoses based on genetic analysis alone have generated conflict and anomalous results.


The foundations of applied molecular genetics are twofold. The first is found in the statistical approaches designed by Fisher and Wright 32 to describe efficiency of selection in producing desired traits in agricultural populations. The second is found in the singular successful attempt in 1908 by A. Garrod to map a metabolic disease, alkaptonuria, to a Mendelian pattern of inheritance.33 Garrod would later offer the concept of “inborn errors of metabolism” to describe a range of metabolic disorders, leading to the general emphasis on genetic disease. The wide acceptance of the concept of genetic diseases, and the confusion of rare monogenic diseases of the Garrod type with the more common

polygenic human diseases, is seen as the single most important historical development underlying the widespread belief in the paradigm in question.34

The tension between agricultural genetics and medical genetics has been described and analyzed most recently by Wahlsten.35 In brief, the argument is that the major statistical tool, analysis of variance (ANOVA), as developed by Fisher, is insensitive to the heredity-environment interaction. This insensitivity is minimized in the agricultural breeding experiments for which ANOVA was designed because large sample size is normally the rule. In medical genetic studies (extended families) or in behavior genetics (twin studies), the sample sizes are small, so that error is large in detecting lack of interaction between heredity and environment. As Wahlsten points out, a newer statistical approach, multiple regression, is replacing ANOVA, but for the kinds of studies being discussed here, the two procedures are essentially equivalent. Experts in agricultural genetics generally accept significant interaction between genes and environment and are extremely cautious in applying heritability coefficients or in assigning any significant numeric value to genetic cause when dealing with complex traits. Their position is that if gene effects are interactive (not additive) with environmental effects, it is incorrect to use ANOVA for assessing genetic contribution to a particular phenotype across a range of environments. Medical geneticists, however, using the same ANOVA but with significantly smaller sample size, not surprisingly do not find evidence for interaction and therefore assume that heredity and environment are additive. They then assign great significance to heritability coefficients and are confident that these numbers describe quantitatively the contribution of separate heredity and environment to any particular phenotype. We have a medical literature, then, that asserts with great confidence, but with serious theoretical reservations from sectors of population genetics, that this or that complex behavior or disease, while having an environmental component, also has a separate genetic component that can be discovered and utilized in pursuit of some hypothetical treatment strategy. It is beyond the scope of this review to enter this controversy fully. It is enough to state the minimum conclusion that medical/behavioral genetics, with a linear view of gene-disease causality, finds itself in serious debate with a significant segment of its parent science, population genetics, which sees complex traits, including disease, as highly interactive and impossible to reduce to genetic elements alone (Figure 5.1).



Since the work of Garrod in 1908, a large number of monogenic diseases have been discovered, and there is a general misconception that all diseases are open to monogenic logic and to solution through gene therapy of some kind. In fact, the total percentage of monogenic diseases has remained constant at less than 2%. While rare monogenic diseases are legitimate targets of the new technology, most of the rest of the 98% of human diseases, including cancer and heart diseases, are not. The latter are polygenic, multifactorial diseases for which genes may be necessary but not sufficient.1

Diseases may be distributed according to whether they are determined before or after fertilization.36,37 Those determined before fertilization (2%) are, of course, genetic and are mostly not preventable. Of those determined after fertilization (98%), there may be multiple causality, including early developmental effects, but in theory at least these are all preventable.

There is a second level at which the biomedical paradigm is in conflict with actual disease distribution. The problem for medical genetic theory is that the common diseases of cancer and of the circulatory system appear to be new; they were not significant causes of death and disability in the early part of the 20th century.37 They are now the major cause of premature death and suffering in the industrial world. Clearly, this sudden shift in causality cannot be based on genetic change. Evolutionary theory and molecular biology agree completely that genetic adaptation due to mutation would take thousands of years and that change due to genetic recombination would also require much more time than the mere 50 to 100 years involved. The reasoning of medical genetics, however, is that these new diseases attack people mostly in older (post-60) age-groups. As such, the responsible genes would be beyond the reach of natural selection, which operates effectively at younger prereproductive ages. This being the case, it is argued that heart and cancer diseases are “old” entities, have always been with us (as have their genes), but show up significantly now because it is only recently that our population has aged sufficiently for them to become a problem. If this is true—so goes the argument—then these are genetic diseases, pure and simple, and may be attacked as such.

But the natural history of our complex diseases shows that, in all probability, these are not genetic diseases but are diseases of civilization.

Of course, they have some genetic basis, but this basis is so broad as to be trivial with regard to providing precise genetic answers. Evidence that diseases of civilization are not simply genetic includes the following. First, twin studies show extremely low concordance for most cancers and heart disease. Second, these same diseases show remarkable variation in identical populations over time and over geographic and migratory patterns. These variations disclose, for example, that diseases tend to be place (environment) specific and that when people migrate, they tend to have those diseases that are common to their host population, not those that are common to the genes they brought with them, that is, not common to their native population. These variations are reversible. Finally, these diseases are rare in populations that have not come under Western habits. Natural history studies all indicate that our major noninfectious diseases are not genetic in any straightforward causal sense; they are diseases associated with changes in environment. That is the message from the past and the present. That message, extended into the future, is that new noninfectious diseases, their prevention and therapy, will also be associated with environmental change.36,37


Hypertension, Myocardial
Infarction, and the ACE Mutation

Restriction fragment length polymorphisms (RFLP) are being used to generate maps of genes and gene products that interact to produce a disease phenotype. The general idea here is that unique DNA sequences (mutations) can be linked to inheritance of phenotype and then mapped to specific chromosomes. Ultimately this analysis may lead to identifying mutated genes of known function and, theoretically, to gene or gene product replacement therapy. While this approach is applicable to singlegene diseases, it is highly suspect when applied to polygenic, multifactorial diseases.

The starting point for much of RFLP work was the analysis by Lander and Botstein 38 applied to the hypertensive rat.39 This work revealed linkage of hypertension to a mutation at the ACE (angiotensin converting enzyme) locus, a gene responsible for converting angiotensin I to angiotensin II, a protein crucial to blood pressure regulation. Subsequent work, however, showed that ACE mutation was not linked with hypertension

in humans.40,41 More recent studies provide a strong suggestion that, even for the hypertensive rat, early developmental changes will neutralize ACE mutation and provide for near normal phenotype. Thus, if young rats are taken from genetic mothers bearing the ACE mutation and nursed by normal mothers of a related strain, the pups show decreased levels of hypertension.42

Myocardial infarction in humans has also been linked to ACE mutation.41 However, in this study many individuals were identified with the identical mutation who had no heart disease. Clearly, other factors are involved. How many other genes or other factors might there be? In studies like this, the question is rarely asked. But the physiology of heart function clearly reveals that ACE-related diseases will most likely be multifactorial, polygenic entities. If so, then one expects that each of the many genes will have a small effect,43 redundancy will be present, and any one gene or even several functionally related genes may be necessary but not sufficient to precipitate a heart disease. In other words, one anticipates that in this situation genetic diagnosis will not be a robust predictor of phenotype. The environment and individual natural history will be major determining factors. In the case of angiotensin-related function, it is clear that redundant epigenetic regulation will dominate a single genetic defect. Why? It is well known that in the normal or diseased human ventricle, ACE is a minor source of angiotensin II. There are many other (gene coded) serine proteases that provide for 90% of ventricular angiotensin II levels.44

We conclude that ACE mutation will predict neither hypertension nor myocardial infarction in humans. While an ACE mutation might have some effect, at the wider physiological–nervous system level, there will be further interactional complexity and phenotypic adaptation, including central nervous system override of renin production. These and other elements of the hypertensive control network will confound simple genetic determinism. Examples include complex cortical and medullary regulation of heart and blood pressure rhythms that are exquisitely sensitive to environmental input and personal experience.45 While the use of ACE inhibitors may be a useful therapy for hypertension, an ACE screen for heart disease is not predicted to be efficient. Yet the biomedical community persists in calling for the use of an ACE gene screen to predict tendency for heart diseases and to emphasize genetic models of hypertension in general.46,47 Why?

Molecular biologists are compelled to find as much detail as possible in gene-based networks like the one for hypertension, and RFLP approaches

do provide the appropriate tool. In time, a gene map of extremely high density for this network will become available. But such maps, for each multifactorial trait or disease, will include perhaps hundreds of genes and interactive gene products all with input from the environment. The complexity of such a system can be described only as indeterminate since, when only six genes are involved in shaping a trait, the total number of phenotypes possible is over 4,000. There will be little of predictive value in the individual bits of genetic information defining this system. Rather, hypertension or other disease phenotype will be defined by the system as a whole and by the responses the system makes to the appropriate internal and external signaling pattern. The search for a screen that depends on a single or even a multiple number of genetic variants is an understandably oversimplified approach born of an optimism that all controls reside in genetic elements. But, as has been pointed out here, such optimism, realistic when applied to the rare monogenic diseases, is misplaced when applied to complex multifactorial systems such as hypertension.


This is a model of multifactorial diseases. It is the major cause of death in North America and in a number of European countries.48 Current research focus is on a hypothesis in which “response to injury” offers the most promise for understanding and perhaps control.49 The hypothesis involves a complex etiology of atherosclerosis that includes disorders of lipid metabolism, clotting, blood pressure regulation, and carbohydrate regulation. The events proximal to the disease include lipid infiltration of blood vessel walls and loss of control of intimal cell proliferation that is postulated by some to involve, in the case of restenosis following angioplasty, tumor suppressor gene (p53) inactivation.50,51 Thus, assuming that the hypothesis is correct, it remains highly unlikely that diagnosis or prediction of atherosclerosis will emerge from gene mutation analysis. Why? Mutation in p53 or related genes is identified as an end product in a long line of causal factors. Epidemiological studies reveal over 200 risk factors at work,52,53 and molecular studies suggest that hundreds of genes may be involved; as many as 200 different genes are probably active in lipid metabolism alone. Using new techniques of linkage disequilibrium,10 one may be able to detect the influences of perhaps 500 genes affecting atherosclerosis. What is the predicted benefit of measuring small effects of 500 genes? Nil. Why? Because each small effect will

display strong environmental shaping, will be open to redundancy at the gene level, and will be interactive with other genes so that cooperative and compensating (epigenetic) effects can take place, all of which makes early diagnosis or prediction of final outcome based on genetic information alone extremely difficult if not impossible.

There is, however, a role for genetic analysis in the diagnosis of complex polygenic diseases like atherosclerosis. New approaches attempt to link genomic variation with specific environmental and physiological states to predict disease phenotype. They recognize that genetic and environmental signals are strongly interactive and that few signals of either kind will exert independent effects on the determination of disease susceptibility. These approaches assume “that interactive effects are translated through quantitative variation of intermediate biological and physiological agents that link discrete genome type variations and variation in risk of disease.” 52 For example, Sing and his collaborators 54 have proposed a nonparametric statistical strategy for selecting combinations of genotypes, intermediate risk factor traits (physiological states), and environmental agents that can be associated with subsets of individuals showing a disease phenotype. One early result of this has been the strong association (odds ratio greater than 2) of high body mass index combined with unique apolipoprotein E genotypes with coronary artery disease. This strategy may be extended to many genomic, physiological, and environmental interactors and may reveal the role of genome type variation in nonlinear relationships with a great variety of other interactors.55 Approaches such as this represent an upper limit of using genetic analysis coupled with other signals in a dynamic epigenetic framework to predict disease outcome. Other examples of nonlinear approaches to predict disease susceptibility rely on dynamical measures of physiological states alone and will be discussed here.

Cancer and Mutation in Regulatory Genes

The purpose of this section is, once again, to focus on an epigenetic perspective as an alternative way of thinking about disease causality. It is not the intent to dismiss mutations as an important aspect of tumor formation. However, a significant and persistent criticism of the mutational theory of cancer remains in evidence, and it behooves us to be reminded of it as a possible missing piece of our mainstream approach to cancer detection and diagnosis. This criticism should be kept in mind as we

review examples here of conflicts in this area. Basically this criticism, drawn from different sources,56–60 states the following:

  1. The mutation of single genes of “major” importance, in itself, is insufficient to cause cancer at least in the early stages. Tumor suppressor genes or oncogenes are examples of major genes.
  2. Early stages are often reversible and display tissuewide cellular changes inconsistent with single-gene mutation causality.
  3. Early-stage changes are seen as epigenetic adaptations to environmental signals. These changes progress through intermediate states to end-stage tumors that do show many mutations that may preclude any spontaneous remission. In what follows here, discussion is restricted to polygenic cancers where there is no Mendelian segregation associated with the phenotype.

Cancer, in its multiple forms, has often been described as one of our most multifactorial and enigmatic diseases. While strong evidence exists for a genetic background, for many cancers much of this evidence is potentially confounded by congenital and familial effects; we forget that many things are inherited in addition to genes. In addition, we know that many forms of cancer have strong environmental determinants. Current research emphasis, however, is on mutation in tumor suppressor genes, which, while they will play some role in cancer, may also prove to be constrained by other factors. Here I analyze several cases and conclude that an epigenetic basis for polygenic cancer is an attractive but missing research component.

The p53 and rb Genes as Tumor Suppressors The most recent trend has been to associate unique cancers with mutation in growth control or tumor suppression genes such as p53 or retinoblastoma (rb).61,62 These genes code for DNA-binding proteins that delay or inhibit cell replication. Mutation in both alleles would then produce defective regulation of growth and tumor formation. If one of these genes is defective at birth, then one inherits a tendency or susceptibility for cancer; the disease itself is then predicted to occur when, through somatic mutation, the second allele is also defective. But it is now clear that some form of redundancy for p53 and rb is present in cells, making it difficult or impossible to use mutational analysis alone for predicting cancer. For example, a mouse has been constructed with both p53 alleles absent (homozygous

knockout).63 In this case, it was expected that growth control in all cells would be defective with dire effects for all affected individuals. However, the affected animals were all normal at birth, and early development and growth was normal. It was only after adulthood was reached that some, but not all, of these individuals developed tumors in excess of that found in control populations. Clearly, the early phases of development that depend on stringent growth controls remain independent of p53 input or have redundant pathways around p53. The same must be said of the p53 mutant adult individuals that did not show any tumor formation. Finally, when a normal p53 gene is inserted into cancer cells, it may or may not restore normal growth regulation.64

More recent evidence shows that p53 protein may form heterodimers with many other cellular proteins,65 including replication protein A, which is involved in the initial stage of DNA replication.66 Thus, p53 regulation is a prime candidate for epigenetic control in which the final effect is modulated by a complex interaction of many bits of genetic and environmental information. Much is learned about DNA replication in p53 studies, but the emerging picture shows not single-gene control of cancer but complex interactive regulation. Epigenetic interactions of p53 protein with other gene products form a basis for explaining the varied effects observed when p53 is mutated in different genetic backgrounds 63 or when wild-type p53 fails to restore normal growth regulation to p53-defective cells.62

A similar story may be told for the retinoblastoma (rb) mutation (for a review, see reference 67), which arises either spontaneously or via heredity associated with a deletion in or absence of chromosome 13 in 20% to 30% of affected cases.68,69 But 20% to 30% is not 100%, so clearly other factors are involved. Many individual rb tumors do not show mutated rb genes.70 In addition, while expression of wild-type rb in some rb-defective cells will restore normal growth,71 such transfection and expression fails to produce normal growth when these cells are transplanted to the eye of nude mice.72 Homozygous rb knockout in the mouse is lethal but only late in development after lineage determination is complete and after millions of cell replications have been completed.62 This gene, therefore, while it plays an important role in cell replication, is not essential during early development and is not sufficient to cause cancer. We must assume that epigenetic control of rb is taking place.

Finally, one of the most outstanding characteristics of neuroblastoma is spontaneous remission at 10- to 100-fold greater than that seen for

any other human cancer.73 Reversibility of tumor growth is normally thought to be inconsistent with mutation causality, especially when one eliminates from consideration the possibility of immune surveillance as the cause of remission.

A newer explanation for remission is apoptosis, but one is left with an epigenetic regulation because some aspect of cellular behavior must be presumed to signal cell death or to engage the so-called apoptosis program. Here again we become aware of the facile nature of molecular thinking on the issue of genetic programming and apoptosis.74 For example, in a recent review we read, “Within a few months of birth the programme is activated: cells then die successfully by apoptosis and the (rb) tumor shrinks.” 73 When one asks the question, “What activates the program?,” the answer is usually in terms of other genes for growth factors or DNA-binding proteins. But cells reacting to stressful signals such as x-irradiation or to a loss of normal tissue environments when explanted into cell culture will display a variety of epigenetic adaptations that might easily trigger cell death.75

Genes for Breast Cancer Human breast cancer work is heavily invested in gene mutation causality even though a large population study tells us that less than 2.5% of breast cancer is associated with genetic determinism.76 Many mutations are found in later stages of a variety of tumors. While it is likely that these play some role, it remains uncertain whether mutations are the cause or the effect of earlier, nongenetic lesions that, if reversed soon enough, would have deflected the tumors altogether.56,77

The latest focus in breast cancer has been on a familial study where linkage has been established for “cancer tendency” to a locus on chromosome 17q21.78 The lod score (see the glossary at the end of this chapter) for linkage was 5.98, well in the range to ensure a high probability of association between the cancer and the genetic anomaly. While the technology of this linkage study may be assumed to be state of the art, we must also be aware of its problems. For example, we do not know what the frequency of this mutation might be in the general population, nor do we know the extent to which other mutations might be present in the suspect or other chromosomes of the affected women. Nor do we know the pleiotropic and epistatic effects that other genes might have in altering the penetrance of the suspected mutation. These are all questions of fundamental importance in elementary genetics.43 We also have

no knowledge of what environmental influences might be required in order for tumor formation to occur in the presence of the mutation. The mutation may be necessary but not sufficient; it may require specific environments or specific other genetic background. Finally, as mentioned at the outset of this discussion, we also know that while there is some relationship of family history to breast cancer, only 2.5% of breast tumors are genetic in origin.76

This breast cancer gene, BRCA1—a putative tumor suppressor gene—has now been isolated.79 BRCA1 germ line mutations are linked to breast and ovarian cancer in a number of small families having multiple cases of these diseases. Women carrying mutant BRCA1 alleles have a significant increase of breast and ovarian cancer so this finding may prove to be extremely useful. The search for the breast cancer gene had been accompanied by an unnecessary hyperbolic publicity in both mass media and scientific press where an anxious expectation was developed that once the gene was found, we would be provided with important new clues for all breast cancer and perhaps for cancer in general. High expectations like this have proved mostly unfounded in the history of cancer research and serve only to frustrate public opinion and undermine confidence in research. It was sobering for many, therefore, to find that in nonfamilial breast and ovarian cancers, constituting more than 95% of the cases, BRCA1 mutations were not involved.80 Thus, this mutation “appears to play no role in common, nonhereditary forms of breast cancer that strike about 173,000 women in the U.S. each year—a finding that undermines some long-held assumptions about how the gene works.” 81

There has also been much attention paid to another tumor suppressor gene, CDKN2, that codes for the cell cycle regulatory protein cyclindependent kinase-4 inhibitor (p16).82 This (mutant) gene has been linked to a variety of tumors and has been a prime suspect for breast cancer. However, a recent report has now examined human breast carcinomas for mutations of this gene with negative results. Evidently, p16 is not involved in the formation of primary breast carcinoma.83 In addition, it has been found that p16 mutations are found in cell lines derived from many tumors but not in primary tumors within the patients, making it clear that so-called carcinogenic mutations may be a pure artifact of cell culture.83 The studies on p16 mutation are consistent with a hypothesis of cancer where early neoplastic change is an epigenetic one that includes mutation as an event after the fact of initiation.



Imaging Techniques

Computed tomography and magnetic resonance imaging have become widespread and extremely expensive additions to diagnosis. The problems inherent in imaging techniques have been recently analyzed 84 and are discussed briefly here as prologue to similar problems turning up with molecular measurements that, while extremely sensitive, are also without proven meaning when applied to disease manifestation.

Imaging techniques, because they are so sensitive, often measure not disease itself but early changes in tissue that are taken as evidence that disease will develop. Early changes may, however, be extremely misleading since they often reflect reversible processes or those with extremely long lag time to any clinical manifestation. As our machines are able to detect the most incipient stages, we experience several problems.84 First, as exemplified by thyroid cancer, is the problem of defining diseases as cellular changes that always progress to serious morbidity. In this case, clinical cancer (tumor size greater than 2 centimeters) is seen in only 0.1% of adults between the ages of 50 to 70 years. However, autopsy using increasingly thin sections of the gland could reveal at least one papillary carcinoma in 36% of adults. It was calculated that as sections became thinner, autopsy would show verifiable papillary cancer in 100% of cases. These “tumors” discovered at earliest stage represent an enormous reservoir of detectable but subclinical disease. Under these circumstances and for a variety of diseases, the patient may never experience clinical symptoms but, under aggressive medical management, may become involved in unnecessary and expensive medical procedures that are predicted to have little positive effect.84

The second major problem that arises has to do with the effect on reported disease frequency where frequency increases as the degree of measurement sensitivity increases. However, without any manifestation, early stage diagnosis makes it appear as if we are experiencing large increases in the disease itself. The third problem is the effect of statistical evaluations of various therapies for a disease. As the time between diagnosis and manifestation increases, it is made to appear as if various therapies are working even when nothing in the way of treatment need be involved in the statistical analysis.84


Antigen and Nucleic Acid Sequence Measurement

Nowhere in medical technology have we greater sensitivity of measurement than in antigen and nucleic acid chemistry. The possibility exists, however, that these measurements are often without predictive value for the diseases for which their measurement was designed. Increased levels of scrutiny can, for example, explain recent reported increased prevalence in breast, prostate, and thyroid cancer.84 Prostate antigen testing, together with other evaluation, may prove useful. However, these tests, used alone, can provide for an enormous increase in reported prevalence and an increased apparent time of survival and, unless carefully applied, could lead to unnecessary treatment.

Polymerase chain reaction (PCR) is a technology used extensively to report “viral loads” in patients and has replaced measurements of actual infective units of pathogen. What PCR does is to amplify by thousands of times sequences of DNA that are present in body fluids at extremely dilute concentration. For example, it has proved to be crucial for diagnosis of HIV-related AIDS, where it is used to amplify vanishingly small amounts of parts of HIV sequences. But such partial sequences tell us nothing about the presence of “live” virus and could be reporting on DNA fragments resulting from a variety of sources (e.g., viral degradation). In addition, PCR is notoriously difficult to quantitate, and recent publications fail to publish standards that might show actual viral numbers. As Nobelest Kary Mullis, the inventor of PCR, himself states in a paper that was refused publication, “The vice of the PCR is that it can find the biochemical equivalent of the needle in the haystack. Viral fragments that are present only in minute quantities can be amplified and identified, but this tells us nothing about whether replicating virus is present in sufficient quantities to do harm.” 85 The HIV-AIDS hypothesis remains plagued by the fact that most AIDS patients, until end-stage disease, rarely show HIV viremia (classically defined by actual virus replication), and diagnosis continues to rely on PCR and antibody measurements. In HIV and other infectious diseases, we may have abandoned, at great scientific cost, traditional rules for establishing disease causality.86 For example, HIV, as many contend, may be caused by agents other than or in addition to HIV. But reliance on PCR tends to conceal the absence of viral units and to conceal the possibility that other causes may be involved.



The conflicts described here arise from attempts to apply a strict geneticreduction analysis to the problem of complex phenotypes. Why has it been so difficult to use single-gene markers to predict polygenic cancer in individuals even when a mutation is present in an important tumor suppressor gene (rb or p53)? Why are there so many false positives and false negatives in predicting heart disease when there is a measurable defect in an important gene (ACE) involved in blood pressure regulation? Finally, why is it that the elimination (homozygous knockout) of both copies of a gene like p53—known to be important for controlled growth of cultured cells—fails to provoke any measurable defects in normal development? On the basis of a linear genetic analysis, these failed predictions should have been viewed as evidence of a failed cancer theory. However, the response from biotechnology has been to rescue the genetic theory in the face of these and many other exceptions to the rule that unique genes have predictable unique effects. Why this response should persistently come up has been treated elsewhere as part of a paradigm shift 87 in biology and medicine.88

The short answers, to the questions of lack of predictive power of gene analysis and of why we have thrown out the facts rather than the theory, are not too difficult. The explanation formulated here is that polygenic disease and growth regulation are not linear processes and cannot therefore be fully analyzed by a linear logic. Rather, they are representatives of complex adaptive systems that are innately unpredictable. To understand the unpredictable nature and other features of such systems, it will be necessary to develop a disease theory similar to what is found in treating nonlinear phenomena.89

What is needed to supplement genetic theory is a theory of biological complex adaptive systems.90 If living systems were seen as complex adaptive systems, then we would not be surprised at the previously mentioned failure of prediction. In fact, they would be expected since such systems are unpredictable and actually seek out alternative pathways when perturbed by new information, either from the outside world or from within. The results of alternative pathway selection would include epigenetic change in the genome and resulting change in pattern of gene expression. In the case of mutation, however, we need to remember that it is taking place within an epigenetic framework. As explained by James Shapiro,91 part of the adaptive response of bacterial cells to stress is

the activation of a whole family of enzymes whose job is to remodel the genome, a job that will predictably increase the frequency of mutation. But in this case the mutation would not necessarily be the result of random process but part of an epigenetic response process that has become maladaptive through chronic stress. One might begin the merger of genetic reductionism and epigenetic complexity with those areas where multigenic systems are known to be coordinated by higherorder cellular responses to environmental conditions. Nobel laureate Barbara McClintock, who described mobile genetic elements long before they were discovered by molecular biology, had always been preoccupied with mechanisms that rapidly reorganize the genome. In one of her last reviews, she wrote of the significance of responses of the genome to challenge. She ended that article as follows: “We know about the components of genomes. … We know nothing, however, about how the cell senses danger and initiates responses to it that often are truly remarkable.” 92

Statements concerning cells “sensing danger” and “initiating responses” strike biologists today more as poetic meanderings than as statements with scientific value. But clearly cells and multicellular organisms display these holistic behaviors. Natural selection operates not merely at a genetic level but at all levels of biological organization,93,94 including whole-cell and organismal behavior. Much evidence exists for the idea that cells do sense danger and respond in a manner that is not explored by reductionistic thinking. For example, when tissues are exposed to X rays, there is a tissuewide, or “field,” response in the cellular population as a whole, a response that is quite separate from gene mutation but that actually induces persistent hypersensitivity to future mutation.95 Cells exposed to the stress of removal from normal tissue constraints in vivo when explanted to cell culture adopt many morphological changes that are heritable and that, when continued over a period of time, give rise to transformation and to mutation.96 The flip side here is the demonstrated ability of liver architecture to constrain tumor growth in an age-dependent fashion. Tumor cells explanted into young rat livers generate tumors at a much lower frequency compared to that seen when these cells are transplanted into older livers.97 Findings like this are difficult to reconcile with singe gene mutation causality but are predicted by an epigenetic theory of cancer that locates control of single cell growth in higher (tissue and organ) levels of biological organization.

Perhaps the most important new insight into cancer is the one having

to do with the source of the many (hundreds and thousands of) mutations that may be found in human tumor cells.98 Cancer cells in culture show a hypermutation phenotype but, surprisingly, not under conditions of rapid growth. Only under conditions of nutritional stress or contact inhibition do these cells recruit an epigenetic response capable of “reorganizing the genome” through increased mutation rates. The result of this reorganization is the production of useful mutations (useful from the cancer cell point of view) allowing cancer cells to escape the various constraints that normally contain tumor expansion and metastasis. Single-gene mutation would be amply buffered by epigenetic mechanisms, including redundancy, but with stress producing 1,000- to 5,000-fold increases in mutation rates, the “cause” has to be located in the epigenetic mechanisms responsible for this extraordinarily high rate.

One may conclude that (1) higher-level epigenetic management and constraint on tumor growth delays clinical cancer and may even reverse it and that (2) epigenetic mechanisms at the intracellular level are responsible for generating increased mutation rates necessary for escape of these cells from the higher-level constraints. In other words, cancer is a cellular, epigenetic disease and not the result of single-gene mutation.

An approach to complex analysis of heart disease with multigenetic causality linked to interactive environments is the work of Sing and his group as mentioned previously (see the section “Atherosclerosis”). At these levels above the cell, for complex physiological systems, chaos theory builds on epigenetic thinking and already is providing new ways to think about complex systems.8–10 This is particularly true for cardiac function, where sinus arrhythmia, long thought to be low-level noise or random fluctuation in heart rate, is now seen as high-order chaos.99 Coupling of heart rate to brain function and thus to experience has long been appreciated as an observable patterned occurrence but was mostly inexplicable through standard physiological experiment.100 Chaos theory is an old story in physiology in general 101 and is able to provide a method of revealing generic patterns in what was thought to be random variation. Recognition of these patterns allows new insights into brainheart physiology and may even allow prediction of sudden cardiac death among patients at risk.99


This chapter draws much from previously published materials by the author. See references section for the exact citations.



Different forms of the same gene.
A protein recognized by the immune system.
Programmed cell death.
A technology used to scan whole bodies for diagnostic purposes.
All genomes, including the human genome, tend to remain constant over long periods of time because of the presence in cells of formidable DNA repair systems. Mutations, for example, occur, but most are repaired.
General term for that part of the cell surrounding the nucleus.
Generally refers to the inheritance of factors and processes that are in addition to genes. Also refers to changes in the genome that do not involve sequence changes in DNA.
Interaction between genes.
When the two alleles are different.
all genes come in allelic pairs; “homozygous” refers to cases in which both alleles are the same.
An international effort to identify every gene in the total of 70,000 to 100,000 genes thought to be present in all humans.
A linear or direct representation of one thing by another.
A technical term used to indicate linkage of a gene to a phenotype.
Defined by inheritance pattern when studied in families.
An epigenetic change involving addition of a chemical group (methyl group) to DNA, thus changing gene expression without changing DNA sequences within the gene.
A phenotype (disease) said to be caused by a single gene mutation.
Polymerase chain reaction; a technique that measures extremely small samples of DNA.
What the organism looks and behaves like; its morphology.
A gene or a protein having many effects.
A phenotype shaped by many genes acting in concert.
Characteristic of old age.
A disease (cancer) of the retina.
An experimental procedure in which genes are randomly made mutant.


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