Genes, Environment-Lifestyle, and Common Diseases
Chapter 4 focuses on diseases that are caused by single genes or by abnormalities of single chromosomes. Much progress has been made in identifying specific mutations that cause these diseases, leading to better risk estimates and, in some cases, more effective treatment of the disease. However, these conditions form only a small portion of the total burden of human genetic disease. Most congenital malformations are not caused by single genes or chromosome defects. Many common adult diseases, such as cancer, heart disease, and diabetes, have genetic components, but again they are usually not caused by single genes or by chromosomal abnormalities.1 These diseases, whose treatment collectively occupies the attention of most healthcare practitioners, are the result of a complex interplay of multiple genetic and environmental∗ factors.
How common is a given disease, such as diabetes, in a population? Well-established measures are used to answer this question.2 The incidence rate is the number of new cases of a disease reported during a specific period (typically 1 year) divided by the number of individuals in the population. The denominator is often expressed as person-years. The incidence rate can be contrasted with the prevalence rate, which is the proportion of the population affected by a disease at a specific point in time. Prevalence is thus determined by both the incidence rate and the length of the survival period in affected individuals. For example, the prevalence rate of acquired immunodeficiency syndrome (AIDS) is larger than the yearly incidence rate because most people with AIDS survive for at least several years after diagnosis.
Many diseases vary in prevalence from one population to another. Cystic fibrosis is relatively common among Europeans, occurring about once in every 2500 births. In contrast, it is quite rare in Asians, occurring only once in every 90,000 births. Similarly, sickle cell disease affects approximately 1 in 600 American blacks, but it is seen much less frequently in whites. Both of these diseases are single-gene disorders, and they vary among populations because disease-causing mutations are more or less common in different populations. (This is in turn the result of differences in the evolutionary history of these populations.) Nongenetic (environmental) factors have little influence on the current prevalence of these diseases.
The picture often becomes more complex with the common diseases of adulthood. For example, colon cancer was until recently relatively rare in Japan, but it is the second most common cancer in the United States. Stomach cancer, on the other hand, is common in Japan but relatively rare in the United States. These statistics, in themselves, cannot distinguish environmental from genetic influences in the two populations. However, because large numbers of Japanese emigrated first to Hawaii and then to the U.S. mainland, we can observe what happens to the rates of stomach and colon cancer among the migrants. It is important that the Japanese émigrés maintained a genetic identity, marrying largely among themselves. Among first-generation Japanese in Hawaii, the frequency of colon cancer rose several-fold—not yet as high as in the U.S. mainland but higher than that in Japan. Among second-generation Japanese on the U.S. mainland, colon cancer rates rose to 5%, equal to the U.S. average. At the same time, stomach cancer has become relatively rare among Japanese-Americans.
These observations strongly indicate an important role for environmental factors in the etiology of cancers of the colon and stomach. In each case, diet is a likely culprit—a high-fat, low-fiber diet in the United States is thought to increase the risk of colon cancer, whereas techniques used to preserve and season the fish commonly eaten in Japan are thought to increase the risk of stomach cancer. It is interesting that the incidence of colon cancer in Japan has increased dramatically during the past several decades as the Japanese population has adopted a more “Western” diet. These results do not, however, rule out the potential contribution of genetic factors in common cancers. Genes also play a role in the etiology of colon and other cancers.
The comparison just discussed is one example of the analysis of risk factors (in this case, diet) and their influence on the prevalence of disease in populations. A common measure of the effect of a specific risk factor is the relative risk. This quantity is expressed as a ratio:
A classic example of a relative risk analysis was carried out in a sample of more than 40,000 British physicians to determine the relationship between cigarette smoking and lung cancer. This study compared the incidence of death from lung cancer in physicians who smoked with those who did not. The incidence of death from lung cancer was 1.66 (per 1000 person-years) in heavy smokers (more than 25 cigarettes daily), but it was only 0.07 in the nonsmokers. The ratio of these two incidence rates is 1.66/0.07, which yields a relative risk of 23.7 deaths. Thus, it is concluded that the risk of dying from lung cancer increased by about 24-fold in heavy smokers compared with nonsmokers. Many other studies have obtained similar risk figures.
Although cigarette smoking clearly increases one’s risk of developing lung cancer (as well as heart disease, as we will see later), it is equally clear that most smokers do not develop lung cancer. Other lifestyle factors are likely to contribute to one’s risk of developing this disease (e.g., exposure to cancer-causing substances in the air, such as asbestos fibers). In addition, differences in genetic background may be involved. Smokers who have variants in genes, such as CYP1A1 and GSTM1, that are involved in the metabolism of components of tobacco smoke are at significantly increased risk of developing lung cancer.
Many factors can influence the risk of acquiring a common disease such as cancer, diabetes, or high blood pressure. These include age, gender, diet, amount of exercise, and family history of the disease. Usually, complex interactions occur among these genetic and nongenetic factors. The effects of each factor can be quantified in terms of relative risks. The following discussion demonstrates how genetic and environmental factors contribute to the risk of developing common diseases.
Traits in which variation is thought to be caused by the combined effects of multiple genes are polygenic (“many genes”). When environmental factors are also believed to cause variation in the trait, which is usually the case, the term multifactorial trait is used.3 Many quantitative traits (those, such as blood pressure, that are measured on a continuous numeric scale) are multifactorial. Because they are caused by the additive effects of many genetic and environmental factors, these traits tend to follow a normal, or bell-shaped, distribution in populations.
An example illustrates this concept. To begin with the simplest case, suppose (unrealistically) that height is determined by a single gene with two alleles, A and a. Allele A tends to make people tall, whereas allele a tends to make them short. If there is no dominance at this locus, then the three possible genotypes (AA, Aa, aa) will produce three phenotypes: tall, intermediate, and short, respectively. Assume that the gene frequencies of A and a are each 0.50. If we look at a population of individuals, we will observe the height distribution depicted in Figure 5-1, A.
Now suppose, a bit more realistically, that height is determined by two loci instead of one. The second locus also has two alleles, B (tall) and b (short), and they affect height in exactly the same way as alleles A and a. There are now nine possible genotypes in our population: aabb, aaBb, aaBB, Aabb, AaBb, AaBB, AAbb, AABb, and AABB. An individual may have zero, one, two, three, or four “tall” alleles, so now five distinct phenotypes are possible (see Figure 5-1, B). Although the height distribution in our fictional population is still not normal compared with an actual population, it approaches a normal distribution more closely than in the single-gene case just described.
From extension of this example, many genes and environmental factors influence height, each having a small effect. Then many phenotypes are possible, each differing slightly from the others, and the height distribution of the population approaches the bell-shaped curve shown in Figure 5-1, C.
It should be emphasized that the individual genes underlying a multifactorial trait such as height follow the mendelian principles of segregation and independent assortment, just like any other gene. The only difference is that many of them act together to influence the trait. More than 100 genes have now been shown to be associated with variation in human height.
Blood pressure is another example of a multifactorial trait. A correlation exists between parents’ blood pressures (systolic and diastolic) and those of their children. The evidence is good that this correlation is partially caused by genes, but blood pressure is also influenced by environmental factors, such as diet, exercise, and stress. Two goals of genetic research are the identification and measurement of the relative roles of genes and environment in the causation of multifactorial diseases.
A number of diseases do not follow the bell-shaped distribution. Instead, they appear to be either present or absent in individuals, yet they do not follow the inheritance patterns expected of single-gene diseases. A commonly used explanation for such diseases is that there is an underlying liability distribution for the disease in a population (Figure 5-2). Those individuals who are on the “low” end of the distribution have little chance of developing the disease in question (i.e., they have few of the alleles or environmental factors that would cause the disease). Individuals who are closer to the “high” end of the distribution have more of the disease-causing genes and environmental factors and are more likely to develop the disease. For diseases that are either present or absent, it is thought that a threshold of liability must be crossed before the disease is expressed. Below the threshold, an individual appears normal; above it, he or she is affected by the disease.
A disease that is thought to correspond to this threshold model is pyloric stenosis, a disorder that presents shortly after birth and is caused by a narrowing or obstruction of the pylorus, the area between the stomach and intestine. Chronic vomiting, constipation, weight loss, and imbalance of electrolyte levels result from the condition, but it sometimes resolves spontaneously or can be corrected by surgery. The prevalence of pyloric stenosis is about 3 per 1000 live births in whites. It is much more common in males than females, affecting 1 of 200 males and 1 of 1000 females. It is thought that this difference in prevalence reflects two thresholds in the liability distribution—a lower one in males and a higher one in females (see Figure 5-2). A lower male threshold implies that fewer disease-causing factors are required to generate the disorder in males.
The liability threshold concept may explain the pattern of recurrence risks for pyloric stenosis seen in Table 5-1. Note that males, having a lower threshold, always have a higher risk than females. However, the sibling risk also depends on the gender of the proband (i.e., the individual from which the pedigree begins). It is higher when the proband is female than when the proband is male. This reflects the concept that females, having a higher liability threshold, must be exposed to more disease-causing factors than males to develop the disease. Thus a family with an affected female must have more genetic and environmental risk factors, producing a higher recurrence risk for pyloric stenosis in future offspring. It would be expected that the highest risk category would be male relatives of female probands; Table 5-1 shows that this is the case.
|RELATIVES||MALE PROBANDS||FEMALE PROBANDS|
Data from Carter CO: Br Med Bull 32(1):21–26, 1976.
A similar pattern has been observed in a study of autism, a behavioral disorder in which the male/female ratio is approximately 4:1. As expected for a multifactorial disorder, the recurrence risks for siblings of male probands (3.5%) is substantially lower than that of siblings of female probands (7%). When the sex ratio for a disease is reversed (i.e., more affected females than males), one would expect a higher recurrence risk when the proband is male.
A number of other congenital malformations are thought to correspond to this model. They include isolated cleft lip and/or cleft palate (CL/P), neural tube defects (anencephaly, spina bifida), clubfoot (talipes), and some forms of congenital heart disease. In this context, isolated means that this is the only observed disease feature (i.e., the feature is not part of a larger constellation of findings, as in CL/P secondary to trisomy 13). In addition, many common adult diseases, such as hypertension, coronary heart disease, stroke, diabetes mellitus (types 1 and 2), and some cancers, are caused by complex genetic and environmental factors and can thus be considered multifactorial diseases.
Whereas recurrence risks can be given with confidence for single-gene diseases (e.g., 50% for typical autosomal dominant diseases, 25% for autosomal recessive diseases), the situation is more complicated for multifactorial diseases. This is because the number of genes contributing to the disease is usually not known, the precise allelic constitution of the parents is not known, and the extent of environmental effects can vary substantially. For most multifactorial diseases, empirical risks (i.e., risks based on direct observation of data) have been derived. To estimate empirical risks, a large series of families is examined in which one child has developed the disease (the proband). Then the siblings of each proband are surveyed to calculate the percentage of siblings who also have developed the disease. For example, in the United States about 3% of siblings of individuals with neural tube defects also have neural tube defects (Box 5-1). Thus the recurrence risk for parents who have had one child with a neural tube defect is 3% in the United States. For conditions such as CL/P that are not lethal or severely debilitating, recurrence risks also can be estimated for the offspring of affected parents. Empirical recurrence risks are, of course, specific for each multifactorial disease.
In contrast to most single-gene diseases, recurrence risks for multifactorial diseases can change substantially from one population to another because gene frequencies as well as environmental factors can differ among populations (note the differences between the London and Belfast populations in Table 5-1).
It is sometimes difficult to distinguish polygenic or multifactorial diseases from single-gene diseases that have reduced penetrance or variable expression. Large data sets and good epidemiologic data are necessary to make the distinction. Several criteria are commonly used to define multifactorial inheritance.
First, the recurrence risk becomes higher if more than one family member is affected. For example, the sibling recurrence risk for a ventricular septal defect (VSD, a type of congenital heart defect) is 3% if one sibling has been affected by a VSD but increases to approximately 10% if two siblings have been diagnosed with VSDs.6 In contrast, the recurrence risk for single-gene diseases remains the same regardless of the number of affected siblings. It should be emphasized that this increase does not mean that the family’s risk has actually changed. Rather, it means that there is more information about the family’s true risk; because they have had two affected children, they are probably located higher on the liability distribution than a family with only one affected child. In other words, they have more risk factors (genetic or environmental) and are more likely to produce an affected child.
Second, if the expression of the disease in the proband is more severe, the recurrence risk is higher. This is again consistent with the liability model because a more severe expression indicates that the affected individual is at the extreme tail end of the liability distribution (see Figure 5-2). His or her relatives are thus at a higher risk for inheriting disease genes. For example, the occurrence of a bilateral (both sides) CL/P confers a higher recurrence risk on family members than does the occurrence of a unilateral (one side) cleft.
Third, the recurrence risk is higher if the proband is of the less commonly affected gender (see the preceding discussion of pyloric stenosis). This is because an affected individual of the less susceptible gender is usually at a more extreme position on the liability distribution.
Fourth, the recurrence risk for the disease usually decreases rapidly in more remotely related relatives (Table 5-2). Whereas the recurrence risk for single-gene diseases decreases by 50% with each degree of relationship (e.g., an autosomal dominant disease has a 50% recurrence risk for siblings, 25% for uncle-nephew relationships, 12.5% for first cousins), it decreases much more quickly for multifactorial diseases. This reflects the fact that many genes and environmental factors must combine to produce a trait. All the necessary risk factors are unlikely to be present in less closely related family members.
|FIRST DEGREE||SECOND DEGREE||THIRD DEGREE||GENERAL POPULATION|
|Congenital hip dislocation||5||0.6||0.4||0.2|
Finally, if the prevalence of the disease in a population is f, the risk for offspring and siblings of probands is approximately . This does not hold true for single-gene traits because their recurrence risks are independent of population prevalence. It is not an absolute rule for multifactorial traits either, but many such diseases tend to conform to this prediction. Examination of the risks given in Table 5-2 shows that the first three diseases follow the prediction fairly well. However, the observed sibling risk for the fourth disease, infantile autism, is substantially higher than that predicted by .
Family members share genes and a common environment. Family resemblance in traits such as blood pressure reflects both genes (nature) and environment (nurture). For centuries people have debated the relative importance of these two types of factors. It is a mistake, of course, to view them as mutually exclusive. Few traits are influenced only by genes or only by environmental factors. Most are influenced by both. It is useful to try to determine the relative influence of genetic and environmental factors (Figure 5-4). This can lead to a better understanding of disease etiology. It can also help in planning public health strategies. A disease in which the genetic influence is relatively small, such as lung cancer, may be prevented most effectively through emphasis on lifestyle changes (avoidance of tobacco). When a disease has a relatively larger genetic component, as in breast cancer, examination of family history should be emphasized in addition to lifestyle modification.
Twins occur with a frequency of about 1 in 100 births in white populations. They are a bit more common in blacks and a bit less common among Asians. Monozygotic (MZ, identical) twins originate when, for unknown reasons, the developing embryo divides to form two separate but identical embryos. Because they are genetically identical, MZ twins are an example of natural clones. Dizygotic (DZ, fraternal) twins are the result of a double ovulation followed by the fertilization of each egg by a different sperm. Thus dizygotic twins are genetically no more similar than siblings. Because two different sperm cells are required to fertilize the two eggs, it is possible for each DZ twin to have a different father. Whereas MZ twinning rates are constant across populations, DZ twinning rates vary somewhat. DZ twinning increases with maternal age until about 40 years, after which it declines.
Because MZ twins are genetically identical, any differences between them should be caused only by environmental effects.7 MZ twins should thus resemble one another very closely for traits that are strongly influenced by genes. DZ twins provide a convenient comparison because their environmental differences should be similar to those of MZ twins, but their genetic differences are as great as those between siblings. Twin studies thus usually consist of comparisons between MZ and DZ twins.8 If both members of a twin pair share a trait (e.g., a cleft lip), it is said to be a concordant trait. If they do not share the trait, it is a discordant trait. For a trait determined totally by genes, MZ twins should always be concordant, whereas DZ twins should be concordant less often, because they, like siblings, share only 50% of their genes. Concordance rates may differ between opposite-sex DZ twin pairs and same-sex DZ pairs for some traits, such as those that have different frequencies in males and females. For such traits, only same-sex DZ twin pairs should be used when comparing MZ and DZ concordance rates, because MZ twins are necessarily of the same sex.
Table 5-3 gives concordance rates for a number of traits. Note that the concordance rates for contagious diseases such as measles are quite similar in MZ and DZ twins. This is expected because a contagious disease is unlikely to be influenced markedly by genes. On the other hand, the concordance rates are quite dissimilar for schizophrenia and bipolar affective disorder, suggesting a sizable genetic component for these diseases. The MZ correlations for dermatoglyphics (fingerprints), which are determined almost entirely by genes, are close to 1.0.
|TRAIT OR DISEASE||CONCORDANCE RATE|
|MZ TWINS||DZ TWINS||HERITABILITY|
|Affective disorder (bipolar)||0.79||0.24||>1∗|
|Affective disorder (unipolar)||0.54||0.19||0.7|
|Blood pressure (diastolic)†||0.58||0.27||0.62|
|Blood pressure (systolic)†||0.55||0.25||0.6|
|Body fat percentage†||0.73||0.22||>1|
|Body mass index†||0.95||0.53||0.84|
|Dermatoglyphics (finger ridge count)†||0.95||0.49||0.92|
|Diabetes mellitus (type 1)||0.55||—||—|
|Diabetes mellitus (type 2)||0.9||—||—|
|Intelligence quotient (IQ)†||0.76||0.51||0.5|
|Myocardial infarction (males)||0.39||0.26||0.26|
|Myocardial infarction (females)||0.44||0.14||0.6|