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Demography in the Age of Genomics: A First Look at the Prospects

Douglas Ewbank

The popular consensus seems to be that genetics is the wave of the future. Information technology was the driving force that changed our economy and our society during the late twentieth century. Genetics is expected to have similar effects on medicine and the social sciences during coming decades. The 1980s and 1990s produced numerous developments in molecular biology, statistics, and computer technology. These developments make it easier to associate observed traits (e.g., diseases, risk factors for disease, personality traits, or differences in protein structures) with specific genes. The resulting changes in our understanding of genetics are so profound that Weiss (1996) has suggested they may amount to a paradigm shift. A few examples suggest the speed of change.

  • The first positional cloning (identification of a gene by virtue of its location in the genome rather than by its biochemical function) occurred in 1986. By 1990, when the Human Genome Project (HGP) began, only a handful of genes had been identified this way. The discovery of the gene for Huntington’s chorea came in 1993, ten years after it was learned that it had to be near one end of chromosome 4. Improvements in molecular biology have greatly speeded up this process. By 1997 the number of genes identified by positional cloning was close to 100 (Collins et al., 1997).

  • The development of new statistical techniques for studying complex traits was marked in 1993 by the publication of three textbooks in



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Cells and Surveys: Should Biological Measures be Included in Social Science Research? 4 Demography in the Age of Genomics: A First Look at the Prospects Douglas Ewbank The popular consensus seems to be that genetics is the wave of the future. Information technology was the driving force that changed our economy and our society during the late twentieth century. Genetics is expected to have similar effects on medicine and the social sciences during coming decades. The 1980s and 1990s produced numerous developments in molecular biology, statistics, and computer technology. These developments make it easier to associate observed traits (e.g., diseases, risk factors for disease, personality traits, or differences in protein structures) with specific genes. The resulting changes in our understanding of genetics are so profound that Weiss (1996) has suggested they may amount to a paradigm shift. A few examples suggest the speed of change. The first positional cloning (identification of a gene by virtue of its location in the genome rather than by its biochemical function) occurred in 1986. By 1990, when the Human Genome Project (HGP) began, only a handful of genes had been identified this way. The discovery of the gene for Huntington’s chorea came in 1993, ten years after it was learned that it had to be near one end of chromosome 4. Improvements in molecular biology have greatly speeded up this process. By 1997 the number of genes identified by positional cloning was close to 100 (Collins et al., 1997). The development of new statistical techniques for studying complex traits was marked in 1993 by the publication of three textbooks in

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Cells and Surveys: Should Biological Measures be Included in Social Science Research? genetic epidemiology (Weiss, 1993; Khoury, 1993; Schulte and Perera, 1993). This development continued through the 1990s with improved computer programs and estimation procedures. Recent developments have reopened the debate about the best way to find genes associated with complex traits (Risch and Merikangas, 1996; Long et al., 1997; Bell and Taylor, 1997; Gambaro et al., 2000). The HGP’s first five-year plan, for 1993–1998, was to map the human genome using marker loci. By 1994, they had already published a map with about three times the resolution that they had planned for 1998. They have now sequenced and checked over 50 percent of the genome thus providing a complete description of the genome of a “consensus” individual. This was accomplished well ahead of the goal set for 2003. The revolution in genetic epidemiology was just becoming apparent in 1989 when there was a meeting of geneticists and demographers to discuss “convergent issues in genetics and demography” (Adams et al., 1990). Reading the resulting volume, it is clear that in 1989 there really were no issues pulling demographers and geneticists together. Genetics was just getting to the point where it could begin to address the kinds of questions that interest demographers. Now, more than ten years later, the nature of the revolution in genetics is clearer and we can begin to consider how it might affect demography. For demographers, and for social scientists in general, there are several options for dealing with genetics. The first is simply to ignore it. Since we are primarily interested in the social and behavioral factors affecting demographic variables, there is a temptation to ignore genetic differences. This may be a reasonable option as long as ignoring genetics doesn’t distort our estimates of the effects of social and behavioral factors. A second option is to use samples of twins (or other related individuals) to control for unobserved heterogeneity associated with genetics. However, samples of twins are hard to collect, especially when there is a need for twins raised apart in different environments. In addition, twin studies don’t allow us to directly address questions about the importance of specific genes. This makes it difficult to understand differences between populations and to forecast the potential impact of developments in genetic medicine. The third strategy is to include in our analyses data on the genetics of individuals or gene frequencies for populations. Adding genetic information to our analyses could reduce the amount of unobserved heterogeneity and produce estimates of the contribution of specific genes to variations among individuals or across populations. This is not yet a real option. As long as most of the genes that have been identified are associated with rare diseases (like Huntington’s chorea or sickle cell anemia),

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Cells and Surveys: Should Biological Measures be Included in Social Science Research? the potential impact of genetics on demographic research is very limited. However, genetic epidemiologists are now searching for genes that have large effects on common conditions. During the next ten years this might lead to discoveries that will substantially alter demographic research. This paper examines how future research on complex traits made possible by the HGP will affect demography. There are two ways in which demographic research might change. First, research on the genetic basis for common diseases and mortality will benefit from applications of demographic multistate modeling. Eventually, this could change epidemiology more than demography. Second, research on the determinants of health and behaviors could expand to include controls for genetic differences. As more genes are linked to common diseases and behaviors, adding genetic data into statistical analyses will become more attractive. However, it is important to be realistic about what we can expect from genetics. In particular, demographers need to think about what kinds of genetic associations will be useful for our purposes. Before turning to the implications for demography of new development in genetics, it is useful to examine the developments in genetic epidemiology during the past 15 years. This review provides a framework within which to discuss the likely developments in genetics in the next five to ten years. AN OUTLINE OF GENETIC EPIDEMIOLOGY The revolution in genetics has been driven largely by developments in molecular biology. However, for demographers the important changes can be more easily described through developments in genetic epidemiology.1 Genetic epidemiology is study of the relationship between genotypes (the particular combination of genes carried by an individual) and phenotypes (observable traits). The choice of statistical methods depends on whether the trait is quantitative (i.e., a continuous variable like body weight) or qualitative (i.e., a discrete variable such as being overweight or a case of diabetes). Genetic variation results from errors in chromosome 1   Ridley’s popular book Genome: The Autobiography of a Species in 23 Chapters (1999) provides a fascinating review of the history of genetics and an exceptionally clear discussion of the complexity of inheritance revealed by recent research. Weiss (1996) provides a summary of how the developments of the 1980s and early 1990s have moved genetics away from a simple Mendelian view. Lander and Schork (1994) provide a nonstatistical discussion of the basic approaches in genetic epidemiology. Weiss (1993) presents the basic statistical methods and an excellent overview of human evolution. A brief overview of the molecular biology that makes the HGP possible can be found in a series of articles by Ellsworth and Manolio (1999a, 1999b, and 1999c) which include excellent glossaries of important terms.

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Cells and Surveys: Should Biological Measures be Included in Social Science Research? duplication which lead to different forms of a gene (termed alleles). The most common difference among alleles is single base-pair differences called single-nucleotide polymorphisms (SNPs). Some mutations render the gene completely incapable of performing its intended function, but most alleles have no noticeable effect on gene functioning. Most genes have only one allele with high frequency and many (often hundreds) rare alleles (Weiss, 1993). An individual’s genotype is defined by the particular combination of alleles he or she carries. Relatively common alleles (found in 1 percent or more of a population) are termed polymorphisms. A gene is not apt to explain much of the variation in risk in a population unless it has common polymorphisms or numerous different alleles that are all associated with substantial excess risk. Major genes or oligogenes for quantitative traits are usually defined as those for which the mean values for two genotypes differ by at least 2.5 times the standard deviation within genotypes (Weiss, 1993). Most variables of interest to demographers are what genetic epidemiologists call complex traits. They are traits that are affected by numerous genes as well as the environment and interactions between environment and genotype. Variables like mortality, health status, and limitations of activities of daily living are extreme cases of complex traits. However, even the individual health problems that demographers consider as components of health and mortality are very complex. The genetics revolution started with breakthroughs that increase the ability of genetic epidemiology to link specific genes with individual traits. The identification of the genes responsible for specific traits then forms the basis for all of the other aspects of the genetic revolution including the promises of medical genetics and the potential future use of genetic information in demographic research. Recent developments will greatly increase the rate of discovery of genes associated with complex traits. It is useful to distinguish four areas of research in genetic epidemiology in humans. The first two examine the role of genetic factors without reference to specific genes. The third area involves research to identify relevant genes by determining their position on individual chromosomes. The fourth area uses genetic differences between populations to study the origin of populations.2 2   Genetic research in nonhuman populations often involves cross-breeding which enables researchers to increase the frequency of a trait (and therefore the associated genes). It can also be used to increase genetic heterogeneity.

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Cells and Surveys: Should Biological Measures be Included in Social Science Research? Twin and Family Studies of the Contribution of Genetics to Observed Differences Among Individuals Genetic epidemiologists have long relied on studies of twins and other related individuals to estimate the relative importance of genetics in determining various traits. They apply variance component models to decompose differences in quantitative traits (like blood pressure) into components associated with genetics, family environment, individual unshared traits, and interactions among these factors. Different sample designs give information about different factors. For example, comparisons of monozygotic (identical) and dizygotic (fraternal) twins provide estimates of the contribution of genetics. Comparisons of monozygotic twins raised apart provide estimates of the contribution of shared environment. One outcome of these studies is estimates of heritability, the proportion of the variation in the distribution of a quantitative trait that is explained by genetics.3 For example, a study of Danish twins estimated that about 25 percent of the variation in life span is genetically determined (Herskind et al., 1996). Twin studies have produced estimates of heritability for a wide range of traits. For example it has been estimated that genes explain 25 percent to 50 percent of the variation in the risk of cancer, IQ scores, risktaking behavior, and sexuality. Estimates of heritability are responsible for much of the excitement (and anxiety) surrounding recent developments in genetics. Demographers and economists have occasionally applied variance component models to twin data (e.g., Behrman et al., 1994). However, they have also used data on twins as controls for genetics to improve estimates of the effects of other variables. For example, they have used data on twins to control for genetic endowments and improve estimates of the economic returns to education (Miller et al., 1995; Behrman et al., 1996). Inheritance Patterns for Genetically Determined Traits Studies of families to determine inheritance patterns use segregation analysis. By examining the proportions of siblings (or more distant relatives) that exhibit a trait, it is possible to distinguish various genetic patterns (e.g., a single recessive gene) and to estimate the rate of penetrance (the probability of developing the trait given a specific genotype). Until the late 1980s this research was primarily focused on Mendelian models, that is, qualitative traits caused by single genes with a high rate of penetrance. This research led to an expansion of genetic counseling. It was 3   The equivalent measure for qualitative traits is a relative risk.

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Cells and Surveys: Should Biological Measures be Included in Social Science Research? generally most successful for diseases associated with clearly defined outcomes with high penetrance and young ages at onset. Recently, the focus has shifted to the search for genes associated with variation in quantitative traits. Quantitative traits are generally multifactorial and polygenic, that is, they are determined by the interaction of several genes or between genes and environment.4 Individual genes contributing to a quantitative trait are called QTLs (quantitative trait loci). The inheritance patterns of traits associated with multiple genes are much more difficult to discern. During the 1980s advances in statistical techniques and computer speed led to the development of QTL models. These models assume that a trait is controlled by one or two important genes with moderate to large effects (termed oligogenes) and numerous other genes with much smaller effects (jointly termed polygenes). These models require strong assumptions about the distributions of the relative importance of these genes including the number of oligogenes. A brief overview of segregation models is provided by Weiss (1990). His textbook on genetic epidemiology (1993) provides a more complete discussion. Segregation studies are complicated by gene-environment interactions. For example, segregation studies in families that exhibit large variation in relevant environmental variables may fail to identify oligogenes. Also, segregation analyses performed in populations with different environments may lead to very different conclusions because the genetic effects may be masked by the environment. The Search for Genes Responsible for Specific Traits The study of inheritance patterns only provides evidence that there are genes associated with a given trait. The next step is to identify the specific loci (i.e., locations on chromosomes) that contain these genes. There are two approaches to locating the loci associated with specific traits. The first is association studies. The simplest association studies compare a trait to the presence of known alleles of a candidate gene.5 For quantitative traits this involves samples of cases and controls. Studies of quantitative traits test for differences in means among genotypes. Candidate genes are often associated with a known protein. For example, the 4   However, see Weiss (1993) for a discussion of the quantitative variability caused by different alleles of a single gene (PAH) associated with PKU, phenylketonuria, a well-known genetic disease. 5   Gambaro et al. (2000) discuss the difficulties in selecting candidate genes. They note that association studies are actually based on candidate alleles, which are even more difficult to identify than candidate genes.

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Cells and Surveys: Should Biological Measures be Included in Social Science Research? vitamin D receptor gene was a logical candidate for involvement in osteoporosis (Ralston, 1997). Alternatively, genes identified through rare alleles can be tested for the effects of more common alleles. For example, rare mutations of the genes encoding type I collagen (COLIA1 and COLIA2) lead to a severe osteoporotic condition. Therefore, a more common polymorphism is a candidate for explaining the more common osteoporosis (Ralston, 1997). The list of candidate genes will probably expand rapidly once the human genome is completely sequenced (Guo and Lange, 2000). In the absence of a candidate gene, it is possible to do a whole-genome scan to look for genes associated with a trait. Testing correlations at many loci raises the problem of multiple comparisons. However, recent analyses have demonstrated that whole-genome scans can be efficient methods for identifying genes associated with specific traits even after adjusting for multiple comparisons (van den Oord, 1999). However, whole-genome scans require a large number of candidate alleles or SNPs, not just candidate genes. The HGP and other groups are beginning to address this need (see below). The availability of a large number of known alleles may make association studies the method of choice for identifying the genes associated with complex traits. Association studies are prone to two common problems that can lead to spurious correlations. First a gene may show a close correlation with the trait because it is very close to the true causal gene on the same chromosome (see the discussion of linkage below).6 This can lead to close associations in one population that are not replicable in other populations since the correlations among neighboring genes will differ among populations. A second problem is population admixture. In a population, a trait that is more common in one ethnic group will appear to be correlated with any allele that also happens to be more common in that group. Therefore, association studies should be performed in relatively homogeneous populations like Finland and Iceland and in small populations of individuals descended from a small number of ancestors.7 The second approach, involving techniques such as linkage analysis, fine mapping, and positional cloning, has been the predominant method used for genetic research during the past decade. It enables researchers to identify first a region of a chromosome, and then a gene based solely on 6   With incomplete mapping of SNPs, it is also possible to find a spurious correlation with an SNP in a noncoding region, which cannot affect a trait. In this way, SNPs can act like marker loci to identify the neighborhood within a gene in which a relevant allele is located (Collins et al., 1997). 7   Another solution to this problem is to use a sample of affected individuals and their parents to control for differences in allele frequencies among populations (van den Oord, 1999).

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Cells and Surveys: Should Biological Measures be Included in Social Science Research? the position of the gene without any knowledge of its function. Linkage takes advantage of the fact that the chromosomes inherited from your parents are not always passed on to your children intact. Instead, the two copies of the chromosome sometimes exchange segments (called recombination). Because of recombination, it is possible to associate the inheritance of a trait with the inheritance of a segment of a chromosome. Loci that are physically close to each other on a chromosome are more apt to remain together after recombination. Loci that are very close will be in linkage disequilibrium.8 It is therefore possible to examine the frequency of the trait in relation to the occurrence of genetic markers (known sequences of nucleotides that occur at specific locations on chromosomes). The relevant gene probably lies between the two markers that are most highly correlated with the presence of the trait. The more markers that are available, the smaller the area identified by linkage analysis. The first goal of the HGP was to produce a finer genetic map to improve the precision of linkage studies. Linkage analysis leads to a candidate region of a chromosome. For example, linkage analysis suggested that there was a gene associated with the risk of Alzheimer’s disease (AD) in the long arm of chromosome 19 (labeled 19q).9 The gene can then be identified within this region through fine mapping based on positional cloning (Ellsworth and Manolio, 1999b). The gene for AD turned out to be the gene for apolipoprotein E (Corder et al., 1993), which is described below. Fine mapping is a time consuming process since there can be hundreds of genes between markers. This process will be eased by the complete sequencing of the human genome. Linkage studies require large pedigrees (i.e., families in which the trait in question is unusually common). Linkage can be very difficult for traits that don’t follow Mendelian inheritance (i.e., a single gene with few alleles). It is also difficult in the case of common alleles. When the risky allele is common, many individuals will be homozygous for the risky allele. Since the two copies of the allele may be linked with different markers, inheritance may not always be associated with the same marker. This problem complicated early linkage studies of Alzheimer’s disease and the identification of a linkage to chromosome 19 (Lander and Schork, 1994; Corder et al., 1993). 8   If the two loci were not linked, the inheritance of an allele of one gene would be independent of the inheritance of an allele of the other gene. Independent inheritance is associated with equilibrium when there is random mating. Therefore, correlated risks of joint inheritance of two alleles constitute disequilibrium. 9   The short arm of each chromosome is labeled p and the long arm is q.

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Cells and Surveys: Should Biological Measures be Included in Social Science Research? Studies of the Origins of Human Populations A third area of research applies knowledge about the geographic distribution of a few dozen alleles (often including genes for blood type) or markers of genotype (e.g., lactose intolerance) to infer historical relationships among populations (Cavalli-Sforza et al., 1994). When combined with archeological and linguistic evidence, these maps provide important insights into the origin of man, ancient migration streams (Owens and King, 1999), and the role of evolution in human history. An excellent example of the use of mapping is research on the geographic distribution of lactose malabsorption (Simoons, 1978; Weinberg, 1999). The Human Genome Project The HGP will significantly increase the speed of discovery of genes associated with specific traits. Linkage analysis and genome-wide scans depend on the availability of numerous markers and maps of the genes that lie between them. The mapping of the human genome will provide a very detailed map, thereby increasing the ability to narrow in on the specific loci associated with a given trait. This development, combined with improved statistical methods and expanded computer power, makes possible large-scale searches for the genes associated with complex traits. The full sequencing will also expand the identification of candidate genes based on an understanding of the functioning of genes (Guo and Lange, 2000). Weiss (1998) points out that the HGP was originally designed to produce a map for an “average” individual. To social scientists it is genetic diversity that is important. Heterogeneity is also central to genetic epidemiology research on humans. The only way we can study the action of a gene is by observing mutations which alter gene functioning. The study of diversity was added to the goals of the HGP in 1998. The goal is mapping 100,000 polymorphisms involving SNPs by 2003 (Collins et al., 1998), taking advantage of the diversity of the U.S. population. Although this is a huge number, it is estimated that there are about 200,000 SNPs in proteincoding regions (cSNPs) which are apt to be most important for understanding disease (Collins et al., 1997). A second project involving the Wellcome Trust and ten international pharmaceutical partners was formed in 1999 to identify 300,000 DNA variants (Cardon and Watkins, 2000). DEMOGRAPHY AND THE GENETICS OF COMPLEX TRAITS Developments in genetic epidemiology during the past fifteen years have greatly expanded the opportunities for identifying the genes associ-

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Cells and Surveys: Should Biological Measures be Included in Social Science Research? ated with variation in complex quantitative traits. As more genes are identified, the potential gain from incorporating genetic information into demographic research will increase dramatically. Measured genotypes associated with common traits are in some ways ideal variables for the kinds of research conducted by demographers and other social scientists. The reason is simple: genotype is fixed at birth. This has two implications for the relationship between genetics and demography. First, demographic models are ideally suited to the study of fixed traits. Second, we can add genetic information to our statistical models and improve the fit without introducing complex correlations associated with joint causation. The following sections discuss potential applications of demographic models to the study of complex traits, and the use of genetic information in research on standard demographic variables. These are the two areas where developments in genetic epidemiology are apt to have the biggest impact on demography and demography is apt to have the biggest impact on epidemiology. Demographic Models for Studying Major Genes Affecting Common Diseases Once a gene for a common, complex condition has been identified, there will be numerous questions about its effect in populations. These problems are apparent in research on the only known gene like this, the apolipoprotein E gene (APOE). APOE is so unique and so heavily studied that few discussions of genetics can avoid using it as an example. It is a major risk factor for both ischemic heart disease (IHD) (Wilson et al., 1996) and Alzheimer’s disease (AD) (Corder et al., 1993; Farrer et al., 1997). The APOE gene has three common polymorphisms labeled e2, e3, and e4.10 Therefore, individuals have one of six possible genotypes, e2/2, e2/3, e2/4, e3/3, e3/4, or e4/4. The e3/3 is the most common genotype, comprising about 60–70 percent in all populations. The e3/4 and e4/4 genotypes are associated with increased risk of both IHD and AD. The e2/2 and e2/3 genotypes are associated with reduced risk of AD. One issue raised by the discovery of major genes involves differences in the amount of excess risk at different ages. For example, the effect of APOE e4 on the risk of AD increases with age up to about age 60 and declines at the oldest ages (Farrer et al., 1997). Similarly, a segregation analysis of the risk of lung cancer suggests that there is a major gene that 10   Numerous very rare mutations of the APOE gene have also been discovered. However, there has been little research examining the effects of these mutations.

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Cells and Surveys: Should Biological Measures be Included in Social Science Research? has a very large effect on the risk under age 60, but only marginal effects after age 80 (Gauderman and Morrison, 2000). The genetic effects on breast cancer also change with age (see discussion below). These changes might be the result of unobserved heterogeneity in the risks of disease, cohort trends in risk (e.g., Colilla et al., 2000), or changes in the nature of the disease with age. For example, very early onset AD might involve a very different natural history than AD at later ages. A second complication arises when a gene is associated with more than one disease. For example, the gene for the vitamin D receptor appears to affect bone density. Alleles that reduce bone density might increase the risk of osteoporosis but reduce the risk of osteoarthritis (Uitterlinden et al., 1997). Most epidemiologic research examines individual diseases. For example, almost all of the research on APOE examines only its relationship with AD or with IHD. One reason for this is that few studies include both a thorough examination for dementia and precise diagnoses of cardiac events or measures of serum lipids. Case-control studies in particular are designed to study one well-defined condition. A third set of issues arises when two or more major genes are identified as being associated with the same disease. Since most epidemiologic research focuses on the effects of single genes, there may be little direct evidence of the combined effects of several genes. If the effects of different genes are not additive, estimates of the effects of alleles of one gene might differ between populations because of unobserved differences at other loci. The interactions of the two genes can be very complex especially if the gene frequencies differ across populations or the effects of each gene change with age. For example, mutations of the PS-1 and PS-2 genes are associated with very early onset AD (Lendon et al., 1997). It appears that the increase in the relative risks of AD under age 60 associated with the e4 allele of APOE are due to complications introduced by PS-1 and PS-2 (Ewbank, unpublished results). Combining Disparate Studies These problems complicate research on mortality differences by APOE genotype. The APOE e4 allele is clearly associated with increased risk of death due to both IHD and AD, at least in males (Ewbank, 1999). There are numerous studies that suggest the importance of APOE for mortality, but few provide direct evidence of mortality differentials by genotype. No single study is large enough to provide solid evidence of the effect of APOE on mortality at various ages. Therefore, it is necessary to combine studies to understand the effects of APOE genotype on the level and age pattern of mortality at the oldest ages. The process of comparing and combining studies is complicated by

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Cells and Surveys: Should Biological Measures be Included in Social Science Research? HGP completes the sequencing of the human genome and begins to catalogue SNPs. SUMMARY AND RECOMMENDATIONS Social and behavioral researchers have always been intrigued by genetics, but the exciting developments in the study of rare genetic diseases have found little application in demography. This could change as genetic epidemiology discovers genes associated with common conditions. However, in the short run, the flood of new genetic research has led to a more complicated view of the genetics of complex traits. Genetic epidemiology has demonstrated that genetic variation in humans does not fit the simple Mendelian model of diseases associated with a small number of genes each with a few alleles (Weiss, 1996). The amount of genetic variation in human populations is much greater than many experts expected twenty years ago. This diversity results from historical differences in environment, population migrations of small groups of related individuals, and numerous random events (Weiss, 1993). The diversity is staggering. For example, research on cystic fibrosis, a “simple genetic” disease, has uncovered more than 800 mutations of the cystic fibrosis gene that are associated with the disease (Kaprio, 2000). This review has examined how future research in the genetic epidemiology of complex diseases might affect demographic research, which provides insight into the characteristics of the types of genes that are most apt to be useful to demographers. Demogenes are those that: are associated with the most common diseases, causes of death, or other variables of interest to demographers; have common polymorphisms associated with substantial variation in risk; have large variations in allele frequencies across populations; and interact with environmental or behavioral characteristics being studied by demographers. APOE is the only gene that has been proven to meet all of these criteria. The impact of genetic research on demography during the next ten to fifteen years will depend on how many additional demogenes are discovered. Demographic research on mortality would be significantly altered if genetic epidemiologists discovered two or three additional genes with impact on overall mortality as large as APOE. Research on functional disability would benefit greatly from four to six genes associated with large attributable fractions for the most common chronic conditions. It is hard to predict how many demogenes there are and how soon

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Cells and Surveys: Should Biological Measures be Included in Social Science Research? they will be discovered. One expert (Kaprio, 2000) recently predicted that during the next five to ten years “genetic dissection of complex traits will continue to yield specific genes, each accounting for only a relatively small fraction of cases.” This seems to be the general consensus. On the other hand, Peyser (1997) predicted that “within a few years, most of the risk factors [for coronary artery disease], both established and proposed, will be found to be associated with specific measured genes.” Predicting the future is complicated by the recent acceleration of research. The Human Genome Project is completing the sequencing of the human genome and is just starting its search for SNPs, and there are still disagreements over the relative advantages of association studies and linkage methods for studying complex traits. The Potential Role of Large-Scale Demographic Surveys in Genetic Research We will almost certainly decide to add the collection of genetic material to large-scale demographic surveys. The question is, should we begin designing supplements to current surveys, or is it premature to collect genetic material before genetic epidemiology has identified more demogenes? There are a few conclusions that follow from the preceding review: Surveys of diverse populations are not useful for identifying the genes associated with specific conditions. Spurious correlations associated with population diversity would completely overwhelm genomewide scans in nationally representative samples of unrelated individuals. Large-scale surveys may have a role to play in replicating studies of the effect of previously identified genes. In this case the diversity in nationally representative samples could be an advantage if the data were analyzed properly. However, demographic surveys would only be useful for replicating findings for phenotypes that are carefully measured in those surveys. Studies of a few measures, like body mass index, might rely on self-reported measures. Others, like blood pressure and insulin levels, might be added to large surveys. However, errors in defining phenotypes severely complicate replication in genetic research. Perhaps the biggest potential for demographic surveys in the next ten years is putting genetic research into a social or public health context. This will certainly be true of genetic effects on behavior. In many cases this might involve demonstrating that individual genes contribute little to understanding complex behaviors. However, given the likelihood of misperceptions about the generalizability of findings in behavioral genetics to everyday life, negative findings could be very important.

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Cells and Surveys: Should Biological Measures be Included in Social Science Research? There is a long time lag between the first plans for a major survey and the availability of useful data. For example, if genetic material was collected in 2002, two more biannual rounds of data collection would provide reasonable follow-up data by 2006. Most of the data analysis would occur after 2007 and would probably have to rely on genes first identified by 2005. If data collection continues until 2020, most of the research would involve genes discovered by 2015. It will take several years to plan the collection and analysis of nationally representative samples and determine the best approach to testing them for a wide range of candidate genes. Given the speed of progress in genetic epidemiology, it would be wise to begin work on the design of appropriate strategies for demographic surveys. Currently available data from epidemiologic surveys could be used to develop models for incorporating genetic information into demographic research. For example, a number of data sets already include APOE genotype, basic social and economic indicators, and prospective data on survival. Some of these surveys probably have additional data on health such as functional health and nursing home placement. It is useful to remember that even if genetic information does not become important for demography until after 2010, this is well within the time horizon of our current graduate students. Ten years from now they will be the young associate professors who will determine how genetics ultimately affects demography. Genes and Demography—A Broader View The preceding discussion started from the perspective of demography by asking what genetic information will add to our current research and what new demographic research it might stimulate. An alternative approach is to ask what demographic questions will be raised by the accelerated pace of discoveries in genetics. Will public discussions of genetics pose demographic questions that we are not currently able to address? Clinicians are already feeling pressure from the public for further information. Patients whose parents had Alzheimer’s disease are asking whether they should get tested for APOE or for rare mutations of the Presenilin genes (Mayeux et al., 1998). Women with familial risk of breast cancer are interested in testing for BRCA1 and BRCA2 mutations (Coughlin et al., 1999). Those who carry these mutations are looking for appropriate prevention strategies. Therefore, while biomedical researchers study how individual mutations cause disease, clinical researchers are struggling to understand what these findings already mean for their patients.

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Cells and Surveys: Should Biological Measures be Included in Social Science Research? As the steady stream of new genetic findings cumulates into a flood, social scientists will be under pressure to figure out what this all means for society. We will face new questions that need to be answered and common perceptions that need to be tested. For example, does the higher prevalence of APOE e4 in African-Americans explain much of the differential in mortality by race? How important can tobacco advertising be if it turns out there is a gene associated with addiction to nicotine? If there is a gene for caution or risk taking, does it explain differences in savings or income? How much of the relationship between poverty and poor health is “simply” due to bad genes? What would be the implications of genes associated with “intelligence?” Newly discovered genes will lead to new thinking about who we are as individuals and what we are as a society. It is almost certain that speculation will outpace evidence. Popular perceptions will stray beyond what has been demonstrated by scientists. The old aphorism about “seeing the forest for the trees” may be replaced by “seeing the person (or the ethnic group) for the genes.” We will be faced with the problem of putting the flood of genetic information into a social and demographic context. Epidemiology will respond to some of these challenges. However, the perspective of most epidemiologists is a desire to understand the causes of specific disease. Demographers, including social demographers and economic demographers, have always concentrated on the bigger picture. To demographers, the social and economic context of health is more than mechanisms complicating disease rates. We are also interested in nonhealth behaviors such as retirement decisions, savings behavior, and caregiving. Adding genetic and bioindicator data to large demographic surveys may be useful to epidemiologists. However, these data will be crucial to demographers if we are to put genes into a wider social context. It is difficult to predict where genetic research will lead us as a society in the next 20 years. For that reason it is difficult to predict what social, economic, and demographic questions will arise and what new avenues of research will open up. However, if the current promises of new genetic discoveries are even partially realized, they could change the questions demographers study and how we study them. REFERENCES Adams, J., D.A.Lam, A.I.Hermalin, and P.E.Smouse, eds. 1990 Convergent Issues in Genetics and Demography. New York: Oxford University Press. Asada, T., Z.Ymagata, T.Kinoshita, A.Kinoshita, T.Kariya, A.Asaka, and T.Kakuma 1996 Prevalence of dementia and distribution of apo E alleles in Japanese centenarians: An almost-complete survey in Yamanashi Prefecture, Japan. Journal of the American Geriatrics Society 44:151–155.

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