Twin Studies of Eating Disorders: A Review

Transcription

1 Twin Studies of Eating Disorders: A Review Cynthia M. Bulik,* Patrick F. Sullivan, Tracey D. Wade, and Kenneth S. Kendler Virginia Institute for Psychiatric and Behavioral Genetics, Department of Psychiatry, Medical College of Virginia of Virginia Commonwealth University, Richmond, Virginia Accepted 3 September 1999 Abstract: Objective: Twin methodology has been used to delineate etiological factors in many medical disorders and behavioral traits including eating disorders. Although twin studies are powerful tools, their methodology can be arcane and their implications easily misinterpreted. Method: The goals of this study are to (a) review the theoretical rationale for twin studies; (b) provide a framework for their interpretation and evaluation; (c) review extant twin studies on eating disorders; and (d) explore the implications for understanding etiological issues in eating disorders. Discussion: On the basis of this review, it is not possible to draw firm conclusions regarding the precise contribution of genetic and environmental factors to anorexia nervosa. Twin studies confirm that bulimia nervosa is familial and reveal significant contributions of additive genetic effects and of unique environmental factors in liability to bulimia nervosa. The magnitude of the contribution of shared environment is less clear, but in the studies with the greatest statistical power, it appears to be less prominent than additive genetic factors by John Wiley & Sons, Inc. Int J Eat Disord 27: 1 20, Key words: twin studies; genetic and environmental factors; eating disorders INTRODUCTION The scientific study of twins is an important step in understanding complex disorders (Cederlof et al., 1982; Martin, Boomsma, & Machin, 1997), and its application has been particularly important in psychiatry. Three decades ago, it was widely believed that autism and schizophrenia resulted from environmental trauma and adverse parenting. Several lines of evidence including twin data on autism (Cook, 1998; Folstein & Rutter, 1997) and schizophrenia (Kendler, 1983) contributed to the current conceptualization of these disorders as having critically important genetic etiological components. Twin studies have also contributed to the reconceptualization of the origins of personality traits from being primarily environmentally determined to being significantly influenced by *Correspondence to: Dr. Bulik, Virginia Institute for Psychiatric and Behavioral Genetics, Department of Psychiatry, Medical College of Virginia of Virginia Commonwealth University, P.O. Box , Richmond, VA, cbulik@hsc.vcu.edu by John Wiley & Sons, Inc. CCC /00/

2 2 Bulik et al. genetic factors (Eaves, Eysenck, & Martin, 1989; Loehlin & Nichols, 1976; Plomin & Daniels, 1987). In the last decade, there have been a number of twin studies of eating disorders in several centers around the world (Australia, Germany, the United Kingdom, the United States, and Sweden). Overall, these studies support the familial aggregation of eating disorders and related traits and suggest that genetic factors play an important etiological role. Given the long tradition of exploring environmental aspects of causality in the field of eating disorders, it is understandable that the twin findings are somewhat controversial (Fairburn, Cowen, & Harrison, in press). In part, controversy can arise from misunderstanding. Twin methodology often appears arcane and complicated even to methodologically sophisticated readers and the results of twin studies are certainly prone to simplification or misinterpretation. Therefore, we have written this paper in order to (a) review the theoretical basis of twin studies; (b) provide a framework for their interpretation and evaluation; (c) review extant twin studies on eating disorders; and (d) explore the implications for understanding etiological issues in eating disorders. TWIN STUDIES THE BASICS Twinning Monozygotic (MZ) or identical twinning occurs at some stage in the first 2 weeks after the first mitosis when the zygote separates and yields two genetically identical embryos. Therefore, any differences between MZ twins who for most intents and purposes share all of their genes provides strong evidence for the role of environmental influences (Plomin, DeFries, McClearn, & Rutter, 1994, pp ). Dizygotic (DZ) or fraternal twinning results from the fertilization of two ova by different spermatozoa. DZ twins are no more similar genetically than nontwin siblings and share on average one half of their genes identical by descent. Thus, differences between DZ twins can result from genetic and/or environmental effects. The goal of the classical twin study is to use the similarities and differences between MZ and DZ twin pairs to identify and delineate genetic and environmental causes for a particular trait. Twin studies are one of the few quasiexperimental means to accomplish this goal in humans, and often the only practical approach. Analytic Approaches There are many ways to analyze twin data including calculating concordance ratios (Plomin et al., 1994), regression-based analyses (DeFries & Fulker, 1985; Sham et al., 1994), and structural equation modeling (SEM; Eaves, 1977; Heath, Neale, Hewitt, Eaves, & Fulkner, 1989; Neale & Cardon, 1992). The essence of these analytic approaches is to compare the similarity of MZ twin pairs with the similarity of DZ twin pairs for the trait under study. Correlation coefficients are convenient ways to summarize the similarity of a type of twin pair (e.g., r MZ and r DZ ). Comparison of these correlations yields information about the etiology of a trait. Measurement of nearly any trait in a large group of humans would reveal variation. This concept and its quantification are fundamental to research in psychology and medicine. Typical analytic approaches to twin data postulate that the overall variation can be decomposed into several different types of causal influences (Table I). These are latent

3 Twin Studies 3 Table 1. Patterns of intrapair correlations and sources of variance implied Sources of Variance Pattern of Correlations Additive Genetic Shared Environment Individual-Specific Environment r MZ =r DZ =0 E r MZ =r DZ >0 C E r MZ =2 r DZ A E r MZ >r DZ, r MZ <2 r DZ A C E Note: r MZ = intrapair correlation for monozygotic twins; r DZ = intrapair correlation for dizygotic twins; A = additive genetic; C = shared environment; and E = individualspecific environment. variables in that their presence is inferred from observed data rather than measured directly (Loehlin, 1987). A familiar example of a latent variable is general intelligence ( g ), which is indexed by observed scores on intelligence tests. The Liability Threshold Model Nearly all biometrical twin analyses of complex disorders are based on the liability threshold model (Falconer, 1965; Pearson, 1901) (see Figure 1). This model assumes: (1) an unobserved or latent variable that indexes liability to a trait is fundamental to the observed trait; (2) the liability consists of a sufficiently large number of genetic and/or environmental effects that are each small in magnitude; (3) the liability is normally distributed in the entire population; and (4) the liability is expressed only if an individual s liability is greater than a certain critical threshold (Falconer, 1960). The most critical assumption of this model that the latent liability is normally distributed is based on the central limit theorem. If a trait is caused by many different factors, all of which are small to moderate in effect and uncorrelated, this theorem predicts that the distribution of this trait will be approximately normal. This theorem is relatively robust in that an approximation of the normal distribution can be produced by as few as Figure 1. The liability threshold model assumes that (1) an unobserved or latent variable that indexes liability to a trait is fundamental to the observed trait; (2) the liability consists of a sufficiently large number of genetic and/or environmental effects that are each small in magnitude; (3) the liability is normally distributed in the entire population; and (4) the liability is expressed only if an individual s liability is greater than a certain critical threshold (Falconer, 1960).

4 4 Bulik et al. five factors of moderate effect size, and the liability threshold model is rather insensitive to deviations from normality (Kendler & Kidd, 1986). Although unlikely to be precisely correct, the liability threshold model is probably a reasonable approximation and is useful both conceptually and statistically. Sources of Variance Additive Genetic Effects (Abbreviation A ) Although a number of different types of genetic influences can be studied in theory (e.g., dominance or epistatic effects), statistical power is usually quite low except for additive genetic effects (Neale, Eaves, & Kendler, 1994). Additive genetic effects result from the cumulative impact of many individual genes each of small effect. As shown in Table 1, the presence of A is inferred when r MZ is greater than r DZ. Additive genetic effects will increase r MZ more than r DZ because MZ twin share all their genes while DZ twins share half identical by descent. If a trait were entirely due to additive genetic effects and could be measured without error, then the MZ: DZ correlation would be 1.0 and 0.5, respectively. Common Environmental Effects (Abbreviation C ) Common environmental effects result from etiological influences to which both members of a twin pair are exposed regardless of zygosity. Thus, common environmental effects contribute equally to r MZ and r DZ. In the simplest case, if r MZ and r DZ are both 1, the trait is entirely determined by common environmental effects. Examples include the social class and religious preference of the family of origin. Individual-Specific Environmental Effects (Abbreviation E ) The second type of environmental effect results from etiological influences to which one member of a twin pair is exposed but not the other. Thus, individual-specific environmental effects decrease r MZ and r DZ. In the simplest case depicted in the first row of Table 1, if r MZ and r DZ are both 0 then the trait is entirely determined by individual-specific environmental effects. Examples include one member of a twin pair being exposed to a traumatic experience not shared with the co-twin. In general, r MZ and r DZ will have intermediate values that do not correspond to the extreme examples above. In these instances, the trait is determined by a combination of A, C, and E. In the second row of Table 1, r MZ =r DZ and the correlations are greater than zero. For this case, the trait is determined by a combination of C and E. In the third row, r MZ is twice r DZ and r MZ is less than one, implying that the trait is determined by both A and E. Finally, in the fourth row of Table 1, r MZ is greater than r DZ but not twice as large; in this instance, the trait is determined by A, C, and E. Qualitative conclusions such as the presence of C or the absence of A are useful, but it is more relevant to quantify the contributions of A, C, and E. It is straightforward to scale the total variance of a trait to one, and to use twin pair correlations to describe the proportions of variance due to A, C, and E. The proportion of variance due to A (additive genetic effects) is a 2 (also known as heritability or, more correctly, as narrow heritability in liability). The proportion of variance due to C is c 2 and the proportion due to E is e 2. The value of e 2 also incorporates measurement error. Obviously, a 2,c 2, and e 2 must sum to the total variance of one.

5 Twin Studies 5 SEM and the Univariate ACE Model Although more complex, approaches based on SEM are particularly attractive for the analysis of twin data for five reasons. SEM is quite flexible and rather complex models can be easily specified. SEM is commonly used in many areas of inquiry, and its extension to twin data is merely a matter of using multiple groups and applying constraints. It provides a scientifically and statistically rigorous framework that allows previously articulated hypotheses to be evaluated by a clearly specified set of statistical rules and competing hypotheses can be directly compared. SEM can be depicted graphically in the form of a path diagram which is convenient and often clarifying. Figure 2 contains a diagrammatic representation of the univariate twin model. Parameter estimates and their confidence intervals can be estimated to allow determination of the best estimate of a parameter available from the data and the accuracy with which the parameter is known (Neale, 1997). This is a critical point as we are usually not just interested in the presence or absence of an effect, but with its effect size and the precision of the estimate. Software enhancements to compute confidence intervals have only recently become available. In this review, we re-fit several models in order to derive the confidence intervals for analyses that were performed prior to the availability of this option. Finally, SEM can be used to determine the statistical model which best fits the observed data. We can specify the components of a model (e.g., the full ACE model contains A, C, and E), calculate the values of r MZ and r DZ expected under the model, and compute how well the observed data fit the expected data. Submodels include the AE, CE, and E only models. It is possible (a) to determine how well each of these models fit the data via a goodness-of-fit 2 test, (b) to test whether submodels of the full ACE model provide a Figure 2. Path diagram of the univariate model for MZ and DZ twins reared together. Latent variables are symbolized by circles and observed variables by rectangles. Variation in the observed phenotypes is due to A (additive genetic influences), C (shared environmental influences), and E (individual-specific environmental influences). Curved lines with an arrow at each end represent correlation coefficients; by definition, the E components are uncorrelated between twins and the C components are identical for each twin (correlation of one). The correlation between the A components is one for MZ twins and 0.5 for DZ twins. The square of the path coefficients from the latent to the observed variables are the proportion of variance due to that particular effect. So, a 2 is the proportion of variance due to additive genetic effects, c 2 the proportion of variance due to common environmental effects, and e 2 the proportion of variance due to individual-specific environmental effects.

6 6 Bulik et al. better fit to the observed data, and (c) to identify the most parsimonious model as a balance between goodness-of-fit and number of parameters (Akaike, 1987; Williams & Holahan, 1994). The process of determining the best-fitting model is described in detail elsewhere (Kendler, 1993; Neale & Cardon, 1992). In effect, this process is an application of the razor of William of Occam ( entities must not be multiplied beyond what is necessary ) and highlights the importance of parsimony. The overzealous application of this principle can itself create problems ( Occam s razor-burn ). For the purposes of this review of twin studies of eating disorders, however, we will focus on the full ACE model, its parameter estimates, and critically their confidence intervals. FRAMEWORK FOR INTERPRETATION AND EVALUATION OF TWIN STUDIES Assumptions of the Twin Method As with any statistical method, the twin paradigm is predicated upon a number of assumptions. Although rather robust, it is important to remain mindful of the accuracy of the assumptions, as well as to what extent violations of the assumptions could bias the results of twin studies. We review four central assumptions. Zygosity Accurate determination of zygosity is critical, as misclassification can potentially invalidate twin study results. Fortuitously, a few self-report questions yield correct zygosity assignment for >95% of twin pairs in comparison to genotypes at multiple highly polymorphic loci (Eaves et al., 1989; Peeters, Van Gestel, Vlietinck, Derom, & Derom, 1998; Spence et al., 1988). Generalizability For findings from twin studies to be generalized to singletons (nontwins), it must be shown that twins are comparable to singletons with respect to the trait under study. Differences between twins and singletons do exist. For example, twins have a shorter average gestation and a higher probability of congenital malformations, low birth weight (Cunningham, MacDonald, & Gant, 1989), perinatal mortality (Naeye, Tafari, Judge, & Marboe, 1978), mental retardation, and cerebral palsy (Russell, 1961). For psychiatric disorders, however, empirical studies have generally found that twins have similar risks as singletons (Kendler, Pedersen, Farahmand, & Persson, 1996; Kringlen, 1967). Generalizability is also supported by the similar lifetime prevalences of eating disorders in population-based twin and singleton samples (Bushnell, Wells, Hornblow, Oakley-Browne, & Joyce, 1990; Garfinkel et al., 1995; Kendler et al., 1991; Walters & Kendler, 1995). Equal Environment Assumption (EEA) The EEA posits that MZ and DZ twins are equally correlated for their exposure to environmental influences that are of etiologic relevance to the trait under study (Plomin et al., 1994). If the EEA is incorrect, then the greater resemblance of MZ twins in comparison to DZ twins could actually be due to environmental factors. A violation of the EEA does not necessarily invalidate the results, but may influence the magnitude of the estimated genetic and environmental components. The majority of studies that have

7 Twin Studies 7 tested the EEA for psychiatric disorders (Kendler & Gardner, 1998) have found little or no evidence for violations of the EEA. There has been some question regarding the validity of the EEA in eating disorders which we review in detail below. Caveats As with any methodology, there are a number of important additional caveats and limitations. Ascertainment Many of the initial studies of eating disorders were conducted in clinically ascertained twin samples which is more efficient when studying rare disorders (Neale et al., 1994). However, there are several reasons to be cautious when interpreting twin studies of clinical samples. Clinical and community cases of bulimia nervosa differ substantially (Bushnell et al., 1994; Fairburn, Welch, Norman, O Connor, & Doll, 1996), with greater severity of illness in the clinic population. In addition, clinically ascertained samples are more likely to evidence comorbidity (Berkson, 1946) and the underlying genetic architecture of hospitalized cases may differ from that of cases ascertained from the general population (Kendler, Heath, Neale, Kessler, & Evans, 1992; McGue, Pickens, & Svikis, 1992; Pickens & Johanson, 1992). Moreover, if more severe forms of the disorder are likely to be referred clinically and if these are more heritable, then heritability estimates will be inflated. There may also be concordant-dependent ascertainment biases, wherein the ascertainment rate differs in affected members of concordant than discordant twin pairs, as well as nonindependent ascertainment biases in which ascertainment rates differ in affected members of concordant pairs where the co-twin has versus has not been ascertained (Kendler & Eaves, 1989). Finally, an ascertained sample omits pairs where both twins are unaffected as these individuals are unlikely to present to treatment settings. This necessitates complex correction strategies for ascertainment (Wade, Neale, Lake, & Martin, 1999) and the estimation of the population prevalence of the disorder, which may not be known with precision. A second ascertainment strategy uses a volunteer twin registry. Participation requires that the twins contact the investigators in response to advertisements or other forms of recruitment. There are potential biases in this sampling procedure: MZ twins tend to be overrepresented in volunteer samples (Lykken, Tellegen, & DeRubies, 1978) and female twins are more likely to participate. Finally, individuals who are more invested in being a twin or who have strong interests in or opinions about the topic under study may be more likely to volunteer. For most purposes, the optimal strategy for twin ascertainment uses population-based twin registries. In this approach, official records (usually birth records) are used to locate all living twins. All twins, regardless of affection status, are contacted for participation. Although some biases continue to exist in terms of who actually participates, ascertainment can be complete and independent of affection status or active volunteering from twins. For rare disorders, however, very large populations of twins are required to provide sufficient numbers of affected twins to enable robust statistical analyses, thus representing an inefficient approach to low prevalence disorders. Statistical Power The statistical power of the classical twin study has received considerable attention in the literature (Martin, Eaves, Kearsey, & Davies, 1978; Neale et al., 1994) which allows a

8 8 Bulik et al. number of general conclusions (most of which also apply to typical epidemiological and clinical studies; Cohen, 1977). First, power is lower for discrete traits than for continuous, normally distributed variables. Second, power for discrete traits is strongly and positively correlated with prevalence. Third, power is also influenced by several general factors (the specified Type I and Type II error rates) and factors specific to twin studies (the ratio of MZ:DZ twins). Finally, and of particular importance, the power of the classical twin study depends on the effect that one is trying to detect. Power is greatest for individual-specific environmental effects, intermediate for common environmental effects, and least for additive genetic effects. To illustrate these issues, Figure 3 illustrates statistical power for a hypothetical twin study of bulimia nervosa (top panel, lifetime prevalence 2.5%) and anorexia nervosa (bottom panel, lifetime prevalence 0.75%). We assume that we have phenotyped 1,000 MZ and 1,000 DZ twin pairs and that the Type I error is The statistical and technical details are described elsewhere (Neale et al., 1994). First, the detection of a 2 and/or c 2 is essentially the same as determining that a disorder aggregates in families. Power would Figure 3. Contour plots of the power of the classical twin study for bulimia nervosa and anorexia nervosa. Assumptions: 2,000 twin pairs (half MZ, half DZ) and Type I error = Common environmental effects (c 2 ) are plotted on the y-axis and additive genetic effects (a 2 ) on the x-axis. The dark shaded areas are regions where statistical power is 80%. For example, the power to detect additive genetic effects for bulimia exceeds 80% only for a thin wedge of fairly high heritability.

9 Twin Studies 9 exceed 80% as long as either a 2 or c 2 was greater than about 30% for bulimia nervosa and greater than about 40% for anorexia nervosa. Second, power is considerably poorer for the detection of c 2 : there is 80% power for bulimia nervosa if c 2 exceeds about 60% and about 70% for anorexia nervosa (the presence of modest amounts of a 2 tends to improve power). Third, power is quite limited for the detection of a 2 although somewhat better for bulimia nervosa than anorexia nervosa. Thus, given the size of the samples studied in the majority of twin studies of eating disorders, there has been sufficient power to detect familial aggregation (a 2 and/or c 2 ); however, the power to distinguish between a 2 and c 2 has been at best modest. It is important to keep three points in mind. First, the use of the verb to detect in the paragraph above means that the 95% confidence interval would not contain zero. Thus, even in regions where power is under 80%, it may be possible to estimate a fairly high parameter value although the confidence interval contains zero. Second, power is a probabilistic concept: parameter estimates in the regions of Figure 3 could be significant by chance. Third, these results are for the univariate ACE model: the modifications discussed below can increase power markedly. Improving Statistical Power Three main strategies have been used to attempt to increase statistical power. First, the process of determining the most parsimonious (best-fitting) twin model increases statistical power by decreasing the number of parameters. Second, it may be possible to use one (Neale et al., 1994) or several (Schmitz, Cherny, & Fulker, 1998) continuous measures as proxy measures of liability to a disorder which generally yields a marked increase in statistical power. Third, the presence of measurement error increases e 2 at the expense of a 2 and/or c 2. If the same twins are phenotyped on two occasions of measurement, then the impact of measurement error can be parsed out to yield substantial gains in statistical power to detect both a 2 and c 2 (Schmitz et al., 1998). All of these strategies have been applied in twin studies of eating disorders as we discuss at length below. Relation Between Observed Variables and the Fundamental Liability Our current diagnostic conceptualizations of eating disorders were developed principally from clinical observations. A key unanswered question is the degree to which these criteria even if of clinical utility capture the basic information relevant to the etiology of eating disorders. We do not know, for example, that our diagnostic concept of bulimia nervosa accurately reflects the underlying genetic architecture. Parameter Estimates There are several common misconceptions about parameter estimates from twin studies (i.e., a 2,c 2, and e 2 ) as discussed in detail elsewhere (Rutter & Plomin, 1997). It is essential to keep several points in mind. First, the parameter estimates describe the causes of variation at the group level. For example, if the point estimate of heritability in liability to self-induced vomiting is 70%, this describes one source of variation in a large group of twins. It has no immediate implication for an individual except as it gives a general understanding about the causes of this behavior. Second, strictly speaking, these parameter estimates are applicable to the group in which they were measured. Generalization to groups with different population histories or who were subject to different environmental effects may not be appropriate. Third, the parameter estimates must be considered in relation to their confidence intervals. For example, in the full ACE model for self-induced vomiting (Sullivan, Bulik, & Kendler, 1998b), the point estimate for the heritability (a 2 )of

10 10 Bulik et al. vomiting was 70%; however, the 95% confidence interval was 3 84%, reflecting the lack of precision associated with that estimate. Therefore, caution must be taken not to over interpret the point estimates of the parameters. SUMMARY OF TWIN STUDIES OF EATING DISORDERS Anorexia Nervosa There have been isolated case reports of clinically ascertained twin pairs with anorexia nervosa (Askevold & Heiberg, 1979; Fichter & Noegel, 1990; Hsu, Chesler, & Santhouse, 1990; Nowlin, 1983). The first systematic study of clinically ascertained twins with AN (Holland, Hall, Murray, Russell, & Crisp, 1984; Holland, Sicotte, & Treasure, 1988; Treasure & Holland, 1989) found that the concordance for MZ twins was substantially greater than for DZ twins. We fit a full ACE model from the data from these reports (assuming a population prevalence of AN of 0.75%) and found evidence of familial aggregation with parameter estimates of 88% (95% CI 33 97) for a 2, 0 (95% CI 0 59) for c 2, and 12% (95% CI 3 31%) for e 2. The observed aggregation appeared to be mostly influenced by additive genetic effects. Only two published studies have explored genetic and environmental contributions to the etiology of anorexia nervosa in a population-based sample (Walters & Kendler, 1995). The concordance rates in MZ and DZ twins for a broad definition of anorexia nervosa were 10% and 22%. The low power attendant to this study s sample size and the rarity of anorexia nervosa (c.f. Figure 3) preclude conclusions regarding the contribution of genetic and environmental factors to the etiology of anorexia nervosa. In a subsequent bivariate analysis of anorexia nervosa and major depression, the heritability of anorexia was estimated to be 58% (95% CI 33 84%) and there was correlated genetic liability between anorexia and major depression (Wade, Bulik, and Kendler, in press). From these twin and family studies (Lilenfeld, Kaye, & Strober, 1997), we can conclude that anorexia nervosa is familial; however, the resolution of the nature of the familiarity will require larger sample sizes or alternative strategies (e.g., pooling data across sites or two-stage sampling procedures; Wade, Neale, Lake, and Martin, 1999). Bulimia Nervosa In Table 2, we present a reanalysis of the twin studies of bulimia nervosa. The primary studies generally presented the best-fitting models; however, in many of the original reports, the confidence to select among submodels was modest and the best-fitting model was often chosen on the basis of parsimony alone. Although parsimony is a virtue, a comprehensive comparison across studies can best be accomplished by presenting the parameter estimates and confidence intervals of the full models for all of the studies. The first section of this table contains the three clinical case series of twins where one or both twins had bulimia nervosa (Fichter & Noegel, 1990; Hsu et al., 1990; Treasure & Holland, 1989). Each study showed greater MZ than DZ concordance for bulimia nervosa. Given the small numbers in each of the studies, we combined these data and fit a full ACE model. In this model, assuming a population prevalence of 2.5%, bulimia nervosa showed evidence of familial aggregation with 47% of the variance due to additive genetic effects, 30% to shared environmental effects, and 23% to unique environmental effects. The confidence intervals for the estimates of all three parameters were broad and contained zero for both a 2 and c 2. The second section of the table presents a reanalysis of the univariate twin analyses of bulimia nervosa. Two studies are from the Virginia Twin Registry (VTR) at different

11 Table 2. Sample Clinical nonsystematic Twin studies of bulimia Author(s), Year Sample Size Concordance Full Model MZ DZ MZ DZ a 2 c 2 e 2 Comments Fichter & Noegel, Female-female pairs only Hsu, Chesler, & Santhouse, Female-female pairs only Treasure & Holland, Female-female pairs only Combined (0 66) 30 (0 56) 23 (9 44) Assumes BN prevalence of 2.5% Univariate Kendler et al., (0 77) 1 (0 65) 46 (23 77) VTR wave 1, narrow definition Bulik, Sullivan, & Kendler, (0 86) 0 (0 68) 49 (14 100) VTR wave 3, narrow definition (0 54) 0 (0 35) 67 (46 94) VTR wave 3, broad definition Wade et al., (0 68) 0 (0 52) 68 (32 100) ATR wave 2 Enhanced power Kendler et al., n/a 28 (7 62) 37 (10 59) 35 (19 49) VTR wave 1, broad definition Bulik, Sullivan, & Kendler, 1998 See text See text 83 (49 100) 0 (0 30) 17 (0 36) VTR waves 1 and 3, broad definition Wade et al., 1999 See text See text 59 (36 68) 0 (0 11) 41 (33 48) ATR waves 1 3 VTR = Virginia Twin Registry; ATR = Australian Twin Registry; BN = bulimia nervosa. Twin Studies 11

12 12 Bulik et al. waves of data collection (Bulik, Sullivan, & Kendler, 1998; Kendler et al., 1991) and one model from the Australian Twin Registry (ATR; Wade et al., 1999). The point estimates presented here differ slightly from the original papers because we present the full models with their confidence intervals rather than the best-fitting models. There is reasonable consistency across these univariate models. The point estimates for a 2 are somewhat variable; however the 95% confidence intervals are overlapping. For c 2, the point estimates are near zero, but the confidence intervals are broad and consistent across studies. There is a more substantial contribution of e 2 (which includes measurement error), as the confidence intervals did not contain zero. The third panel presents the three studies that used varying strategies to enhance statistical power to detect a 2 and c 2 by including bulimia nervosa diagnoses in a multivariate twin model (Kendler et al., 1995) or incorporating more than one occasion of measurement into the model (Bulik et al., 1998; Wade et al., 1999). The effects of increasing statistical power are reflected in the narrower confidence intervals. In Kendler et al. (1995), the confidence intervals for a 2,c 2 were narrower than the univariate models and did not contain zero. Much has been made of the point estimates from Kendler et al. (1995) especially the fact that c 2 was greater than a 2. Note, however, that the 95% CIs contain nearly the same regions and are consistent with the univariate results. It is critical to note that the two studies that corrected for measurement error had greater power to detect both c 2 and a 2. In these studies, the point estimates of a 2 were higher and the confidence intervals suggested a substantial contribution of a 2. The point estimates of c 2 were quite low, but the confidence intervals allowed for the possibility of some contribution of shared environment. In both studies, the confidence intervals for the a 2 and c 2 estimates did not overlap. Thus, by controlling for measurement error and increasing statistical power, it appears that the contribution of a 2 to liability to bulimia nervosa is more substantive than the contribution of c 2. Twin Models of Component Behaviors and Continuous Measures of Disordered Eating and Attitudes Another approach to improve statistical power includes performing twin modeling on component behaviors of the syndrome of bulimia nervosa or using continuous measures that are hypothesized to be etiologically related to eating disorders (Neale & Cardon, 1992). In the following summary, we present the full models when possible; however, we were unable to fit full models of all of these analyses if the relevant data were not presented in the original manuscripts. Sullivan et al. (1998b), using the data from the VTR, dismantled the syndrome of bulimia nervosa into its component behaviors of objective binge eating and self-induced vomiting. As the prevalences of these individual behaviors were higher than the full syndrome of bulimia nervosa, our power to detect a 2 and c 2 was greater as reflected in the narrower confidence intervals. For binge eating, the estimates were a 2, 46% (95% CI 22 58%); c 2, 0 (95% CI 0 18%); and e 2, 54% (95% CI 42 68%). For vomiting, the estimates were a 2, 70% (95% CI 03 84%); c 2, 0 (95% CI 0 59%); and e 2, 30% (95% CI 16 50%). Given the greater power of these analyses, we were able to detect a 2 for both behaviors (the confidence intervals did not contain zero). The confidence intervals for c 2 did not eliminate the possibility of a contribution of shared environment to either behavior. Five studies have examined four different twin populations using continuous measures of disordered eating or associated attitudes. The earliest study (Holland et al., 1988) examined the Eating Disorder Inventory (EDI; Garner, Olmsted, & Polivy, 1984) and only

13 Twin Studies 13 reported a heritability estimate for the Drive for Thinness subscale which was near 1.0, although the standard errors were large. Rutherford, McGuffin, Katz, and Murray (1993) also studied the EDI in twins and reported heritability estimates ranging from 28 to 52% for subscales. Shared environment was not included in any of the best-fitting models. In two samples of adolescent twins from the Minnesota Twin Family Study aged 11 and 17 years, there were marked differences between the two cohorts on the EDI (Klump, Mc- Gue, & Iacono, in press). For the younger adolescents, there were contributions of both a 2 and c 2 to most subscales. For the older group, however, the contribution of a 2 appeared to outweigh the contribution of c 2. These results suggest the influence of developmental age effects on the structure of genetic and environmental sources of liability to eating disorders. Studies from the Australian Twin Registry examined measures of dietary restraint and concern about eating, weight, and shape from the Eating Disorders Examination (EDE; Fairburn & Cooper, 1993; Wade, Martin, & Tiggemann, 1998). The AE model provided the best fit for the total EDE score with a 2 estimated at 62% (95% CI 21 71%). Individual variation of three of the EDE subscale measures was also best explained by the AE model, with heritability ranging from 32 to 62%. The exception was the Weight Concern measure, best explained by the CE model with the contribution of c 2 estimated to be 52% (95% CI 43 64%). Summary For anorexia nervosa, the clinical data are intriguing; however, the limited number of studies and the low power preclude definitive conclusions about the role of genes and environment in the etiology of anorexia nervosa. The clinical and community-based univariate studies of bulimia nervosa are strikingly consistent regarding the relative contribution of genes and shared environment to the etiology of bulimia nervosa. In studies with greater power and consistent across two different twin populations (Virginia and Australia) there is strong evidence that a reasonable proportion (and perhaps most) of the observed familial aggregation of bulimia nervosa is due to additive genetic effects. In addition, although the point estimates tend to be low, the confidence intervals consistently allow for the possible contribution of shared environment to the etiology of bulimia nervosa, although the magnitude of that contribution appears to be less than additive genetic effects. Although we cannot be sure of etiologic homogeneity between the diagnosis of bulimia nervosa and the constructs measured by the EDI and the EDE, there is replicated evidence across different populations of twins of a substantial contribution of additive genetic factors to the aggregation of these traits. Shared environment may be particularly relevant for weight concerns. Finally, there is evidence of developmental changes in the relative contribution of genes and environment to these traits that will best be explored in longitudinal studies. ADDRESSING THE CRITIQUES OF TWIN STUDIES OF EATING DISORDERS Diagnostic Issues Are diagnoses of bulimia nervosa in population-based studies of twins too broad? All of the twin studies of bulimia nervosa extracted diagnoses from standard and widely used

14 14 Bulik et al. structured diagnostic interviews. There are several lines of evidence to suggest that the interviews accurately identified threshold cases of bulimia nervosa. First, the prevalence of bulimia nervosa in the Virginia and Australian twin samples is similar to other epidemiological studies (Bushnell et al., 1990; Garfinkel et al., 1995; Hoek, 1991). Second, Wade et al. (1997) conducted a direct comparison of a comprehensive structured interview (Structured Clinical Interview for DSM-III-R; SCID) (Spitzer, Williams, Gibbon, & First, 1990) with a specialized semistructured interview for eating disorders (EDE) and found that agreement between the two types of instruments was fair to moderate. Third, despite differences in diagnostic methodology, there has been general agreement across sites, ascertainment strategies, instruments, and approaches (i.e., diagnoses vs. symptoms) regarding the relative contributions of genes and environment to the etiology of bulimia nervosa and related traits. Moreover, when the diagnostic criteria have been broadened in order to increase statistical power, we have taken care to show that there is not an etiological disjunction between threshold and subthreshold cases. Although a two-group comparison between clinical and subclinical cases has been suggested (Fairburn et al., in press), this approach is less informative and less powerful than the approaches we have taken which have found no evidence of a discontinuity across varying diagnostic thresholds for bulimia nervosa (Kendler et al., 1991; Sullivan, Bulik, & Kendler, 1998a). The syndrome of bulimia nervosa appears to lie on a continuum of severity that differs quantitatively but not qualitatively. EEA in Eating Disorders Is the observed heritability of bulimia nervosa an artifact of a violation of the EEA? Studies that evaluated the EEA in eating disorders have supported its validity with two exceptions. A possible violation of the EEA was first reported by Hettema, Neale, and Kendler (1995) who examined the extent to which adult physical similarity (presumed to be a measure of the EEA) impacted on twin resemblance for five psychiatric disorders. They incorporated physical similarity directly into the twin model to observe the effect that variable had on estimates of a 2 and c 2. Physical similarity had no effect on twin resemblance for any disorder except bulimia nervosa. In the best-fitting model, physical similarity virtually eliminated any detectable effects of a 2. Caution must be taken when evaluating the results of this study. The findings were unstable, because a trichotomization of the physical similarity measure eliminated the effect for the narrow definition of bulimia nervosa, and the parameter estimates were similar to the initial univariate report (Kendler et al., 1991). The authors hypothesized that twin resemblance for bulimia nervosa was affected by the similarity of their environmental treatment which was a result of physical similarity. The physical similarity ratings of the twins were made on photographs taken around the age of 30. At that time, most of the women had already developed bulimia nervosa and many had already recovered. Thus, the measure could have reflected the effect of both twins having had bulimia on body weight, shape, and physical appearance rather than represent a cause of twin similarity. Moreover, a comprehensive study by Klump, Holly, Iacona, McGue, and Wilson (in press) improved upon the approach taken by Hettema et al. by using photographs that were more contemporaneous with the measurement of disordered eating. In this study, using a variety of statistical approaches, they found no systematic effect of physical similarity on twin similarity again supporting the validity of the EEA in twin studies of eating attitudes and behaviors. The second possible violation of the EEA was observed in Bulik et al. (1998) who tested

15 Twin Studies 15 the EEA using logistic regression of six putative measures of environmental similarity: childhood treatment, adolescent co-socialization, similitude, physical similarity, degree of adult contact, and parental approach to twin rearing. The six measures of common environment had no independent effects on twin similarity for binge eating, although greater adolescent co-socialization predicted twin concordance for bulimia nervosa. If the co-socialization variable truly indexes the environment, then the greater tendency for MZ than DZ twins to socialize together in adolescence could influence concordance for bulimia nervosa. However, differential co-socialization patterns in MZ and DZ twins may themselves be partially under genetic control. This was best illustrated in twin studies of cannabis abuse by Kendler and Prescott (1998) who found that adolescent co-socialization predicted concordance for cannabis use. There are two possible reasons for increased co-socialization among MZ pairs only one of which is consistent with a violation of the EEA. First, MZ twins might socialize together more frequently than DZ twins because of social expectations (or a belief that MZ twins should hang out together more). Alternatively, MZ twins may spend a greater amount of time together because of genetic factors, since individuals who are genetically similar tend to select the same environments. Only the social expectations model would constitute an EEA violation. Kendler and Prescott (1998) tested these hypotheses and found that individuals who are genetically similar tended to seek out similar environments, thus upholding the EEA. Similarly, it is possible that genetic factors contribute to binge eating and self-induced vomiting in part by increasing the probability of an individual placing herself in an environment in which bulimic behavior is encouraged. This process is called genetic control of sensitivity to the environment (Kendler & Eaves, 1986) and does not constitute a violation of the EEA. How do we place these two findings in context and how do we evaluate the EEA? Even when a broad range of environmental variables is sampled (i.e., Bulik et al., 1998; Sullivan et al., 1998b), there are many other variables that index similarity of treatment. It is also critical to note that differential treatment of MZ and DZ twins itself does not constitute a violation of the EEA. It has to be shown that the type of treatment for which MZ twins were more similarly exposed than DZ twins directly influenced the risk for the psychiatric disorder in question (Kendler, Neale, Kessler, Heath, & Eaves, 1994). Perhaps the strongest support for the EEA in eating disorders used an informative method developed by Scarr (1968) which takes advantages of twins who are misinformed about their zygosity (Kendler, Neale, Kessler, Heath, & Eaves, 1993). This approach examines trait similarity in twins first as a function of actual zygosity, and then as a function of perceived zygosity. If parental or environmental treatment is influenced by preconceptions about zygosity, then perceived zygosity should influence twin similarity. Using this approach, no evidence was found that the social and personal expectations associated with being an MZ or DZ twin had any effect on twin resemblance for psychiatric disorders including bulimia nervosa. In sum, in the context of the variety of examinations of the EEA in eating disorders, there is little evidence to suggest that EEA has substantially biased results. The appropriate question to ask is not whether an EEA violation exists, but rather the extent to which violations of the EEA could be contributing to overestimations of heritability. Shared Versus Nonshared Environment One of the most frequently asked questions concerns the relative infrequency with which shared environment has been found to contribute to etiology of bulimia nervosa. Dissecting this observation requires a considerable degree of objectivity.

16 16 Bulik et al. First, if one only looks at the point estimates, it appears that there is minimal contribution of shared environment. As we have emphasized, a true appreciation of the method necessitates an evaluation of the confidence intervals. In all of the studies we have reported, the confidence intervals do not eliminate the possibility of a contribution of c 2 to the etiology of bulimia nervosa. Second, it is possible that the true contribution of shared environment is substantially less than the contribution of additive genetic effects to the etiology of bulimia nervosa. It is illustrative to discuss what this would not mean. This does not mean that family environment has no influence on psychopathology. Indeed, nonshared environment can include aspects of family environment (Silberg et al., 1994). Research strongly suggests that children raised in the same families experience surprisingly different environments (Dunn & Plomin, 1990). For example, if both members of a twin pair experienced separation from their mother due to a prolonged hospitalization, their experience of that separation could differ dramatically. In addition, the relative absence of shared environmental effects does not mean that the environment does not impact on liability to eating disorders. Indeed, all studies have found that a substantial portion of the variance resulted from nonshared environmental factors. Third, although numerous clinical studies have found associations between eating disorders and family environment (Fallon, Sadik, Saoud, & Garfinkel, 1994; Humphrey, 1986; Wonderlich, 1992; Woodside, Shekter-Wolfson, Garfinkel, & Olmsted, 1995; Woodside, Shekter-Wolfson, Garfinkel, Olmsted, Kaplan, et al., 1995), few have taken into account the presence of gene-environment correlations. For example, there may be aspects of the child s behavior that are genetically mediated (e.g., picky eating) that elicit certain types of parenting styles (e.g., overcontrolling at mealtime). Similarly, genetically mediated features of the child such as perfectionism and focus on physical appearance could influence choice of environment (e.g., choosing gymnastics over soccer as a primary sport). Thus, to label either overinvolved parenting at mealtimes or participation in gymnastics as purely environmental variables precludes recognition of the contribution of genetic factors to these variables. Fourth, shared environmental factors may be important to the etiology of bulimia nervosa, but may only exert their influence in concert with an individual s genetic makeup. Thus, the independent main effect of shared environmental factors may be small, but their effects via one of several types of Gene Environment interactions might be quite profound (Kendler & Eaves, 1986). CONCLUSIONS AND IMPLICATIONS OF THE FINDINGS OF TWIN STUDIES OF EATING DISORDERS The most methodologically sophisticated family studies have shown that anorexia nervosa, bulimia nervosa and related eating disturbances are strongly familial (Hudson, Pope, Jonas, Yurgelun-Todd, & Frankelburg, 1987; Kassett et al., 1989; Lilenfeld, 1997; Lilenfeld et al., 1998). Twin studies confirm the observed familiality for both eating disorders. Due to the rarity of the condition, we remain limited in the conclusions we can draw regarding genetic and environmental contributions to the etiology of anorexia. Twin studies of bulimia, however, reveal a contribution of additive genetic effects and unique environmental factors to liability. The magnitude of the contribution of shared environment is less clear, but it appears to be less prominent than additive genetic factors. These findings have several implications. First, the consistency with which additive