Robert Plomin and C. S. Bergeman.
Behavioral and Brain Sciences (1991) 14, 373-427.
Abstracts. Evidence for genetic influence on environmental measures will emerge in quantitative genetic analyses if genetically influenced characteristics of individuals are assessed by these environmental measures. Recent twin and adoption studies indicate substantial genetic influence when measures of the environment are treated as phenotypes in genetic analyses. Genetic influence has been documented for measures as diverse as videotaped observations of parental behavior toward their children, ratings by parents and children of their family environment, and ratings of peer groups, social support, and life events. Evidence for genetic influence on environmental measures includes some of the most widely used measures of environment – the Home Observation for Measurement of the Environment, the Family Environment Scales, and the Social Readjustment Rating Scale of life events, for example. The goal of this article is to document and discuss these findings and to elicit commentary that might help to shape the course of research on this topic, which has far-reaching implications for the behavioral and brain sciences.
In the traditional stimulus-response model, the environment is independent of the organism. It is something imposed on the organism from the outside, like an electrical shock applied to the paw of a mouse. This view allows no role for DNA, because the organism has nothing to do with the environment that impinges on it. In the social and behavioral sciences, however, measures of the environment often must blur the distinction between environment and organism in the search for experiences that affect the individual.
For example, in developmental psychology, the parents are viewed as a major source of environmental influence: Many measures of home environment directly assess parental behaviors – parental responsiveness, for example. Parental behavior assessed on measures of home environment might well reflect organismic characteristics, such as the personality of the parents (Plomin 1986), as well as that of the children (Bell 1968). Although other measures of the home environment — parental education and occupation, and the perennial item, the number of books in the home – assess parental behavior less obviously and are unlikely to be influenced by characteristics of children, these measures may nonetheless reflect such organismic characteristics of parents as their IQ. Environmental measures outside the home (e.g., peers, life events, and social support) might also involve characteristics of the organism, as individuals select, modify, and even create their environments.
Finally, the majority of environmental measures used in the social and behavioral sciences involve self-report; self-perceptions filter through the complex feelings, personality, and cognition of the individual and thus incorporate the organism in the measure of the environment. A few measures of the environment may have nothing to do with the organism (e.g., accidents and illnesses or classroom size) although organismic involvement or lack of involvement must be assessed rather than assumed. For example, such personality traits as risk-taking might influence accident proneness, individuals differ in their susceptibility to disease, and classroom size might be related to socioeconomic status.
Blurring the distinction between environment and organism may be necessary for identifying environmental influences relevant to behavioral development; indeed, this seems essential to study the environment as experienced. Including characteristics of the organism in measures of the environment has an important implication, however: The ubiquitous genetic influence found for most organismic characteristics can result in genetic influence on ostensible measures of the environment.
The basic quantitative genetic model is widely used to break down the variance of such phenotypes as behavior into genetic and environmental components of variance (e.g., Plomin, DeFries et al. 1990). Quantitative genetic analyses using twin and adoption designs have shown some hereditary influence on nearly all behavioral phenotypes investigated. Environmental measures are now being considered within the quantitative genetic model as a measured index of the anonymous environmental component of variance. (See Figure 1a.) For example, in behavioral genetic analyses of the variance of children’s IQ scores, specific measures of parenting can be incorporated into a parent-offspring model as an index of environmental variance affecting children’s IQ; one can thereby assess the contribution of the environmental index to children’s IQ independent of parental IQ (Bergeman & Plomin 1988; Coon et al. 1990; Plomin et al. 1985; Rice et al. 1988; Thompson et al. 1986). These analyses also indicate the extent to which the environmental index may be associated genetically with the IQ of parents and children.
This is an appropriate way to address genetic effects on an environmental measure in a quantitative genetic framework. This traditional approach, however, is limited to finding genetic influences on an environmental measure only to the extent that the environmental measure relates to the particular behavioral phenotype under investigation. Specifically, genetic influence on an environmental measure will be estimated in these analyses only if genotype-environment correlation occurs between the measure of the environment and the particular behavioral phenotype (Plomin 1986). Genotype-environment correlation refers literally to a correlation between genetic and environmental deviations as they affect a particular phenotype. For example, a genotype-environment correlation occurs for a particular measure of behavior (e.g., musical ability) if children are exposed to musical training on the basis of their genetic propensities. In addition to this quantitative genetic perspective on genotype-environment correlation, sociobiologists have begun to consider genotype-environment correlation at the level of comparisons between species (Jones 1980; Lumsden 1989).
A more radical position is that environmental measures should be considered as phenotypes themselves. (See Figure 1b.) That is, a measure of the environment can be treated as a phenotype and analyzed using quantitative genetic methods to break down its variance into genetic and environmental components. This environment-as-phenotype approach allows us to turn the spotlight on the environmental measures themselves, exploring the relative degree of genetic and environmental influence on these measures regardless of their association with a particular phenotype, unlike genotype-environment correlation analyses, which limit the search for genetic influence on an environmental measure to its association with a particular phenotype. It should be emphasized that our argument is that measures of the environment – not the environment itself – should be conceptualized as phenotypes. Environments have no DNA and can show no genetic influence. Measures of the environment, as our examples suggest, may be perfused with characteristics of individuals, however. To the extent that this is the case, measures of the environment can show genetic influence. Consider such an environmental construct as parental responsiveness. We might think of this as existing “out there,” independent of individuals. When we measure the construct, however, we are in fact measuring parental behavior, and this measure can be analyzed as a phenotype in quantitative genetic analyses to determine the extent to which interindividual genetic and environmental differences contribute to phenotypic variance for this measure. If the measure is really “out there,” independent of individuals, it will show no genetic influence.
Although it sounds odd to consider environmental influence on a measure of the environment, we again emphasize the word measure and consider the example of parental responsiveness. If a measure of parental responsiveness shows no genetic influence in quantitative genetic analyses, this means that differences in responsiveness among parents is the result of nongenetic factors. In quantitative genetics, environmental influence refers to this residual component of variance, which is much broader than the systematic psychosocial environments that behavioral scientists usually consider. For example, it includes such nonhereditary biological factors as illness and accidents, nutrition, and even nonhereditary events related to DNA itself.
This way, the environment-as-phenotype perspective goes beyond genotype-environment correlation in considering genetic influence on measures of the environment. For example, in terms of genotype-environment correlation, a measure of parental responsiveness would be viewed as an index of the environmental component of variance for a behavioral measure such as IQ (see Figure 1a). Genetic influence on this environmental index would be estimated in the context of a parent-offspring model of some specific phenotype (e.g., IQ), and genetic influence would be found only to the extent that the environmental index is associated with the IQ of the parent and that of the offspring. In contrast, the environment-as-phenotype approach might well find evidence for genetic influence on parental responsiveness (in an observational study of twin parents’ responsiveness towards their children, for example) even if parental responsiveness is not at all related to IQ. That is, genetic influence on parental responsiveness might come from other sources – personality, for example – or, as discussed later, from genetically influenced patterns of interaction with one’s environment that are independent of our traditional dimensions of personality. Although this distinction between environment-as-phenotype and genotype-environment correlation is important for finding evidence for genetic influence on environmental measures, it should be noted that in terms of developmental processes (in contrast to components of variance) genetic influence on any phenotype is best conceptualized in terms of genotype-environment correlation in which genetic dispositions work themselves out in transactions with the environment (Scarr & McCartney 1983).
One might argue that finding genetic influence on environmental measures means that the measures are not really measures of the environment. If one adopts this position, then by definition there can be no genetic influence on environmental measures. Although we disagree with this position, the present article can be reformulated to accommodate it: Our goal is to explore the extent of genetic influence on measures that are thought to be and are widely used as indices of the environment. Regardless of whether one would attempt to define away genetic influence on measures of the environment, it should be of interest to learn the extent to which genetic factors affect available measures of the environment.
The goal of this article is to document the importance of genetic influence on measures of the environment. For this reason, the body of the article consists of a litany of studies using diverse methods and measures with data relevant to the issue of genetic influence on environmental measures. Recognizing that environmental measures can be significantly influenced by heredity leads to questions about mechanisms that might mediate this genetic influence. In the last section (sect. 3) we allow ourselves to speculate about mechanisms that might underlie genetic influence on environmental measures. In the absence of empirical evidence on this topic, however, we keep these speculations brief. We hope that success in achieving our goal of providing convincing evidence of genetic influence, on environmental measures will stimulate the next steps in this program of research on the nature of nurture, steps that include investigating the antecedents and sequelae of genetic influence on environmental measures.
2. Evidence for genetic influence on measures of the environment
Most of the relevant research on the nature of nurture involves proximal measures of the family environment, as distinguished from such distal measures as SES (socioeconomic status) and parental education. After a lengthy review of work on such proximal measures of family environment, the few available genetic analyses of other environmental measures are described – for example, SES and education, television viewing, peers, social support, and life events.
Space does not allow us to present the basic concepts and methods of quantitative genetics. Background information about twin and adoption methods and analyses is available in an earlier Behavioral and Brain Sciences article (Plomin & Daniels 1987) and in a recent textbook (Plomin, DeFries et al. 1990).
2.1. Proximal family environment
At least three measurement issues can affect the genetic contribution to proximal measures of family environment: method, source, and target. Methodologically, measures of the family environment vary from completely objective ones – videotape observations of mothers interacting with their children, for example – to subjective ratings by parents and by children. Such an objective measure of childrearing as videotaped interactions between parent and child could incorporate genetically influenced characteristics of the parent as well as those of the child. As mentioned earlier, ratings can contribute additional genetic influence. For ratings, the magnitude and type of genetic influence could differ as a function of the source of information – typically, the parent, the child, or an observer in the home.
Method and source potentially affect the results of any measures, not just genetic estimates of family environment. A third issue is more specific to genetic analyses of measures of family environment: the target of the genetic analysis. For example, in comparisons between identical and fraternal twins, the twins could be the parents or the children. In an observational study of twin parents in which the environmental measure is the childrearing of these parents in their separate families, any characteristics of the parents might be relevant to their childrearing. In contrast, in an observational study of twin children in which the childrearing of the parents of the children is assessed, genetic influence is limited to characteristics of children to which parents respond. That is, genetically influenced characteristics of the parents will not contribute genetic variance to measures of childrearing in the typical case in which the twins are the children, except for the diluted hereditary resemblance between adult parents’ characteristics and related characteristics of their children.
Thus, for observational studies, it could be hypothesized that childrearing measures show greater genetic influence in studies in which the target twins are parents rather than children, but unfortunately, no such studies have been reported. When the method involves ratings rather than observations, genetic influence could be adduced as perceptions filter through genetically influenced characteristics such as personality (regardless of whether the target twins are parents or children). There may be important interactions between method, source, and target as well. For example, in the case in which ratings are made by parents and the target twins are the parents’ children,’ genetic influence on the subjective processes entailed in the parents’ ratings will not show up in analyses of their twin children. In contrast, when ratings are made by the twin children themselves, genetic influence on the children’s subjective processes related to their ratings will be incorporated in this assessment of the family environment.
2.1.1. Studies of parents as targets. Only one genetic investigation of the family environment studied parents rather than children as targets, and this was a questionnaire study (Plomin, McClearn et al. 1989). Moreover, parenting per se was not assessed, but rather perceptions of the general family environment, with the widely used Family Environment Scales (FES; Moos & Moos 1981). To the extent that the FES assesses not only characteristics of the parents but also characteristics of the spouse and children, it could dilute estimates of genetic influence when parents are the target of the genetic analysis. The study is part of the Swedish Adoption Twin Study of Aging (SATS A), mentioned several times in this article, which uses one of the most powerful designs in the armamentarium of behavioral genetics: identical (MZ) and fraternal (DZ) twins reared apart (MZA and DZA) and matched groups of MZ and DZ twins reared together (MZT and DZT; McClearn et al, submitted; Pedersen et al., in press). An abbreviated version of the FES was completed by 179 reared-apart twin pairs and 207 reared-together pairs in relation to the twins’ current family, that is, the family consisting of the adult subject, spouse, and children. The results, summarized in Table 1, suggest that genetic factors affect the FES. The average MZA correlation is .25, ranging from .10 for cohesion to .45 for culture. Because the MZA correlation directly estimates heritability, this suggests that about 25% of the variance of the FES scales is the result of genetic influence. The average DZ correlation is .10, suggesting a heritability of about 20%. The average MZT correlation is .28 and the average DZT correlation is .09; thus, the classical twin estimate of heritability that doubles the difference between the MZT and DZT correlations is 38%. The estimate of heritability that doubles the difference between the MZA and DZA correlations is 30%. The average of these four estimates suggests that the heritability of the FES scales is about 25%, with the lowest heritability for achievement (14%) and the highest for culture (47%). Because of the large standard errors of correlations, considerable sampling fluctuation can be seen. Model-fitting analyses are particularly helpful in this regard because they analyze data from all four groups simultaneously and provide tests of statistical significance for parameter estimates. The SATS A model used in these and subsequent SATSA reports is presented in detail elsewhere (Plomin, McClearn et al. 1988; Plomin, Pedersen et al. 1988). The results of model fitting verify the conclusions reached on the basis of examining the simple correlations: The average model-fitting heritability estimate is 24%, with the lowest estimate for achievement (12%) and the highest estimate for culture (40%). Heritability estimates were statistically significant (p < .05) for Expressiveness, Cultural, Organization, and Control, and marginally significant (p < . 10) for Conflict and Active.
Thus, this first study of FES ratings in which the parents are the target twins suggests that about a quarter of the variance is the result of genetic differences among individuals, which is similar to the magnitude of genetic influence on personality measures in the same study (McClearn et al., submitted). The most surprising of these results is that the reared-apart twins rated different families similarly, and that this was the case to a greater extent for MZA than for DZA twins. Unless the rearing families of separated twins were similar (because of selective placement), this evidence for genetic influence allows two interpretations: Either heredity is involved in general perceptions (e.g., “looking at life through rose-colored glasses”) or members of the two families in fact responded similarly to genetically influenced characteristics of the separated twins.
2.1.2. Studies of children: Twin studies. In all other genetic studies of the family environment, the targets are children rather than parents; most of these studies also use parental and child ratings rather than observations. The earliest relevant work was not conducted for the purpose of investigating genetic influence on measures of the family environment. The goal of the research was to address the “equal environments” assumption of the twin method by investigating whether MZ twins are treated more similarly than DZ twins (Lehtovaara 1938; Loehlin & Nichols 1976; Smith 1965; Wilson 1934; Zazzo 1960). Although MZ twins were found to be treated more similarly by their parents for some measures, the bottom line was that such MZ-DZ differences in treatment do not relate to twin differences in behavior (Plomin, DeFries & McClearn 1990).
It was not asked, however, why ratings of MZ twins’ treatment are more similar than those of DZ twins in the first place; genetic influence on these measures of family environment could be part of the answer. An alternative possibility is that attributional biases because of labelling twin pairs as MZ or DZ could lead to these results. This is unlikely, however, because research with twins whose zygosity is mistaken by their parents has shown that true rather than mistaken zygosity governs twin similarity (Scarr 1968; Scarr & Carter-Saltzman 1979).
188.8.131.52. Loehlin & Nichols (1976) study. The largest and most thorough study of this type involved 850 pairs of high school twins (Loehlin & Nichols 1976). Among the more than 1,000 items in the study (with twin results for each item helpfully included in an appendix) are several items that involve parents’ ratings and children’s ratings of childrearing variables. The parental rating items consist of same-different judgments concerning the parents’ treatment of each child. For most items, parents rarely indicated that they treated their children differently, regardless of whether the twin children were identical or fraternal. Similarly, parent ratings in another study of twins from 1 to 6 years of age yielded twin correlations in excess of .90 for measures of childrearing (Cohen et al. 1977). Taken at face value, these results suggest that parents do not respond to genetic differences between their children, although the possibility looms large that parents deny differential treatment of their children because this goes against social conventions. Observational studies might tell a different story.
For a few items in the Loehlin & Nichols study, however, parents reported that they treated their children differently to some extent, which makes it possible to ask whether differential treatment is greater for DZ than for MZ pairs. Table 2 lists the percentage of discordant MZ and DZ pairs for these items. Discordances are consistently greater for DZ twins than for MZ twins, which suggests some genetic influence. The average percent discordance is 11% for MZ twins and 21% for DZ twins. Although data of this type permit no precise estimate of the magnitude or significance of genetic influence, at least some modest genetic influence is suggested by these results.
The Loehlin & Nichols data set also includes several items relating to the twin children’s perceptions of their parents’ treatment. Intraclass correlations for these quantitative ratings are listed in Table 3. Each item shows greater resemblance for MZ twins than for DZ twins – significantly so for all but one item – suggesting that heredity affects adolescents’ perceptions of their parents’ treatment. The average twin correlations are .55 and .34, respectively, for MZ and DZ twins for the items in Table 3, suggesting substantial genetic influence.
184.108.40.206. Lytton’s study. Another study motivated by the equal environments assumption of the twin method is important for several reasons, despite its small sample size (17 MZ, 29 DZ pairs; Lytton 1977; 1980). Although interview data were involved in the measures of parental treatment, the primary data were derived from observational ratings of mothers interacting with their twins. These observations of mother-child interaction appear to show greater differential behavior than do parental reports of differential treatment discussed in the previous section. Similar to the parental report items that show differential treatment, these observational data suggest that parents treated MZ twins more similarly than DZ twins: Seven parental treatment variables showed significantly greater variance within DZ pairs than within MZ pairs (twin correlations were not reported). These parental treatment variables included use of material rewards, amount of play, support of dependence, encouraging mature action, monitoring, use of reasoning, and play frequency.
The most interesting feature of this study was its coding of parent-initiated actions, defined as parental actions that were not preceded by a child’s action within the previous 10 seconds. These measures of parent-initiated action were summarized in four categories: command/prohibition, suggestion, positive action, and negative action. The only category of parent-initiated treatment that suggested genetic influence was parents’ suggestions. The author concluded that parents of MZ twins are more likely than parents of DZ twins to respond to rather than create greater similarity in their children, thus supporting the equal environments assumption of the twin method. In the present context, these results can be reinterpreted as indicating genetic influence on parental treatment in response to characteristics of children. This finding fits well with expectations of genetic influence on childrearing as discussed earlier: Because this is a study of twin children that involves objective observations of childrearing, we would expect the results to show genetic influence on parents’ play and monitoring that is in response to their children rather than on parent-initiated measures. This pioneering study is an exemplar of the type of research that is needed to understand the processes underlying genetic influences on environmental measures.
220.127.116.11. Rowe’s studies. The previous studies inadvertently obtained data relevant to genetic influence on measures of the environment in their investigations of the equal-environments assumption of the twin method. Two twin studies by David Rowe (1981; 1983) were the first with the explicit goal to assess genetic influence on environmental measures. Adolescent twins were asked to rate parental treatment in two separate studies using different environmental measures and different samples. In his first study, Rowe (1981) assessed three dimensions: acceptance-rejection, control-autonomy, and firm-lax control, using an abbreviated version of Schaefer’s Children’s Reports of Parental Behavior Inventory (Schaefer 1965). Results of these two twin studies are summarized in Table 4. For ratings of both mother and father, the twin results suggested a significant and substantial genetic influence on acceptance-rejection. The two control-related dimensions, however, showed no indication of genetic influence.
Similar results were obtained in a second study of adolescents’ ratings on the Family Environment Scales and included nontwin siblings in addition to twins. Two second-order factors were derived that are similar to the warmth and control dimensions typically found in childrearing research. The warmth dimension (called acceptance-rejection) refers not only to affection but also to parents’ supportiveness toward the child. Control (restrictiveness-permissiveness) involves the parents’ attempts to set and enforce rules and to organize the child’s life. The twin correlations indicate significant genetic influence for the warmth dimension, but no genetic influence was found for the control dimension. It is noteworthy that nontwin siblings in this study were as similar as DZ twins for both the warmth and control dimensions, suggesting that twins are not more sensitive than nontwin siblings in terms of perceived differences in parental treatment toward them and their same-aged cotwins.
It is especially interesting that in both of Rowe’s studies parental warmth – but not parental control – showed genetic influence. Loehlin & Nichols’s data also provide some support for this hypothesis. In Table 2, the item, “Was spanked more often as a child,” is problematic for this hypothesis, because it yielded the greatest difference between MZ and DZ twin discordances. Although spanking seems to be a control item, it is different from the typical control item that assesses family organization (e.g., assignment of chores), and it could be argued that this item actually involves warmth more than control. This could be determined empirically if correlations were available between spanking items and other control items. If spanking is more a matter of warmth than control, this item’s apparent genetic influence would support the hypothesis of greater genetic influence for parental warmth than control. The other items in Table 2 tend to support the hypothesis. Other than the spanking item, the two items with the largest difference between MZ and DZ twins are warmth items: item 265 (“Was rocked and held more often as a child”) and item 243 (“Was closer to the mother”). Two clear control items (“Had stricter discipline as a child”; “Had stricter discipline as an adolescent”) showed the smallest differential treatment of MZ and DZ twins. The twins’ own ratings in Loehlin & Nichols’s study (Table 3) are measures of parental warmth rather than of control, and all these items suggest genetic influence.
18.104.22.168. SATSA. The hypothesis that genetic influence is greater for parental warmth than for control was also supported in a SATSA analysis of retrospective ratings of childhood family environment viewed half a century later (Plomin, McClearn et al. 1988). Although the twins in this study are adults, it is a study of twins as children in the sense that they reported retrospectively about themselves as children in relation to the family environment in which they were reared.
Despite the procedural differences between SATSA and Rowe’s two studies of adolescents, the SATSA results, shown in Table 5, generally confirm Rowe’s findings. Perceptions of control show the lowest MZA correlation and the lowest model-fitting estimate of heritability, whereas warmth-related dimensions of expressiveness and conflict are significantly heritable; the cohesion scale shows only marginally significant (p < .06) heritability, however. Two second-order FES factors are similar to the factors reported by Rowe (1983). A warmth dimension called relationship consists of the cohesion, expressiveness, and conflict scales, and a control dimension called system maintenance includes the control and organization scales. Model-fitting heritability estimates for these warmth-related and control-related factors were .38 and .11, respectively, replicating Rowe’s finding of greater genetic influence for parental warmth than parental control.
SATSA adds to Rowe’s results by suggesting that genetic influence is found not only for warmth, but for nearly all FES dimensions other than control. The correlations for MZ twins reared apart from early in life are particularly impressive because these individuals were reared in different families. This could mean that genetic influence is in the eye of the beholder, that is, heredity may be involved in subjective characteristics that affect perceptions of the family environment. It is also possible, however, that members of the two families responded similarly to genetically influenced characteristics of the separated MZ twins.
These results may be especially pertinent to a new area of attachment research that focuses on adult parents’ descriptions of their relationships with their parents when they were children (Main et al. 1985). The possibility of genetic influence on such a measure looms large and becomes even more interesting as an alternative interpretation of the finding that such representations of one’s own attachment as a child relate to attachment patterns as a parent.
22.214.171.124. SIDE. Two other relevant studies involved self-reports on the Sibling Inventory of Differential Experience (SIDE, Daniels & Plomin 1985), which was developed to assess nonshared experiences of siblings in relation to parents and each other, as well as peers. Siblings rate their experiences relative to their siblings rather than in an absolute sense. For example, one of the SIDE differential parental treatment items is, “Mother has been sensitive to what we think and feel.” Each sibling answers on a 5-point scale in which 1 represents “toward sibling much more,” 3 means “same toward my sibling and me,” and 5 means “toward me much more.” The relative scoring of the SIDE can be transformed to “absolute difference scores” to assess perceived differences in experience regardless of which twin was favored. These difference scores do not permit the calculation of sibling correlations as in previous studies because the SIDE asks siblings to rate their experiences relative to their siblings.
The SIDE was used in a twin study (Baker & Daniels 1990) and in a sibling adoption study (Daniels & Plomin 1985) that compares nonadoptive siblings (biological siblings in nonadoptive families) and adoptive siblings (genetically unrelated children adopted early in life into the same family). The twin study included adult twins who responded retrospectively about the family in which they were reared, and the sibling adoption study included adolescents and young adults. If genes affect the SIDE measures, mean differences for DZ twins will exceed those for MZ twins, and adoptive pairs will exceed those for nonadoptive pairs, because the magnitude of genetic differences within pairs is in the order: MZ < DZ = nonadoptive sibling < adoptive siblings.
Table 6 lists mean absolute differences on the SIDE reported by MZ and DZ twins and nonadoptive and adoptive siblings. The twin comparisons indicate significant genetic influence both for parental affection and control and for sibling closeness and jealousy. The sibling adoption design, however, yields less evidence for genetic influence. Adoptive sibling differences are not significantly greater than nonadoptive sibling differences in their ratings of parental treatment. Ratings of treatment by one’s sibling, however, consistently show greater differences within adoptive sibling pairs than nonadoptive sibling pairs, although the difference is significant only for sibling closeness, the scale most clearly related to warmth. These are the first studies to consider genetic influence on sibling behavior toward a target child rather than parental behavior.
This suggestion of greater genetic influence in the twin data as compared to the adoption data needs to be replicated and tested for generalization to other environmental measures, it may not be coincidental that twin data for personality questionnaires also yield greater evidence for genetic influence than adoption data (Plomin & Nesselroade 1990). This finding can be explained genetically by epistatic genetic variance, higher-order interactions among genes that are entirely shared by MZ twins but not by DZ twins or other first-degree relatives who are the subjects of adoption studies. An environmental explanation has been called the MZ assimilation effect, in which MZ twins experience more similar environments than DZ twins. In personality research to date, it appears that both epistasis and an MZ assimilation effect may be responsible for twin estimates of heritability that exceed adoption studies of first-degree relatives (Plomin, Chipuer & Loehlin 1990).
For environmental measures, however, it is not yet clear that adoption data yield lower estimates of genetic influence than do twin studies. As indicated in the following section, some adoption data implicate substantial genetic influence on environmental measures.
2.1.3. Studies of children: Adoption studies. The Colorado Adoption Project (CAP) provides sibling adoption data for several types of measures. The CAP is mentioned several times in this article: It is a combined adoption/family, prospective, longitudinal study consisting of 245 adopted children studied yearly beginning at 12 months of age (Plomin, DeFries & Fulker 1988). Also assessed are the adoptees’ biological and adoptive parents, matched nonadoptive families, and younger adoptive and nonadoptive siblings. (The phrase “adoptive siblings” refers to unrelated children adopted into the same family.)
The CAP included the widely used observation/interview instrument, the Home Observation for Measurement of the Environment (HOME; Caldwell & Bradley 1978), assessed for nonadoptive and adoptive siblings when each child was 12 months and 24 months old. The HOME is problematic for detecting genetic influence on the family environment in that many of the items cannot be expected to reflect genetic differences among siblings because the items are not specific to the child. For example, such items as number of books and pets in the home will not differ for the two children and thus cannot display genetic influence in the sibling adoption design. Nonetheless, some of the HOME items are specific to each child, and these items make it possible to explore genetic influence on this objective environmental measure.
As shown in Table 7, nonadoptive and adoptive sibling correlations for the HOME general score are .50 and .36, respectively, at 12 months, and .50 and .32 at 24 months, suggesting that parental behavior reflects genetic differences among children. Model-fitting analyses confirmed this conclusion, showing significant genetic influence at both ages (Braungart et al, in press). The significant correlations for adoptive siblings indicate, not surprisingly, that the HOME also assesses environmental influences shared by siblings. The surprise is that genetic influence should count for so much for this objective measure of the home environment, especially when it is limited to parental responses to genetically influenced characteristics of the children, as discussed earlier.
Sibling correlations for subscales of the HOME suggest that evidence for genetic influence on the HOME is found for a toys scale, which assesses the number of toys of different types (e.g., challenging toys, muscle activity toys, and push-pull toys). Although the number of toys of different types might not appear to be sensitive to the particular child (because toys can be handed down from older to younger siblings) it is possible that parents buy toys for each child that reflect the child’s particular interests. The restriction-punishment results are also consistent with the possibility of some genetic influence. The sibling correlations for the HOME scale that assess encouraging developmental advances suggest genetic influence at 12 months, but not at 24 months. Maternal involvement indicates no genetic influence at either age. Although a HOME-like measure constructed for the CAP for use at 3 and 4 years showed little genetic influence, the sibling sample is much smaller at these ages and the measure is problematic in other ways as well (Plomin, DeFries & Fulker 1988).
CAP also provides the only videotaped observations of mother-child interactions that can be investigated for genetic influence. Ratings were made from videotapes of mothers interacting with each of the siblings when the child was 1, 2, and 3 years old (Dunn & Plomin 1986; Dunn et al 1986; Dunn et al. 1985). In addition to its objectivity, an important feature of this measurement strategy is that, unlike the HOME, maternal behavior specific to each child is assessed. At each age, mother and child were videotaped in three 5-minute sessions: a structured task (teaching), a moderately structured task (play with a specific set of toys), and an unstructured task (free play). Factor analysis of various behavioral counts and ratings yielded affection, control, and verbal factors. Nonadoptive and adoptive sibling correlations for these three factors at 1, 2, and 3 years of age are shown in Table 8. Despite the small sample sizes, the affection factor consistently shows nonadoptive correlations that are substantially greater than adoptive correlations. No genetic influence is suggested for the control and verbal factors, with the single exception of control at 3 years. Although the small sample size calls for caution in drawing conclusions, this is somewhat offset by the replication of results across the three years.
2.1.4. Studies of proximal family environment: Summary. Measures of the family environment show genetic influences in both twin and adoption studies, with different methods (e.g., in studies in which children or their parents rate the children’s environment), and with diverse measures of environment, including ratings, the observation/interview HOME measure, and ratings of videotape observations of mother-child interaction.
Although we hypothesized that childrearing studies of parents show greater genetic influence than studies of children, the only relevant comparison yields similar heritabilities for the two types of studies. As mentioned earlier, however, the FES used in the SATS A study in which the target was parents is not specific to childrearing. It assesses the general atmosphere of the family environment, and this could cloud genetic involvement of the respondent. More support can be found for a second hypothesis: in studies of children, parental ratings show less genetic influence than ratings by the children themselves, which is expected on the basis of the earlier discussion of components of genetic influence on environmental measures. A recurrent finding across all designs is that measures of warmth show greater genetic influence than measures of control, an unexpected finding.
Are genetic influences on environmental measures limited to measures of the proximal family environment, which may be especially caught up in the genetic concatenations among family members? The following sections review genetic research on other measures of the environment.
2.2. SES and education
Parental education and socioeconomic status (SES) are among the most widely used Indices of the home environment in studies of children’s development, and for this reason the question of possible genetic involvement in these measures should be raised. Genetic analyses of these variables require that the target sample be parents because SES and parental education do not. vary for children in the family.
Both SES and parental education appear to show genetic influence, which is not surprising, given that their correlation with IQ is greater than .50 (Jensen 1980). For example, a study of 1,900 pairs of 50-year-old male twins yielded MZ and DZ twin correlations of .42 and .21, respectively, for occupational status, and .54 and .30 for income (Fulker & Eysenck 1979; Taubman 1976). An adoption study of occupational status yielded a correlation of .20 between biological fathers and their adult adopted-away sons (2,467 pairs; Teasdale 1979). A study of 99 pairs of adopted-apart siblings yielded a correlation of .22 (Teasdale & Owen 1981). All these results are consistent with a heritability of about .40 for occupational status. Years of schooling also shows substantial genetic influence; for example, MZ and DZ twin correlations are typically about .75 and .50, respectively, suggesting that heritability is about 50% (e.g., Taubman 1976). Recent SATSA analyses confirm these findings of substantial genetic influence on occupational status and years of education (Lichtenstein & Pedersen, in press), as does an analysis of Norwegian twins, which also suggests that IQ is to some extent responsible for genetic variance in occupational status and years of education (Tambs et al. 1989).
2.3. Television viewing
Time spent watching television by children could be viewed as a measure that depends directly on the child’s own behavior and thus is not really an environmental measure. Children’s television viewing has been used as an environmental measure in thousands of studies that investigate the consequences of television viewing (Pearl et al. 1982). Despite the huge research effort to investigate its consequences, little is known about the causes of individual differences in children’s television viewing (Bryant 1990). It is not merely a function of parental restrictions – 70% of parents put no restrictions on the amount of time their children watch television (Lyle & Hoffman 1972) – which makes it more plausible to consider characteristics of children, including genetically influenced characteristics, among the provenances of this measure.
Individual differences in the amount of television viewing in children were investigated as part of the CAP (Plomin, Corley et al. 1990). Both the sibling and the parent-offspring adoption designs yielded evidence for significant genetic influences. For example, the correlation for amount of television viewing in early childhood for nonadoptive siblings is .48, whereas the correlation for adoptive siblings is only .26, suggesting substantial genetic influence.
Peers represent a potentially important category of extra-familial environmental influence. The SIDE measure of nonshared environment includes three scales that assess characteristics of peer groups. The twin and sibling adoption studies described earlier (Baker & Daniels 1990; Daniels & Plomin 1985) suggest that these peer characteristics show substantial genetic influence, as indicated in Table 9. These SIDE peer scales suggest even greater influence than the SIDE parental and sibling scales (see Table 6).
2.5. Social support
SATSA data suggest that measures of social support involve genetic influence (Bergeman et al. 1990). Nine items from a modified version of the Interview Schedule for Social Interaction (ISSI; Henderson et al. 1980) were administered. Two scales were analyzed: quantity and quality (perceived adequacy). Twin correlations, listed in Table 10, estimate significant genetic influence for the quality scale, but not the quantity scale. Although twins are similar for the quantity measure, DZ twins are nearly as similar as MZ twins on average, indicating correlated environmental influence. Model-fitting heritability estimates are 0% for the quantity measure and 30% for the quality measure.
2.6. Life events
The most recent discovery of genetic influence on environmental measures involves a widely used class of measures, life events. A measure of life events based on the Social Readjustment Rating Scale (Holmes & Rahe 1967), used in more than 1,000 studies (Holmes 1979), was modified for older Individuals in the H-70 study in Gothenburg, Sweden (Persson 1980), and included in SATSA (Plomin, Lichtenstein et al. 1990). Considerable disagreement exists concerning the best way to assess life events, and there is dissatisfaction with such traditional questionnaire measures as the Social Readjustment Rating Scale (e.g., Paykel 1983). The value of this research does not rest on its use of a particular measure of life events, however, because its goal was merely to assess genetic influence on a standard measure of life events used in many studies. Knowing that other measures might yield different results is a possibility that offers an obvious direction for future research in this area. A traditional total life events score was constructed by summing each reported event weighted by the average importance assigned to the event by all individuals who completed the questionnaire. In addition to the total score, scales were constructed to address the possibility that distinctions between controllable events (e.g., serious conflicts with child) and uncontrollable events (e.g., serious illness in child) may be important (Thoits 1983).
The twin correlations and model-fitting estimates of heritability are listed in Table 11. For the total life events score, the correlation for MZA is .49, suggesting significant and substantial genetic influence. The patterns of correlations for all four groups of twins were consistent with a hypothesis of genetic influence; the model-fitting estimate of heritability is 40% for the total life events measure.
The distinction between controllable and uncontrollable events appears to be important: The correlations for MZA are .54 for controllable life events and .22 for uncontrollable events. Maximum likelihood model-fitting analyses yielded significant estimates of genetic”influence for all of the scales, but the heritability estimates are 43% for controllable events and 18% for uncontrollable events.
3. Summary and implications
Figure 2 summarizes the magnitude of genetic influence on environmental measures for those studies that permit heritability estimates. Heritabilities are plotted in relation to a dimension of presumed subjectivity/objectivity of the measures. Although this is meant only as a first rough attempt to classify environmental measures that have been used in genetic studies, there is likely to be little disagreement concerning the general ordering of measures along the subjective-objective dimension. For example, ratings of videotape observations seem more objective than interviews concerning the amount of television viewing. The latter appear to be more objective than self-report ratings of social support, and these seem more objective than self-report questionnaires about the warmth of the family environment.
An interesting feature of the results summarized in this manner is that genetic influence does not appear to be limited to subjective environmental measures. This finding suggests that genetically influenced characteristics of individuals responsible for genetic influence on environmental measures extend beyond the subjective processes involved in self-report ratings. Genetic influence appears to be not just in the eye of the beholder, but also in the behavior of the individual.
It should be emphasized again that these results by no means imply that the variance of environmental measures is entirely genetic in origin. Indeed, these data suggest that nongenetic factors are primarily responsible for variance on environmental measures. Moreover, as always, much more research is needed. The initial research on the nature of nurture that we described involves a hodgepodge of measures and a smattering of ages. More specifically, gender differences have not yet been given adequate attention, primarily because samples are not large enough to detect genetic differences between the genders.
Nonetheless, as it stands, this evidence for genetic influence on environmental measures challenges the reasonable assumption that measures labelled as environment are in fact measures of the environment. Indeed, these findings suggest that environment measures often show as much genetic influence as do measures of such behavior as personality. Even though these are very early days in research on the nature of nurture, the results would so far seem to shift the burden of proof to those who continue to assume that environmental measures are free of genetic influence.
Developmentalists have considered the issue of the direction of effects in socialization (Bell 1968); evidence for genetic influence on measures of the family environment can be assimilated in this context. Wachs and Gruen (1982), for example, have emphasized orgaeismic specificity, by which they mean that environmental influences are mediated by the organism. It must be said, however, that the issue of the direction of effects receives far more “lip service” than actual research. Also, reckoning with genetic influence on measures of the family environment goes beyond the effects of child characteristics on parents’ childrearing. For example, genetically influenced characteristics of parents can contribute to genetic influence on environmental measures independently of characteristics of the children. These findings are likely to have the greatest impact on areas of environmental research other than proximal measures of the family environment because the possible contribution of organismic characteristics has rarely been broached in these areas. For example, to our knowledge the huge literature on life events has never considered the possible role of genetic influence.
One direction for research on the nature of nurture is to continue to sort out the relative magnitude of genetic influence on environmental measures. For the field of behavioral genetics, it is interesting that genetic research on environmental measures holds out the hope that some measures are substantially influenced by genetics and others are not, unlike research on personality, for example, where nearly all dimensions show moderate genetic influence (Loehlin 1982). Research in this vein may prove useful in a practical sense in identifying environmental measures that are relatively free of genetic influence. Although heritability does not imply immutability, environmental measures free of genetic influence would seem more likely to show effects of intervention, and they would permit more straightforward interpretations of environmental influence in other research using measures of the environment.
In addition, sorting out the extent of genetic involvement for diverse environmental measures might provide clues as to the mechanisms of genetic influence. For example, why does parental affection show greater genetic influence than parental control? Why does the quality of social support show greater genetic influence than the quantity of support? Why do ratings of controllable life events show greater genetic influence than uncontrollable life events?
Such questions as these lead to what will surely be a major direction for research at this interface between nature and nurture: exploration of the processes by which genetic influence affects measures of the environment. As discussed in the introduction, environmental events have no DNA; genetic effects on environmental measures must be the result of covariation with genetically influenced characteristics of the individual. We suggest that it might be useful to consider the degree of genetic influence on an environmental measure as an index of the extent to which characteristics of the organism are assessed by the environmental measure. But this does not take us very far toward identifying the specific processes by which genetic influence emerges in analyses of environmental measures, because any genetically influenced characteristic of individuals can contribute to genetic influence on measures of the environment. Furthermore, the criterion that these organismic characteristics should be heritable is not particularly helpful, because nearly all such characteristics are moderately heritable, for example, those most often studied by behavioral geneticists: cognitive abilities, personality dimensions, and mental disorders.
The first step in this direction is to explore behavioral correlates of environmental measures. Few analyses of this type have been reported, but the results so far do not promise that traditional dimensions of behavior can account for genetic influence on environmental measures (Plomin 1986). For example, genetic influence on the HOME might be thought to result from parental IQ. The HOME correlates only .13 with parental IQ in CAP, however. The HOME correlates at about the same level with extraversion and neuroticism. Other associations between behavioral and environmental measures are reasonable but weak. For example, the FES second-order factor of personal growth correlates about. 20 with several major dimensions of personality — emotionality, activity, sociability, extraversion, and neuroticism, for example. SIDE scales also yield significant correlations with personality; the most heritable SIDE scale involves peers, and this scale correlates significantly but modestly with fearfulness, shyness, sociability, and activity. We have conducted analyses in SATSA of the personality correlates of life events and again find only modest correlations. The controllable life events score correlates only .06 with neuroticism and .12 with extraversion; the highest correlation (.21) for uncontrollable life events was found with a sensation-seeking scale.
Although these patterns of correlations between behavioral measures and environmental measures are weak, it is possible that in concert they can begin to account for genetic influence on environmental measures. These are just phenotypic correlations, however, and do not demonstrate genetic mediation between behavioral and environmental measures. Multivariate genetic analyses of the phenotypic covariation between behavioral measures and environmental ones are needed to determine the extent to which such behavioral measures can account for genetic influence on environmental measures. To our knowledge, no such multivariate analyses have been reported in which an environmental measure is analyzed as a phenotype.
There are three reasons, however, why we do not expect such multivariate analyses of genetic overlap between behavioral and environmental measures to yield simple answers. First, answers to the question of processes that mediate genetic influence on environmental measures are likely to differ as a function of method, source, and target. For example, it seems reasonable to expect that different genetic processes are involved in genetic influences on objective and subjective measures. For subjective measures, genetic influence can accumulate as ratings are filtered through the feelings, personality, and cognitions of individuals. This issue can be addressed by multivariate analyses of the genetic covariance between environmental measures.
Second, even if multivariate genetic analyses uncover genetic correlations between environmental measures and traditional dimensions of behavior, a genetic correlation is just a correlation, and does not prove that genetic influence on the environmental measure is epiphenomenal to the genetic influence on the behavioral measure. Indeed, a hypothesis that interests us goes the other way around: Genetic influence on the ways organisms interact with their environment might be responsible for the ubiquitous genetic influence found for behavior. This is the essence of Scarr & McCartney’s (1983) developmental theory of how people make their own environment. Only longitudinal analyses can begin to disentangle such questions of cause and effect.
Third, traditional dimensions of behavior may show few important genetic associations with measures of the environment because of the possibility that environmental measures extract genetically influenced patterns of reactions of individuals to their environment that are not tapped by our traditional measures of behavior. With respect to family environment for example, traditional measures of personality and cognition designed to be context-free seem unlikely to capture entirely the genetically influenced concomitants of the intense, emotion-laden context of family relationships. Attempts to assess context-specific behavioral dimensions may be more fruitful and could lead to new insights about behavior at the interface between nature and nurture. We suggest that behavioral genetic studies of attributional processes may be useful in this regard.
Finally, in addition to broaching the topic of the antecedents of genetic influence on environmental measures, multivariate analyses can be used to address their sequelae. That is, given that both environmental and behavioral measures are influenced genetically, it is possible that associations between environmental measures and behavioral outcomes are also mediated genetically. For example, if measures of life events are heritable, associations between measures of life events and psychopathology might be mediated in part by genetic influences shared by the two domains. Multivariate genetic analyses are also appropriate to assess genetic covariance between environmental measures and outcome measures, although the same caveat is in order: A causal direction from environment to outcome cannot be attributed to genetic correlations between environmental measures and outcome measures. For example, a multivariate analysis of the HOME and children’s IQ using CAP nonadoptive and adoptive sibling data found little evidence for genetic mediation of the phenotypic association between HOME and children’s IQ (Braungart et al., in press). We are aware of no other published reports of multivariate genetic analyses in which an environmental measure is treated as a phenotype. As mentioned in the introduction, however, parent-offspring model-fitting analyses of IQ have incorporated environmental measures as indices of IQ-relevant environmental variance; these studies have found some evidence for genetic mediation of the link between environmental indices and IQ.
Success in all of these research directions could be facilitated by the development of more sophisticated measures of the environment. For example, in relation to family environment, measures are needed that are specific to a child rather than general to a family. We also need better measures of experience (the subjective, experienced environment) in contrast to measures of the objective environment. Most important, we need measures that move beyond the passive model of the individual as merely a receptacle for environmental influence to measures that can capture the individual’s active selection, modification, and creation of environments – this lies at the heart of the interface between nature and nurture.
In summary, it is remarkable that research reported to date, using diverse measures and methods, so consistently converges on the conclusion that genetic influence is significant and substantial on widely used measures of the environment. This finding has far-reaching implications for environmental studies of the behavioral and brain sciences; the bottom line is that labelling a measure environmental does not make it environmental. Nonetheless, this is only a first step in a sprawling, unexplored land. Much remains to be learned, for example, about the degree of genetic influence on the many facets of environmental influence, about the antecedents and sequelae of genetic influence on environmental measures, and about the developmental course of the nature-nurture interface. Our motivation in writing this target article was to provide a solid foundation for this new field of research by documenting the evidence for genetic influence on environmental measures with the hope that this will stimulate further research on the nature of nurture.
Nature and nurture
The point of our target article is very simple. If one takes a measure of the environment and treats it as a phenotype in a quantitative genetic analysis such as a twin or adoption study, one often finds evidence for genetic influence. We intentionally chose to focus on the basic phenomenon – that environmental measures show genetic influence – rather than spinning off into the ramifications of this finding, which we felt would have diluted the impact of the article. In our response to the melange of commentaries, we hope that the forest is not lost for the trees: Nineteen commentaries agree with our general point that the provenance of widely used measures of the environment includes genetics. Two additional commentaries (by Bradley & Caldwell and Graham) seem to accept this conclusion, and for four others it is not clear whether they accept it or not (Duyme & Capron, Hay, Socha, and Wachs). Only five of the commentaries clearly disagree with the finding (Baumrind, Bookstein, Hirsch, Schönemann & Schönemann, and Thelen). The latter commentaries, however, question the use of quantitative genetic methods to investigate genetic influence on any phenotype, not just the application of these methods to the analysis of environmental measures which is the topic of the target article. We begin by responding to the latter commentaries. Next we consider issues concerning the interpretation and implications of the finding. The final section of our Response addresses suggested directions for research on the nature of nurture.
All the new data mentioned in these commentaries confirm the existence of genetic influence on environmental measures. McGue et al. mention dissertation research (Moster 1990) that replicates our finding that controllable life events show greater genetic influence than uncontrollable ones (Plomin, Lichtenstein et al. 1990). Kendler notes that preliminary twin analyses at the Medical College of Virginia also find heritable variance on measures of life events and social support. Bouchard & McGue (1990) replicated our finding of significant genetic influence on the Family Environment Scale (Plomin, McClearn et al. 1988); the average heritability in their study was .21 and in ours it was .25. Goodman & Stevenson present welcome new data concerning the equal-environments assumption of the twin method, specifically in relation to measures of the environment. They find that parents respond more similarly to identical twin children, even when the parents mistakenly think that their children are fraternal twins. We have also become aware of the results of a family study of depression that suggests that a common familial factor predisposes individuals both to depression and to stressful life events (McGuffin et al. 1988).
General criticisms concerning quantitative genetics
As mentioned previously, five of the commentaries question the use of quantitative genetic methods to investigate genetic influence on any phenotype. They raise objections that have been considered for a long time in quantitative genetics. For example, Baumrind is concerned about restriction of range, selective placement, and the unequal environments assumption of the twin method. Schönemann & Schönemann focus on error of measurement and nonadditive genetic variance. (The other three commentaries – by Bookstein, Hirsch, and Thelen – are discussed later.) Such issues have been discussed extensively in the quantitative genetic literature. We had thought it was no longer necessary to provide a general discussion of quantitative genetics in our target article, but these commentaries lead us to begin with a brief statement about quantitative genetics as applied to the study of behavior.
One of the most dramatic shifts in the behavioral sciences occurred during the 1980s, when antipathy toward human behavioral genetics turned into acceptance. For example, a survey of more than 1,000 social and behavioral scientists and educators indicated that most have accepted a significant effect of heredity on IQ scores, traditionally one of the most controversial areas in behavioral genetics (Snyderman & Rothman 1988). [See also Jensen: “‘Total Perceived Value’ as the Basis of Assortive Mating in Humans” BBS 12(3) 1989.] Indeed, with increasing frequency, we caution that the rush of the behavioral sciences away from environmentalism may be going too far, to a view that all behavior is biologically determined. Against this background of remarkable change, it is almost nostalgic for us to see these antigenetic commentaries. For example, diatribes of the sort in Hirsch’s commentary were commonplace 15 years ago; this atavism should serve to remind us how far the social and behavioral sciences have come since the 1970s. Unfortunately, Hirsch devotes his commentary to congratulating people who have written antigenetic tracts rather than addressing our target article, which limits what we can respond to. If behavioral genetics is, as Hirsch says, “much ado about nothing,” why does he need to keep repeating himself as the decades go by, while other behavioral scientists shake themselves free from the shackles of environmentalism and accept a more balanced view that recognizes genetic as well as environmental influences on individual differences in behavioral development?
We have trouble understanding how a serious scientist can any longer deny the ability of quantitative genetic methods to detect genetic influence on complex quantitative traits, including behavioral traits, or deny the results of quantitative genetic research that converge on the conclusion that genetic influences are important. Artificial selection studies provide a powerful demonstration of the impact of heredity on the behavior of nonhuman animals by showing that we can successfully select animals whose offspring reliably and appreciably differ for behavior. For example, in one of the longest mammalian selection studies of behavior, replicated high and low lines were selected for activity in a brightly lit open field, an aversive situation thought to assess emotional reactivity (DeFries et al. 1978). After 30 generations of selection, a 30-fold difference exists between the activity of the high and low lines, and there is no overlap between them.
For human behavior, no quantitative genetic methods as powerful as selection studies exist. Human behavioral genetic research relies on family, adoption, and twin designs. As in family studies of nonhuman animals, family studies of human behavior assess the extent of resemblance for genetically related individuals, although such studies cannot disentangle possible environmental sources of resemblance. That is the point of adoption studies. Genetically related individuals adopted apart give evidence of the extent to which familial resemblance is the result of hereditary resemblance.
Twin studies are like natural experiments in which the resemblance of identical twins, whose genetic identity can be expressed as a genetic relatedness of 1.0, is compared to the resemblance of fraternal twins, first-degree relatives whose coefficient of genetic relatedness is 0.50. If heredity affects a trait, identical twins should be more similar for the trait than fraternal twins. As in studies of nonhuman animals, family, adoption, and twin studies can be used to estimate the magnitude of genetic influence as well as its statistical significance.
Consider a quantitative physical trait such as height. Correlations for first-degree relatives are 0.45, whether reared together or adopted apart. Identical and fraternal twin correlations are 0.90 and 0.45, respectively, again regardless of whether they are reared together or adopted apart. These results indicate significant genetic influence. Heritability is a descriptive statistic of effect size that estimates the proportion of phenotypic variance that can be accounted for by genetic variance. For these height data, heritability is estimated as 90%. These same methods can be used to investigate genetic influence on behavioral characteristics and yield evidence for appreciable and nearly ubiquitous genetic influence (Plomin, DeFries et al. 1990). The point of our target article is that when these methods are applied to measures of the environment treated as phenotypes, they sometimes yield evidence for genetic influence, as well.
Although we feel that the specific points raised by these commentaries are not really the issue, we will respond to them. The treatment of error by Schönemann & Schönemann seems bizarre. They use twin correlations to estimate reliability, but twin correlations can be low when measures are very reliable, which is precisely the case with the data that Schönemann & Schönemann abuse. Similarly, their attempts at a purely environmental model simply ignore the possibility of genetic influence and rename as correlated environments any excess similarity of identical twins as compared to fraternal twins. We groan for the Schönemanns’ contortions as they try to avoid the obvious, parsimonious interpretation of behavioral genetic data: Genetic influence is important.
Concerning Bookstein’s example of “attractivity” between parent and child, how would he explain a finding in which his measure of parent-child attractivity is more similar for identical twins than for fraternal twins, especially for twins reared apart? It is odd that after arguing that there is “no way to claim understanding of genetic influence,” Bookstein concludes that “inasmuch as no one appears to disagree with the target article’s principal claim, one wonders why it was written at all.”
Thelen misconstrues quantitative genetics. Genetic influence does not refer to genes that “belong to the organism in a material and permanent way,” nor does the environment refer to “all that affects behavior from outside the organism.” She attacks as oversimplified and biologically untenable the sketch of a straw man that she has drawn. Thelen also says that because of the inadequacies of their assumptions behavioral geneticists are compelled to invoke constructs like genetic influence on environmental measures and nonshared environment to fill in the holes in the model. This is a strange misrepresentation of what we think are among the most important discoveries that have emerged from human behavioral genetics research. Because quantitative genetic methods make it possible to consider both genetic and environmental influences rather than assuming that one or the other is paramount, quantitative genetic analyses of behavior have led to these two exciting discoveries at the interface of nature and nurture: (1) Environmental influences salient to behavioral development are “nonshared,” that is, they make children in a family different from rather than similar to one another (Plomin & Daniels 1987); and, now, (2) the most widely used measures of the environment show substantial genetic influence when treated as phenotypes In genetic analyses.
In the rest of this section, we consider other issues raised by these and other commentaries that address general issues in quantitative genetics rather than issues specific to the investigation of genetic influence on environmental measures.
Interactionism (Baumrind, Bradley & Caldwell, Bronfenbrenner, Socha, Thelen)
Several commentaries attack quantitative genetics because it does not sufficiently take into account interactions between genetic and environmental effects (Baumrind, Bradley & Caldwell, Bronfenbrenner, Socha, Thelen). In our view, mistaken notions of nature versus nurture have too often been replaced with the equally mistaken view that the effects of heredity and environment cannot be analyzed separately, a view called interactionism (Plomin et al. 1977). Obviously, there can be no behavior without both an organism and an environment. The scientifically useful question is: For a particular behavior, what causes differences among individuals?
Thelen and Socha refer to a “dynamical systems theory of development” presented by Oyama (1985). We agree that behavior is a complex system and that it can involve emergent properties. But to say that genetic and environmental effects interact and therefore cannot be disentangled is wrong. It should be noted that Oyama accepts the validity of quantitative genetic research. For example, she states that she is not “disputing the possibility or utility of genetic analysis or of accounting for phenotypic differences by specific environmental or genetical variation” (p. 3; see also, pp. 37-38 and 86). We agree with exhortations that we should be “unpacking the developmental system to determine temporal priority, level of analysis, transfer of control, and interrelatedness of variables” (Oyama 1985, p. 165). These exhortations would have more impact, however, if they were accompanied by a plan for research to accomplish these worthy goals.
The empirical findings that have emerged from quantitative genetic research on human behavior are novel and exciting, and are emerging at an accelerating rate. This pace will quicken as quantitative genetics begins to capitalize on the spectacular advances in molecular genetic techniques, developments that presage a bright future for this growing field. We are not aware of similar advances made by interactionists – proponents of the “dynamical systems theory of development (DSTD)” – who seem to spend more of their time telling others what to do rather than doing things themselves. Our irritation with such nay-sayers leads us to issue a challenge to the DSTDers: Let us make a date 10 years from now at the turn of the century to compare the relative contributions to the study of development that have been made by our two approaches.
Correlations and causation (Wachs)
Although Wachs says he has no quarrel with the possibility that genetic factors may be associated with measures of the environment, he objects to making causal statements when correlations are used. Although we all learn that one of the first rules of statistical inference is that a correlation does not imply causation, this rule is wrong – correlations can imply causation. For example, all experiments are analyzed in terms of correlations or regressions; analysis of variance is merely a computational shortcut for regression. The issue is not the statistic, but the extent of control over variables. We cannot manipulate human genotypes as we can in selection studies of nonhuman animals in the laboratory. As in other quasi-experimental approaches, however, we can randomize one variable while we study the effects of the other. For example, parent-offspring resemblance could result from shared heredity or shared environment. How can we disentangle these two possibilities? The adoption design randomizes heredity in order to investigate the effect of shared environment by studying genetically unrelated individuals adopted together into the same family. The effects of shared heredity can be investigated by randomizing shared family environment in studies of genetically related individuals adopted apart. These correlations can be used to impute causation in the sense of decomposing phenotypic variance into genetic and environmental components of variance.
Process (Bronfenbrenner, Wachs)
Bronfenbrenner, Wachs, and several other commentators contend that finding heritability is not useful in telling us about underlying processes, “the processes through which genotypes are transformed into phenotypes,” as Bronfenbrenner puts it. Developmentalists profess interest in process more than outcome, whereas behavioral geneticists are viewed as “merely” studying outcomes on some measured traits. The word process is used in different ways. It is sometimes used to refer to mechanisms of development, the hyphen in cause-effect relationships. In this sense, behavioral genetic research addresses process to a much greater degree than most developmental research, which is usually descriptive rather than etiological. Usually, however, the process-outcome distinction refers to different levels of analysis in that process implies more basic levels of analysis. The problem with this distinction is that one researcher’s process is another researcher’s outcome. For example, an IQ test is often viewed as an exemplar of an outcome measure because it involves performance on a test. IQ tests were designed to assess cognitive processes such as reasoning and problem solving, however. In contrast, learning is viewed as an exemplar of a process, even though learning is a construct that refers to changes in measured performance over time. Although most developmentalists would view measures of learning performance as more basic than performance on psychometric measures of cognitive abilities, to a neuroscientist thinking about neurotransmitters and neuromodulators, both types of measures would appear to be molar outcomes of complex neural processes.
In summary, we do not find the distinction between process and outcome helpful. The issue is levels of analysis, and quantitative genetic methods can be applied to any level of analysis. Most exciting is the use of multivariate quantitative genetic methods to explore the etiological nexus among different levels of analysis, which as we indicated (see also Hewitt), should be the next step in research on the nature of nurture.
Model fitting (Willerman)
Willerman is perturbed by model-fitting, which represents the state of the art in quantitative genetic analyses. The first version of our target article relied on the basic data of twin and adoption correlations and scarcely mentioned model fitting. Reviewers generally felt, however, that our case would be stronger if we provided tests of significance and model-fitting estimates of parameters. In the final version we added model-fitting results when possible, but we still emphasized the basic data of twin and adoption correlations because we felt that this was the level at which the data would convince most scientists that environmental measures are influenced by genetic factors. Nonetheless, as we indicated, model-fitting analyses are helpful because they yield parameter estimates based on simultaneous analyses of data from several groups, such as identical and fraternal twins reared apart and reared together. They provide tests of statistical significance for parameter estimates, and they make it possible to test alternative models. We noted that the results of model fitting verify the conclusions reached on the basis of examining the simple correlations.
Willerman also makes a specific point about model fitting in relation to the distinction between additive and nonadditive genetic variance. As we indicated, this distinction is difficult to make empirically because essentially it is based on a pattern of twin correlations in which the DZ correlation is less than half the MZ correlation. We could just pretend that all genetic variance is additive, as is usually done when heritability is estimated from twin correlations, by doubling the difference between MZ and DZ correlations. We prefer, however, to consider the possibility of nonadditive genetic influence even though we have only a crude index of it. Schönemann & Schönemann and Duyme & Capron also criticized our use of different models in our model-fitting analyses. It is a major strength of model fitting, however, that alternative models can be compared. For example, we can compare models that consider the possibility of nonadditive as well as additive genetic variance. This is, in fact, one of the problems with Schönemann & Schönemann’s supposed “model violations”: They ignore the possibility of nonadditive genetic variance. Moreover, the point is that regardless of the specific model, evidence for significant genetic influence emerges.
Miscellaneous issues (Boomsma & Molenaar, Kendler, Wachs, Willerman)
Wachs raises the concern that heritability estimates based on data from identical twins reared apart may reflect environmental similarity because of larger ecological similarities. He says that heritability estimates are valid only if being reared in different families means being reared in totally different environments. We disagree. All that is important is that the separated identical twins are reared in uncorrelated environments, not necessarily different ones. Quantitative genetic analyses address individual differences in a population and attribute these observed differences to genetic and environmental sources of variance. Larger ecological contexts – educational opportunities shared by all twins in Sweden, for example – cannot be a source of individual differences, just as even larger environmental contexts, such as the lack of sunshine during the winter months in northern Sweden, is not a variable because it is the same for all individuals in northern Sweden.
Willerman argues that the use of data for twins reared apart may be misleading because twins reared apart have not had a cotwin in the home, and therefore cannot be affected by potential assimilation or contrast-rating artifacts. Is that not a strength rather than a weakness of the reared-apart twin design?
Hay and Kendler note that we did not emphasize the distinction between shared and nonshared environment. This is a topic of great interest to us (e.g., Plomin & Daniels 1987), but it is not a distinction that we felt was important to highlight when the point we are trying to make is that environmental measures are influenced by genetic factors.
Interpretations and implications concerning the nature of nurture
Most of the comments focused on issues of interpretation and implications of finding genetic influence on environmental measures rather than general issues concerning quantitative genetic analysis. For both interpretations and implications, some commentators felt we went too far and others felt we did not go far enough.
Interpretations about nature
Heritability and environmental measures. Other commentators, among them Hay and Wachs, are concerned about the interpretation of finding heritability for measures of the environment. What we mean by the phrase “genetic influence on environmental measures” is that genetic variance (i.e., heritability) is found for these measures. This is the way we talk about heritability for other phenotypes such as personality and cognitive abilities. Finding heritability does not indicate the process by which genetic variance occurs; the developmental process is likely to involve genotype-environment transactions of the sort Wachs describes. We certainly do not equate heritability with genetic determination. For example, what does it mean when we find that a measure of life events is heritable? There are no schlimazel (Yiddish for “crooked luck”) genes that attract life’s pies in the face. Genetic differences among children might predispose some children to be reckless sensation seekers who put themselves at greater risk for accidents, however. [See Zuckerman: “All Parents Are Environmentalists Until They Have Their Second Child” BBS 10(1) 1987.]
Hay questions our conclusion that genetic influence is significant and substantial for widely used measures of the environment. He says that “genetic estimates are small and virtually never significant at better than the 5% significance level.” Most important, as noted at the beginning of this Response, each attempt so far to replicate the findings described in our target article has shown genetic influence. The reason for the repeated appearance of the 5% significance level in our target article is simply that we chose 5% probability as a standard cut-off for significance; most of the differences are significant at much better than the 5% level. Concerning the magnitude of heritability, most of the available studies did not report heritability and the data were not analyzed using model-fitting procedures. Of those studies in which heritability estimates were reported, the average heritability estimates were 24% for parents’ ratings of their current family environment (Table 1), 26% for retrospective ratings of family environment (Table 5), 15% for social support (Table 10), and .34 for life events (Table 11). The average of these average heritability estimates is .25. Is it not significant if genetics explains 25% of the variance of these widely used measures of the environment?
Hay also notes that very large sample sizes are needed to prove differential heritability, that is, that some environmental measures are more heritable than others. Replication may be a more useful strategy, however. When we find, for example, that three studies in a row using different designs, samples, and measures suggest greater genetic influence for warmth than control, it would be silly not to pay attention to this finding. In the new replication of our results, Bouchard and McGue (1990) again find that the FES second-order control factor shows heritability of about half the magnitude of the heritability for other FES factors. Similarly, another new study mentioned earlier (Moster 1990) replicated our finding that controllable life events show greater genetic influence than uncontrollable life events.
Genetic influence on the environment per se (Kendler, Rutter, Tellegen).Rutter, on the other hand, argues that our position is not radical enough: Genetics influences the distribution of environments, not just environmental measures. Kendler also suggests evolutionary reasons for thinking about genetic influence on the environment itself. Rutter says that it is a “logical non sequitur” to say that finding genetic influence on environmental measures challenges the assumption that measures labelled as environment are in fact measuring the environment. (See also Tellegen.) What we said is that evidence for genetic influence on widely used environmental measures implies that labelling a measure as environmental does not make it an environmental measure. This is not a logical non sequitur, it is a truism or even a tautology. If genetic factors influence environmental measures, then environmental measures are not solely influenced by environmental factors. Rutter’s main point is a good one: Genetic influences could substantially affect an environmental measure, but the association between an environmental measure and an outcome could be environmentally mediated.
Jeopardy and IQ (Turkheimer & Gottesman). Turkheimer & Gottesman argue that finding genetic influence on environmental measures “weakens our already shaky confidence in the meaningfulness of traditional analyses of heritability.” On the contrary, we see Turkheimer & Gottesman’s example which is meant to cast doubt on traditional analyses of heritability, as a good example of why such analyses represent a useful first step in understanding a phenomenon.
Turkheimer & Gottesman pose the hypothetical example of children adding one IQ point for every 10 hours they watch Jeopardy. Turkheimer & Gottesman wonder who would care about heritability if such an association were found. We care. In the thousands of articles on the “effects” of television viewing, this association would be viewed as environmental: Watching jeopardy increases IQ. But what if the tendency to watch Jeopardy shows genetic influence? Turkheimer & Gottesman say that this tendency undoubtedly has a genetic component, but we would counter that this must be assessed rather than assumed, because some environmental measures show little genetic influence. Furthermore, if it is so obvious that individual differences in watching television are genetically influenced, why have the thousands of articles on children’s television viewing never mentioned genetics? Indeed, we bet that watching Jeopardy does not show genetic influence because it is too specific a response.
Nonetheless, if significant genetic influence were found for the tendency to watch Jeopardy – for example, identical twins are more similar than fraternal twins – we would also want to know something about the effect size. How much of the variance in children’s Jeopardy-watching results from genetic factors? This is heritability – merely a descriptive statistic that describes the effect size of genetic influence, just as a correlation is a descriptive statistic that describes the effect size of an association. Heritability does not tell us what processes are responsible for genetic influence, just as a correlation does not tell us what processes are responsible for the association.
If genetic influence on Jeopardy-watching is significant and its effect size substantial, then it raises the possibility of a different interpretation of the Jeopardy-IQ association. The association might be mediated genetically rather than being caused because Jeopardy-watching increases IQ. We need to assess the extent of the genetic contribution to the Jeopardy-IQ association before we can interpret the association. Turkheimer & Gottesman say that the important discovery that needs to be made is that an activity exists that can increase IQ. If the Jeopardy-IQ association is mediated genetically, however, then watching Jeopardy does not increase IQ – brighter children watch Jeopardy.
Contrary to Turkheimer & Gottesman’s interpretation, the point of the Plomin et al. (1977) reply to Roberts (1967) – that it matters whether the effects of genes are mediated through the external environment or through, say, the ribosomes – is not “that variance partitioning can be deceptive, because variance can sometimes be manipulated.” The point was to say that genotype-environment correlation cannot be attributed to genotype as Roberts wished to do, because it is a correlation that involves both genotype and environment. Genotype-environment correlation complicates the partitioning of variance, but it is not “deceptive” or “manipulated.” Neither genetic influence nor genotype-environment correlation stands in the way of programs that enable new environmental interventions. Change the mix of genetic and environmental influence, and descriptive statistics such as heritability that describe that mix will change, just as we would expect a correlation to change when changes occur in the contributors to the association.
H² = 0 (Turkheimer & Gottesman). As can be seen from the commentaries, not everyone is willing to agree with Turkheimer & Gottesman’s conclusion that “H² = 0 is no longer an interesting null hypothesis.” We are not willing to accept that conclusion either. Because genetic influence is so ubiquitous, it is more important to find phenomena that do not show genetic influence than it is to find genetic influence on yet another new measure. For this reason, we think it is especially interesting that the early results of research on the nature of nurture suggest that parental, affection shows greater genetic influence than parental control, that the quality of social support shows greater genetic influence than the quantity of support, and that controllable life events show greater genetic influence than uncontrollable life events.
Genotype-environment correlation (McGue et al.). McGue et al. argue that the terminology of genotype-environment correlation is not too narrow to encompass the nature of nurture. We agree. What we said was that quantitative genetic analyses that limit the search for genetic influences on environmental measures to traditional genotype-environment correlation are limited. We agree that at the level of process genetic influence on environmental measures needs to be phrased in terms of genotype-environment correlation, a correlation between genetic propensities and aspects of the environment. They quote from their recent Science article (Bouchard et al. 1990) concerning effective experiences, but they do not mention their good phrase, “nature via nurture” (in contrast to nature vs. nurture), that captures their view (and that of Scarr & McCartney, 1983) that genotype-environment correlation can be interpreted causally in terms of genes driving experience. Although it is tempting to lean in that interpretative direction, we think it is safer to treat genotype-environment correlation as a correlation that involves joint effects of genes and environment without assuming that variation in one causes variation in the other. In the absence of data to the contrary, it is just as reasonable to view genotype-environment correlation as incorporating bidirectional, reciprocal, developmental transactions between genetic and environmental factors.
Which environmental measures? (Duyme & Capron). Finally, we do not agree with Duyme & Capron’s statement that a measure of the environment may be submitted to genetic analysis only if it is a reflection of behaviors for which it might be possible to localize the genes. Any ostensible environmental measure can be submitted to quantitative genetic analysis. The analysis, rather than our a priori assumptions, will tell us if there is any heritable influence. Also, Duyme & Capron argue that evidence for genetic influence on SES is confounded with prenatal effects. Father-offspring resemblance in adoption studies is not confounded with prenatal effects, however; twin results do not suffer from this problem and yield similar results suggesting genetic influence.
Interpretations about nurture
The commentaries raise many issues concerning the interpretation of nurture in nature-of-nurture analyses.
Validity and reliability (Hay, Wachs). We agree with Hay and Wachs that just the fact that a measure of the environment has been widely used in the literature does not necessarily guarantee its validity or reliability. For example, we agree that videotapes of interactions between mothers and their infants may not be valid (they are reliable), but don’t we get some credit for trying to move beyond paper-and-pencil measures? Similarly, despite the HOME’s methodological problems, isn’t this observational measure a step in the right direction away from research that solely assesses parental childrearing attitudes? We can only use measures that exist, and we will gladly use more valid and reliable measures of the environment as they are developed.
Importance of environmental variance (Goodman & Stevenson, Graham, Johnson). Several commentators emphasize that the bulk of variance of environmental measures is environmental in origin. These comments suggest that we need to repeat what we said: The data suggesting genetic influence on environmental measures by no means imply that the variance of environmental measures is entirely genetic in origin. On the contrary, these data suggest that nongenetic factors are primarily responsible for variance on environmental measures. It is not news to say, however, that environmental measures are environmental in origin. The news is that widely used measures of the environment often show as much genetic influence as measures of behavior such as personality do. Graham says that we have shown ourselves to be “obviously ambivalent” about this when we say on the one hand that “nongenetic factors are primarily responsible for variance on environmental measures” and on the other hand that “genetic influence is significant and substantial on widely used measures of the environment.” Explaining something like 25% of the variance of a complex phenomenon in the social and behavioral sciences is a major achievement, even though it does not account for the majority of the variance.
Environment and measures of the environment (Graham, Tellegen). Several commentaries also addressed the distinction that we draw between the environment and environmental measures. Graham states that “surely the second should be a simple reflection of the first.” Tellegen sees our position as a “contradiction” or “sign of ambivalence” because we discuss our results operationally in terms of genetic Influence on environmental measures rather than assuming that our index of the environment is the environment itself. We wrote against the position that some critics have taken, namely, that if genetic influence is found for a particular measure, then the measure cannot really be a measure of the environment. For example, we disagree with Crusio’s argument that measures should not be called “environmental” if they reflect properties of the individual. Tellegen’s suggestion that we focus on the environment in terms of a latent variable rather than fallible measures of the environment is not to the point.
We agree with Tellegen (and Rutter and others) that the matter is partly semantic regarding how the environment is defined. But we still maintain that the traditional concept of the “environment-out-there” can show no genetic influence because it has no DNA. As we indicated in the first paragraph of our target article, the environment independent of the organism (such as the electric shock applied to the paw of a mouse) does not have DNA, whereas measures of the environment used in the behavioral sciences almost always, and perhaps necessarily, blur the distinction between environment and the organism. Surely we can agree that it is absurd to consider genetic influences on environmental factors such as the weather (temperature, pressure, cloudiness), even though individuals’ reactions to these meteorological conditions differ. Similarly, such life events as economic depression and epidemics of disease are not genetic per se. The issue is that psychological environments are not often like this. Psychological environments are not “out there,” imposed on a passive organism, but rather “in here,” experienced by an active organism who perceives, interprets, modifies, selects, and creates environments. If environment is defined “in here” as experience, then environments per se, not just measures of these environments, can of course show genetic influence.
We made what we think is a conservative decision to limit our discussion to the operational, empirical level of measures of the environment. Our thinking was this: The conceptual issue of the interface between genes and. the environment is debatable and somewhat semantic, but what is clear is that if one analyzes measures of the environment as phenotypes in quantitative genetic analyses, one finds significant and substantial genetic influence.
Other models (Tellegen, Wachs). Tellegen’s expository model depicted in his Figure 1 limits genetic influence on environmental measures to genetic Influence mediated by a particular behavioral trait such as extraversion. His model is functionally similar to the model depicted in Figure 1a in our target article, except that the usual double-headed arrow between an environmental index and behavior is replaced with two separate paths. The same can be said for the model presented in Wachs’s commentary. As we indicated, this is an appropriate model for the analysis of genotype-environment correlation for a particular behavioral trait, but it is limited to finding genetic influence on the environmental measure only to the extent that the environmental measure relates to the particular behavioral phenotype under investigation. Our environment-as-phenotype approach is more radical In that it. considers genetic influence on environmental measures regardless of their association with any particular behavioral phenotype.
Biased recall (McGue et al., Willerman). McGue et al. mention some recent work (Finkel & McGue 1990) on the reliability of retrospective reports of rearing that they interpret as suggesting biased recall. It seems likely that a retrospective report – indeed, any self-report measure – shows biases of recall. We need to understand why such processes of recall and attribution show genetic influence. The data that McGue et al. describe suggest an interesting association between neuroticism and individuals’ perceptions of family nurturance, although we cannot conclude that these data necessarily involve bias just because parents of these individuals do not show the association.
Willerman also notes several possible problems, such as low recall accuracy and reconstruction of past experiences as assessed by self-report. He suggests that adults who believe that their lives have not gone well might be more inclined to have more negative recollections of childhood. This is an empirical question about what mediates genetic influence on a measure involving retrospective ratings of the family rearing environment. Willerman might be right that genetic variance on such a measure arises for reasons of attribution and reconstruction rather than, say, personality. As indicated in our target article and discussed later in this Response, a major direction for research on the nature of nurture is the exploration of processes by which genetic variance emerges for measures of the environment. Willerman assumes that genetic influence on self-report measures of family environment is nothing but personality. This may prove to be the case, but our first effort to explore this issue empirically concludes that most of the genetic influence on family environment measures is independent of genetic influence on major dimensions of personality (Chipuer et al., submitted). Although Willerman considers only self-report measures of family environment, it is particularly impressive to us that significant heritability is found as well for objective measures of the environment such as the HOME and ratings of videotapes of mother-child interaction.
Hay also questions an aspect of the results in one of the studies involving twins reared apart; he notes that identical twins reared apart are somewhat more similar than identical twins reared together (Tables 1 and 11). Contrary to Hay’s lecture about statistical significance in the rest of his commentary, he fails to note that these differences are not significant. Furthermore, he neglects to mention that, in the same tables, data for fraternal twins do not conform to his hypothesis, and that other data from the same study do not show correlations for identical twins reared apart that exceed correlations for identical twins reared together (Tables 1 and 5).
Implications for socialization research (Rowe, Scarr, Schulsinger). Rowe and Scarr use findings of genetic influence on environmental measures and the importance of nonshared environment to cudgel socialization researchers who have ignored genetic influence for so long. More progress is likely to be made, however, by emphasizing that these findings provide an exciting opportunity to look at old issues in a new way. Schulsinger seems almost sad about these findings, suggesting that they open a Pandora’s box. We agree with his specific suggestions, such as the need for valid measures and good hypotheses. We wish Schulsinger would cheer up about these findings, however: We are confident that in the end these findings will foster more and better research on the environment.
Baumrind criticizes genetic research for not having implications for socialization researchers. For example, she says that finding genetic influence on environmental measures “offers little to the socialization researcher.” This position seems refuted by the commentaries from such socialization researchers as Bradley & Caldwell, Bronfenbrenner, Caspi, Lytton, Rutter, Tellegen, and Wachs. If, as Baumrind says, the concern of the socialization researcher is “how to help caretakers in various socio-ecological niches to nurture nature most effectively,” how does she know what that “nature” is that she proposes to nurture?
Implications for behavioral genetic analyses (Boomsma & Molenaar, Bronfenbrenner, Crusio, Hay). Crusio notes that finding genetic influence on environmental measures implies that genotype-environment correlation is important and deserves greater attention in behavioral genetic analyses of variance. Boomsma & Molenaar suggest that behavioral genetic estimates of shared environment, as meager as they are, may be inflated by genetic influence on shared environment when assessed in twin studies. This may explain why twin studies yield higher estimates of shared environmental influence than studies of adoptive siblings do. We agree with both of these points, although we suggest that behavioral genetic research that incorporates specific measures of the environment will take us farther than attempts to refine the traditional anonymous components of variance approach.
Hay recommends that multivariate genetic analyses should be conducted on environmental measures. We agree that such analyses might yield interesting results about the covariance structure of environmental measures.
Bronfenbrenner suggests that the major need for behavioral genetic analyses is not just to include any measure of the environment, but measures of reciprocal social interactional processes such as indices of mutual mother-infant responsiveness. He also makes the important recommendation that such analyses be repeated using two or more contexts such as different social groups that present contrasting conditions.
Political implications (Baumrind, Graham). For some commentators, concerns about the political implications of finding genetic influence seem to be the root cause of unease with genetics. For example, only at the end of Baumrind’s commentary do we see what seems to be motivating her strenuous attempt to discredit quantitative genetic research conceptually and methodologically rather than accepting the much simpler explanation that genetics is important:
When social problems seem intransigent, as so many do today, scientists as well as politicians turn easily to biological explanations. The thrust of the target article (whatever the motives of its authors) Is to elevate genetic determinism as an explanation for human behavior. Cultural determinism and genetic determinism both undermine the attribution of personal responsibility to the individual as a moral agent.
Graham also seems upset by the political implications of these findings. We agree with Graham that finding genetic influence on environmental measures is important without making any claims for its practical value. Now that this issue is on the table, however, we will expose our old-fashioned philosophy of science. We believe that finding genetic influence is compatible with a wide range of social action, including no action at all. Values help us decide what we want to do with such knowledge, and we believe that on the whole better decisions can be made with knowledge than without. For example, finding genetic influence on IQ by no means implies that “them what gots, gets.” Depending on our values, we could argue that scarce educational resources should go to those who most need them to function adequately in our society.
As another example, Graham asks whether finding genetic influence on children’s television viewing should discourage us from interventions to cut down the time children spend watching television. It should not, but for reasons other than the reason given by Graham. He suggests that because genetic factors account for only a quarter of the variance in television viewing, its effect is too weak to carry practical implications. We suggest that decisions to intervene to discourage children’s television viewing depend on our values, not on the magnitude of genetic influence. Graham’s value is that television viewing is bad for children. Our value on this topic happens to be much more extreme: We hate television with a passion, and would blind all the one-eyed monsters in the world if given the chance. Our proposed violent intervention comes from our own values, however, and is unrelated to whether or not genetic factors affect individual differences in television viewing. Other people have different values. For example, an “individuals’ rights” perspective might be that television viewing is a victimless crime that one ought to be able to perpetrate on oneself if that is what one wants to do. Regardless of what our values lead us to recommend, knowledge of etiology might be helpful. For example, given Graham’s implied value that television viewing is bad for children, it would be helpful in his hypothetical television intervention studies to consider the possibility that some children are at greater “risk” for television viewing than others and to target interventions specifically for these children, even if only a quarter of the variance in television viewing is accounted for by genetic factors.
Directions for future research on the nature of nurture
Simonton’s case study of genius is an example of the heuristic value of research on the nature of nurture. In 1988, Simonton wrote a book on genius that proposed an interesting “chance-configuration” theory. In a chapter discussing the origins of genius, he considered parental loss and orphanhood, birth order, cultural enrichment, role models, formal education, and the Zeitgeist. Heredity is mentioned only in passing on a single page. On the basis of Simonton’s present commentary, however, we can hope that another book of genius is budding that considers possible genetic origins of genius and of associations between socialization factors and genius. In this section, we highlight suggestions that commentators have made for future research on the nature of nurture. The major categories of suggestions involve conceptualizations of the environment, measures of the environment, and multivariate analyses. We were especially pleased with the commentary by Waldman & Weinberg because it was entirely forward-looking. We agree wholeheartedly with their exhortation (and Wachs’s) for greater interaction between behavioral geneticists and environmentally oriented researchers. We agree with all four directions they outline: development of reactive and active measures of environmental interactions; identification of method, source, and target factors; investigation of mediators and moderators; and consideration of developmental changes. Their suggestions for research on moderators and consideration of development changes take on special significance because these ideas were not discussed in our article nor in the other commentaries.
Conceptualizations of the environment
Genotype → environment processes (Scarr). Scarr proposes two principles that point to future directions for research and elaborate her theory for genotype → environment effects, a theory that emphasizes that people make their own environments (Scarr & McCartney 1983). Her first principle is that the environment is best construed as an array of opportunities for behaviors to develop. For example, the family can be seen as a diverse opportunity structure for its members.
Several implications of this principle were considered in our target article and the commentaries. For example, we are interested in the possibility that genetic influence on the ways in which organisms interact with their environments might be responsible for the ubiquitous genetic influence found for behavior. Kendler indicates a similar interest: Genes may alter risk for psychiatric disorders by influencing pathogenic or protective features of the environment. Socha extends this idea even further by considering environmental influences on DNA itself, that is, transcriptional and translational changes in responses to the environment. For example, a very active area of molecular genetic research involves DNA responses to environmental stress (e.g., Scandalios 1990).
Scarr’s second principle is that environments have nonlinear effects on behavioral development. Specifically, she suggests that low-quality environments affect development, but environments in the adequate to superior range have little effect beyond genotype-environment correlation. Bradley & Caldwell suggest that both genetic and environmental influences are most powerful at the negative extreme. Future research would do well to investigate these testable hypotheses about etiological differences between the low extreme and the rest of the distribution. A related hypothesis is that results could differ, not just at the extremes of the distribution for a particular population, but in such atypical populations as the severely handicapped (Bradley & Caldwell), in different populations (Duyme & Capron), or in different cultures such as peasant families living in the Peruvian Andes (Baumrind). Mowe reminds us that cross-cultural differences do not lead to simple interpretations.
Parental effects and child effects (Lytton). Lytton presents a very interesting hypothesis concerning the finding that parental warmth appears to show more genetic influence than parental control. His observational work suggests that, for warmth, the direction of effects is from child to parent, whereas for control the direction is more from parent to child. He also suggests that parental control might be shown to be influenced genetically in a twin study of parents, which is in fact suggested by our SATSA findings (Plomin et al. 1989). We agree with Lytton’s suggestion that more research is needed to distinguish between parental effects and child effects. His is one of the only studies of this sort, which is why we referred to it as a pioneering study that is an exemplar of the type of research that is needed.
Moos’s taxonomy (Caspi). Caspi describes Moos’s (1973) sixfold taxonomy, which classifies aspects of the environment. Although, as Caspi indicates, there is as yet little agreement about which aspects and levels of the environment should be analyzed, such classifications will be useful in designing research on the nature of nurture. We agree with Caspi’s provocative argument that we need to keep measures of person and environment as distinct as possible from each other if we ever hope to integrate them explicitly in an interactional theory. Interactionists will not like this position, however.
Objectivity and subjectivity (Tellegen). Tellegen’s distinction between objective-consensual and subjective-experiential environmental latent variables is a good one. It can be viewed as a subset of the suggestion by Waldman & Weinberg to consider method, source, and target variance. These issues could be investigated in a multi-method mode by modeling a common latent trait involving different rating sources including self-report (i.e., a “consensual” common factor). Variance unique to each rating source could then be considered; variance unique to the self-report measure is one way to operationalize the subjective-experiential notion. This objective-subjective distinction is likely to prove useful because genetically influenced processes that can contribute to these two types of environmental measures differ. The subjectivity/objectivity dimension does not so far appear to be related to the extent of genetic influence on environmental measures, however. (See our Figure 2.)
Models of genotype-environment correlation (Tellegen, Wachs). The genotype-environment models proposed by Tellegen and Wachs are helpful in extending our thinking about the interface between nature and nurture. As mentioned earlier, we see these models as similar to the traditional genotype-environment correlation models presented in their simplest form in our Figure 1a. In our view, such models are limited to finding genetic influence on an environmental measure only to the extent that the environmental measure relates to the particular behavioral phenotype included in the analysis. Nonetheless, models of this sort represent one way to think about the organismic phenotypes that contribute genetic variance to measures of the environment. We return to this issue in the final section on multivariate analysis.
Experience (Bronfenbrenner, Caspi, McGue et al., Rutter). Several commentators, including McGue et al. and Rutter, agree with our point that we need better measures of experience, the subjective environment. Caspi, however, finds the distinction between subjective and objective approaches to the environment unsatisfying and discusses an interesting alternative approach that attempts to assess the stimulus context of situations (Block & Block 1981).
Reactive and active measures (Bradley & Caldwell, Caspi). We also emphasized that we need measures that move beyond the passive model of the individual as merely a receptacle for environmental influence, to measures that can capture the individual’s active selection, modification, and creation of environments, a point that Caspi extends in his commentary and in his longitudinal research on social selection. (See also McGue et al.) Bradley & Caldwell remind us that we also need measures of reactive genotype-environment correlation that assess the reaction of others to genetically-influenced characteristics of the individual. Caspi suggests that this is a better way to think about subjective aspects of the environment.
Behavioral/nonbehavioral and person/physical measures (Bronfenbrenner, Graham). Graham suggests that researchers should distinguish between behavioral and nonbehavioral measures of the environment, such as the number of persons per room, because he believes that the genetic contribution to the latter is not measurable. The distinction between behavioral and nonbehavioral measures might be useful, although most environmental measures used by behavioral scientists are not easily divided between those that are behavioral and those that are not. There is probably a great deal of overlap between Graham’s distinction and Bronfenbrenner’s suggestion that we consider personal and physical processes.
Many commentators underscored a point mentioned at the end of our article: A major direction for research at the interface between nature and nurture is multivariate investigation of the antecedents and sequelae of genetic influence on measures of the environment. We stated that multivariate genetic analyses of the phenotypic covariation between behavioral and environmental measures are needed to determine the extent to which such behavioral measures can account for genetic influence on environmental measures. Concerning sequelae, it is possible that associations between environmental measures and behavioral outcomes – for example, life events and depression – are also mediated genetically, if both are influenced genetically. As Rutter notes, a measure of the environment can be influenced genetically, but its association with an outcome could be mediated entirely environmentally.
Such multivariate analysis is the title and main theme of the commentary by Hewitt. Johnson suggests that the liberalism-conservatism dimension that pervades personality and shows substantial genetic influence could be an important mediator of genetic influence on experience. Johnson also proposes a novel strategy for finding mediators of genetic influence on environmental measures. He suggests starting with a strongly genetically influenced trait of parents and investigating how experiences differ for individuals high and low on the trait.
Several commentators criticize our paper for not discussing the processes by which genetic factors affect measures of the environment (Rutter, Socha, Tellegen, Wachs). As mentioned earlier, we felt that the target article would have greater impact if it focused on the phenomenon of genetic influence on environmental measures, which could be solidly documented, rather than speculating about the sources or sequelae of this genetic influence about which very little is as yet known.
Since writing our target article, we have worked on a paper that uses a trivariate quantitative genetic model to investigate the extent to which genetic influence on environmental measures can be accounted for by genetic influence on extraversion and neuroticism (Chipuer et al., submitted). We find that most of the genetic variance on environmental measures is independent of genetic variance on these two major dimensions of personality, suggesting that these personality dimensions do not by themselves explain the puzzle of genetic influence on environmental measures.
Several commentators (Baumrind, Kendler, Lytton, and Socha) predict that molecular genetic studies will help settle questions about genetic influence on behavior. Imagine being able to identify behavior- or environment-relevant DNA variation directly in individuals rather than resorting to indirect estimates of a genetic component of variance derived from twin and adoption studies. Advances in molecular biology are well on the way to making this fantasy a reality (Plomin 1990). It was only 10 years ago that the now standard techniques of the “new genetics” of recombinant DNA were first employed to identify genes responsible for disorders. We predict that 10 years from now, at the turn of the century, molecular genetic techniques will have revolutionized human behavioral genetics. Kendler’s caution is well taken, however: As genetics moves toward reductionist molecular models, it is also important to move out into the environment to understand the inevitably complex interplay between our genes and the environment around us.
In closing, we thank the commentators who contributed their ideas about interpretations and implications and, especially, about directions for future research. We hope that this discussion will stimulate research that leads to new measures of the environment as well as mechanisms to explain genetic influence on environmental measures.