Different approaches can be used to define depression and anxiety: they can be viewed as diagnostic disorders (e.g., major depressive disorder (MDD), generalized anxiety disorder, separation anxiety) that are either present or absent, as a dimension of symptoms ranging from none to many, or as temperamental/personality features (e.g., emotionality, neuroticism, behavioral inhibition) that are continuously distributed within the general population (Murray, Creswell, & Cooper, 2009; Pickles & Angold, 2003). There is good evidence to support a dimensional approach. For instance, symptoms of depression and anxiety that fall below the diagnostic threshold are associated with functional impairment and increase the risk of future episodes that meet the diagnostic criteria (Angold, Costello, Farmer, Burns, & Erkanli, 1999; Pickles et al., 2001; Van den Oord, Pickles, & Waldman, 2003). Nevertheless, it also needs to be borne in mind that high symptom scores cannot be equated with clinical disorder. An additional challenge that research in this area has had to contend with is how to incorporate information when different informants rate children’s symptomatology. In general, family studies have used a categorical approach to defining depression and anxiety and have mainly included clinically referred samples. In contrast, twin and adoption studies of childhood anxiety and depression have generally adopted a dimensional approach to defining psychopathology in community samples.

Estimates of the prevalence of any childhood anxiety disorder are in the order of 3–12 % although rates vary according to the type of anxiety disorder examined (e.g., simple phobia, generalized anxiety, separation anxiety) (Costello et al., 1996; Simonoff et al., 1997). In general, epidemiological studies show that rates of any anxiety disorder are higher in children than adolescents (Costello, Mustillo, Erkanli, Keeler, & Angold, 2003) and are higher in females than males (Lewinsohn, Hops, Roberts, Seeley, & Andrews, 1993; Lewinsohn, Zinbarg, Seeley, Lewinsohn, & Sack, 1997). The gender difference in prevalence appears to emerge early in life (Lewinsohn, Gotlib, Lewinsohn, Seeley, & Allen, 1998). Estimating precise rates of continuity over time is complex given findings that some types of anxiety disorder such as social anxiety increase in prevalence from childhood to adolescence, others such as separation anxiety decrease, and others such as specific phobia show low levels of continuity from childhood to adolescence (Costello et al., 2003). Nevertheless, both anxiety disorders and symptoms during childhood and adolescence show some continuity with anxiety disorders during adult life (Goodwin, Fergusson, & Horwood, 2004; Gregory et al., 2007; Pine, Cohen, & Brook, 2001). However, results of one prospective follow-up study suggest that children with anxiety disorders show relatively well-adjusted adult outcomes unless they had a comorbid depressive disorder (Last, Hansen, & Franco, 1997). Adolescent anxiety disorders (particularly separation anxiety, generalized anxiety, and panic) also appear to act as a risk factor for major depression in adult life (Rutter et al., 2006).

MDD during childhood is relatively uncommon and the 12-month prevalence ranges from 0.5 to 3 % (Birmaher et al., 1996; Harrington, 1994). During adolescence, the prevalence of MDD and depressive symptoms that fall below the diagnostic threshold increase dramatically (Lewinsohn, Rhode & Seeley 1998). Estimates of the 12-month prevalence of depressive disorder in adolescence range from 2 to 8 %, and the figure for lifetime adolescent depression is 20 % (Birmaher et al., 1996; Costello, Erkanli, & Angold, 2006; Harrington, 1994). In childhood an equal proportion of boys and girls are affected (or a slight excess of boys). However, in adolescence, the ratio of affected females to males is 2:1 which mirrors the pattern that is seen in adult life (Costello et al., 2006; Harrington, 1994). High levels of depressive symptoms that fall below the diagnostic threshold are associated with functional impairment and deliberate self-harm and predict future episodes of depressive disorder (Angold et al., 1999). A significant proportion of depressed adolescents continue to have mental health problems and poor social outcomes in adult life (Dunn & Goodyer, 2006; Rutter et al., 2006).

Depression and anxiety in young people co-occur more commonly than would be expected by chance with rates of anxiety disorders in adolescents with depression ranging from 20 to 75 %. Variation in estimates is at least partly due to study differences in age distribution and time frames for estimating comorbidity (Angold, Costello, & Erkanli, 1999; Kovacs & Devlin, 1998). This co-occurrence has been identified in both clinical studies of children and adolescents and general population samples that have examined subclinical levels of depression and anxiety symptoms (Brady & Kendall, 1992; Kovacs & Devlin, 1998). Both clinical- and population-based studies tend to report that anxiety symptoms or disorders typically precede depressive symptoms or disorders (Avenevoli, Stolar, Li, Dierker, & Merikangas, 2001; Kovacs, Gatsonis, Paulauskas, & Richards, 1989) although there are exceptions to this finding (Moffitt et al., 2007) and certain types of anxiety disorder, namely, panic disorder, are more likely to follow than precede depression (Lewinsohn et al., 1997). However, the general pattern that anxiety commonly precedes depression has led to suggestions that anxiety may be a developmental precursor of depression (Merikangas, 1993). As reviewed by Rutter, Kim-Cohen & Maughan (2006), the precise underlying reasons and direction of the sequential relationship between anxiety and depression are currently unclear, and it seems likely that the answers may differ for different types of anxiety disorder. However, it seems safe to conclude that in general, anxiety disorders constitute a risk factor for later depression.

The vast majority of twin studies find evidence for a significant genetic component to depressive symptomatology (reviewed in Rice et al., 2002a) with average heritability estimates around 40 %. However, it is apparent that there is wide variation in the genetic parameter estimates across different studies (range 11–72 %) which seem at least partly attributable to measurement (i.e., interview versus questionnaire) and rater effects (i.e., who reports on the child’s symptoms). It is widely known that correlations between different informants are modest (Cantwell, Lewinsohn, Rohde, & Seeley, 1997; Verhulst, Dekker, & van der Ende, 1997), and it seems that each informant provides meaningful information for their own perspective (e.g., Boomsma, van Beijsterveldt, & Hudziak, 2005; Verhulst et al., 1997). Thus, it may be that different informants rate slightly different phenotypes or are influenced by different factors (Hay et al., 1999). The rater differences for twin studies of depression are particularly puzzling as their direction differs across different studies (Eaves et al., 1997; Rice, Harold, & Thapar, 2002b; Thapar & McGuffin, 1994). This issue requires further investigation as it has implications for refining the phenotype for molecular genetic studies. One study has attempted to overcome this issue by using latent variables and decomposing genetic and environmental influences on variation that is common to both parent and child ratings (Kendler, Gardner, & Lichtenstein, 2008). Another possible explanation for the wide variation in genetic estimates across twin studies is that the etiology of depressive symptoms differs between childhood and adolescence. One consistent finding from twin studies is that the influence of genetic factors on depression is small and nonsignificant in childhood and increases in adolescence (Eley & Stevenson, 1999; Rice et al., 2002b; Scourfield et al., 2003; Silberg et al., 1999; Thapar & McGuffin, 1994). These observed age-related differences in the contribution of genes and environment present the possibility that such effects might partly contribute to differences in parameter estimates between studies. To summarize, approximately 30–50 % of the variation in adolescent depressive symptoms is due to genetic influences, but for depressive symptoms in childhood, the figure is generally much smaller and nonsignificant.

Questionnaire measures of anxiety show significant genetic influence (Eaves et al., 1997; Eley et al., 2003; Legrand, McGue, & Iacono, 1999; Rice, van den Bree, & Thapar, 2004; Thapar & McGuffin, 1997). Some interesting findings arise from the few studies that have separately examined different dimensions of anxiety disorder symptoms (Eaves et al., 1997; Eley et al., 2003). There is fairly consistent evidence from these studies that for separation anxiety, shared environmental influences are present (Eaves et al., 1997; Eley et al., 2003; Silberg, Rutter, Neale, & Eaves, 2001). This is in contrast to results from other dimensions of anxiety such as generalized anxiety disorder, which is substantially genetically influenced (Silberg et al., 2001). As is the case with depressive symptoms, rater effects again appear to be important—two studies of self-rated anxiety found no evidence for a genetic component to anxiety symptoms (Eaves et al., 1997; Thapar & McGuffin, 1995—boys only).

There have been fewer studies of internalizing symptoms than of depressive and anxiety symptoms. However, results are quite consistent across studies, showing modest genetic influences between 30 and 40 % (e.g., Deater-Deckard, Reiss, Hetherington, & Plomin, 1997; Gjone & Stevenson, 1997; Schmitz, Fulker, & Mrazek, 1995). Boomsma et al. (2005) did not find any marked changes in the contribution of genetic factors to variation in the anxiety/depression scale of the CBCL between the ages of 7 and 12 years which is in contrast to the age-related findings in the genetic etiology of depression between childhood and adolescence although this difference could be because that particular study did not include adolescent twins.

There has only been one study of depressive disorder in young people (Glowinski et al., 2003) and no twin study of anxiety disorders in children and adolescents although the etiology of depression and anxiety disorder symptom counts derived from a semi-structured clinical interview has been examined (e.g., Eaves et al., 1997; Silberg et al., 1999). Instead, a number of studies have used the DeFries and Fulker regression method (DeFries & Fulker, 1985) which involves examining the etiology of selected high scorers on questionnaire measures and comparing this to the etiology of lower symptom levels (Deater-Deckard et al., 1997; Eley, 1997; Gjone et al., 1996; Rende, Plomin, Reiss, & Hetherington, 1993; Rice et al., 2002b).

Glowinski et al. (2003) undertook a twin study of adolescent MDD in females and reported a heritability estimate of 40 % (95 % confidence interval = 24, 55). This estimate is consistent with results from a meta-analysis of adult twin studies which reported a heritability estimate of 37 % (95 % confidence interval = 31, 42) for MDD (Sullivan, Neale, & Kendler, 2000). However, the evidence from twin studies using the DF method to examine high levels of depressive symptomatology is very consistent and shows that these are less heritable and more influenced by shared environmental factors than depressive symptoms within the normal range. This surprising finding might reflect measurement differences (high scores on questionnaire are not synonymous with depressive disorder). This explanation was evaluated by Glowinski et al. (2003) where they compared heritability estimates for a broad phenotype of sadness and/or anhedonia lasting 2 weeks to that of a diagnosis of MDD. They found that the broader phenotype was largely influenced by shared environmental influences whereas a diagnosis of MDD depended on both heritable and environmental factors. As these authors pointed out, this suggests that shared environmental factors may be important in the etiology of a broad depressive phenotype but not in the diagnosis or syndrome of MDD. This finding is in contrast to the available evidence from adults where the genetic correlation between depressive and anxiety symptoms with diagnostic criteria is high (Gjerde et al., 2011). Nonetheless, the findings from childhood/adolescent anxiety/depression illustrate the importance of precision in diagnostic definitions for molecular genetic studies and of not necessarily assuming concordance between findings from childhood, adolescent, and adult depression. On the basis of current evidence, it would seem inappropriate to focus gene finding studies on adolescents with high levels of depressive/internalizing symptoms. Thus, in contrast to adolescent MDD which shows moderate genetic influences accounting for around 40 % of the phenotypic variation, high levels of depressive symptoms are mainly influenced by shared environmental factors. It seems likely that this difference in etiology reflects subtle differences between the phenotype of depressive disorder and high levels of depressive symptoms assessed by questionnaire.

Results from studies examining the genetic and environmental contributions to covariation between anxiety and depression are relatively consistent. Two cross-sectional studies (Eley & Stevenson, 1999; Thapar & McGuffin, 1997) show that anxiety and depression symptoms are associated mainly because they share a common genetic liability. Silberg et al. (2001) used a longitudinal design to examine the genetic and environmental architecture of the association between three dimensions of anxiety (overanxious disorder (OAD), simple phobias, and separation anxiety) and depression in girls. They found evidence for a common set of genes influencing early (between ages 8 and 13) OAD and simple phobias and later depression (between ages 14 and 17) although later OAD and phobias did also have unique genetic influences. Another longitudinal study again found that early anxiety and later depression shared a common genetic etiology and that the link could not be explained by earlier depression (Rice et al., 2004). Thus, both cross-sectional and longitudinal studies suggest that anxiety and depression in childhood and adolescence are associated because they share a common genetic liability.

Longitudinal twin studies suggest that genetic influences on depression and internalizing symptoms differ between childhood, adolescence, and adulthood (Kendler et al., 2008; Lau & Eley, 2006; Scourfield et al., 2003). There is evidence for both genetic attenuation (where genetic influences that impact at one developmental period decline in their influence during subsequent periods) and genetic innovation (where “new” genetic influences become active over time) (Kendler et al., 2008; Lau & Eley, 2006; O’Connor, McGuire, Reiss, Hetherington, & Plomin, 1998; Scourfield et al., 2003). Nevertheless, there is some inconsistency in the pattern of results across longitudinal studies, and this is likely at least in part attributable to the measure of depressive symptoms employed and the age range of the sample studied. A number of studies have reported that both genetic and non-shared environmental influences contribute to the continuity of depression/internalizing symptoms over time during childhood (Bartels et al., 2004) and early to late adolescence (Kendler et al., 2008; Lau & Eley, 2006; O’Connor, McGuire, Reiss, Hetherington, & Plomin, 1998). In contrast, two other studies have reported a role for shared environmental influences in explaining continuity of symptoms (Schmitz et al., 1995; Scourfield et al., 2003). One explanation for some of the inconsistencies across studies is that genetic influences on continuity of symptoms are found only within particular developmental periods (Lau & Eley, 2010). Thus, studies that cross developmental periods (e.g., from childhood to adolescence, from adolescence to early adulthood) tend to find evidence for genetic innovation (i.e., new genetic influences become active over time) (Kendler et al., 2008; Lau & Eley, 2006; Scourfield et al., 2003). It has been suggested that genetic innovation across the transition from childhood to adolescence may be due to genetic influences on hormonal changes associated with puberty (Angold, Costello, & Worthman, 1998; Kendler et al., 2008), structural brain changes (Giedd et al., 2010), or greater gene–environment correlation which increases around adolescence as young people have greater independence in selecting and shaping their environments. One twin study found evidence to suggest that gene–environment correlation with stressful life events accounted for the increase in genetic influences on depression from childhood to adolescence (Rice, Harold, & Thapar, 2003). Reports of genetic heterogeneity between childhood, adolescent, and adult depression (Kendler et al., 2008) suggest that developmental genetic differences may provide an explanation for the low levels of continuity between childhood and adult depression (Harrington et al., 1990; Weissman et al., 1999).

Adoption studies provide another approach to disentangling genetic and environmental influences on behavior. As with twin studies, adoption studies depend on comparisons of the similarity between pairs of relatives who differ in their degree of genetic relatedness. For example, comparisons of the similarity between biological parents and children and adoptive parents and children can be made with greater similarity between biological parents and children consistent with a role for genetic factors, while greater similarity between adoptive parents and children consistent with a role for shared environmental factors. The strengths and weaknesses of adoption studies are described elsewhere (e.g., Plomin et al., 2008; Rutter, Pickles, Murray, & Eaves, 2001).

Adoption studies of child depression/anxiety (and other types of genetically sensitive parent–offspring design described below) have been remarkably consistent in finding that the intergenerational transmission of depression/anxiety is primarily due to environmental factors. There have been three adoption studies of depression/anxiety in childhood/adolescence (Eley, Deater-Deckard, Fombonne, Fulker, & Plomin, 1998; Tully, Iacono, & McGue, 2008; Van den Oord, Boomsma, & Verhulst, 1994). Van den Oord et al. (1994) examined internalizing symptoms in an international adoptee sample using a sibling design. Correlations between nonbiological (adoptive) siblings were as high as those between biological siblings suggesting strong shared environmental influences and no evidence for substantial genetic effects. Eley et al. (1998) studied the Colorado Adoption Project cohort using both a sibling and a parent–offspring design and again found no evidence for genetic effects. In fact correlations between parents and children were very low suggesting non-shared environmental influences (although slightly different phenotypes were examined in parents and offspring which may have reduced correlations between the generations). However, in line with the results of van den Oord and colleagues, when mothers rated their children’s internalizing problems, correlations were higher and suggested some shared environmental influences (which may be partly attributable to shared rater variance as this pattern was only observed when mothers rated their own and their child’s symptoms). The most recent adoption study by Tully et al. (2008) examined the similarity between adoptive (unrelated) parents and adolescents for lifetime MDD as well as a control sample of non-adopted children and their biological parents. Adoptive adolescents whose unrelated parents had experienced lifetime MDD showed elevated rates of depression compared with adopted children whose unrelated parents had not had MDD (odds ratio = 2.19) which is consistent with an important shared environmental component to the intergenerational transmission of depression. Inherited influences did make some contribution as the same comparison in the biologically related group resulted in a slightly, though not significantly, higher risk to offspring (odds ratio = 2.96).

Thus, the results from adoption studies are at odds with those from twin studies in that adoption studies find that genetic influences are unimportant in the etiology of depressive symptoms. There are several possible explanations for these contrasting findings: First, twin studies rely on comparisons being made between siblings of exactly the same age; thus any developmental differences in the etiology and phenomenology of depression will be controlled for. In contrast, any developmental differences between the phenotype in the parent and offspring generations as well as any genetic heterogeneity between the generations may influence results (i.e., if different sets of genes influence depression in offspring and parents). Passive gene–environment correlation may account for some of the differences in results between twin and adoption studies of depression in childhood and adolescence. Passive gene–environment correlation occurs when children are exposed to family environments provided by their parents that are correlated with their genetic characteristics. It does not seem unlikely that this may occur with depression given that it is well documented that children of depressed parents often experience a disharmonious or stressful family environment (e.g., compromised parenting, exposure to stressful family live events) (Downey & Coyne, 1990) in addition to presumably inheriting genes that increase vulnerability to depression. In a classic twin design, passive gene–environment correlation would generally be subsumed within the genetic parameter estimate. The adoption design is thought to remove passive gene–environment correlation. Thus, this might account for differences in the findings of adoption and twin studies and also suggests that passive gene–environment correlation may play a role in the etiology of depression in young people. Finally, passive rGE may also occur in the prenatal environment where maternal genotype is correlated with exposure to the prenatal environment. This is another way in which heritability estimates from twin studies may, in theory, be inflated. Examples of prenatal rGE have been reported for childhood conduct problems and symptoms of attention deficit hyperactivity (D’Onofrio et al., 2008; Rice et al., 2009; Thapar et al., 2009). However, this has not yet been examined for depression or anxiety in young people.

Ongoing research is examining genetic and environmental contributions to the parent–child transmission of depression using alternative research designs such as the children of twins design (Silberg, Maes, & Eaves, 2010; Singh et al., 2011) and an IVF design (Lewis, Rice, Harold, Collishaw, & Thapar, 2011; Thapar et al., 2007). Like adoption studies, these other types of parent–offspring research design have also implicated the importance of environmental factors in the intergenerational transmission of depression. Two independent studies have used the children of twins design to examine this question. The rationale of the children of twins design is that the offspring of adult identical (monozygotic; MZ) twins will be social cousins but genetic half-siblings. To take the example of depression, if parental depression involves a genetically mediated risk to offspring, the risk should apply as much to the offspring of the MZ co-twin as to the children reared by the MZ twin with depression. In contrast, this will not apply to the same extent to the offspring of fraternal (dizygotic; DZ) twins because they share only 50 % of their segregating genes on average whereas MZ twins share 100 % of their genes. Two studies have used this approach and found evidence consistent with environmental transmission of depression within families (Silberg et al., 2010; Singh et al., 2011). One study used an IVF design to examine the transmission of maternal depression to child internalizing problems (Lewis et al., 2011). In this design, parents differ in genetic relatedness to their children as a result of assisted reproductive technologies using either the parents own gametes or donated gametes, and the design is informative for disentangling prenatal influences from maternal genetic factors as well as for examining genetic and environmental influences on the intergenerational transmission of psychopathology and other traits (Thapar et al., 2007). Again, that study found support for environmental transmission of depression although there was evidence that child gender may contribute to the generational transmission of maternal depression with environmental factors contributing slightly more to mother–daughter transmission than mother–son transmission. Thus, studies using different types of research design to examine the intergenerational transmission of evidence depression have been very consistent. The intergenerational transmission of anxiety has not yet been well examined, and it is not known if there is genetic heterogeneity between generations and how this might affect results. Future molecular genetic studies may be helpful in addressing this question. Preliminary evidence suggests that passive rGE contributes to the intergenerational transmission of depression (Harold et al., 2011; Silberg et al., 2010) and this is one of the several reasons for differences in findings between twin studies of child/adolescent depression and intergenerational studies of depression.

The pattern of strength and weakness of these newer genetically sensitive intergenerational designs is considered elsewhere (D’Onofrio et al., 2003; Thapar et al., 2007). However, a brief consideration of their strengths in comparison to the classic twin and adoption methods is given below. Both designs provide a useful approach for examining questions of environmental causation in that they are able to identify whether associations between an environmental risk factor (e.g., maternal smoking in pregnancy) and a child outcome are attributable to gene–environment correlation and therefore inconsistent with a true environmental influence (Thapar & Rutter, 2009). One particular strength of the COT design is the ability to simultaneously estimate genetic and environmental influences in the parent and child generation in addition to genetic and environmental transmission paths without the need for strong assumptions (For instance, that the same genes influence both generations which is an assumption required by the extended twin family design; D’Onofrio et al., 2003). One particular strength of the IVF design is the separation of maternal genotype from the prenatal environment in unrelated mother–child pairs meaning that the design provides a powerful test of prenatal passive rGE (Thapar et al., 2007). The inclusion of groups of children who are related to one parent and not the other (i.e., children born following sperm donation and egg donation) in the IVF design also allows a comparison of the relative influence of maternal and paternal genotype on child outcomes (Rice & Thapar, 2010).

Molecular genetic studies of childhood/adolescent anxiety and depression are in their infancy and have been guided by results from studies of adult depression (see Lau & Eley, 2010 for a review). Because most common susceptibility variants are likely to confer only small increases in risk for psychopathology, large sample sizes are required; therefore, there have been inconsistencies in genetic association findings to date. Molecular genetic studies of anxiety and depression in young people have tended to use a candidate gene approach and have focused on functional polymorphisms in genes involved in pathways thought to be important in depression including stress response and hypothalamic–pituitary–adrenal (HPA) axis functioning. Of the small number of genetic association studies of childhood/adolescent MDD, most have relied on small sample sizes. Pharmacogenetic studies of adolescent depression have recently begun following reports of genetic variation influencing treatment responses to antidepressants in adults (Lekman et al., 2008). Two small pharmacogenetic studies have reported genetic influences on poor treatment outcome in adolescent depression (Brent et al., 2010; Kronenberg et al., 2007). The first reported lower efficacy of citalopram (a selective serotonin reuptake inhibitor; SSRI) and higher suicidality scores for adolescents homozygous for the S variant of the serotonin transporter gene (Kronenberg et al., 2007). Since the S variant is associated with higher levels of serotonin in the synaptic cleft, and an SSRI will essentially have this same effect (by preventing the reuptake of serotonin into the cell), this is a biologically plausible result. The second examined antidepressant response in adolescents unresponsive to an SSRI and reported that genotypes in FKBP5, a gene that codes for a protein causing subsensitivity of the glucocorticoid receptor (Tatro, Everall, Kaul, & Achim, 2009), are associated with suicidal events and behavior (Brent et al., 2010).

In children/adolescents, one small study has reported significant association between the S variant and depression using both a case–control design and a family-based association design (Nobile et al., 2004). However, the S variant has also been associated with childhood aggression as opposed to depression (Beitchman et al., 2006). Thus, molecular genetic association studies of childhood/adolescent depression have only just begun and tend to rely on small samples which means that replication of findings is particularly important. As described below, there are also issues relating to how best to define the phenotype for molecular genetic studies of depression in young people. However, there are a number of ongoing molecular genetic studies of recurrent adult MDD with an early onset (Levinson et al., 2007; Mill et al., 2008).

Two main distinctions between types of gene–environment interplay have been made: gene–environment correlation (rGE) and gene–environment interaction (G × E). Although gene–environment correlation and interaction are considered separately, it is likely that gene–environment correlation and interaction may simultaneously influence risk for depression and anxiety (Eaves, Silberg, & Erkanli, 2003). rGE simply refers to the fact that genes and environment are correlated and is defined as genetic influences on exposure to the environment. Three types of gene–environment correlation have been differentiated—passive, active, and evocative (Plomin et al., 2008). Active and evocative gene–environment correlations arise because the child’s genetically influenced characteristics and behavior correlate with the environment that they “create,” evoke, or select, while passive rGE arises because parental genes are correlated with the environments they provide for their child. In practice, it is difficult to differentiate active and evocative rGE, but they are thought to be subtly different—evocative rGE refers to an individual evoking a response from another person because of their behavior (which is partly influenced by their genes), while active rGE refers to an individual actively seeking out an environment that is correlated with their genes, sometimes known as “niche-fitting” (Scarr & McCartney, 1983). In most circumstances, parents provide both genes and environment for their children (Jaffee & Price, 2008; Plomin et al., 2008) which means that the rearing environment is correlated with genetic characteristics in the parental generation, and because parents pass genes on to their offspring, also in the child generation. Passive rGE can therefore be said to refer to the possibility that in the parental generation, a seemingly environmental factor (e.g., family stress, parenting style) is in fact a marker of parental genetic predisposition (e.g., to psychopathology, personality style) and these same genes then co-segregate in the parent’s offspring (Jaffee & Price, 2008).