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Childhood maltreatment the corticotropin-releasing hormone receptor gene and adult depression in the general population.

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Neuropsychiatric Genetics
Childhood Maltreatment, the Corticotropin-Releasing
Hormone Receptor Gene and Adult Depression in the
General Population
Hans J€orgen Grabe,1* Christian Schwahn,2 Katja Appel,1 Jessie Mahler,1 Andrea Schulz,1
Carsten Spitzer,3 Kristin Fenske,4 Sven Barnow,5 Michael Lucht,1 Harald J€urgen Freyberger,1
Ulrich John,6 Alexander Teumer,7 Henri Wallaschofski,2 Matthias Nauck,2 and Henry V€olzke8
Department of Psychiatry and Psychotherapy, University of Greifswald, Greifswald, Germany
Institute of Clinical Chemistry and Laboratory Medicine, University of Greifswald, Greifswald, Germany
Department of Psychosomatic Medicine and Psychotherapy, University of Hamburg, Hamburg, Germany
Department of Biological and Clinical Psychology, University of Greifswald, Greifswald, Germany
Institute of Clinical Psychology, University of Heidelberg, Heidelberg, Germany
Institute of Epidemiology and Social Medicine, University of Greifswald, Greifswald, Germany
Interfaculty Institute for Genetics and Functional Genomics, University of Greifswald, Greifswald, Germany
Institute for Community Medicine, University of Greifswald, Greifswald, Germany
Received 5 March 2010; Accepted 1 September 2010
Dysregulations of the hypothalamic-pituitary-adrenal (HPA)
axis have been implicated in the pathogenesis of depressive
disorders and the corticotropin-releasing hormone (CRH) was
found to modulate emotional memory consolidation. Recently,
two studies have reported an interaction between childhood
abuse and the TAT–haplotype of the CRH-Receptor Gene
(CRHR1) connecting childhood adversities and genetic susceptibility to adult depression. We tested the hypothesis of an
interaction of childhood maltreatment with single nucleotide
polymorphisms (SNPs) and haplotypes of the CRHR1 gene not
previously investigated. Caucasian subjects (n ¼ 1,638) from the
German general population (Study of Health in Pomerania,
SHIP) were analyzed. As in the previous studies, childhood abuse
and neglect were assessed with the Childhood Trauma Questionnaire (CTQ) and depression with the Beck Depression Inventory
(BDI-2). The CRHR1-SNPs were genotyped on the Affymetrix
Genome-Wide Human SNP Array 6.0 platform. We identified an
interaction between the TAT–haplotype and childhood physical
neglect. The interaction with physical neglect showed significant
(P < 0.05) results in 23 of the 28 SNPs, with rs17689882
(P ¼ 0.0013) reaching ‘‘gene-wide’’ significance. Although we
did not replicate the specific interaction of abuse and the
TAT–haplotype of the CRHR1 gene we confirmed the relevance
of an interplay between variants within the CRHR1 gene and
childhood adversities in the modulation of depression in adults.
The largest effect was found for rs17689882, a SNP previously not
analyzed. Relevant sample differences between this and prior
studies like lower BDI-2 scores, less childhood maltreatment and
higher psychosocial functioning may account for the differences
in gene–environment interaction findings. 2010 Wiley-Liss, Inc.
2010 Wiley-Liss, Inc.
How to Cite this Article:
Grabe HJ, Schwahn C, Appel K, Mahler J,
Schulz A, Spitzer C, Fenske K, Barnow S,
Lucht M, Freyberger HJ, John U, Teumer A,
Wallaschofski H, Nauck M, V€
olzke H. 2010.
Childhood Maltreatment, the CorticotropinReleasing Hormone Receptor Gene and Adult
Depression in the General Population.
Am J Med Genet Part B 153B:1483–1493.
Additional Supporting Information may be found in the online version of
this article.
Grant sponsor: Federal Ministry of Education and Research (joint grant
together with Siemens Healthcare, Erlangen, Germany); Grant numbers:
01ZZ9603, 01ZZ0103, 01ZZ0403, 03ZIK012; Grant sponsor: German
Research Foundation; Grant number: 1912/5-1.
Hans J€
orgen Grabe and Christian Schwahn contributed equally to this
*Correspondence to:
Hans J€
orgen Grabe, M.D., Department of Psychiatry and Psychotherapy,
Ernst-Moritz-Arndt-University of Greifswald, HANSE-Klinikum Stralsund,
Rostocker Chaussee 70, 18437 Stralsund, Germany.
Published online 18 October 2010 in Wiley Online Library
DOI 10.1002/ajmg.b.31131
Key words: major depression; hypothalamic-pituitary-adrenal
(HPA) axis; childhood neglect; general population; epidemiology; CRHR1 gene; gene–environment interaction
As psychosocial and genetic factors contribute to the vulnerability
to major depressive disorders (MDD) the investigation and
understanding of possible interactions between environmental and
genetic factors have increasingly gained importance [Sullivan et al.,
2000; Zimmermann et al., 2008; Caspi et al., 2010]. There is growing
evidence that dysregulation of the hypothalamic-pituitary-adrenal
(HPA) axis is an important mechanism in the pathophysiology of
MDD [Holsboer et al., 1982; Heim et al., 1997; De Kloet, 2004]. The
physiological stress response is primarily mediated by the release of
hypothalamic corticotrophin-releasing factor, also known as corticotropin-releasing hormone (CRH). This stimulates adrenocorticotropin release from the anterior pituitary, which in turn induces
the release of cortisol from the adrenal cortex. The activity of CRH
at the CRH type 1 receptor (CRHR1) in extrahypothalamic regions
is also thought to produce symptoms of anxiety and depression
[Holsboer, 1999; Reul and Holsboer, 2002]. In addition, elevated
CRH concentrations in the cerebrospinal fluid and altered CRHR1
messenger RNA expression were found in depressed patients
[Nemeroff et al., 1984; Merali et al., 2004]. Some studies have
suggested that early life stress precedes the HPA axis hyperactivity
observed in MDD [Heim and Nemeroff, 2001; Carpenter et al.,
2004]. Accordingly, Bradley et al. [2008] investigated the association between childhood abuse and 10 single nucleotide polymorphisms (SNPs) of the CRHR1 gene in a sample of 422 subjects who
were predominantly African American (97.4%) with low socioeconomic status and high rates of lifetime trauma. The subjects were
evaluated while waiting for their medical appointments in a large,
urban, public hospital. A distinct TAT haplotype formed by the
three most significant CRHR1 SNPs (rs7209436, rs110402, and
rs242924) appeared to moderate the effect of child abuse on the risk
for adult depressive symptoms. The authors reported that these
effects were supported by similar findings in a smaller independent
sample (n ¼ 199) with subjects who were predominantly Caucasian
(87.7%). Despite some differences in the genotype distribution in
the different strata of the replication sample the authors did not
provide any P values for the gene environment interaction in this
sample. The appropriate method for the additive interaction analyses with a binary outcome measure would have been the calculation of the Relative Excess of Risk due to Interaction (RERI) [Knol
et al., 2007; Grabe et al., 2009]. Thus, it remained questionable if the
findings of the first sample have been replicated by the second
‘‘supportive’’ Caucasian sample. In an extended sample of the
previous study Heim et al. [2009] confirmed the protective effect
of the rs110402 A-allele against the negative emotional consequences of childhood abuse in the male subsample only. This effect was
carried by the exposure ‘‘physical abuse’’ only but not by the other
exposure dimensions (sexual and emotional abuse). Polanczyk et al.
[2009] replicated the interaction between childhood maltreatment
and the TAT haplotype of the CRHR1 gene in the E-Risk Study
comprising 999 Caucasian females. This interaction was not confirmed in the Dunedin Multidisciplinary Health and Development
Study comprising 899 males and female participants. Only in the ERisk Study the retrospective self-report Childhood Trauma Questionnaire (CTQ) [Bernstein et al., 2003] was used like in the study of
Bradley and coworkers, but not the Dunedin Study that relied on
five different sources of information generating a cumulative
exposure of the first decade of life [Polanczyk et al., 2009].
Replication of findings in gene–environment interaction research is very important to the field but often replication is
compromised by the use of different assessment procedures for
life events and outcomes. We applied therefore, the same questionnaires for the assessment of childhood maltreatment and
depression as in the study of Bradley et al. [2008]. We tested the
hypothesis of an interactive effect between childhood maltreatment
and variants of the CRHR1 gene in a large Caucasian general
population sample from a rural region in Germany. Importantly,
our sample showed less depression, lower rates of childhood
maltreatment and higher psychosocial functioning as compared
to prior studies. First, we aimed to replicate the specific finding on
the interaction with the TAT–haplotype. Second, we selected the
most effective childhood stressor for interaction effects from the
TAT–haplotype interaction analyses and extended the analyses to
other SNPs and haplotypes of the CRHR1 gene that were not fully
covered by the previous studies.
Sampling and Phenotyping Methods
Sample and sample recruitment. Data from the Study of
Health in Pomerania (SHIP) were used [John et al., 2001; Grabe
et al., 2005]. The target population was comprised of adult German
residents (20–79 years) in northeastern Germany living in three
cities and 29 communities, with a total population of 212,157. The
net sample (without migrated or deceased persons) comprised
6,267 eligible subjects, of which 4,308 Caucasian subjects participated in SHIP-0 (1997–2001). Follow-up examination (SHIP-1)
was conducted 5 years after baseline and included 3,300 subjects.
All participants gave written informed consent. Since 2007, the
‘‘Life-Events and Gene–Environment Interaction in Depression’’
(LEGENDE) study has been ongoing which is based on the SHIP-1
sample. Of the SHIP-1 sample (n ¼ 3,300), 80 subjects were deceased and 677 subjects refused participation in the LEGENDE
study. N ¼ 1,683 subjects have already participated in this ongoing
study, 860 subjects have not responded yet to invitation letters and
are in the process of telephone recruitments and home visits. From
1,683 consecutively recruited subjects with complete data sets five
subjects were excluded from the final analyses because of the
diagnosis of bipolar disorder based on a diagnostic interview
[Wittchen et al., 1998]. Preliminary analyses including these five
subjects indicated however, that the inclusion or exclusion did not
change the results. Forty subjects were excluded a priori because of
an unreliable or inconsistent performance during the interview
according to the judgment of the interviewer. Thus, 1,638 were
eligible for the genetic analyses of the genetic main effect and of the
haplotype block structure in our sample (Table I). Because of some
missing values in the CTQ [Gast et al., 2001; Bernstein et al., 2003],
abuse could be analyzed in 1,611 subjects, emotional neglect in
1,614 subjects and physical neglect in 1,616 subjects. The Beck
TABLE I. Description of the Sample (n ¼ 1,638)
(School) educationa
<10 years/elementary school
10 years/junior high
High school
University degree
Employment statusa
Housewife/maternity leave
Part-time employed
Fully employed
Social welfarea
Household monthly income (n ¼ 1,602)b
<500 s
500–899 s
900–1,299 s
1,300–1,799 s
1,800–2,299 s
2,300–2,799 s
2,800–3,299 s
3,300–3,799 s
3,800 s
Marital statusa
Divorced/living separately
Partnership (married or living with partner)a
Abuse (n ¼ 1,611)c
Physical neglect (n ¼ 1,616)
Emotional neglect (n ¼ 1,614)
Phenotype measures. Current depressive symptoms were assessed using the BDI-2, which is a 21-item self-report questionnaire
with high reliability and validity [Beck and Steer, 1987]. The BDI-2
was used as continuous outcome measure in all analyses. The CTQ
was used for the self-report screening of childhood maltreatment
including emotional, sexual, and physical abuse as well as emotional
and physical neglect [Gast et al., 2001; Bernstein et al., 2003]. It has a
total of 28 items that are rated on a five-point Likert scale with
higher scores indicating a high degree of traumatic experiences. In
addition to a dimensional scoring procedure, the manual provides
threshold scores to determine the severity of abuse and neglect
(none ¼ 0, low ¼ 1, moderate ¼ 2, and severe to extreme ¼ 3). The
distribution of the BDI-2 scores dependent on the different qualities
and levels of exposure (CTQ) is given in Table II. In independent
studies, the CTQ was reported to have good reliability and validity;
additionally, the five-factor model (i.e., the five subscales reflecting
the different types of childhood trauma) has been empirically
confirmed [Scher et al., 2001].
Dichotomized variables (0 and 1 vs. 2 and 3) were created for each
dimension. A dichotomized composite score of abuse (emotional,
physical, and/or sexual) was coded according to Bradley et al.
[2008]. At least one ‘‘moderate to severe’’ score in any of these
three abuse dimensions was required to yield a positive overall
abuse rating. Given the relatively high rate of physical and
emotional neglect both dimensions were analyzed independently
from abuse.
Genetic Methods
SNPs and genotyping. DNA was isolated from leukocytes by
standard phenol/chloroform extraction. The SHIP-0 sample was
genotyped using the Affymetrix Genome-Wide Human SNP Array
6.0. Hybridization of genomic DNA was performed according to
the manufacturer’s standard recommendations. The genetic data
analysis workflow was created using the Software InforSense.
Genetic data were stored using the database Cache (InterSystems).
Genotypes were determined using the Birdseed2 clustering
algorithm. For quality control purposes, several control samples
where added. The overall genotyping efficiency of the GWA was
From the Affymetrix 6.0 data set, all 37 SNPs covering the intronic
and exonic regions of the CRHR1 gene were selected (HapMap II
comprises 46 SNPs in the CEU sample). The SNPs were included in
a detailed gene–environment analyses when the minor allele frequency (MAF) was >0.1, the Hardy–Weinberg equilibrium (HWE)
was P > 0.01 and the genotype rate was >95%. These criteria were
met by 28 SNPs, which were then entered in further analyses.
Data available for 1,637 subjects.
1s to 1.3 $.
Abuse includes moderate to severe sexual, emotional or physical abuse.
Depression Inventory (BDI-2) mean score [Beck and Steer, 1987] of
the sample was 6.3 (SD 7.0) and the mean age was 53.6 (SD 13.6)
years (range, 25.4–85.2 years). SHIP and LEGENDE were approved
by the local Institutional Review Board and conformed to the
principles of the Declaration of Helsinki.
Statistical Analyses
CRHR1 gene and linkage disequilibrium (LD) structure. The
gene encoding CRHR1 is located on chromosome 17q21.31 and
contains 13 exons spanning 51 kb. We used Haploview [Barrett
et al., 2005] to determine the LD structure of the 28 SNPs within the
CRHR1 gene in our sample (n ¼ 1,638) and to test for HWE. Using
the confidence intervals according to Gabriel et al. [2002] and the
solid spine of LD method one haploblock was defined. The Four
TABLE II. Mean Scores (Standard Deviation) of BDI-2 Dependent on Levels of Neglect or Abuse (Childhood Trauma Questionnaire)
Level of neglect or abuse (CTQ); BDI-2 (mean score SD)
Physical neglect
Emotional neglect
Maximum of neglect
Physical abuse
Emotional abuse
Sexual abuse
Maximum of abuse
5.2 6.1 (n ¼ 973)
5.5 6.4 (n ¼ 991)
5.0 6.1 (n ¼ 765)
6.1 6.8 (n ¼ 1,487)
5.8 6.4 (n ¼ 1,442)
6.1 6.8 (n ¼ 1,507)
5.7 6.4 (n ¼ 1,300)
7.4 8.0 (n ¼ 376)
6.9 6.9 (n ¼ 429)
6.7 7.0 (n ¼ 512)
7.6 8.7 (n ¼ 70)
8.6 9.5 (n ¼ 132)
7.7 6.7 (n ¼ 56)
7.8 8.6 (n ¼ 168)
8.6 7.5 (n ¼ 198)
8.3 9.2 (n ¼ 85)
8.3 8.1 (n ¼ 215)
9.0 8.7 (n ¼ 41)
8.1 7.5 (n ¼ 29)
8.2 10.2 (n ¼ 43)
7.7 8.5 (n ¼ 88)
8.3 8.5 (n ¼ 69)
9.6 9.2 (n ¼ 109)
8.7 8.6 (n ¼ 140)
10.4 9.9 (n ¼ 31)
17.2 12.2 (n ¼ 23)
14.2 12.3 (n ¼ 17)
11.5 10.6 (n ¼ 55)
ANOVA, P-value
The maximum of neglect is the highest level of physical and emotional neglect.
The maximum of abuse is the highest level of physical, emotional, and sexual abuse.
Gamete Rule algorithm yielded a three-block structure spanning
regions of 23 kb, 1 kb, and 16 kb (Figs. 1 and 2). Block 1 comprised
the three top-SNPs from Bradley et al. [2008] (rs7209436, rs110402,
and rs242924) that constituted the TAT–haplotype. Thus, the
three-block solution was most appropriate to compare our results
to the previous studies and to extent the associations to other parts
of the CRHR1 gene. Ten of the 13 exons of the CRHR1 gene were
located in block 3.
FIG. 1. CRHR1 linkage disequilibrium (LD) map (n ¼ 1,638) demonstrating the physical location and LD pattern of the selected CRHR1 SNPs from the
Affymetrix Genome-Wide Human SNP Array 6.0. Initially, all 37 SNPs covering the CRHR1 gene were selected. The SNPs were included in detailed
genetic analyses when the minor allele frequency (MAF) was >0.1, the Hardy–Weinberg equilibrium (HWE) was P > 0.01 and the genotype rate
was >95%. Finally, 28 SNPs as indicated were entered into the genetic analyses. The LD plot was generated with Haploview using r2 as the measure
of LD, which ranges from 1 (or complete LD, indicated by black squares) to 0 (or absence of LD, indicated by white squares). The numbers within the
LD plot indicate D0 (if no number is indicated, D0 ¼ 1). In our sample, we identified three separate blocks based on the Four Gamete Rule algorithm.
FIG. 2. Haplotypes of the three blocks as proposed by Haploview
[Barrett et al., 2005] based on our sample (n ¼ 1,638).
Haplotype Analysis
Haplotype analysis has become an essential step when investigating
an association between several SNPs within a gene and a phenotype.
Haplotype-based analysis may help to differentiate the true effect of
an SNP from what is due to its linkage disequilibrium with other
variants [Tregouet et al., 2003; Frere et al., 2006]. Haplotypes may
serve as better markers for unknown functional variants than SNPs
alone, and importantly, they may also define functional units in
which the effects cannot be predicted from what is known of the
individual effect of each SNP that enters into their combination.
When investigating unrelated individuals, haplotypes in general
cannot be deduced from genotypes and must be statistically inferred. We therefore used THESIAS [Tregouet and Garelle, 2007;
Miyamoto et al., 2008] (, a multipleimputation algorithm that never assigns haplotypes to individuals
(unlike software that treats haplotypes as observed), and is therefore
not subjected to type I error inflation [Curtis and Sham, 2006;
Tregouet and Garelle, 2007]. The THESIAS algorithm includes the
possibility of (1) testing the null hypothesis of no association of
haplotypes with the phenotype by means of the likelihood ratio test
(with k 1 degrees of freedom for k haplotypes), (2) testing for the
absence of deviation from the (by default) assumption of additivity
of haplotype effects on the phenotype, and (3) adjusting for
covariates and testing for interactions between these covariates
and haplotypes. First the previously described TAT–haplotype was
analyzed with THESIAS. Then the haplotypes were selected based
on the haplotypes under the LD generated by haploview [Barrett
et al., 2005] for frequencies >1% for our data set (n ¼ 1,638) (Fig. 2)
for detailed analyses. We present the direct effects of the haplotypes
on the BDI score and, based on the results of the individual SNPs,
confirmatory interaction analyses between the haplotypes and
physical neglect. By linear combinations, the BDI mean scores for
each haplotype in the interaction analyses were determined. Given
the testing of 3 or 4 haplotypes per block, we chose an a-level equal
to 0.0125 for the analysis of each block. The direct effects of each
haplotype on the BDI score are included in Table III.
Regression analyses for interaction effects. Linear regression
models were used to estimate the interaction effect between childhood abuse, emotional and physical neglect and the SNPs. A
maximum sample size of 1,638 subjects was informative for these
analyses. First, interactions between the three environmental stressors (separately) for each SNP were calculated. In agreement with
Bradley et al. [2008] an additive genetic model was applied.
TABLE III. Direct and Interaction Effects With Physical Neglect for the Haplotypes of the Three Blocks Within the CRHR1 Gene With
Replacement of Missing Genotypes Using THESIAS [Tregouet and Garelle, 2007]
Genetic main factor;
(n ¼ 1,616)
Gene–environment interaction; physical neglect
(n ¼ 1,616)
Environment factor
Block 1
Block 2
Block 3
under LD (n ¼ 1,616) P-Value
Mean BDI
(95% CI)a
haplotype; haplotype;
mean BDIa mean BDIa
for interaction
(95% CI)
2.8 (2.5–3.1)
3.1 (2.6–3.5)
2.8 (2.2–3.3)
3.3 (2.7–3.9)
1.5 (0.3 to 2.7)
0.9 (2.4 to 0.6)
2.3 (4.0 to 0.6)
0.9 (0.5 to 2.4)
2.9 (2.6–3.2)
3.0 (2.7–3.4)
2.7 (2.2–3.3)
1.6 (0.5 to 2.8)
0.3 (1.6 to 0.9)
2.4 (4.1 to 0.6)
2.8 (2.5–3.1)
2.9 (2.2–3.5)
2.8 (2.2–3.4)
3.4 (2.7–4.1)
1.1 (0.0 to 2.3)
0.8 (0.7 to 2.3)
2.3 (4.1 to 0.6)
0.3 (2.2 to 1.6)
Mean estimated BDI value per haplotype; a combination (addition) of two haplotypes determine one BDI score.
Depression (continuous BDI score) was the dependent variable
and all models were adjusted for gender and age groups (10-year
intervals). As the BDI scores were not normally distributed, we
applied a bootstrap approach (10,000 bootstrap samples), which is
robust against non-normal distributions, to obtain the P values and
the CI of the regression coefficients or linear combinations. Because
the bootstrap statistic was unbiased, percentile CI was chosen.
Analyses were performed with STATA/MP software, version 10.1
(StataCorp LP, College Station, TX).
Correction for multiple testing. The 28 SNPs within the CRHR1
gene were linked to a varying degree. It was therefore not appropriate to adjust for 28 independent tests. For the interaction analysis
with all 28 SNPs, the a-level was adjusted for multiple testing
according to the spectral decomposition techniques by Nyholt
[2004]. Calculations were carried out using the program SNPSpD
( Based on
this analysis, the a-level for the SNP analysis was corrected to be
equal to 0.00386.
Power Analysis
We analyzed the power of our study in detail. We performed the
power analyses with varying effect sizes (1–5 points of change in
BDI score per allele), varying minor allele frequency (MAF) (0.1;
0.2; 0.45) and varying proportion of exposed subjects (9–16.5%
according to the prevalence of exposure) using Quanto 1.2.3.
( [Gauderman, 2002; Gauderman and
Morrison, 2006]. For the power calculation, a sample size of
1,600 subjects, a mean outcome (BDI) of 6.3 with a standard
deviation of 7.0, a marginal effect of 3.0 by the environment factor
and of 0.0 by the genetic factor was assumed. The mode of
inheritance was assumed to be additive. To specifically address the
power of the replication of the findings of Bradley et al. [2008], a two
-sided a-level of 0.05 was chosen (Fig. 5). The power analysis for a
two-sided a-level of 0.00386 (gene-wide significance) is presented
in Supplemental Figure 1.
Gene–Environment Interaction of the
No interaction was observed between childhood abuse (THESIAS:
coefficient for interaction 0.6 (95% CI 0.6 to 1.8); P ¼ 0.3381),
emotional neglect (coefficient for interaction 0.7 (95% CI 0.4 to
1.9); P ¼ 0.1961) and the TAT–haplotype in the total sample. When
analyzing female and male subjects separately, no interaction
emerged (Tables I and II supplement). There was, however, an
interaction between the TAT–haplotype and physical neglect in the
total sample. Analyzing males and females separately females
showed a somewhat weaker effect (Table III supplement). In the
case of physical neglect, the estimated BDI score of carriers of
this TAT–haplotype increased by 1.4 points (95% CI 0.2–2.6;
P ¼ 0.0229) due to the interaction (for one copy of the
Based on the interaction between the TAT–haplotype and
physical neglect in our sample all further analyses were performed
with physical neglect.
Gene–Environment Interaction of Haplotypes in
the First, Second, and Third Haplotype Block
The finding of the interaction between the TAT–haplotype and
physical neglect was confirmed when analyzing the first haplotype
block of the CRHR1 gene (Table III): The interaction between the
first haplotype (comprising the TAT-sequence) and physical neglect increased the estimated BDI score by 1.5 (95% CI 0.3–2.7;
P ¼ 0.0156) points (per copy of the haplotype). The interaction
between the third haplotype (comprising the CGG-sequence) and
physical neglect decreased the BDI score by 2.3 (95% CI 4.0 to
0.6; P ¼ 0.0098) points.
Interactions between distinct haplotypes and physical neglect
also emerged in the second and third haplotype blocks (Table III).
The GTG-haplotype of the second block and the second haplotype
of the third block (located at position 3 in Table III and Fig. 2)
decreased the estimated BDI scores in interaction with physical
neglect by 2.4 (95% CI 4.1 to 0.6; P ¼ 0.0073) and 2.3 (95%
CI 4.1 to 0.6; P ¼ 0.0086), respectively. The interaction with the
ACC–haplotype of the second block correspondingly increased the
estimated BDI score by 1.6 (95% CI 0.5 – 2.8; P ¼ 0.0061) per copy
of this haplotype.
Gene–Environment Interaction of the SNPs
The P-values for the interaction between the 28 SNPs of the CRHR1
gene and physical neglect are given in Figure 3. Assuming an a-level
equal to 0.05, physical neglect showed statistically significant
interactions in 23 of the 28 SNPs, with the top marker rs17689882
(P ¼ 0.0013) reaching gene-wide significance when adjusting for 28
SNPs (Figs. 3 and 4). Although interactions with childhood abuse
and emotional neglect were not included in the further hypothesis,
the corresponding P-values were indicated in Figure 3 for informative reasons.
Direct Gene Effects
None of the SNPs and none of the haplotypes were directly
associated with the BDI score (P > 0.1).
Gender Effects in Gene–Environment Interaction
of the SNPs
In direct response to the recent paper of Heim et al. [2009] we
further analyzed putative gender effects for our top SNP
(rs17689882) and the top SNP of Heim et al. (rs110402) in
interaction with physical neglect, abuse, and physical abuse.
Although the interaction between rs17689882 and physical neglect
was statistically present in males (P ¼ 0.0021) but statistically
absent in females (P ¼ 0.1536), the P value for the three-way
interaction was statistically not significant (P ¼ 0.4727). Analyzing
the interaction between rs17689882 and abuse/physical abuse no
significant three-way interaction with gender emerged (P > 0.3).
Also in the gender stratified analyses, no significant results emerged
for males or females with both exposures (P > 0.3).
Analyzing gender effects for rs110402 in interaction with physical neglect, abuse, and physical abuse, no significant findings
FIG. 3. Interaction effects of CRHR1 single-nucleotide polymorphisms (SNPs) with physical neglect, emotional neglect and child abuse on adult
depression. The y-axis shows the P values (log scale) for the interaction effects of the 28 SNPs for an additive genetic model. *Indicates the three
top markers from Bradley et al. [2008] forming the TAT–haplotype. $Indicates each of the three haplotype blocks (first block left side). #Indicates
two exonic SNPs. Horizontal dashed lines indicate P ¼ 0.05 (nominally significant) and P ¼ 0.00386 (significance level after correction for multiple
testing as described in the text).
emerged for the three way interaction with gender (P > 0.4). Also in
the gender stratified analyses, no significant results emerged for
males or females with abuse/physical abuse (P > 0.5). However,
rs110402 showed a trend for interaction with physical neglect in the
gender stratified analyses (males: P ¼ 0.0794; females: P ¼ 0.1727;
Table IV supplement).
We identified an interaction between the TAT–haplotype and
childhood physical neglect in which the TAT–haplotype emerged
rather as a risk than as a protective haplotype as has previously been
reported [Bradley et al., 2008; Polanczyk et al., 2009].
Following our approach, we analyzed further haplotypes within a
three block structure of the CRHR1 gene and 28 SNPs in total with
physical neglect, the only CTQ dimension that interacted with the
TAT–haplotype in our general population sample. In contrast to
our best SNP (rs17689882) which was located in the third haplotype
block, all three top markers (rs7209436, rs110402, rs242924)
from the study of Bradley et al. [2008] that constituted the
TAT–haplotype were located within our first haplotype block and
were only modestly linked to our top marker (r2 ¼ 0.21) in the third
block. As far as we can conclude, our top marker has not been
covered by the SNPs analyzed in the study of Bradley et al. [2008].
Therefore, we propose that rs17689882 is most closely linked to a
causative variation. Further, we identified additional markers
(including the two exonic markers rs16940674 and rs16940665)
and haplotypes that conferred the effect of interaction in our
sample compared to the African American sample of Bradley
et al. [2008].
Most of our significant SNPs were located in intronic regions of
the CRHR1 gene in the third haplotype block; however, 10 of the 13
exons were located in this haplotype block. This indicates that our
relevant markers could be in LD with causative exonic markers in
this region.
Two SNPs (rs16940674 and rs16940665) in haplotype block 3
were exonic but did not change the amino acid sequence of the
receptor protein. There is, however, accumulating evidence that
synonymous SNPs can affect splicing or mRNA stability, thereby
altering the gene products [Chamary et al., 2006].
Polanczyk et al. [2009] replicated the interaction between childhood maltreatment and the TAT haplotype of the CRHR1 gene in
the E-risk study comprising 999 Caucasian mothers of twins.
However, this interaction was not confirmed in the Dunedin
Multidisciplinary Health and Development Study comprising
899 male and female participants. Only the E-Risk study used the
retrospective self-report CTQ [Bernstein et al., 2003] like in our
study and the study of Bradley et al. [2008] but not the Dunedin
Study which relied on five different sources of information generating a cumulative exposure index by counting the number of
maltreatment experiences during the first decade of life [Polanczyk
et al., 2009]. As Polanczyk et al. [2009] primarily analyzed the
TAT–haplotypes no statement could be given on the role of the
SNPs and haplotypes of the second and third block of the CRHR1
gene in their samples.
Based on non-replications and critical meta-analyses [Munafo
et al., 2009; Risch et al., 2009] there has been much debate on the
validity and the comparability of findings in gene–environment
interaction research. Besides the ethnical heterogeneity of different
samples under investigation [Koenen and Galea, 2009; Caspi et al.,
2010], the different statistical approaches [Grabe et al., 2009;
Schwahn and Grabe, 2009] and the use of different clinical endpoints, the assessment of the complex nature of environmental
exposures remains an unsolved issue. However, for the first time in
G E interaction research several independent studies have used
the CTQ. This allows for direct comparisons of the exposure
between the studies. Why did we find an interaction with physical
neglect but not with abuse? In our general population sample, a
relatively low rate of 9% of childhood abuse (already combining
emotional, physical, and sexual abuse) was found versus 12% of
FIG. 4. Childhood physical neglect interacts with the SNPs of the
CRHR1 gene to decrease the risk for depression in adults. A: Mean
Beck Depression Inventory (BDI) scores (n ¼ 1,616) were
associated with childhood physical neglect (Childhood Trauma
Questionnaire) (P < 0.001). The error bars indicate robust SEM.
B: Interaction effect between rs17689882 and childhood
physical neglect on adult depression is presented. The sample
sizes for no-to-mild childhood physical neglect and moderate-tosevere physical neglect are as follows: AA, 46, and 5,
respectively; AG, 463, and 92, respectively; GG, 818, and 168,
respectively. The presented P-value is based on the allelic
analysis as described in the Materials and Methods Section. Note
that by choosing this allelic model, the P-value is not based on a
small group size.
emotional neglect and 16.5% of physical neglect (Table II). In
contrast, Bradley et al. [2008] reported 37% of their sample had
been exposed to childhood abuse that was identically measured
with the CTQ [Bernstein et al., 2003]. In the extended sample
(n ¼ 1,063) of the original Bradley et al. [2008] paper Heim et al.
[2009] confirmed the protective effect of the rs110402 A-allele
against the negative emotional consequences of childhood abuse in
the male subsample only. Moreover, this effect was carried by the
exposure ‘‘physical abuse’’ only but not by the other exposure
dimensions (sexual and emotional abuse). These results point to
gender differences and to differential effects of various exposures on
the G E interaction, as found in our study. Our results provided
some support for gender effects as the interaction between
rs17689882 and physical neglect was statistically present in males
(P ¼ 0.0021) but statistically absent in females (P ¼ 0.1536) in the
gender-stratified analysis. However, the P-value for the three-way
interaction (including gender) was statistically not significant. Also
rs110402 showed a trend for interaction with physical neglect in the
gender stratified analyses in males.
Moreover, is it possible that physical neglect represents a more
sensitive measure of general familial dysfunction in an overall
higher functioning sample with relatively low rates of childhood
abuse. Another impressive difference between both samples is
reflected by the respective BDI scores: in our general population
sample the BDI score was much lower (6.3; SD 7.0) compared to the
mean BDI score (14.4; SD 13.1) from the study of Bradley et al.
[2008]. Further, the first sample from the study of Bradley et al.
[2008] was composed of 97.4% African Americans with low
socioeconomic status who were recruited while waiting for their
medical appointments in a large, urban, public hospital. In contrast,
our Caucasian sample reflected the German general population
living in a rural area. Given those major differences in the sample
and exposure structure we consider it as plausible, that a different
childhood stressor emerged in interaction analyses in our sample.
We therefore conclude that we found clear statistical evidence that
aversive childhood conditions interact with variants within the
CRHR1 gene.
From a biological perspective it is likely that variants of the
CRHR1 gene influence memories with emotional content which are
specifically relevant for the development of depressive disorders. In
fact, CRH mediates the effects of emotional arousal on memory
consolidation [McGaugh, 2004] and blockade of CRHR1 receptors
has been shown to impair the consolidation of fear memory
[Roozendaal et al., 2002; Hubbard et al., 2007]. Polanczyk et al.
[2009] suggested that subjects carrying the protective variants of the
CRHR1 gene might have an impaired activation of the fear memory
consolidation which could result in a relatively unemotional cognitive processing of memories of aversive childhood experiences.
Moreover, the protective variants of the CRHR1 gene have been
associated with deceased cortisol responses to the dexamethasone/
CRH test in adults who have been exposed to childhood maltreatment [Heim et al., 2009; Tyrka et al., 2009]. Both mechanisms could
contribute to the protective interaction of the CRHR1 gene with
childhood maltreatment against depression. It is important to
consider that also G E interactions act within a biological network
that is influenced by other gene variants too. One recent study
points to a significant G G E interaction with the serotonin
transporter promoter gene polymorphism (5-HTTLPR) [Ressler
et al., 2010]. The protective variants of the CRHR1 gene exerted
their effect only in the carriers of the 5-HTTLPR LL-genotype.
Thus, unmeasured differences in the distribution of the 5-HTTLPR
genotypes could also be responsible for different findings in the
CRHR1 gene childhood maltreatment studies.
Some potential limitations and strengths of our study need to be
discussed. The power is always a matter of concern in genetic
analyses. We therefore analyzed the power of our study in detail.
The largest effects in the study of Bradley et al. [2008] were
associated with the SNPs of the TAT–haplotype (rs7209436,
rs110402, and rs242924). All three SNPs had an MAF >0.45 in
our sample (corresponding to Fig. 5A). Nine percent of our sample
was exposed to sexual, emotional, or physical abuse (by the
definition used by Bradley et al. [2008]). Thus, an effect of 2.5 points
of BDI change per allele with a power >80% (a-level of 0.05) could
have been detected in our study, which is much smaller than the
effect of about seven BDI points per allele reported by Bradley et al.
[2008]. The same applies to emotional neglect with an even higher
power (12% of our subjects). Our top marker (rs17689882) had an
MAF equal to 0.205, and the proportion of subjects exposed to
physical neglect was 16.5% (corresponding to Fig. 5B). Thus, a BDI
change as small as 2.2 points per allele could have been detected with
a power >80%. Choosing an a-level of 0.00386 (‘‘gene-wide’’
significance) and an MAF equal to 0.205, a BDI change due to an
interaction as small as 3.0 points per allele could have been detected
with a power of 80% (Fig. 1, Supplement). Despite the acceptable
power of our analyses, false negative and positive findings cannot be
ruled out completely.
Haplotype analyses can provide important information regarding the combined effects of SNPs. Based on the THESIAS software,
we were able to include all SNPs within a haplotype block into one
analysis. Thus, we were not forced to select a limited number of
tagSNPs for the analyses to get a sufficient coverage of allelic
variation within each haplotype block. The THESIAS software,
however, does not provide the ability to run bootstrap analyses.
Therefore, no robust estimates could be calculated in haplotype
analyses to overcome the non-normal distribution of the BDI
scores. In order to estimate the magnitude of a possible type I
error of interaction analyses we made a priori comparisons between
regression analyses with and without bootstrap at the level of
individual SNPs. The differences between bootstrap and conventional CIs were small and only minor differences in P values with
and without bootstrapping emerged (mean standard deviation:
0.0005 0.0020). We therefore consider our results in haplotype
interaction analyses to be largely unaffected by the non-normal
distribution of the BDI scores. It may be important to keep in mind
that the CRHR1-SNPs were genotyped on the Affymetrix GenomeWide Human SNP Array 6.0 platform. We applied reasonable
quality procedures and selected SNPs with a minor allele
frequency > 0.1, a HWE with P > 0.01 and a genotype rate >95%.
Bradley et al. [2008] recruited their study subjects while waiting
for their medical appointments or while waiting with others who
were scheduled for medical appointments in the waiting rooms of a
large, urban, public hospital. In order to avoid major selection
effects (e.g., treatment samples) that may bias the results, we
consecutively recruited the subjects from the general population
who were participating in an ongoing epidemiological study. Due
to refusal in participation (n ¼ 677) and the fact that the recruitment of subjects who were non-responders to the postal invitation
is still ongoing in the LEGENDE study, our analyses may not be fully
representative to the German general population. Based on the
DSM-IV stem questions for depression [Wittchen et al., 1999]
implemented in SHIP-0 and -1, 18.1% of the recruited subjects for
LEGENDE versus 20.1% of the non-recruited subjects (deceased,
refused, or non-responders) in LEGENDE reported depressive
symptoms in the SHIP-1 survey (P ¼ 0.14). No differences emerged
between the subjects who already participated in LEGENDE
(N ¼ 1,683) and the other subjects from the baseline cohort (SHIP
-0) in the rate of the screening diagnosis of depression at baseline
(P > 0.8). Thus, when comparing the rate of the previous screening
FIG. 5. Power simulation for gene–environment interactions in our
sample with varying effect sizes b (1–5 points of change in BDI
score per allele) and varying proportion of exposed subjects. The
minor allele frequency was 0.45 (A), 0.2 (B), and 0.1 (C). We
simulated the power of our analyses with varying effect sizes,
varying minor allele frequency (MAF) (0.1; 0.2; 0.45) and
(9–16.5% according to the prevalence of exposure). The mode of
inheritance was assumed to be additive, and a two-sided alpha
level of 0.05 was chosen (Quanto 1.2.3) [Gauderman and
Morrison, 2006].
diagnoses of depression no major selection bias emerged between
the participants of LEGENDE and the representative baseline
(SHIP-0) and follow-up cohorts (SHIP-1). Comparing the mean
BDI-2 score of our sample (6.3; SD 7.0) with another sample from
Germany used in a validation study of the BDI-2 (n ¼ 315), only
minor differences emerged (7.69; SD ¼ 7.52) [Kuhner et al., 2007].
We therefore suggest that the generalizability of our results at least
to other Caucasian general population samples with relatively low
rates of childhood maltreatment should be good.
Although the mean BDI score in our sample was much lower
(6.3; SD 7.0) compared to the mean BDI score (14.4; SD 13.1) from
the study of Bradley et al. [2008], interaction effects with the CRHR1
gene were found. For homozygote carriers we found a change of
3–BDI points due to interaction, which is lower than previously
reported [Bradley et al., 2008]. The changes of 3–4 BDI points due
to interaction in our sample, however, reflected a relative change in
BDI scores of about 50%, which we consider a relevant effect.
As we analyzed a dimensional depression score, we cannot
generalize our findings to the risk of having a lifetime episode of
major depression or not. Such analyses need a different statistical
approach, and the reduced power compared to the dimensional
endpoint needs to be addressed [Knol et al., 2007; Grabe et al.,
Although we did not replicate the specific interaction of childhood abuse and the TAT–haplotype of the CRHR1 gene we
confirmed the interaction of variants within the CRHR1 gene with
aversive childhood conditions in the modulation of the susceptibility for depressive symptoms in adulthood.
There are no conflicts of interest. External financial support in the
past 5 years: Hans Jo€rgen Grabe: German Research Foundation;
Federal Ministry of Education and Research Germany; speakers
honoraria from Bristol-Myers Squibb, Eli Lilly, Novartis, Eisai,
Wyeth, Pfizer, Boehringer Ingelheim, Servier, and travel funds from
Janssen-Cilag, Eli Lilly, Novartis, AstraZeneca, and SALUS-Institute for Trend-Research and Therapy Evaluation in Mental Health.
Carsten Spitzer: Travel funds and speakers honoraria from JanssenCilag and Boehringer Ingelheim; research grant from the ‘‘Stiftung
zur Aufarbeitung der SED-Diktatur.’’ Christian Schwahn, Katja
Appel, Jessie Mahler, Andrea Schulz, Kristin Fenske: none. Sven
Barnow: German Research Foundation; Federal Ministry of Health
Germany. Michael Lucht: German Research Foundation; Federal
Ministry of Education and Research; speakers honoraria from
Sanofi-Aventis, Eli Lilly Co. and Sanofi-Synthelabo. Harald J.
Freyberger: German Research Foundation; Social Ministry of the
Federal State of Mecklenburg-West Pomerania; Family Ministry of
the Federal Republic of Germany; speakers honoraria from AstraZeneca, Lilly, Novartis and travel funds from Janssen-Cilag. Ulrich
John: German Research Foundation; German Cancer Aid; European Union; Federal Ministry of Education and Research Germany;
Social Ministry of the Federal State of Mecklenburg-West Pomerania of Germany. Alexander Teumer: none. Henri Wallaschofski:
Research grants from Pfizer, Novartis, Novo Nordisc, SanofiAventis and the German Research Foundation. Matthias Nauck:
Research grants from the Federal Ministry of Education and
Research Germany, BioRad Laboratories GmbH, Siemens AG,
Zeitschrift f€
ur Laboratoriumsmedizin, Bruker Daltronics, Abbott,
Jurilab Kuopio, Roche Diagnostics, Dade Behring, DPC Biermann
and Becton Dickinson. Henry V€olzke: Research grants by SanofiAventis, Biotronik, the Humboldt Foundation, the Federal Ministry of Education and Research (Germany) and the German Research Foundation. SHIP is part of the Community Medicine
Research net of the University of Greifswald, Germany, which is
funded by the Federal Ministry of Education and Research (grants
no. 01ZZ9603, 01ZZ0103, and 01ZZ0403), the Ministry of Cultural
Affairs and the Social Ministry of the Federal State of MecklenburgWest Pomerania. Genome-wide data have been supported by the
Federal Ministry of Education and Research (grant no. 03ZIK012)
and a joint grant from Siemens Healthcare, Erlangen, Germany and
the Federal State of Mecklenburg-West Pomerania. The University
of Greifswald is a member of the ‘‘Center of Knowledge Interchange’’ program of the Siemens AG. This work was also funded by
the German Research Foundation (DFG: GR 1912/5-1). H.J.G. and
C.S. had full access to all of the data in the study and take
responsibility for the integrity of the data and the accuracy of the
data analysis. We would like to thank Daniela Becker, Varinia
Popek, Frauke Grieme, Daniel Grams, and Andrea Rieck for their
contribution to the study (organization, data collection, and data
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