An investigation of child maltreatment and epigenetic mechanisms of mental and physical health risk

Abstract

In the present investigation, differential methylation analyses of the whole genome were conducted among a sample of 548 school-aged low-income children (47.8% female, 67.7% Black, M age = 9.40 years), 54.4% of whom had a history of child maltreatment. In the context of a summer research camp, DNA samples via saliva were obtained. Using GenomeStudio, Methylation Module, and the Illumina Custom Model, differential methylation analyses revealed a pattern of greater methylation at low methylation sites (n = 197 sites) and medium methylation sites (n = 730 sites) and less methylation at high methylation sites (n = 907 sites) among maltreated children. The mean difference in methylation between the maltreated and nonmaltreated children was 6.2%. The relative risk of maltreatment with known disease biomarkers was also investigated using GenoGo MetaCore Software. A large number of network objects previously associated with mental health, cancer, cardiovascular systems, and immune functioning were identified evidencing differential methylation among maltreated and nonmaltreated children. Site-specific analyses were also conducted for aldehyde dehydrogenase 2 (ALDH2), ankyrin repeat and kinase domain containing 1 (ANKK1), and nuclear receptor subfamily 3, group C, member 1 (NR3C1) genes, and the results highlight the importance of considering gender and the developmental timing of maltreatment. For ALDH2, the results indicated that maltreated girls evidenced significantly lower methylation compared to nonmaltreated girls, and maltreated boys evidenced significantly higher methylation compared to nonmaltreated boys. Moreover, early onset–not recently maltreated boys evidenced significantly higher methylation at ALDH2 compared to nonmaltreated boys. Similarly, children with early onset–nonrecent maltreatment evidenced significantly higher methylation compared to nonmaltreated children at ANKK1. The site-specific results were not altered by controlling for genotypic variation of respective genes. The findings demonstrate increased risk for adverse physical and mental health outcomes associated with differences in methylation in maltreated children and indicate differences among maltreated children related to developmental timing of maltreatment and gender in genes involved in mental health functioning.

Full text

An investigation of child maltreatment and epigenetic mechanisms
of mental and physical health risk
DANTE CICCHETTI,
a,b
SUSAN HETZEL,
a
FRED A. ROGOSCH,
b
ELIZABETH D. HANDLEY,
b
AND
SHEREE L. TOTH
b
aUniversity of Minnesota Institute of Child Development; and bUniversity of Rochester Mt. Hope Family Center
Abstract
In the present investigation, differential methylation analyses of the whole genome were conducted among a sample of 548 school-aged low-income
children (47.8% female, 67.7% Black, Mage ¼9.40 years), 54.4% of whom had a history of child maltreatment. In the context of a summer research camp,
DNA samples via saliva were obtained. Using GenomeStudio, Methylation Module, and the Illumina Custom Model, differential methylation analyses
revealed a pattern of greater methylation at low methylation sites (n¼197 sites) and medium methylation sites (n¼730 sites) and less methylation at high
methylation sites (n¼907 sites) among maltreated children. The mean difference in methylation between the maltreated and nonmaltreated children was 6.2%.
The relative risk of maltreatment with known disease biomarkers was also investigated using GenoGo MetaCore Software. A large number of network objects
previously associated with mental health, cancer, cardiovascular systems, and immune functioning were identified evidencing differential methylation
among maltreated and nonmaltreated children. Site-specific analyses were also conducted for aldehyde dehydrogenase 2 (ALDH2), ankyrin repeat and kinase
domain containing 1 (ANKK1), and nuclear receptor subfamily 3, group C, member 1 (NR3C1) genes, and the results highlight the importance of considering
gender and the developmental timing of maltreatment. For ALDH2, the results indicated that maltreated girls evidenced significantly lower methylation
compared to nonmaltreated girls, and maltreated boys evidenced significantly higher methylation compared to nonmaltreated boys. Moreover, early onset–not
recently maltreated boys evidenced significantly higher methylation at ALDH2 compared to nonmaltreated boys. Similarly, children with early onset–
nonrecent maltreatment evidenced significantly higher methylation compared to nonmaltreated children at ANKK1. The site-specific results were not altered
by controlling for genotypic variation of respective genes. The findings demonstrate increased risk for adverse physical and mental health outcomes
associated with differences in methylation in maltreated children and indicate differences among maltreated children related to developmental timing of
maltreatment and gender in genes involved in mental health functioning.
Child maltreatment represents a pathogenic relational envi-
ronment that confers significant risk for maladaptation and
psychopathology across both psychological and biological
domains of development (Cicchetti & Lynch, 1995; Cicchetti
&Toth,2015a,2015b). The development sequelae accompany-
ing child maltreatment not only result in adverse consequences
during infancy, childhood, and adolescence, but also often
initiate a negative developmental cascade that continues
throughout the life span (Masten & Cicchetti, 2010). The
proximal environment involving the nuclear family, as well
as more distal factors associated with the community and cul-
ture more broadly, transact to undermine typical biological
and psychological developmental processes in children who
have experienced maltreatment (Cicchetti & Lynch, 1993).
Child maltreatment ushers in motion a probabilistic path of
epigenesis for abused and neglected children characterized by
an increased likelihood of failure and disruption in the suc-
cessful resolution of salient developmental tasks, resulting
in a profile of relatively enduring vulnerability factors that in-
crease the probability of the emergence of maladaptation and
psychopathology (Cicchetti, 1989; Cicchetti & Lynch, 1995;
Vachon, Krueger, Rogosch, & Cicchetti, 2015). Long before
psychopathological conditions appear in adulthood among
maltreated individuals, a host of deviations in developmental
processes are likely to have occurred during childhood.
Because maltreated children experience the extremes of care-
giving casualty, they provide one of the clearest opportunities for
scientists to discover the myriad ways in which psychological
stressors can affect biological systems. Comparisons between
maltreated and nonmaltreated children can elucidate our under-
standing of the caregiving processes that contribute to the devel-
opment of regulated neurobiological systems. Numerous inter-
connected neurobiologic systems are negatively affected by the
various stressors associated with maltreatment. Moreover, each
of these neurobiological systems influences and is influenced
by multiple domains of psychological and biological develop-
ment. It is highly probable that a numberof the symptoms and dis-
orders that maltreated children develop eventuate in conjuction
with disturbances and dysregulation in neurobiological systems.
Maltreated children are exposed to atypical caregiving and
high stress. Two questions come to the fore. How does this
Address correspondence and reprint requests to: Dante Cicchetti, Institute
of Child Development, University of Minnesota, 51 East River Road, Min-
neapolis, MN 55455; E-mail: cicchett@umn.edu.
This research was supported by grants from the Emerald Foundation and the
Jacobs Foundation (to D.C.) and National Institute of Mental Health Grant
MH083979 (to D.C. and F.A.R.).
Development and Psychopathology, 2016, page 1 of 13
#Cambridge University Press 2016
doi:10.1017/S0954579416000869
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affect gene regulation? What are the implications for mental
and physical health? Epigenetics involves the investigation
of changes in gene function that do not alter the DNA se-
quence, but instead provide an extra layer of transcriptional
control that regulates gene activity and plays an important
role in the acute regulation of genes in response to environ-
mental changes. Epigenetics is a promising avenue for both
basic and intervention research on child maltreatment (Szyf
& Bick, 2013; Weder et al., 2014). Epigenetic research thus
has the potential to elucidate mechanisms to explain how mal-
treatment experiences confer risk for physical and mental
health problems (Szyf & Bick, 2013; Toth, Gravener-Davis,
Guild, & Cicchetti, 2013; Yang et al., 2013).
DNA is not a static entity as it was once thought to be
(Mill, 2011; Szyf & Bick, 2013). Epigenetic processes play
a dynamic role in regulating gene expression and are respon-
sive to the environment (Mill, 2011; Roth, 2013; Toth et al.,
2013). A number of epigenetic mechanisms have been
proposed linking prenatal exposure to maternal stress and
early infant outcomes (Monk, Spicer, & Champagne, 2012;
Roth, 2013). In addition, postnatal environmental exposures,
such as early adversity or the quality of mother and child in-
teractions, also may lead to shifts in developmental trajecto-
ries via epigenetic pathways (Toth et al., 2013).
The most investigated epigenetic mechanism in research
on child maltreatment has been DNA methylation. Altera-
tions in methylation, which manifest as changes in cytosine
nucleotide–phosphate–guanine nucleotide (CpG) sites in
the DNA sequence, may persist in a stable form over a long
time. Alterations in the structure of chromatin influence
gene expressions. Specifically, genes are switched off when
chromatin is silent, and they are switched on (i.e., expressed)
when chromatin is active. These dynamic states of chromatin
are controlled by a number of processes, including epigenetic
patterns of DNA methylation. Individual differences in DNA
methylation thus are a potential biomarker of the contribu-
tions that environmental factors make to the divergence in
phenotypes of maltreated individuals who possess similar ge-
netic endowments.
Researchers examining methylation have adopted an epi-
genome-wide approach, as well as a focus on candidate genes
of interest. Investigations that have utilized a genome-wide ap-
proach havefound that thesemethylation changes occurat many
gene loci (Szyf & Bick, 2013). The majority of human epige-
netic investigations on child maltreatment have been conducted
with adults and have assessed maltreatment retrospectively. For
example, in a small sample of maltreated and nonmaltreated
males (maltreated, n¼12; nonmaltreated, n¼28), Suderman
et al. (2014) found genome-wide methylation profiles in adult
DNA associated with child adversity that justify future explora-
tion of epigenetic regulationas a mediatingmechanism for long-
term health outcomes.
Research with animals that experience abuse demonstrates
that experience-induced changes in DNA methylation occur
in brain circuits that have functional relevance to trajectories
of normal and abnormal behavior. Accordingly, experience-
driven DNA-methylation changes in maltreated individuals
also have implications for disruptions in neural circuitry
and behavior (Szyf & Bick, 2013). Likewise, using salivary
DNA specimens, whole-genome methylation differences
have been found between maltreated and nonmaltreated chil-
dren at 2,868 CpG sites. A substantial number of genes impli-
cated in prostate, colorectal, breast, colon, and ovarian cancer
were contained in the set of genes that showed differential
methylation between maltreated and nonmaltreated children
(Yang et al., 2013). The results of these investigations suggest
that epigenetic mechanisms may be associated with risk for de-
veloping major health problems in later life (Yang et al., 2013).
Several investigations have used a candidate gene ap-
proach to examine the effects of child abuse and neglect on
methylation. Epigenetic regulation is a viable candidate
mechanism through which caregiving behaviors, including
child maltreatment, may exert long-term effects on hypothal-
amus–pituitary–adrenal activity and neuronal function (Szfy
&Bick,2013). Candidate genes utilized in this regard to date
include the serotonin transporter gene, the glucocorticoid re-
ceptor gene (nuclear receptor subfamily 3, group C, member
1[NR3C1]), and FK506 binding protein 5 gene (FKBP5).
These studies have found that child maltreatment and traumatic
experiences were associated with increased methylation of
NR3C1 (Perroud et al., 2011; van der Knapp et al., 2014;
Yang et al., 2013)andFKBP5. For example, McGowan
et al. (2009) found increased methylation of the exon 1F
NR3C1 promotor among suicide victims with a history of child
maltreatment compared to controls. Regarding FKBP5,Klen-
gel et al. (2013)foundthatFKBP5, an important functional
regulator of the stress hormone system, increased the risk of
developing stress-related psychiatric disorders in adulthood
through allele-specific (i.e., a particular functional polymor-
phism), childhood trauma-dependent DNA methylation in
functional glucocorticoid response elements of FKBP5.Addi-
tional research has begun to emerge regarding methylation in
other genes, including dopaminergic genes such as dopamine
receptor D2/ankyrin repeat and kinase domain containing 1
(DRD2/ANKK1) and child maltreatment. For instance, Groleau
et al. (2014) found a trend-level increase in methylation of
DRD2 among women with bulimia-spectrum disorders and a
history of child sexual abuse compared to control women.
The present study examined genome-wide methylation in
a large (N¼548) sample of maltreated and nonmaltreated
children from low-socioeconomic stress backgrounds, as
well as site-specific genes methylation. Parameters of mal-
treatment also were assessed (e.g., onset/developmental tim-
ing of maltreatment; see Barnett, Manly, & Cicchetti, 1993;
Cicchetti & Barnett, 1991; Manly, 2005).
Hypotheses
1. Maltreated children will evince significant differences in
methylation across the epigenome, relative to demographi-
cally comparable nonmaltreated children.
D. Cicchetti et al.2
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2. Compared to nonmaltreated children, maltreated children
will demonstrate greater adverse physical and mental
health risk based on patterns of differential methylation as-
sociated with disorder outcomes.
3. Maltreated children will exhibit differential methylation
of specific genes negatively associated with health out-
comes, compared to nonmaltreated children.
4. Gender, ancestry, and genotype differences will be con-
trolled or examined interactively with maltreatment status.
Developmental timing of maltreatment experiences will
be related to further variation in site-specific methylation.
Method
Participants
The participants in this investigation included 548 children
(262 female, 286 male) who attended a research summer
camp program designed for school-aged low-income mal-
treated (n¼298) and nonmaltreated (n¼250) children. Chil-
dren were on average 9.40 years old (SD ¼0.88). The sample
was racially (67.7% Black, 20.6% White, and 11.7% Biracial
or other race) and ethnically (20.6% were Latino) diverse. In-
formed consent was obtained from parents of maltreated and
nonmaltreated children for the child’s participation in the sum-
mer camp program and for examination of any Department of
Human Services (DHS) records pertaining to the family.
Children in the maltreated group were recruited through a
DHS liaison who examined Child Protective Services reports
to identify children who had been maltreated and/or were part
of a family with a history of maltreatment. Children living in
foster care were not recruited for the current investigation be-
cause of the frequent instability in foster care placements. The
DHS liaison contacted eligible families and explained the
study. Parents who were interested in having their child parti-
cipate provided signed permission for their contact informa-
tion to be shared with project staff. These families were repre-
sentative of those receiving services through the DHS.
Comprehensive reviews of all DHS records for each family
were conducted. Maltreatment information was coded by
trained research staff and a clinical psychologist, using the
Barnett et al. (1993) nosological system for classifying child
maltreatment. Coding is based on all available information
and does not rely on DHS determinations.
Because maltreating families primarily have low socioeco-
nomic status (National Incidence Study; Sedlak et al., 2010),
nonmaltreating families were recruited from those receiving
Temporary Assistance to Needy Families in order to ensure so-
cioeconomic comparability between maltreated and nonmal-
treated families. A DHS liaison contacted eligible nonmaltreat-
ing families and described the project. Parents who were
interested in participatingsigned a release allowing their contact
information to be given to project staff for recruitment. The
families were recruited as nonmaltreated families aftercompre-
hensive DHS record searches confirmed the absence of any
documented child maltreatment.Families who received preven-
tative DHS services due to concerns over risk for maltreatment
were not included within the nonmaltreated comparison group.
In order to further verify a lack of DHS involvement, trained re-
search assistants interviewed the mothers of children recruited
for the nonmaltreatment group using the Maternal Child Mal-
treatment Interview (Cicchetti, Toth, & Manly, 2003)and
reviewed records in the year following camp participation to as-
sure that all information had been assessed.
Children in the maltreated and nonmaltreated groups were
comparable on a number of family characteristics (see Table 1).
These include maternal education, x2(1, N¼545) ¼2.91, p.
.05, marital status, x2(3, N¼545) ¼4.46, p..05, and family
history of receiving public assistance, x2(1, N¼544) ¼0.81,
p..05.
Procedures
Day camp procedures. Maltreated and nonmaltreated chil-
dren were randomly assigned to groups of 10 same-sex and
same-age peers. Within these groups 5 children were mal-
treated and 5 were nonmaltreated. Each group was led by
three trained camp counselors who were unaware of child
maltreatment status and study hypotheses. Children partici-
pated in recreational activities throughout the week. After
child assent was obtained, children participated in research
assessments conducted by trained research assistants. DNA
samples via saliva also were obtained from children, as de-
scribed below. All research assistants were unaware of child
maltreatment status and study hypotheses.
Measures
Maltreatment Classification System (MCS). The MCS (Bar-
nett et al., 1993) is designed to assess individual children’s
maltreatment experiences. The MCS utilizes DHS records
to make independent determinations of maltreatment. The
MCS classifies the subtypes that each child experienced, fre-
quency of occurrence, subtype severity, and developmental
periods of occurrence in order to designate the recency, onset,
and chronicity of maltreatment. Subtypes of maltreatment in-
clude neglect, emotional maltreatment, physical abuse, and
Table 1. Family demographic characteristics
Maltreated Nonmaltreated
Marital status
Never married 36.2% 36.2%
Married 17.1% 18.2%
Living with partner 22.5% 17.4%
No longer married 24.2% 29.6%
Maternal education
Less than high school graduate 24.2% 18.1%
Family history of receiving
public assistance 99.7% 100%
Note: All group contrasts were nonsignificant.
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sexual abuse. Neglect refers to failure to provide for the
child’s basic physical needs for adequate food, clothing, shel-
ter, and medical treatment. Neglect also includes lack of
supervision, moral–legal neglect, and educational neglect.
Emotional maltreatment involves extreme thwarting of chil-
dren’s basic emotional needs for psychological safety and se-
curity. Examples include belittling and ridiculing the child,
extreme negativity and hostility, child abandonment, suicidal
or homicidal threats, and extreme negativity and hostility.
Physical abuse involves nonaccidental physical injury to
the child such as bruises, welts, burns, choking, and broken
bones. Sexual abuse involves attempted or actual sexual con-
tact between the child and caregiver for purposes of the care-
giver’s sexual satisfaction or financial benefit. Examples of
sexual abuse range from exposure to pornography or adult
sexual activity to sexual touching and fondling to forced in-
tercourse with the child.
The MCS has demonstrated reliability and validity in classi-
fying maltreatment in a number of studies (Bolger & Patterson,
2001;Dubowitzetal.,2005; English et al., 2005;Manly,2005;
Smith & Thornberry, 1995). DHS recordswere coded using the
MCS by trained research staff and a clinical psychologist. All
coders achieved adequate reliability before coding records
used for the study. Kappas for the presence of each of the mal-
treatment subtypes ranged from 0.90 to 1.00; intraclass correla-
tions for severity ratings of individual subtypes of maltreatment
ranged from 0.83 to 1.0.
In the present study, 72.1% of the maltreated children had
experienced neglect, 59.4% experienced emotional maltreat-
ment, 27.2% physical abuse, and 8.7% experienced sexual
abuse. Therefore, emotional maltreatment and neglect were
pervasive throughout the sample while physical and sexual
abuse occurred less frequently. Consistent with other samples
of maltreatment, the majority of children in this study experi-
enced more than one subtype of maltreatment. More specifi-
cally, 58.9% of children had experienced two or more sub-
types of maltreatment (M¼1.75 SD ¼0.72), and 10 out
of the 15 possible combinations of the four maltreatment sub-
types were present in the sample.
Developmental timing variables indicate the occurrence of
maltreatment in discrete developmental periods, including in-
fancy, toddlerhood, preschool, early school age, and later
school age. This information is used to define developmental
period of maltreatment onset and recency of maltreatment.
We defined a categorization of onset and recency groups by
dichotomizing onset into early (prior to age 5) and later
(age 5 and older), and recency as recent (age 5 or older)
and not recent ( prior to age 5). These groupings then define
three onset/recency groups used in analyses: early onset–
not recent, early onset–recent, and late onset–recent.
DNA collection, extraction, and genotyping
Trained research assistants obtained DNA samples from partic-
ipants by collecting saliva using the Oragene DNA Self-Collec-
tion kits. DNA was purified from 0.5 ml of Oragene-DNA so-
lution using the DNA Genotek protocol for manual sample
purification using prepIT-L2P. Sample concentrations were de-
termined using the Quant-iT PicoGreen dsDNA Assay Kit
(P7589, Invitrogen). Single nucleotide polymorphism (SNP)
genotyping was conducted using Applied Biosystems TaqMan
SNP Genotyping Assays for rs671 at Chr.12:112241766
in ALDH2 and (C__11703892_10) and rs1800497 at
Chr.11:113270828 in ANKK1 (C___7486676_10). The
NR3C1 gene mutation rs41423247, commonly known as
Bcll, was genotyped using previously reported primer and
probe sequences (1). Individual allele determinations were
made using TaqMan Genotyping Master Mix (Applied Biosys-
tems, Catalog 4371357) with amplification on an GeneAmp
9700 (Applied Biosystems) and analyzing the endpoint fluores-
cence using a Tecan M200 and data analyzed with JMP 8.0
(SAS, Inc.). Human DNA from cell lines was purchased from
Coriell Cell Repositories for all representative genotypes and
confirmed by sequencing using dye terminator cycle sequenc-
ing on an ABI 3130xl. These positive controls and no template
controls were run alongside study samples representing 9% of
the total data output. Any samples that were not able to be ge-
notyped to a 95% or greater confidence were repeated under
the same conditions.
The call rate for the ALDH2 SNP rs671 was 99.8%. The
frequency distribution of the ALDH2 SNP was GG ¼100%.
Because of the lack of genotypic variation for ALDH2,
this SNP was not included in supplemental analyses. The
call rate for the ANKK1 SNP rs1800497 was 99.6%. The
ANKK1 SNP distribution did not deviate from Hardy–Wein-
berg equilibrium, x2(1) ¼0.45, ns. The frequency distri-
bution of the ALDH2 SNP was as follows: G/G ¼47.3%,
A/G ¼42.0%, and A/A ¼10.6%. A/G and A/A genotypes
were combined for analyses. The call rate for the NR3C1
SNP was 99.8%. The frequency distribution did not deviate
from Hardy–Weinberg equilibrium, x2(1) ¼0.02, ns, and
was as follows: C/C ¼56.2%, C/G ¼37.2%, and G/G ¼
6.4%. C/G and G/G were combined for analyses.
To address potential population stratification, ancestral
proportion testing was conducted. DNA from study partici-
pants was subjected to SNP genotyping of the Burchard
et al. panel of 106 SNPs (Lai et al., 2009; Yaeger et al.,
2008), known to be informative for ancestry from Africa, Eur-
ope, and Native America. The SNPs were genotyped using
the iPLEX platform from Sequenom Bioscience, Inc., which
uses the Sequenom MassArray. Samples are subjected to sin-
gle base primer extension (SBE) with fluorophore labeled nu-
cleotides from primers designed for SNPs of interest. The
samples including the SBE products were placed on the iPLEX
platform and matrix-assisted laser desorption/ionization time
of flight was used to identify the allele based on the fluoro-
phore passing the detector at the expected time associated
with the mass of the SBE primer. The SNP genotyping results
were then recoded and uploaded into STRUCTURE v2.3.4,
which uses algorithms developed by Pritchard and colleagues
(Falush, Stephens, & Pritchard, 2003,2007; Hubisz, Falush,
Stephens, & Pritchard, 2009). Three SNP tests were excluded
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based on high allele call rates of the non-DNA containing
wells. The data from the remaining 103 loci were uploaded
into the software and set to analyze with an Admixture model
of ancestry and initialization of the simulation on the GALA
cohort (initialize of POPINFO). The simulation was set to
run with a burn-in of 10,000, 1,000 Markov chain Monte Carlo
repetitions, and assuming three populations within the group.
The results of the simulations were subsequently identified
as percent association to each ancestry group based on the
known ancestry of the GALA cohort.
To facilitate Maltreatment Race interaction tests, a
grouping variable using ancestral proportion continuous
scores was created with multinomial logistic regression to
classify cases. Parent-reported race/ethnicity (coded 1 ¼Afri-
can American, 2 ¼Caucasian, 3 ¼Hispanic, and 4 ¼other
race/ethnicity) was predicted from proportion African ances-
try and proportion Native American ancestry. Given the large
proportion of African American children in our sample, we
then created a binary variable to classify those with predomi-
nately African ancestry (n¼361, 65.9%) versus other ances-
tral compositions (n¼187, 34.1%).
DNA methylation
Salivary DNA samples were collected from participants
using Oragene DNA collection tubes (DNA Genotekw).
DNA was later isolated from 450 ml of Oragene-DNA/saliva
solution using the PrepIT-L2P protocol. The diluted DNA
samples were submitted to the BioMedical Genomics Center
(BMGC) at the University of Minnesota for quality analysis
and testing of whole-genome methylation analysis using the
HumanMethylation450 BeadChip (Illumina). The samples
were assayed for quality by determining the concentration,
using the Quant-iT PicoGreen dsDNA Assay Kit (Invitrogen,
Item P7589) and real-time polymerase chain reaction (Taq-
Man) quantification of human DNA concentration. All sam-
ples passed BMGC quality control standards, and a normal-
ized 0.5 mg human DNA for each participant was utilized
in the subsequent methylation analyses.
Each 0.5 mg DNA sample was subjected to bisulfite con-
version using the EZ-96 DNA Methylation Kit (Zymo Re-
search, D5003) that converts unmethylated cytosine bases
to uracils. This method utilizes the methyl group attached to
a cytosine as a protecting group to deamination and subse-
quent conversion to a uracil. After bisulfite conversion, the
total amount of DNA was increased by methylation-specific
amplification using a whole-genome amplification process,
which copies the converted uracils to thymine bases. The
DNA was then enzymatically fragmented in an end-point
fragmentation process.
Microarray processing and analysis of the Illumina Infi-
nium HumanMethylation 450K BeadChip was also done
by the University of Minnesota’s BMGC. This covers over
485,000 individual sites with single nucleotide resolution
of CpG sites both inside and outside CpG islands. The
450K BeadChip offers comprehensive genome-wide cover-
age including 99% of RefSeq genes with high quality by
using more than 600 negative controls. Bisulfite-converted
samples were then hybridized to these BeadChips followed
by washing and staining per protocols prescribed by Illumina.
The microarray bead chips were then imaged using a HiScan
SQ system.
The fluorescence data was subsequently analyzed using
the Methylation Module v1.9.0 of the GenomeStudio soft-
ware package v2011.1 (Illumina). All data was background
corrected and negative control normalized, producing average
beta values. This average beta value represents the relative
quantity of methylation at an individual site ranging from 0
to 1 (unmethylated to completely methylated). Tests that pro-
duced different results from technical replicates, originating
from the same source individual and collection type, of study
participant samples were identified as poor and removed from
subsequent analyses. This was accomplished by using differ-
ential methylation analysis of replicate sample-average beta.
Criteria for exclusion of CpG loci based on lack of precision
within technical replicates was identified by selecting sites
with jDiffScorej.13, which is equivalent to a pvalue of
,.01. Tests corresponding to these suspect loci (N¼
5,244), those tests with pvalues of greater than .01 (N¼
1,603), and SNP tests (N¼65) were excluded (N¼6,638,
1.4%). Beta values were analyzed using principal component
analysis in Partek Genomics Suite (Partek Inc.) software. Re-
view of the data distribution identified two samples as outliers
that were subsequently removed from further analyses.
Data analytic plan
Differential methylation analysis of beta values was per-
formed comparing maltreated to nonmaltreated children after
subtracting background noise and normalizing to array con-
trols using GenomeStudio, Methylation Module, and the Illu-
mina Custom Model. The resulting measure of this calcula-
tion set is delta beta, which represents the amount of
change to the average beta at a site, or relative percentage
methylation difference between defined groups. Positive
delta beta values indicate an elevation in relative methylation
and a negative value indicates a reduction. Because there are
males and females in both the maltreated and the nonmal-
treated groups, the X and Y chromosome data were removed
from subsequent analyses. To correct for multiple testing, the
significance threshold to determine differential methylation
was set to 5.010–7, which is consistent with prior research
(i.e., Yang et al., 2013) and recommendations by Raykan,
Down, Balding, and Peck (2011). To investigate the associa-
tion of child maltreatment with known disease biomarkers,
the delta beta values of differentially methylated loci ( p,
5.010–7 ) were then analyzed using GeneGo MetaCore Soft-
ware (Thomas-Reuters, MetaCore Version 6.23).
Site-specific methylation analyses were performed using
SPSS v.21. Prior to conducting analyses, beta values were
transformed using the M-value method, which has been
shown to be more statistically valid for differential analyses
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of methylation levels compared to beta values (Du et al.,
2010). Analyses of the three genes under investigation (i.e.,
ALDH2, ANKK1, and NR3C1) progressed as follows:
1. For ALDH2, because of multiple CpG sites in the first
exon region, exploratory factor analyses (EFAs) with Pro-
max rotation were conducted for data reduction purposes.
2. Independent ttests were conducted to compare methylation
levels at specific CpG sites among maltreated and nonmal-
treated children.
3. Analyses of covariance (ANCOVAs) were then conducted
examining maltreatment effects on methylation levels
(maltreatment as a binary yes/no variable) including child
gender and race as covariates to determine if significant
maltreatment differences in methylation levels were statis-
tically significant with the inclusion of these relevant
covariates and to determine whether sex and/or race may
moderate maltreatment effects on methylation levels.
4. Finally, ANCOVAs were tested to determine the effect of
maltreatment timing (onset and recency) on methylation
levels, controlling for child age and sex and all interactions.
Maltreatment was operationalized in two ways depending
on analyses. For the first set of ANCOVAs, maltreatment was
treated as a binary variable (yes/no). For the second set of
ANCOVAs, maltreatment was operationalized into four
groups: nonmaltreated (n¼250), early onset–not recent (n
¼127), early onset–recent (n¼84), late onset–recent (n¼
74) for the examining of the impact of maltreatment timing
on methylation level.
Results
Preliminary analyses
Maltreated (n¼298) and nonmaltreated children (n¼250)
did not differ on age, t(546) ¼–0.84, ns, and families’ cur-
rent receipt of public assistance, x2(1) ¼0.81, ns. Maltreated
children were more likely to be male, 57.7%; x2(1) ¼8.00,
p¼.005, and of non-African ancestry, 43.8%; x2(1) ¼7.95,
p¼.005, compared to nonmaltreated children (45.6% male,
32.0% non-African ancestry).
Differential methylation analysis
The delta beta information was binned to 10% increments
based on the expected amount of percent methylation from
the nonmaltreated group, or expected average beta. The results
indicated a pattern of greater methylation among maltreated
children at 0.0–0.69 methylation and decreased methylation
among maltreated children at 0.70 methylation (see Figure 1
for graphicalrepresentation). Data were further classified as low
methylation (0%–29%), medium methylation (30%–69%), and
high methylation (70%–100%). Table 2 illustrates that mal-
treated children had greater methylation at low methylation sites
(n¼197 sites) and medium methylation sites (n¼730 sites)
and less methylation at high methylation sites (n¼907 sites).
Finally, the mean difference in methylation between the mal-
treated and nonmaltreated participants was 6.2% with a range
of 1%–64% for the differentially methylated loci ( p,5.0 
10–7). The location within genes of significantly different
CpG sites ( p,5.0 10–7) and associated select functional
gene regions are reported in Table 3. This illustrates that
much of the statistically significant CpG loci were found in in-
tergenic and gene body regions (34.4% and 42.6% respec-
tively). After controlling for multiple comparisons, maltreated
and nonmaltreated children had significantly different
methylation values at a total of 1,876 CpG sites ( p,5.0 
10–7, all sites).
Disease by biomarker analysis
To investigate the potential association of maltreatment with
known diseases, as indexed by genes, proteins, and other
biomarkers, the delta beta values of differentially methyl-
ated loci ( p,5.0 10–7) from the HumanMethylation450
BeadChip were analyzed using GeneGo MetaCore Software
(Thomas-Reuters, MetaCore Version 6.23). The GeneGo
MetaCore software uses a Fisher exact test with Benjimini–
Hochenberg false data recovery rate corrections. For each
Table 2. Genome-wide methylation difference for
maltreated children compared to expected methylation
(nonmaltreated children)
Nonmaltreated
Methylation
Maltreated Methylation
Greater Less Total
Low (0.00–0.29) 130 67 197
Medium (0.30–0.69) 430 300 730
High (0.70–1.0) 407 500 907
Total 967 867 1834
Figure 1. (Color online) Genome-wide methylation difference for maltreated
children compared to expected methylation (nonmaltreated children).
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health index, Table 4 lists the number of network objects as-
sociated with the health index and the number of objects that
were differentially methylated for maltreated versus nonmal-
treated groups. The significance values for differential
methylation rates are lower than the adjusted false discovery
rate ( pvalues, indicating significant maltreated/nonmal-
treated group differences). A large number of network objects
(i.e., proteins, peptides, and other biologically functional
molecules) previously associated with mental health, cancer,
cardiovascular systems, and immune functioning were iden-
tified evidencing differential methylation among maltreated
and nonmaltreated children.
Site-specific analyses
ALDH2. An EFA was conducted with CpG sites cg10449070,
cg13955512, cg18780217, cg21470387, and cg24546205
from the first exon region. The results were not indicative of
a one- or two-factor solution; therefore, each CpG site was
analyzed separately. Independent samples ttests revealed a
significant difference between maltreated and nonmaltreated
children at site cg24546205, t(535) ¼–2.27, p¼.02, such
that maltreated children evidenced significantly higher
methylation than nonmaltreated children. There were no sig-
nificant differences between maltreated and nonmaltreated
children on methylation level at the other four ALDH2 sites.
Next, full-factorial ANCOVAs including maltreatment
(yes/no), gender, and race were conducted with the five
ALDH2 CpG sites as outcomes in separate models. Even
with the inclusion of gender, race, and all interactions, mal-
treated children continued to evidence significantly higher
methylation at site cg24546205 compared to nonmaltreated
children, F(1, 528) ¼4.92, p¼.03. No other significant mal-
treatment main effects were found for ALDH2 loci. Girls evi-
denced significantly higher methylation levels at site
cg18780217, F(1, 538) ¼4.44, p¼.04, compared to
boys. No other significant gender main effects were found
for the ALDH2 CpG sites. There were no significant race
main effects at any of the ALDH2 loci.
For cg13955512, a significant Maltreatment Gender in-
teraction was found, F(1, 536) ¼15.41, p,.001 (see
Figure 2a). Probing the interaction revealed that maltreated
girls evidenced significantly lower methylation compared to
nonmaltreated girls, F(1, 255) ¼7.97, p¼.005, and mal-
treated boys evidenced significantly higher methylation com-
pared to nonmaltreated boys, F(1, 281) ¼7.32, p¼.007.
Probed differently, results indicated that among nonmal-
treated children, there was no difference in methylation level
between boys and girls at this site. Among maltreated chil-
dren, however, results indicated that boys evidenced signifi-
cantly higher methylation compared to girls, F(1, 290) ¼
17.45, p,.001. None of the other interactive effects were
significant in any models.
To examine the role of maltreatment timing (onset and re-
cency), the above ANCOVAs were repeated, substituting the
binary maltreatment yes/no variable for the four-group mal-
treatment timing variable described above. None of the mal-
treatment timing main effects were significant for any of the
ALDH2 loci. The results indicated a significant main effect of
gender at cg13955512, F(1, 515) ¼11.45, p,.001. The
main effect of gender at cg13955512 was further clarified
by a significant Maltreatment Timing Gender interaction,
F(3, 515) ¼5.44, p,.001 (see Figure 2b). Probing the in-
teraction revealed a significant effect of maltreatment timing
among boys, F(3, 268) ¼2.99, p¼.03. Bonferroni contrasts
indicated that nonmaltreated boys evidenced significantly
lower methylation compared to early onset–not recently mal-
treated boys ( p¼.046). None of the other Bonferroni con-
trasts reached significance. Although there was also a signif-
icant effect of maltreatment timing among girls, F(3, 247) ¼
Table 3. Number of loci significantly different at p ,5.0 ×1027between the maltreated and
nonmaltreated participants grouped by CpG location within gene and associated select
functional gene regions
Location
No. of CpG
Sites Diff. Total CpG Sites
Differen. Methylated
(%)
Intergenic 646 115,900 5.57 ×1023
1st exon 36 21,712 1.43 ×1023
3′UTR 82 16,709 4.91 ×1023
5′UTR 90 40,714 2.21 ×1023
Gene body 799 156,771 5.10 ×1023
TSS200+TSS1500 223 115,900 1.92 ×1023
Functional Gene Region
Promoter associated 90 94,758 9.50 ×1024
Enhancers 466 100,712 4.63 ×1023
DNase hypersensitive sites 177 57,547 3.08 ×1023
Note: CpG, Cytosine nucleotide–phosphate–guanine nucleotide; Diff., different; Differen., differentially; UTR, untrans-
lated region.
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2.75, p¼.04, none of the Bonferroni contrasts reached statis-
tical significance. Probed differently, results indicated that
among nonmaltreated children, there was no difference in
methylation level between boys and girls. However, among
children with early onset–not recent maltreatment, F(1, 120)
¼7.59, p¼.007, early onset–recent maltreatment, F(1, 80)
¼4.62, p¼.04, and late onset–recent maltreatment, F(1,
69) ¼6.99, p¼.01, boys consistently demonstrated signifi-
cantly higher methylation compared to girls. There were no sig-
nificant main effects of race, and none of the other interactions
were significant for any of the other ALDH2 CpG sites.1
ANNK1. Because there was only one CpG site in the first exon
region for ANKK1, an EFA was not conducted. The results of
an independent samples ttest indicated that maltreated children
evidenced significantly higher methylation than nonmaltreated
children at site cg06976250, t(546) ¼–2.08, p¼.04. An AN-
COVA examining the effect of maltreatment status controlling
for race, gender, and all interactions continued to support a sig-
nificant difference between nonmaltreated and nonmaltreated
children at this CpG site, F(1, 539) ¼9.04, p¼.003. More-
over, girls evidenced significantly higher methylation than
boys, F(1, 539) ¼9.26, p¼.002, and children with African
ancestry evidenced significantly higher methylation than chil-
dren of non-African ancestry, F(1, 539) ¼13.08, p,.001.
None of the interactions were statistically significant.
To investigate the role of maltreatment timing (onset and re-
cency) on methylation at ANKK1, the above ANCOVA was re-
peated substituting the binary maltreatment yes/no variable for
the four-group maltreatment timing variable. A significant
effect of maltreatment timing was found, F(3, 518) ¼3.70,
p¼.01 (see Figure 3). Bonferroni contrasts indicated that
children with early onset–non recent maltreatment evidenced
significantly higher methylation compared to nonmaltreated
children ( p¼.02). None of the other Bonferroni contrasts
were statistically significant. As found in the previous model,
girls evidenced significantly higher methylation compared to
boys, F(1, 518) ¼7.31, p¼.007, and children with African
ancestry evidencedhigher methylation compared to children of
non-African ancestry, F(1, 518) ¼8.16, p¼.004.2
NR3C1. An EFA was conducted with CpG sites cg20753294,
cg18146873, cg08818984, and cg26720913 from the first
Table 4. Select disease indices that are differentially methylated among maltreated and nonmaltreated children
Diseases p
FDR-Adj.
p
No. of Signif.
Diff. Network
Objects
Total Network
Objects
Cancer
Melanoma 3.7E-14 1.5E-12 217 3884
Endometrial neoplasms 4.7E-12 7.5E-11 483 11261
Prostatic neoplasms 4.8E-12 7.5E-11 449 10275
Kidney neoplasms 1.7E-10 1.7E-09 454 10631
Leukemia 3.2E-09 2.4E-08 172 3254
Gastrointestinal neoplasms 3.0E-08 1.7E-07 449 10834
Lung neoplasms 3.4E-08 1.9E-07 676 17844
Breast neoplasms 9.4E-07 4.2E-06 372 8894
Liver neoplasms 1.1E-06 4.9E-06 175 3619
Stomach neoplasms 6.2E-06 2.4E-05 183 3925
Colorectal neoplasms 6.6E-06 2.5E-05 389 9534
Pancreatic neoplasms 2.8E-05 9.7E-05 126 2572
Cardiovascular and hematologic systems
Hemorrhagic disorders 1.6E-14 7.1E-13 139 2083
Blood protein disorders 2.2E-12 3.9E-11 128 1982
Cardiovascular diseases 2.8E-09 2.1E-08 185 3565
Immune systems
Psoriasis 8.0E-13 1.7E-11 47 414
Asthma 1.4E-11 1.9E-10 88 1196
Autoimmune diseases of the nervous system 8.6E-09 5.8E-08 76 1108
Mental health and central nervous system
Schizophrenia and disorders with psychotic
features 6.6E-14 2.4E-12 79 918
Affective disorders, psychotic 7.5E-11 8.7E-10 47 471
Bipolar disorder 2.1E-10 2.1E-09 46 469
Depressive disorder 2.0E-06 8.3E-06 42 557
Note: FDR-Adj., False discovery rate adjusted; Diff., different.
1. Given the lack of variability of the ALDH2 SNP, we were unable to con-
duct supplementary analyses controlling for genotype.
2. The above full factorial analyses of variance were repeated with the inclu-
sion of ANKK1 genotype variation (rs1800497) as a factor. The pattern of
results did not change with the inclusion of genotype.
D. Cicchetti et al.8
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exon region for NR3C1. The results were not indicative of a
one or two-factor solution; therefore, each CpG site was ana-
lyzed separately. Independent samples ttests did not support
significant differences at any NR3C1 site between maltreated
and nonmaltreated children. Furthermore, ANCOVAs includ-
ing gender and race also did not indicate any significant main
effects of maltreatment status. Girls evidenced significantly
higher methylation at cg08818984, F(1, 539) ¼5.67,
Figure 2. Effect of (a) maltreatment status and (b) developmental timing on ALDH2 methylation varied by gender. Mal, Maltreated; NMal, non-
maltreated; ENR, early onset–not recent; ER, early onset–recent; LR, late onset–recent. *p,.05. **p,.01.
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p¼.02, and cg26720913, F(1, 538) ¼6.36, p¼.01, com-
pared to boys. None of the other main effects of maltreatment
status, gender, or race were statistically significant. For site
cg18146873, there was a significant maltreatment by race
interaction, F(1, 539) ¼4.50, p¼.03 (see Figure 4). Probing
the interaction revealed that maltreated children with African
ancestry evidenced significantly lower methylation compared
to nonmaltreated children with African ancestry, F(1, 333) ¼
5.65, p¼.02. The effect of maltreatment status was nonsignif-
icant for children of non-African ancestry, F(1, 206) ¼0.72,
ns. Probed differently, the results indicated that among non-
maltreated children, there was no difference between children
Figure 3. The effect of maltreatment developmentaltiming on ANKK1 methylation. Significant contrast: early onset–not recent .nonmaltreated.
p,.05.
Figure 4. The effect of maltreatment status on NR3C1 methylation varied by race. Mal, maltreated; NMal, nonmaltreated. *p,.05.
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of African ancestry and other children, F(1, 246) ¼0.66, ns.
However, among maltreated children, those of African ances-
try evidenced significantly lower methylation compared to
maltreated children of other ancestral backgrounds, F(1,
293) ¼5.46, p¼.02. None of the other interactive effects
were significant at any of the NR3C1 sites.
The next set of ANCOVAs examined the effect of maltreat-
ment timing on methylation controlling for gender and race
and all interactions. No significant main effects of maltreat-
ment timing or race were found for any NR3C1 sites. Boys evi-
denced significantly lower methylation at sites cg08818984,
F(1, 518) ¼4.30, p¼.04, and cg26720913, F(1, 517) ¼
5.14, p¼.02, compared to girls. For site cg18146873, there
was a significant maltreatment timing by gender interaction,
F(3, 518) ¼3.46, p¼.02. However, probing the interaction
revealed no significant contrasts. There were no other signifi-
cant interactions for any NR3C1 CpG sites.3
Discussion
This investigation was conducted on one of the largest sam-
ples to date of methylation differences between maltreated
and nonmaltreated children. Because most of the studies of
DNA methylation have been on adults who retrospectively re-
ported having been maltreated, the fact that the participants in
this investigation were maltreated children examined prospec-
tively, and not adults, is very concerning. The sample was care-
fully recruited, with demographically comparable low-income
children who have confronted the stress associated with poverty
and adversity. The maltreated children all had Child Protective
Services documented maltreatment experiences (Barnett et al.,
1993; Cicchetti & Barnett, 1991;Manly2005), and we were
able to ascertain the developmental timing of maltreatment
events since the birth of the targeted child.
We found, as have Yang et al. (2013) and others, that there
were significant differences between maltreated and nonmal-
treated children in methylation across the epigenome. Mal-
treated children tended to have higher levels of methylation
at CpG sites where nonmaltreated evinced low and medium
levels of methylation, as well as lower methylation levels at
CpG sites where nonmaltreated children evinced high levels
of methylation (see Table 2 and Figure 1). The pattern of
methylation differences was related to differential physical
and mental health risk for maltreated and nonmaltreated chil-
dren. Maltreated children exhibited differential methylation
in genes associated with various cancers, cardiovascular
and hematologic disease, immune disorders, as well as major
psychiatric disorders (e.g., schizophrenia, bipolar disorder,
and depression). Thus, the genome-wide epigenetic study
of child maltreatment demonstrates an increased risk for men-
tal and physical disorders and suggests liabilities for future
health outcomes.
In addition, we conducted site-specific analyses of
methylation on CpG sites of three genes: ALDH2, ANKK1 (as-
sociated sites of DRD2), and NR3C1 to examine variation
among maltreated children on these genes related psychological
functioning and disorders. Not all maltreated children exhibitt he
same degree of methylation. The effects of developmental tim-
ing, gender, and race were evaluated. Analyses also controlled
for genotype variation. The site-specific results were not altered
by controlling for genotypic variation of respective genes.
For NR3C1 (the glucocorticoid receptor gene), among
African American children, maltreated children evinced
much lower methylation than nonmaltreated children, and
among maltreated children, African American children had
lower methylation than children who were not African Amer-
ican. Although prior studies have shown that early maltreat-
ment and trauma are associated with increased methylation
of NR3C1 (Perroud et al., 2011; van der Knapp et al.,
2014), the role of ancestral variation has not been well stud-
ied. Our findings suggest that this is an important considera-
tion for future research.
The ANKK1 gene, also referred to as Taq1A, was origi-
nally associated with the DRD2 gene. Because of its close
proximity to DRD2, ANKK1 is believed to regulate DRD2
(Samochowiec, Samochowiec, Puls, Bienkowski, & Schott,
2014). Our results suggest that maltreated children (boys
and girls) with early onset but not recent maltreatment had
significantly higher ANKK1 methylation than nonmaltreated
children suggesting a potential dopaminergic pathway of mal-
treatment risk that may be related to the early experience of
maltreatment. Similarly, given the documented association
of ALDH2 and risk for alcohol use disorders (e.g., Dick &
Foroud, 2003), our findings that maltreated boys with early
onset but not recent maltreatment showed the largest
methylation differences compared to nonmaltreated boys in-
dicates another potential pathway of epigenetic risk.
Together, our site-specific findings add to the existing lit-
erature by demonstrating that the effects of child maltreatment
on methylation in the glucocorticoid receptor gene (NR3C1),
dopaminergic gene (ANKK1), and alcohol-metabolizing gene
(ALDH2) are complex and highlight the need for examining
individual characteristics such as gender, race, and develop-
mental timing of maltreatment. Given the associations of
these three genes with various psychological outcomes, ex-
plicating the role of epigenetic modifications of these genes
in the development of psychopathology for maltreated chil-
dren may be of critical importance.
Future directions
In the future, it will be important to study other manifestations
of stress-related risk in maltreated children, such as allostatic
load and constituent biomarkers and immune functioning, in
order to understand how methylation may affect these pro-
cesses related to future disease. Future epigenetic research
needs to determine whether African American children are
more protected by lower methylation of NR3C1 or if lower
3. The above full factorial analyses of variance were repeated with the inclu-
sion of glucocorticoid receptor genotype variation (rs41423247) as a fac-
tor. The pattern of results did not change with the inclusion of genotype.
Child maltreatment and epigenetic mechanisms of mental and physical health risk 11
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methylation of this gene suggests greater risk for mental and
physical problems. In addition, research needs to examine
linkages of differential methylation in maltreated children
to behavioral outcomes. Maltreated children exhibit increased
behavioral problems and psychopathology (Cicchetti & Toth,
2016; Cicchetti & Valentino, 2006; Vachon et al., 2015);
methylation may serve as a mediator between maltreatment
and adverse outcomes. Finally, investigations of other high-
risk groups of children who experience varying degrees of in-
consistent caregiving (e.g., Esposito et al., 2016) will not only
offer insight into the specificity of these neurobiological sys-
tem dysregulations in response to maltreatment and its asso-
ciated stressors but also contribute to the development of
more knowledge of which aspects of caregiving experiences
are critical for normal neurobiological growth (Toth, Sturge-
Apple, Rogosch, & Cicchetti, 2015).
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