Interactions between endogenous factors,
e.g., gene allelic variants, and exogenous
factors, e.g., exposure to specific environ-
ments, can be reasonably suspected as the
cause of the most common chronic com-
munication disorders. However, the elucida-
tion of genetic factors and their interactions
with non-genetic factors in behavioral
diseases has been challenged. To date, two
main strategies have been used for the iden-
tification of genes for diseases of unknown
molecular etiology. Over two decades, link-
age analysis and positional cloning of the
disease gene were effective in the identifica-
tion of genes for Mendelian or monogenic
single-gene disorders (Botstein and Risch,
2003; McKusick, 2007). The methodological
and statistical threshold criteria, applied for
linkage analysis in families, provides robust
evidence for identification of chromosomal
locus and the gene mutation characterized
by high penetrance. Among common neu-
ropsychiatric diseases, the approach was
successful in the identification of genes
for familial early onset Alzheimer’s disease
(AD) carrying autosomal-dominant muta-
tions in PSEN1, PSEN2 genes (Rogaev et al.,
1995; Sherrington et al., 1995). Another
genetic approach, genome-wide associa-
tion (GWA), or direct genetic association
analysis in candidate-genes, employs rela-
tively common genetic variations across the
genome or in selected genes for compara-
tive analysis in case–control groups. GWA
has been applied for hundreds phenotype
traits, including neuropsychiatric conditions
(Wellcome Trust Case Control Consortium,
2007; Donnelly, 2008). The genetic associa-
tion methodology generated much more
controversial data in interpretation (Pearson
and Manolio, 2008). In most cases, the GWA
studies identified only a minor proportion
of genetic contribution to common ill-
nesses with little medical diagnostic value.
Nonetheless, the genetic association method
revealed some convincing data. For exam-
ple, as confirmed in numerous studies, ApoE
ε4 allelic isoform is a common risk factor
for AD in ethnic groups of Caucasian and
Asian origin. Analysis of two large Caucasian
cohorts stratified by age showed that the
ApoE-genotype dependent lifetime risks
for AD are similar to those of Mendelian-
disease genes with major risk effect (Genin
et al., 2011). Unlike AD, however, no mutant
gene convincingly inherited as a Mendelian
trait has yet been described for schizophre-
nia and affective disorders, despite the fact
that many mutigenerational pedigrees are
available. GWA employs relatively common
single-nucleotide polymorphisms (SNPs)
across the genome. The recently emerged
concept that rare genetic variations, rather
than common population variations, under-
lie common diseases challenges the standard
genetic association approach in neuropsy-
rare and non-coding variations
Indeed, rare copy-number variations
(CNVs) in a few chromosomal loci have
been found in a small proportion of schizo-
phrenia samples (Karayiorgou et al., 1995;
International Schizophrenia Consortium,
2008; Mulle et al., 2010; Vacic et al., 2011).
Among numerous genes implicated for
schizophrenia, a few candidate-genes, such
as DISC or NPAS3, with disrupted struc-
ture due to chromosomal translocations in
single patients, can be noted (Millar et al.,
2000; Kamnasaran et al., 2003).
Emerging new generation sequenc-
ing technology, termed deep or massively
parallel sequencing (MPS), may transform
the field. Direct sequencing of all genes, or
preferably whole-genome sequences, will
provide complete genetic information of
the patient. However, from our current
knowledge on population genetic variabil-
ity, we can expect >3.5 million SNPs and
>1,000 CNVs of genomic sequences per
individual in comparison to reference
genome sequence. Thus, excluding genetic
“background” and identification of disease-
related variations in the individual genomes
remains a challenge in study of behavior
Currently available MPS platforms (e.g.,
Illumina or Complete Genomics) produce
biases to different types of errors in nucleo-
tide sequences (Lam et al., 2011). Another
challenge is a biological interpretation of
genetic variations found via direct genome
sequencing analysis. Application of high-
throughput functional assays in cellular
models and in animals testing the biologi-
cal significance of suspected mutations or
polymorphisms in selected genes may be
one possible solution.
De novo mutations
Another recently proposed approach is the
identification of genes via analysis of de
novo mutations in exome. Despite reduced
reproductive fitness, the rate of incidence
for schizophrenia and autism is relatively
stable (0.4–1%) in worldwide popula-
tions (Bassett et al., 1996; Saha et al., 2005;
Center for Disease Control and Prevention,
2009). The Hardy–Weinberg concept could
explain, in part, the long-term maintenance
of recessive mutant alleles in populations.
Another explanation is that spontaneous
de novo mutation process contributes to
the relatively frequent population occur-
rence of schizophrenia and autism. In this
event, we may anticipate the increased risk
for disease with increased parental age due
to age-dependent increased mutation fre-
quency in gametogenesis. Indeed, the effect
of paternal (but not maternal) age on the
risk for schizophrenia has been reported.
Meta-analysis studies suggest that there is
an increased relative risk for schizophre-
nia from 1.84 to 4.62 in children of fathers
with an older age of fatherhood (Hubert
et al., 2011). Instead of searching for large
families and pedigrees, the small nuclear
families with one proband and no other
schizophrenia cases in the family history
are a subject of interest. Recently, puta-
tive causative de novo mutations were
found in an analysis of genomic exomes
of 4 of 20 analyzed probands with autism
(O’Roak et al., 2011). Two studies employed
sequencing of exomes for schizophrenia.
Genomics of behavioral diseases
Evgeny I. Rogaev*
Brudnick Neuropsychiatric Research Institute, University of Massachusetts Medical School, Worcester, MA, USA
www.frontiersin.org April 2012 | Volume 3 | Article 45 | 1
published: 02 April 2012
On one hand, the genomic regions which
are targets for the epigenomic regulations
are under pressure of negative selection.
These regions are characterized by a rela-
tive deficiency in SNPs (Tolstorukov et al.,
2011). On the other hand, the mutations
in these regions, if they occur, may directly
affect the chromatin structure or DNA
methylation, thereby affecting the capac-
ity of these regions for either epigenomic
stress-induced or cell specific (e.g., neu-
I believe it would be reasonable to
hypothesize that the genetic–epigenomic
interactions (GEI) play a significant role
in common human illness, particularly in
behavioral diseases, including major psy-
chiatric diseases, such as schizophrenia,
affective disorders, autism, or even in age-
related dementias. Any SNP or structural
genetic variation in cis-position located at
a distant regulatory region or nearby gene
only a small portion of the genome, <1%, is
represented by exome, most of the sponta-
neous germline mutations must be located
in non-coding genomic regions. Therefore,
the complete genome sequencing in search
of de novo mutations in neuropsychiatric
diseases is a promising strategy. The search
for mutations in sequences for enhanc-
ers, insulators, and repressors, which are
often physically distant (e.g., >1 Mb)
from the gene transcriptional start, along
with 5′-and 3′-UTRs (untranslated gene
regions) and promoter gene regions must
Unlike genetics, epigenomic variations are
generally reversible alterations that can be
identified by analysis of the state of DNA
methylation or histone modifications spe-
cific for active and non-active chromatin
In summary, in 35 probands from fam-
ily trios, several different genes bearing
de novo mutations were found. With one
exception for a DGCR2 gene located on
schizophrenia-associated 22q11.2 microde-
letion region, the mutant genes are not pre-
sented in putative schizophrenia-associated
chromosomal loci or pathways predicted
in previous studies (Girard et al., 2011; Xu
et al., 2011). There was no match between
the genes with de novo mutations found in
independent families. Thus, the pathogenic
significance of the identified mutations for
schizophrenic trait must be further verified.
Estimates made from preliminary analy-
sis of parent–child genome sequences sug-
gest a rate of de novo spontaneous germline
mutation ∼1.1 × 10−8 bp for human haploid
genome (Roach et al., 2010). It would cor-
respond to ∼60–70 new mutations per dip-
loid genome in each individual, which are
non-inherited from the parents. Because
Figure 1 | epigenomic and genetic interactions in regulation of
chromatin and gene activity underlying behavior traits. Genes can be
regulated via genomic elements (enhancers, repressors, insulators) in
tissue-specific manner. Epigenomic modification of chromatin in brain cells
can be affected by programmed transformation, environmental factors, and
genetic variations in the regulatory regions. Alteration of the transcription of
the gene can also be caused by mutation in the regulatory elements without
obvious changes in chromatin. Open or active chromatin structures are
identified by the mapping of DNAse-hypersensitive sites and by Chip-seq
technology, which tracks the genomic regions forming complexes with
differentially modified histones (K) or the sequences binding with transcription
Rogaev Genomics of behavioral diseases
Frontiers in genetics | Behavioral and Psychiatric Genetics
April 2012 | Volume 3 | Article 45 | 2
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variations in the same individual genomes
can be detected by MPS using rapidly
progressing methodologies for analysis of
DNA methylation with single-nucleotide
resolution, DNAse-I hypersensitive sites,
or Chip-seq data for transcriptional start
sites or transcriptional regulatory ele-
ments. For example, Chip-seq can track
transcriptional start sites via detection of
sites for histone H3 trimethylated at lysine
4 (H3K4me3) and other transcriptional
regulatory elements including enhanc-
ers enriched with histone H3 acetylated
at lysine 27 (H3K27Ac) and histone H3
monomethylated at lysine 4 (H3K4me1).
DNAse-I hypersensitive sites (at least
∼1–2% of genome) mark open or active
chromatins associated with majority of
regulatory and transcriptional start sites.
Beyond neuropsychiatric illnesses, I sug-
gest that GEIs may also underlie changes in
non-pathogenic behavioral traits and that
the interplay between genetic variations and
epigenomic modifications could be identi-
fied through the study of non-conventional
animal models. Genomic sequencing, cou-
pled with epigenomic studies, provides per-
spective in the identification of alterations
in genome correlated with rapid behavior
changes under certain selection process in
rodents (e.g., in rats) or, in follow-up arti-
ficial selection in domesticated animals.
Rapid changes in behavior, from native
aggressive defending reaction to tolerant
or even to “the man’s best friend” behav-
ior in Canidae species, can be achieved in
just a few generations, as demonstrated in
the domestication experiments of silver
foxes selected for tameability (Spady and
Ostrander, 2008; Trut et al., 2009; Parker
et al., 2010). The patterns of genetic altera-
tions underlying changes in this behavior
paradigm have yet to be identified. Perhaps
it is not an exaggeration to speculate that
elucidation of such a mechanism may also
contribute to understanding the evolution
of normal and abnormal social behavior in
humans, and even of our own tolerance or
intolerance of each other.
Supported, in part, by NIH/NINDS
Investigator Award, NIH/NIA AG029360, EU
FP7 HEALTH-RF (ADAMS project), Rostok
Group. I would like to thank A. Grigorenko
for her assistance with figure preparation.
can alter the constitutive or tissue-specific
state of active chromatin and regulation
of the gene (Figure 1). Certain epigenetic
mutations can be silent, but will manifest
due to programmed epigenomic transfor-
mations during development, the aging
process, or triggered under specific con-
ditions, e.g., hypothalamus – axilatory
mediated stress conditions, or exposure
to infection or chemical compounds. We
could speculate that the silent variations
affecting the epigenomic state play a role
in some psychiatric disorders with revers-
ible clinical manifestation (schizophrenia
and bipolar disorder) or stress-induced and
Despite the fact that the genetic role of
schizophrenia is well-established, and that
multiple informative schizophrenia families
have been available for a long time, there
is, as yet, no robust evidence for mutations
in genes altering the protein structure in
any significant number of schizophrenia
cases as observed, e.g., in AD. Classical
twin analysis showed that the contribution
of non-genetic factors to schizophrenia is
at least 50%. The number of autism spec-
trum disorder (ASD) cases continues to
increase. Currently, ASD is diagnosed in 1
of 110 children (Center for Disease Control
and Prevention, 2009). The cause of the
rising rate for ASD is unknown. Recent
re-evaluation of large cohorts of twins also
demonstrated that there is a significant role
of non-genetic factors in ASDs, and up to
37–38% of genetic factors in contrast to the
previous conception of an ∼90% heritability
in autism. A surprisingly significant role of
shared twin environment, evidenced by high
dizygotic twin concordance, was observed
(Hallmayer et al., 2011). The hypothesis of
GEI seems to be very relevant to ASD and
Modified genomic DNA and chromatin
complexes can be extracted from neuronal
cells separately from glial cell popula-
tions from postmortem brain specimens
(Matevossian and Akbarian, 2008). We can
attempt to determine whether structural or
single-nucleotide variations in individual
genomes (genetic variations) correlate
with individual variations in DNA meth-
ylation or methylation/acetylation histone
modifications (epigenomic variations) in
the same loci in neuronal cells. Genetic
variations can be identified by whole-
genome MPS re-sequencing. Epigenomic
Rogaev Genomics of behavioral diseases
www.frontiersin.org April 2012 | Volume 3 | Article 45 | 3
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Received: 02 February 2012; accepted: 12 March 2012;
published online: 02 April 2012.
Citation: Rogaev EI (2012) Genomics of behavioral diseases.
Front. Gene. 3:45. doi: 10.3389/fgene.2012.00045
This article was submitted to Frontiers in Behavioral and
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Copyright © 2012 Rogaev. This is an open-access article
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