REVIEW ARTICLE

Next-Generation Sequencing in Schizophreniaand Other Neuropsychiatric DisordersMatthew Schreiber,1,2 Michael Dorschner,1,3,4 and Debby Tsuang1,4*1Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA2Mental Health Services, VA Puget Sound Health Care System, Seattle, WA3Department of Genome Sciences, University of Washington, Seattle, WA4Geriatric Research, Education, and Clinical Center, VA Puget Sound Health Care System, Seattle, WA

Manuscript Received: 7 January 2013; Manuscript Accepted: 13 March 2013

Schizophrenia is a debilitating lifelong illness that lacks a cure

and poses a worldwide public health burden. The disease is

characterized by a heterogeneous clinical and genetic presenta-

tion that complicates research efforts to identify causative ge-

netic variations. This review examines the potential of current

findings in schizophrenia and in other related neuropsychiatric

disorders for application in next-generation technologies, par-

ticularly whole-exome sequencing (WES) and whole-genome

sequencing (WGS). These approaches may lead to the discovery

of underlying genetic factors for schizophrenia andmay thereby

identify and target novel therapeutic targets for this devastating

disorder. � 2013 Wiley Periodicals, Inc.

Key words: schizophrenia; genetics; sequencing; whole exome;

autism

INTRODUCTION

Schizophrenia is a debilitating lifelong illness that lacks a cure and

poses a worldwide public health burden. The symptoms and course

of schizophrenia are variable, with an age of onset beginning in late

adolescence but spanning several decades. Neurobiological factors

are known to play a major role in the disease, yet no definitive

diagnostic tests exist, which can make it challenging to diagnose.

Mirroring these clinical complexities, the genetic basis of schizo-

phrenia is also something of a labyrinthine puzzle.

Even prior to the molecular genetic era, observational [Gottes-

manandWolfgram, 1991; Faraone et al., 1999] and epidemiological

[Tsuang, 1994] twin, adoption, and family studies suggested that a

complex interplay of genetics and environment led to the develop-

ment of schizophrenia [Slater and Tsuang, 1968; Tsuang

et al., 1974]. These studies have shown, for example, that the

risk of schizophrenia is elevated 10-fold for individuals with an

affected first-degree relative and 50-fold for individuals with both

parents affected. They have also demonstrated that the estimated

heritability of the disease is as high as 80% [Tsuang, 1993; Gejman

et al., 2011].

Studies of schizophrenia have also shown that the clinical

heterogeneity of schizophrenia [St Clair et al., 1990] likely reflects

etiological heterogeneity at the molecular genetics level [Tsuang

and Faraone, 1995]. Linkage studies have demonstrated that mul-

tiple loci contribute to the genetics of schizophrenia in families,

suggesting the likely existence of locus heterogeneity. Decreased

penetrance and unknownmodes of inheritance further complicate

the genetic picture of schizophrenia, slowing gene discovery efforts.

This review briefly surveys schizophrenia genetics, examining

the recent findings in schizophrenia—including several tantalizing

discoveries—and in other related neuropsychiatric disorders that

demonstrate the potential of next-generation technologies, partic-

ularly whole-exome sequencing (WES) and whole-genome se-

quencing (WGS). We anticipate that these approaches may lead

to exciting new ways of uncovering the underlying genetic factors

for schizophrenia and may thereby identify and target novel

therapeutic targets for this devastating disorder.

MODES OF TRANSMISSION AND HYPOTHETICALMODELS

Numerous modes of transmission have been tested to explain the

complex genetic architecture of schizophrenia, and these inves-

tigations have led to the proposal of twomain hypothetical models.

The advent of high-density genotyping panels facilitated genome-

How to Cite this Article:Schreiber M, Dorschner M, Tsuang D.

2013. Next-Generation Sequencing in

Schizophrenia and Other Neuropsychiatric

Disorders.

Am J Med Genet Part B. 162B:671–678.

�Correspondence to:

Debby Tsuang, M.D., M.Sc., VAPSHCS, GRECC, S-182 1660 S.

Columbian Way, Seattle, WA 98108.

E-mail: dwt1@uw.edu

Article first published online in Wiley Online Library

(wileyonlinelibrary.com).

DOI 10.1002/ajmg.b.32156

� 2013 Wiley Periodicals, Inc. 671

Neuropsychiatric Genetics

wide association studies to directly test the common-disease com-

mon-variant (CDCV) hypothetical model, which posits that com-

mon variants with modest effects on a disease contribute in an

interactive manner to confer disease susceptibility [Reich and

Lander, 2001; Smith and Lusis, 2002; Hirschhorn and

Daly, 2005; Iyengar and Elston, 2007]. According to the CDCV

model, a disorder results from the interaction ofmultiple common,

small-effect genetic variants with environmental risk factors that

exceed a biological threshold for developing a disorder. The Hap-

Map project facilitated the identification of disease susceptibility

genes through indirect linkage disequilibrium mapping of single-

nucleotide polymorphisms (SNPs). Specifically, by examining a

subset of SNPs (tagSNPs), researchers can capture information

about correlated SNPs that have not been genotyped, and given the

precepts of the CDCVmodel, this reduces the number of SNPs that

have to be genotyped.

Alternatively, the common-disease rare-variant (CDRV) model

posits that complex traits are characterized by allelic heterogeneity

and that disease etiology is thus caused by multiple rare variants

which act collectively, each with moderate to high penetrance

[Smith and Lusis, 2002; Iyengar and Elston, 2007]. Therefore,

according to this model, the presence of many individually rare

mutations in individual families or subjectsmay increase the risk of

developing schizophrenia, and each mutation may be unique to

those families or individual subjects. Studies based on evolutionary

theories have demonstrated that for complex diseases like schizo-

phrenia, allelic heterogeneity might be extensive, with multiple

susceptibility alleles of independent origins. The CDRV is further

supported by a recent analysis that has shown that rare variants are

more likely to be disease-predisposing than are common variants

[Gorlov et al., 2008].

The CDCV and CDRV models are not mutually exclusive

[Goldstein and Chikhi, 2002]; rare deleterious mutations are

known to occur in genes that also harbor common variants with

modest effects on disease risk [Bodmer and Bonilla, 2008]. This

phenomenonhas beenobserved, for example, in variants associated

with lipid levels: eleven of the 30 genes that carry common variants

associated with lipid levels also carry known rare alleles that are of

large effect inMendelian dyslipidemias [Cohen et al., 2006; Romeo

et al., 2007]. It is likely that in a heterogeneous, complex genetic

disorder such as schizophrenia, a subset of casesmay be attributable

to rare mutations with large effects while another subset may

develop the disorder as a result of an interaction of multiple

common variants of small effect.

LINKAGE AND GWA STUDIES IN SCHIZOPHRENIA

Although commonly used genetic methods have successfully iden-

tified single genes that cause many rare genetic disorders, these

approaches have been less successful in complex disorders like

schizophrenia. Multiple disease-related genetic loci have been

reported by genetic linkage studies and GWASs in schizophrenia

[Stefansson et al., 2009; Ripke et al., 2011], yet relatively few

causative genes have been found.

Linkage studies have identified numerous regions that show

evidence of linkage to schizophrenia. In a genome scan meta-

analysis, Ng et al. [2009a] found that only two regions, one on

chromosome 5q (142–168 Mb) and another near the chromosome

two centromere (106–134 Mb), demonstrate suggestive evidence

for linkage in all ethnicities, and limiting their analysis to samples of

European ancestry only added one additional region with evidence

of a suggestive linkage, chromosome 8p. Several other regions

showed nominal evidence for linkage and several regions were

nearly significant (6p, 10p, 13q, 15q, 18p, and 22q), but none

achieved genome-wide significance [Ng et al., 2009a]. The limited

statistical power of these linkage studies are likely related to the

composition of their study samples. Given that schizophrenia is

associated with social isolation and reduced reproductive fitness,

large, multi generational families that are ideal for linkage studies

are few and far in between.

Because of the need for larger sample sizes, recent genetic

studies have shifted from family-based studies to case–control

studies. GWASs with adequate sample sizes and marker

densities are a direct attempt to test the CDCV model. Studies

with tens of thousands of cases and controls and �500,000–1

million SNP genotypes are adequately powered to identify

variants that have frequencies higher than 5% and increases

in disease risk as small as 1.2-fold. Simulations in one of

these studies suggested that, together, common polygenic

variations might account for up to 30% of the total variation

in schizophrenia liability [Stefansson et al., 2009]. The Schizo-

phrenia Psychiatric Genome-Wide Association Consortium re-

cently assembled and conducted a two-stage mega-analysis

of GWASs that included 51,695 individuals. They replicated

two previously implicated schizophrenia loci (6p21.32–p22.1

and 18q21.2) and found genome-wide significance for five

novel schizophrenia loci (1p21.3, 2q32.3, 8p23.2, 8q21.3,

and 10q24.32–q24.33) [Ripke et al., 2011]. However, the odds

ratios for these SNPs were modest (as expected with the

CDCV model) and many were intragenic and therefore

unlikely to be functional. Moreover, because GWASs rely on

the detection of common polymorphisms that are themselves

not necessarily causative for disease but are often close to

causative variants, GWASs are not adequately powered for asso-

ciation studies of all variants. Complementary approaches such

as next-generation sequencing (NGS) are thus necessary to

complement GWASs.

CYTOGENETIC, ARRAY-BASED, AND COPY NUMBERVARIATION (CNV) IDENTIFICATION STUDIES INSCHIZOPHRENIA

Rare chromosomal anomalies that are detected using cytogenetics

and karyotyping have long been identified as causative and are

highly penetrant in subsets of families with schizophrenia. Cyto-

genetic abnormalities that have been identified include micro-

deletions on chromosomes 5q22, 9q32, and 21q11.2 and

inversions on chromosomes 2p11–q13, 4p15.2, 9p11–q13,

10p12–q21, and 18p11.3–q21.2 (reviewed by Bassett et al., 2000).

A1:11 (q42.1; q14.3) translocation in aScottish pedigreewith ahigh

frequency of schizophrenia showed thatDISC1was one of the genes

disrupted due to this translocation [Millar et al., 2000]. This

discovery has led to many investigations of DISC-1 in neurodevel-

672 AMERICAN JOURNAL OF MEDICAL GENETICS PART B

opment and psychiatric disorders. Other cytogenetic abnormalities

that are associated with schizophrenia include velo-cardio-facial

syndrome (VCFS; Karayiorgou et al., 1995) or chromosome 22q11

deletion (22q11D). Approximately 24–31% of individuals with the

22q11D meet diagnostic criteria for schizophrenia or schizoaffec-

tive disorder [Pulver et al., 1994;Murphy et al., 1999]. Furthermore,

although 22q11D syndromes occur in only 0.016% of the general

population, they have been found in 0.3–2% of adults diagnosed

with schizophrenia and in 6% of early-onset schizophrenia cases

(onset<13 years). Yet despite clear associationswith the deletion of

the gene cluster in the VCFS region, no specific causative gene has

been identified. Several excellent candidates genes (e.g., COMT)

exist in this region, with established roles in neural development

that are currently under active investigation. Although these cyto-

genetic abnormalities exhibit a wide range of phenotypes (in other

words, they are pleiotropic), a subset of cases develop symptoms

that are clinically indistinguishable from idiopathic schizophrenia

cases [Bassett et al., 1998]. This finding led to the hypothesis that

structural genomic variantsmay be responsible for schizophrenia, a

theory that has prompted some of the recent, more-detailed CNV

studies that are described below. A recent multicenter study,

including more than 3,391 cases and 3,181 controls, found that

13 individuals with schizophrenia harbored >500 kb deletions

in this 22q11.2 region and none in controls [International

Schizophrenia Consortium, 2008]. Although such cytogenetic

aberrations are rare, when they are found they can be informative

for diagnostic and research purposes.

Using newer technologies, such as GWAS arrays and array

comparative genomic hybridization (aCGH), investigators have

detectedother rare genomic rearrangements andCNVs in subsetsof

cases with schizophrenia. For example, recurrent deletions at

1q21.11, 15q11.3, 15q13.3, 22q11.2, and the 2p16.3 neurexin 1

locus have been found to increase the risk of developing schizo-

phrenia [Tam et al., 2009]. Although these studies were initially

too small to show associations between single CNVs and the

disease, they also identified novel candidate genes such as

ERBB4, SLC1A3, RAPGEF4, and CITI within these regions.

However, genetic models that account for new mutations do not

sufficiently explain the risk of schizophrenia in the general popula-

tion, and the fact that some unaffected individuals also appear to

carry the same CNVs raises the possibility of decreased penetrance

or true pathogenicity. CNVs associated with schizophrenia may

either disrupt single or multiple genes; therefore, the search for

all types of genetic variations is necessary.

Large-scale array-based studies, CNV analyses [International

Schizophrenia Consortium, 2008; Walsh et al., 2008; Xu

et al., 2008; Bassett et al., 2010a; Kirov et al., 2012], and exome-

sequencing studies [Girard et al., 2011; Xu et al., 2011] have

determined that de novo mutations involving chromosomal seg-

ments and single genes play a role in sporadic cases with schizo-

phrenia [Bassett et al., 2010b]. Although these de novo mutations

are rare, they have been informative regarding the relevant phe-

notypes that may be associated with schizophrenia. For example,

schizophrenia-associated CNVs have been discovered in individu-

als with other apparently unrelated phenotypes such as autism,

mental retardation, and seizures. In particular, de novo CNVs

appear to predominate among severe cases with early onset and

developmental disabilities and may therefore affect reproductive

fitness.

NGS, WES, AND WGS

A major obstacle to gene discovery, until recently, has been our

inability to conduct comprehensive genome-wide sequencing and

to develop statistical models that incorporate multiple susceptibil-

ity variants. Advances in both NGS techniques and analytical

methods, coupled with increasingly faster and cheaper computa-

tion power, have now alleviated some of these limitations. These

recent advances set the stage for the kinds of comprehensive

analyses that are necessary to identify underlying rare genetic

variants, particularly in regard to family-based samples. The iden-

tification of rare variants with large effects via family studies could

rapidly translate into a discovery of the biological underpinnings of

disease and novel therapeutic targets. Sequence data, including

noncoding regions, now provide the opportunity to perform

comprehensive analyses that will identify schizophrenia suscepti-

bility genes. This will represent a significant step toward the

identification of novel pathways underlying the pathogenesis of

schizophrenia and other related neuropsychiatric disorders.

The revolutionary advances of NGS have ushered in an era of

whole-exome sequencing (WES), whole-genome sequencing

(WGS), and transcriptome analyses. NGS technologies have

made large-scale sequencing possible and feasible. These platforms

have truly revolutionized genetic studies, using new techniques and

technology to obtain vast amounts of DNA sequence data. Indeed,

the volume of data obtained has increased exponentially because of

novel technical approaches to sequencing that involve massively

parallel sequencing [Bras et al., 2012]. For example, WES incor-

porates the targeted capture of the entire exome (i.e., all exons)

followed by sequencing, and this methodology provides investi-

gators with a comprehensive list of variants within the coding

portion of the genome. See Table I for examples of currently

available commercial exome-capture products.

Complex bioinformatic methods align sequence data for quality

control, which is critical for identifying sequence variants that differ

between study subjects and reference exomes. In WES, sequencing

is targeted to all exons, and the amount of sequencing required for

each sample is greatly reduced to about 2% of the total genome,

which allows an unbiased search for potential causative variants.

Although it remains unknown how much genetic variation that

occurs outside the exons is likely to contribute to human disease,

it is also currently feasible to interrogate complete genomes.

Because targeted capture is no longer necessary, WGS has the

advantage of producing more complete and uniform sequence

coverage, which allows for more accurate identification of, for

example, structural variants. However, because the computational

and analytical burden increases substantially with WGS, new

bioinformatics and computational methods are necessary and

are currently being developing alongside these technological

advances (Table II).

As far as NGS approaches are concerned, WES is currently

more commonly utilized thanWGS, primarily becauseWES offers

three key advantages: lower cost, the ability to focus on regions

where mutations can be more quickly identified and more readily

SCHREIBER ET AL. 673

interpreted, and the ability to rapidly identify groups of genes that

may participate in functional networks [Avramopoulos, 2010]. In

contrast toWES,WGSprovides researcherswith the opportunity to

see the whole range of genetic variation; WES identifies �20,000

variants per individual sequenced [Ng et al., 2009b] whereas

genome sequencing identifies �4,000,000 variants [Bentley et al.,

2008] per individual sequenced. At present, WGS can be prohibi-

tively expensive, as well as posing vastly increased challenges in data

analysis and interpretation—for example, the expected increase in

noise relative to an uncertain gain in signal poses additional

challenges [Shendure, 2011]. This review focuses on the success

of WES, but it is likely that as technology continues to advance,

WGS will become the gold standard and it is therefore important

to anticipate and consider the implications of this shift. Both WES

and WGS will have a profound impact on clinical medicine by

improving diagnostic accuracy and developing more effective

therapeutic strategies [Biesecker et al., 2012]. The genome-wide

study of expressed genes through RNA analysis, or transcriptomes,

in a variety of tissues is another technique that investigators are

beginning to apply to psychiatric disorders [Glatt et al., 2009]. This

will be an area in which NGS will greatly increase the ability of

investigators to study changes in disease-relevant tissues.

In relation to schizophrenia, WES may help unravel two persis-

tent questions: first, what accounts for the apparent “missing

heritability” that remains after several generations of molecular

genetic studies of schizophrenia? And second, how does schizo-

phrenia persist in the population, given that fecundity is reduced in

affected individuals? By making large amounts of sequence data

available from specific individuals with schizophrenia, WES will

help to solve both of these important questions.

THE IMPLICATIONS FOR SCHIZOPHRENIA OF WESON INTELLECTUAL DISABILITY RESEARCH

WEShas the potential to transform the investigation of the genetics

of neuropsychiatry diseases like schizophrenia. For example, a

recent study demonstrated the power of this method to identify

novel mutations in a cohort with severe intellectual disability [de

Ligt et al., 2012], another disorder that also exhibits substantial

genetic heterogeneity. In this study, de novomutations were found

in 53 of 100 subjects. In 13 subjects, these mutations occurred in

genes predicted to play a role in causing intellectual disability.

Potentially causative mutations were identified in the novel candi-

date genes of 22 of these patients. For three of these patients, a

second set of affected individuals revealed mutations in genes that

were uncovered in the initialWES study, which strongly implicated

DYNC1H1, GATAD2B, and CTNNB1 as novel genes causing

intellectual disability. This type of work is rapidly advanced by

the expanding publicly available databases of common human

genetic polymorphisms, in which the allele frequencies generated

from sequencing the genomes of several reference populations are

readily available for comparison [Abecasis et al., 2012]. This has

been, to date, the most successful strategy for gene identification in

rare Mendelian disorders.

TABLE I. Examples of Commercial Vendor, Exome Capture, Target, Genomic Size, and the Number of Genes Targeted

Vendor Exome capture product Target Size (Mb) GenesNimbleGen/Roche SeqCap EZ human library v3 CCDS, RefSeq, Gencode, Vega, mirBase 64 >20,000

SeqCap EZ Exome þ UTR CCDS, RefSeq, Gencode, Vega,mirBase plus 32 Mb UTR

96 >20,000

Agilent Sureselect all eExon v5 CCDS, RefSeq, Gencode, mirBase,TCGA and UCSC

50 21,522

Sureselect all exon v5 þ UTR CCDS, RefSeq, Gencode, mirBase,TCGA and UCSC plus 21 Mb UTR

71 21,522

Illumina TruSeqExome CCDS, RefSeq, Gencode, mirBase 62 20,794

TABLE II. Commonly Used Bioinformatics Software Tools for Next-Generation Sequence Analysis

Task Software/Tool Reference URL

Sequence alignment Burrows wheeler aligner (BWA) Li and Durbin [2009] http://bio-bwa.sourceforge.net/MAQ Li et al. [2008] http://maq.sourceforge.net/ELAND Bentley et al. [2008] http://www.illumina.com

Variant identification Genomic analysis toolboxkit (GATK)

DePristo et al., [2011];McKenna et al. [2010]

http://www.broadinstitute.org/gatk/

Sequence annotation SeattleSeq http://snp.gs.washington.edu/SeattleSeqAnnotation137/

Annovar Wang et al. [2010] http://www.openbioinformatics.org/annovar/

See http://seqanswers.com/wiki/Software for a comprehensive list of bioinformatics tools.

674 AMERICAN JOURNAL OF MEDICAL GENETICS PART B

THE IMPLICATIONS FOR WES ON AUTISM

Autism shares much with schizophrenia as a paradigmatic neuro-

psychiatric disorder [Sullivan et al., 2012]. Both autism and schizo-

phrenia are neurodevelopmental disorders with underlying

etiologies that may overlap, and a recent study suggests they share

causative mechanisms. A recent study found that autism and

schizophrenia families showed overlapping elevated risk for both

disorders [Sullivan et al., 2012]. Autism and schizophrenia are also

syndromic, with constellations of symptoms that can vary across

patients, giving rise to the terms autism- and schizophrenia-spec-

trum disorders. Autism, like schizophrenia, clearly has strong

familial components. However, autism has one advantage in ge-

netic analysis compared to schizophrenia in that, because the

diagnosis is typically made in childhood, parental involvement is

more certain, which increases the likelihood of obtaining both

genotype and phenotype on the parents. The many commonalities

of autism and schizophrenia suggest that progress in autism

genetics might presage future success in schizophrenia genetics.

Several groups have shown an increased CNV burden in pro-

bands with autism [Sebat et al., 2007; Pinto et al., 2010; Sanders

et al., 2011], making this a robust and replicated finding. One

limitationofCNVstudies is that the genomic regions implicated are

relatively large, and identifying specific genes that are responsible

for a phenotype is difficult. For example, CNV studies in autism

have identified several genes that are associated with an increased

risk of developing autism, such as SHANK2 [Berkel et al., 2010] and

NRXN1 [Kim et al., 2008; Kirov et al., 2009]. Interestingly, both

proteins have also been implicated in schizophrenia risk, again

suggesting overlap in the genetic risk factors for both disorders

[Carroll and Owen, 2009]. However, other studies do not support

this overlap [Vorstman et al., 2012].

Oneof the advantages ofWES is that a specific gene that harbors a

genetic variant can be identified and its functional role and related

biological pathways can be further investigated. Publicly available

bioinformatics resources can help to predict the function of specific

types of mutation, including into those that are more likely to

change the function of the protein (e.g., a nonsense mutation

coding for a premature stop codon) and those that are less likely

to change the function of the protein (e.g., a missense mutation

producing a predicted conservative amino acid change). An initial

study in autism suggested that this approach is likely to be highly

productive, strongly implicating de novomutations in the etiology

of autism by showing that missense mutations were enriched in

probands suffering from autism-spectrum disorders [O’Roak

et al., 2011]. Indeed, WES studies in autism suggest that the risk

of disease is related to rare single-nucleotide variants in several

genes, such as SCN (including a sodium channel alpha subunit,

SCN1A) [O’Roak et al., 2011], CHD8, and KATNAL2. Several

genes with potential roles in neurodevelopment were implicated in

this study. This finding has been replicated, and of particular

interest, a second voltage-gated sodium channel, SCN2A, was

found to have two independent nonsense mutations in affected

individuals but not in unaffected family members [Sanders

et al., 2012]. An independent study taking a similar WES approach

provided evidence that codingmutations in a wide range of critical

genes contribute to autism risk, and more particularly, the study

found that mutations in genes CHD8 and KATNAL2 were likely to

be important genetic risk factors [Neale et al., 2012].

These encouraging, substantive results in applying WES to

autism hold promise for similar studies in schizophrenia. Because

some of the clinical, neurodevelopmental, and familial features of

autism and schizophrenia overlap, it is conceivable that similar genes

will act as risk factors for both disorders, perhaps dependent on the

genetic background of individual families (e.g., gene–gene interac-

tions, unique founder mutations within each family). By extending

the reach of WES to other neuropsychiatric disorders, researchers

can now take advantage of the successes in autism research.

WES FINDINGS IN SCHIZOPHRENIA

As yet, the literature in this emerging field remains small but very

exciting in its suggestion thatWESwill be productive in identifying

rare variants that may be causative in schizophrenia. One approach

to examine whether rare variants may be inherited (vs. those

occurring de novo) includes the study of trios (i.e., an affected

proband and his/her parents). This strategy has been successful in

gene identification in other diseases, such as intellectual disability

[Vissers et al., 2010] and autism [O’Roak et al., 2011]. Both of these

studies usedexomesequencingofpatient–parent trios to identifyde

novo mutations in a complex trait that is characterized by extreme

genetic heterogeneity. Such family-based methods can be used to

determine whether variants are more likely inherited or de novo,

with de novomutationsmore likely to be pathogenic if both parents

are unaffected. In addition to single-nucleotide changes, small

insertions or deletions (i.e., indels) can also be detected, and the

impact on the predicted protein product can be assessed.

Similar to success in autism genetics, WES in schizophrenia has

also generated some encouraging findings. Consistent with an a

priori hypothesis of the CDRV model is that sporadic cases will

likely have an accumulation of rare de novo mutations, which is a

reflection of an elevated rate ofmutations. This notion is supported

by epidemiological studies showing that advanced paternal age

increases the risk for developing schizophrenia (as the mutation

rate is increased in older fathers’ gametogenesis [Kong et al., 2012])

and that schizophrenia persists at a significant rate in populations

despite reduced reproductive fitness. A few early findings that the

rate of de novo mutation was higher in schizophrenia samples [Xu

et al., 2011, 2012] compared to the rate found in the general

population supports this view [Awadalla et al., 2010]. In this light,

Girard et al. [Girard et al., 2011] found 15 de novo mutations

(including four nonsense mutations) in eight probands with spo-

radic schizophrenia; this observed mutation rate exceeds the pre-

dicted germline mutation rate, which ranges from 1.1 � 10�8 to

3.8 � 10�8 per nucleotide per generation [Conrad et al., 2011].

That thesemutations are predicted to affect gene function supports

the hypothesis that these genes are likely associated with the

schizophrenia phenotype. Interestingly, one of the genes identified

with a novel stop codon, KPNA1, affects immunoglobulin gene

recombination. This gene is of interest as immune factors are

hypothesized to play a key role in the underlying pathogenesis of

schizophrenia [Brown, 2006;Muller and Schwarz, 2010]. However,

since this studyonly includes a small sample size, replication studies

including much larger samples, are necessary to link these genes

SCHREIBER ET AL. 675

with schizophrenia conclusively. Another study [Xu et al., 2011]

found 40 de novo mutations in 27 cases, with predicted functional

effects in 40 genes. The mutations in this study showed excess

non-synonymous gene changes in patients with schizophrenia,

which further supports the hypothesis that de novo mutations

mayplay a large role in the risk for schizophrenia. A follow-up study

conducted functional assays of these genes and found that the

mutations associated with schizophrenia were predicted to be

in genes enriched for expression in the prenatal period and to

be expressed in the hippocampus and prefrontal cortex [Xu

et al., 2012]. This is encouraging in that it corresponds well with

other converging lines of evidence that aberrant brain development

in the prenatal period is critical to the emergence of schizophrenia.

These studies clearly demonstrate the feasibility and the potential

of WES to rapidly move to investigation of specific potentially

functional genes.

Finally, one recent study combined several strategies to maxi-

mize data mining. The initial findings of 166 sets of genomic or

exomic sequence data were followed up the genotyping in a large

independent cohort [Need et al., 2012]. Although no sequence

variants reached significance across the study, several variants were

only found in schizophrenia cases, thereby suggesting potential

links to schizophrenia. In particular, a missense mutation in KL

(koltho) was identified in 5 of 2,780 schizophrenia cases but not

observed in 7,417 controls. KL has been potentially linked to

vitamin D metabolism, which has been previously reported to be

a risk factor for schizophrenia [Need et al., 2012].

CHALLENGES IN WES

An important challenge in performing WES and other NGS meth-

ods in schizophrenia—or in any complex neuropsychiatric disor-

der for that matter—is that the amount of data generated by WES

and WGS is daunting. Computational algorithms vary across

laboratories, with no general consensus for the best way to process

data.

One way to reduce the amount of data is to restrict the size of the

region of interest that is being investigated. For example, candidate

sequencing in large family pedigrees can be focused to areas with

significant genetic linkage signals [Wijsman, 2012]. The reduction

of target genomic regions can also decrease the chance of discarding

meaningful variants. This method has been developed and utilized

inapilot studyof autism[Marchani et al., inpress], and it has shown

promise in schizophrenia pedigrees, including the identification of

functionally clustered genes that increase the risk of schizophrenia

[Timms et al., in press]. However, the deluge of potentially disease-

causing variants from any given set of experiments still makes

sorting and interpreting sequence data a monumental task.

Variant filtering strategies vary across laboratories and must

balance false from true discoveries. The prevalence of most neuro-

psychiatric disorders is sufficiently common in the general popula-

tion that the standard variant-filtering strategies will require

adjustment. For example, we generally set minor allele frequency

cutoffs to reflect the prevalence of the disorder. If one assumes

autosomal dominant inheritance in a subset of families with schizo-

phrenia, a disorder that occurs in�1% of the population, then the

allele frequency of the causative alleles should be lower than 5%;

therefore, alleles that occur at a frequency of >5% should be

excluded. And of course, if the prevalence of the disease varies

within the relevant ethnic groups, the cutoffs should be adjusted

accordingly. When combined with family studies in which signifi-

cant linkage signals have been obtained, focused areas of the genome

can be further interrogated. In fact, this complementarymethod has

already produced novel results in schizophrenia genetics: in a study

of a set of multiplex families with schizophrenia, mutations were

identified in three genes with roles in modulating glutamatergic

signaling function, GRM5, PPEF2, and LRP1B [Timms et al., in

press]. This grouping of genes lends support to the well-established

hypothesis that glutamatergic hypofunction plays a role in schizo-

phrenia pathogenesis. The finding also illustrates the potentially

powerful interplay between WES results and hypotheses derived

from other lines of research. Tools that assist investigators in the

prioritization and interpretation of variants are urgently needed.

THE FUTURE

The arrival ofWES and otherNGSmethods herald the beginning of

a new era, not just for schizophrenia research but also for research

into nearly every complex neuropsychiatric disorder. An abun-

dance of new sequencing data will soon be available, and we will

benefit greatly from the ability to combine genetic data generated by

multiple methods (such as, for example, combining linkage and/or

GWAS data with WES data). WGS will become increasingly more

cost-effective as sequencing costs decrease and bioinformatics tools

improve, and these advances will open up the possibility of detect-

ing noncoding genetic changes in regulatory regions.

In addition, the availability of a large catalog of variants that

are associated with an entire spectrum of neuropsychiatric

disorders will dramatically increase our understanding of the

predominant gene pathways that underlie specific disorders like

schizophrenia and diagnostic classification within and across

disorders. Indeed, overlapping clinical symptoms across diag-

nostic disorders could be manifestations of shared “final path-

ways,” which cause a cascade of downstream effects that can lead

to many different neuropsychiatric syndromes. Diagnostically,

syndromes may be classified by the affected underlying biological

pathways rather than by phenotypes. Furthermore, treatment

can be targeted to specific pathways.

This new era of genetic research in neuropsychiatric disorders is

built on the foundations of many decades of dedicated psychiatric

genetics investigations. The last several hundred years of careful

phenotyping and subject and family collections established the

complex genetics of psychiatric disorders, and now the next gener-

ation of investigators can be optimistic that new techniques will

bring us closer to an understanding of the molecular genetic

underpinnings of neuropsychiatric disorders. The possibility of

finally putting these puzzles together is nearly within our grasp,

bringing us ever closer to developing new therapeutic strategies.

ACKNOWLEDGMENTS

The authors thank Andrew David for his editorial assistance. Drs.

Schreiber and Tsuang are employed by the US Department of

Veterans Affairs.

676 AMERICAN JOURNAL OF MEDICAL GENETICS PART B

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