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Keywords:

  • schizophrenia;
  • genetics;
  • sequencing;
  • whole exome;
  • autism

Abstract

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MODES OF TRANSMISSION AND HYPOTHETICAL MODELS
  5. LINKAGE AND GWA STUDIES IN SCHIZOPHRENIA
  6. CYTOGENETIC, ARRAY-BASED, AND COPY NUMBER VARIATION (CNV) IDENTIFICATION STUDIES IN SCHIZOPHRENIA
  7. NGS, WES, and WGS
  8. THE IMPLICATIONS FOR SCHIZOPHRENIA OF WES ON INTELLECTUAL DISABILITY RESEARCH
  9. THE IMPLICATIONS FOR WES ON AUTISM
  10. WES FINDINGS IN SCHIZOPHRENIA
  11. CHALLENGES IN WES
  12. THE FUTURE
  13. ACKNOWLEDGMENTS
  14. REFERENCES

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 presentation that complicates research efforts to identify causative genetic variations. This review examines the potential of current findings in schizophrenia and in other related neuropsychiatric disorders for application in next-generation technologies, particularly whole-exome sequencing (WES) and whole-genome sequencing (WGS). These approaches may lead to the discovery of underlying genetic factors for schizophrenia and may thereby identify and target novel therapeutic targets for this devastating disorder. © 2013 Wiley Periodicals, Inc.


INTRODUCTION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MODES OF TRANSMISSION AND HYPOTHETICAL MODELS
  5. LINKAGE AND GWA STUDIES IN SCHIZOPHRENIA
  6. CYTOGENETIC, ARRAY-BASED, AND COPY NUMBER VARIATION (CNV) IDENTIFICATION STUDIES IN SCHIZOPHRENIA
  7. NGS, WES, and WGS
  8. THE IMPLICATIONS FOR SCHIZOPHRENIA OF WES ON INTELLECTUAL DISABILITY RESEARCH
  9. THE IMPLICATIONS FOR WES ON AUTISM
  10. WES FINDINGS IN SCHIZOPHRENIA
  11. CHALLENGES IN WES
  12. THE FUTURE
  13. ACKNOWLEDGMENTS
  14. REFERENCES

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 schizophrenia is also something of a labyrinthine puzzle.

Even prior to the molecular genetic era, observational [Gottesman and Wolfgram, 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 development 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 multiple loci contribute to the genetics of schizophrenia in families, suggesting the likely existence of locus heterogeneity. Decreased penetrance and unknown modes 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, particularly whole-exome sequencing (WES) and whole-genome sequencing (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 HYPOTHETICAL MODELS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MODES OF TRANSMISSION AND HYPOTHETICAL MODELS
  5. LINKAGE AND GWA STUDIES IN SCHIZOPHRENIA
  6. CYTOGENETIC, ARRAY-BASED, AND COPY NUMBER VARIATION (CNV) IDENTIFICATION STUDIES IN SCHIZOPHRENIA
  7. NGS, WES, and WGS
  8. THE IMPLICATIONS FOR SCHIZOPHRENIA OF WES ON INTELLECTUAL DISABILITY RESEARCH
  9. THE IMPLICATIONS FOR WES ON AUTISM
  10. WES FINDINGS IN SCHIZOPHRENIA
  11. CHALLENGES IN WES
  12. THE FUTURE
  13. ACKNOWLEDGMENTS
  14. REFERENCES

Numerous modes of transmission have been tested to explain the complex genetic architecture of schizophrenia, and these investigations have led to the proposal of two main hypothetical models. The advent of high-density genotyping panels facilitated genome-wide association studies to directly test the common-disease common-variant (CDCV) hypothetical model, which posits that common 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 of multiple common, small-effect genetic variants with environmental risk factors that exceed a biological threshold for developing a disorder. The HapMap 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 CDCV model, 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 subjects may 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 schizophrenia, 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 phenomenon has been observed, 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 in Mendelian 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 cases may 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

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MODES OF TRANSMISSION AND HYPOTHETICAL MODELS
  5. LINKAGE AND GWA STUDIES IN SCHIZOPHRENIA
  6. CYTOGENETIC, ARRAY-BASED, AND COPY NUMBER VARIATION (CNV) IDENTIFICATION STUDIES IN SCHIZOPHRENIA
  7. NGS, WES, and WGS
  8. THE IMPLICATIONS FOR SCHIZOPHRENIA OF WES ON INTELLECTUAL DISABILITY RESEARCH
  9. THE IMPLICATIONS FOR WES ON AUTISM
  10. WES FINDINGS IN SCHIZOPHRENIA
  11. CHALLENGES IN WES
  12. THE FUTURE
  13. ACKNOWLEDGMENTS
  14. REFERENCES

Although commonly used genetic methods have successfully identified 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 Schizophrenia Psychiatric Genome-Wide Association Consortium recently 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 association studies of all variants. Complementary approaches such as next-generation sequencing (NGS) are thus necessary to complement GWASs.

CYTOGENETIC, ARRAY-BASED, AND COPY NUMBER VARIATION (CNV) IDENTIFICATION STUDIES IN SCHIZOPHRENIA

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MODES OF TRANSMISSION AND HYPOTHETICAL MODELS
  5. LINKAGE AND GWA STUDIES IN SCHIZOPHRENIA
  6. CYTOGENETIC, ARRAY-BASED, AND COPY NUMBER VARIATION (CNV) IDENTIFICATION STUDIES IN SCHIZOPHRENIA
  7. NGS, WES, and WGS
  8. THE IMPLICATIONS FOR SCHIZOPHRENIA OF WES ON INTELLECTUAL DISABILITY RESEARCH
  9. THE IMPLICATIONS FOR WES ON AUTISM
  10. WES FINDINGS IN SCHIZOPHRENIA
  11. CHALLENGES IN WES
  12. THE FUTURE
  13. ACKNOWLEDGMENTS
  14. REFERENCES

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. Cytogenetic abnormalities that have been identified include microdeletions 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). A 1:11 (q42.1; q14.3) translocation in a Scottish pedigree with a high frequency of schizophrenia showed that DISC1 was one of the genes disrupted due to this translocation [Millar et al., 2000]. This discovery has led to many investigations of DISC-1 in neurodevelopment 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 schizoaffective 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 associations with 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 cytogenetic 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 variants may 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 detected other rare genomic rearrangements and CNVs in subsets of 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 schizophrenia [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 population, 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 segments and single genes play a role in sporadic cases with schizophrenia [Bassett et al., 2010b]. Although these de novo mutations are rare, they have been informative regarding the relevant phenotypes that may be associated with schizophrenia. For example, schizophrenia-associated CNVs have been discovered in individuals 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

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MODES OF TRANSMISSION AND HYPOTHETICAL MODELS
  5. LINKAGE AND GWA STUDIES IN SCHIZOPHRENIA
  6. CYTOGENETIC, ARRAY-BASED, AND COPY NUMBER VARIATION (CNV) IDENTIFICATION STUDIES IN SCHIZOPHRENIA
  7. NGS, WES, and WGS
  8. THE IMPLICATIONS FOR SCHIZOPHRENIA OF WES ON INTELLECTUAL DISABILITY RESEARCH
  9. THE IMPLICATIONS FOR WES ON AUTISM
  10. WES FINDINGS IN SCHIZOPHRENIA
  11. CHALLENGES IN WES
  12. THE FUTURE
  13. ACKNOWLEDGMENTS
  14. REFERENCES

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 susceptibility variants. Advances in both NGS techniques and analytical methods, coupled with increasingly faster and cheaper computation 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 identification 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 susceptibility 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 incorporates the targeted capture of the entire exome (i.e., all exons) followed by sequencing, and this methodology provides investigators 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.

Table I. Examples of Commercial Vendor, Exome Capture, Target, Genomic Size, and the Number of Genes Targeted
VendorExome capture productTargetSize (Mb)Genes
NimbleGen/RocheSeqCap EZ human library v3CCDS, RefSeq, Gencode, Vega, mirBase64>20,000
 SeqCap EZ Exome + UTRCCDS, RefSeq, Gencode, Vega, mirBase plus 32 Mb UTR96>20,000
AgilentSureselect all eExon v5CCDS, RefSeq, Gencode, mirBase, TCGA and UCSC5021,522
 Sureselect all exon v5 + UTRCCDS, RefSeq, Gencode, mirBase, TCGA and UCSC plus 21 Mb UTR7121,522
IlluminaTruSeqExomeCCDS, RefSeq, Gencode, mirBase6220,794

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).

Table II. Commonly Used Bioinformatics Software Tools for Next-Generation Sequence Analysis
TaskSoftware/ToolReferenceURL
  1. See http://seqanswers.com/wiki/Software for a comprehensive list of bioinformatics tools.

Sequence alignmentBurrows wheeler aligner (BWA)Li and Durbin [2009]http://bio-bwa.sourceforge.net/
 MAQLi et al. [2008]http://maq.sourceforge.net/
 ELANDBentley et al. [2008]http://www.illumina.com
Variant identificationGenomic analysis toolbox kit (GATK)DePristo et al., [2011]; McKenna et al. [2010]http://www.broadinstitute.org/gatk/
Sequence annotationSeattleSeq http://snp.gs.washington.edu/SeattleSeqAnnotation137/
 AnnovarWang et al. [2010]http://www.openbioinformatics.org/annovar/

As far as NGS approaches are concerned, WES is currently more commonly utilized than WGS, primarily because WES offers three key advantages: lower cost, the ability to focus on regions where mutations can be more quickly identified and more readily interpreted, and the ability to rapidly identify groups of genes that may participate in functional networks [Avramopoulos, 2010]. In contrast to WES, WGS provides researchers with 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 prohibitively 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 persistent questions: first, what accounts for the apparent “missing heritability” that remains after several generations of molecular genetic studies of schizophrenia? And second, how does schizophrenia 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 WES ON INTELLECTUAL DISABILITY RESEARCH

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MODES OF TRANSMISSION AND HYPOTHETICAL MODELS
  5. LINKAGE AND GWA STUDIES IN SCHIZOPHRENIA
  6. CYTOGENETIC, ARRAY-BASED, AND COPY NUMBER VARIATION (CNV) IDENTIFICATION STUDIES IN SCHIZOPHRENIA
  7. NGS, WES, and WGS
  8. THE IMPLICATIONS FOR SCHIZOPHRENIA OF WES ON INTELLECTUAL DISABILITY RESEARCH
  9. THE IMPLICATIONS FOR WES ON AUTISM
  10. WES FINDINGS IN SCHIZOPHRENIA
  11. CHALLENGES IN WES
  12. THE FUTURE
  13. ACKNOWLEDGMENTS
  14. REFERENCES

WES has 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 novo mutations 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 candidate 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 initial WES 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.

THE IMPLICATIONS FOR WES ON AUTISM

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MODES OF TRANSMISSION AND HYPOTHETICAL MODELS
  5. LINKAGE AND GWA STUDIES IN SCHIZOPHRENIA
  6. CYTOGENETIC, ARRAY-BASED, AND COPY NUMBER VARIATION (CNV) IDENTIFICATION STUDIES IN SCHIZOPHRENIA
  7. NGS, WES, and WGS
  8. THE IMPLICATIONS FOR SCHIZOPHRENIA OF WES ON INTELLECTUAL DISABILITY RESEARCH
  9. THE IMPLICATIONS FOR WES ON AUTISM
  10. WES FINDINGS IN SCHIZOPHRENIA
  11. CHALLENGES IN WES
  12. THE FUTURE
  13. ACKNOWLEDGMENTS
  14. REFERENCES

Autism shares much with schizophrenia as a paradigmatic neuropsychiatric disorder [Sullivan et al., 2012]. Both autism and schizophrenia 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-spectrum disorders. Autism, like schizophrenia, clearly has strong familial components. However, autism has one advantage in genetic 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 probands with autism [Sebat et al., 2007; Pinto et al., 2010; Sanders et al., 2011], making this a robust and replicated finding. One limitation of CNV studies 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].

One of the advantages of WES 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 novo mutations 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 coding mutations 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 interactions, 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

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MODES OF TRANSMISSION AND HYPOTHETICAL MODELS
  5. LINKAGE AND GWA STUDIES IN SCHIZOPHRENIA
  6. CYTOGENETIC, ARRAY-BASED, AND COPY NUMBER VARIATION (CNV) IDENTIFICATION STUDIES IN SCHIZOPHRENIA
  7. NGS, WES, and WGS
  8. THE IMPLICATIONS FOR SCHIZOPHRENIA OF WES ON INTELLECTUAL DISABILITY RESEARCH
  9. THE IMPLICATIONS FOR WES ON AUTISM
  10. WES FINDINGS IN SCHIZOPHRENIA
  11. CHALLENGES IN WES
  12. THE FUTURE
  13. ACKNOWLEDGMENTS
  14. REFERENCES

As yet, the literature in this emerging field remains small but very exciting in its suggestion that WES will 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 used exome sequencing of patient–parent trios to identify de 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 novo mutations more 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 of mutations. 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 sporadic schizophrenia; this observed mutation rate exceeds the predicted germline mutation rate, which ranges from 1.1 × 10−8 to 3.8 × 10−8 per nucleotide per generation [Conrad et al., 2011]. That these mutations 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; Müller and Schwarz, 2010]. However, since this study only includes a small sample size, replication studies including much larger samples, are necessary to link these genes 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 may play 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 maximize 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

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MODES OF TRANSMISSION AND HYPOTHETICAL MODELS
  5. LINKAGE AND GWA STUDIES IN SCHIZOPHRENIA
  6. CYTOGENETIC, ARRAY-BASED, AND COPY NUMBER VARIATION (CNV) IDENTIFICATION STUDIES IN SCHIZOPHRENIA
  7. NGS, WES, and WGS
  8. THE IMPLICATIONS FOR SCHIZOPHRENIA OF WES ON INTELLECTUAL DISABILITY RESEARCH
  9. THE IMPLICATIONS FOR WES ON AUTISM
  10. WES FINDINGS IN SCHIZOPHRENIA
  11. CHALLENGES IN WES
  12. THE FUTURE
  13. ACKNOWLEDGMENTS
  14. REFERENCES

An important challenge in performing WES and other NGS methods in schizophrenia—or in any complex neuropsychiatric disorder 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 in a pilot study of autism [Marchani et al., ], and it has shown promise in schizophrenia pedigrees, including the identification of functionally clustered genes that increase the risk of schizophrenia [Timms et al., ]. 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 neuropsychiatric disorders is sufficiently common in the general population 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 schizophrenia, 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 significant linkage signals have been obtained, focused areas of the genome can be further interrogated. In fact, this complementary method 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., ]. This grouping of genes lends support to the well-established hypothesis that glutamatergic hypofunction plays a role in schizophrenia 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

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MODES OF TRANSMISSION AND HYPOTHETICAL MODELS
  5. LINKAGE AND GWA STUDIES IN SCHIZOPHRENIA
  6. CYTOGENETIC, ARRAY-BASED, AND COPY NUMBER VARIATION (CNV) IDENTIFICATION STUDIES IN SCHIZOPHRENIA
  7. NGS, WES, and WGS
  8. THE IMPLICATIONS FOR SCHIZOPHRENIA OF WES ON INTELLECTUAL DISABILITY RESEARCH
  9. THE IMPLICATIONS FOR WES ON AUTISM
  10. WES FINDINGS IN SCHIZOPHRENIA
  11. CHALLENGES IN WES
  12. THE FUTURE
  13. ACKNOWLEDGMENTS
  14. REFERENCES

The arrival of WES and other NGS methods herald the beginning of a new era, not just for schizophrenia research but also for research into nearly every complex neuropsychiatric disorder. An abundance 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 detecting 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 diagnostic disorders could be manifestations of shared “final pathways,” 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 generation 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.

REFERENCES

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MODES OF TRANSMISSION AND HYPOTHETICAL MODELS
  5. LINKAGE AND GWA STUDIES IN SCHIZOPHRENIA
  6. CYTOGENETIC, ARRAY-BASED, AND COPY NUMBER VARIATION (CNV) IDENTIFICATION STUDIES IN SCHIZOPHRENIA
  7. NGS, WES, and WGS
  8. THE IMPLICATIONS FOR SCHIZOPHRENIA OF WES ON INTELLECTUAL DISABILITY RESEARCH
  9. THE IMPLICATIONS FOR WES ON AUTISM
  10. WES FINDINGS IN SCHIZOPHRENIA
  11. CHALLENGES IN WES
  12. THE FUTURE
  13. ACKNOWLEDGMENTS
  14. REFERENCES
  • Abecasis GR, Auton A, Brooks LD, DePristo MA, Durbin RM, Handsaker RE, Kang HM, Marth GT, McVean GA, Consortium GP. 2012. An integrated map of genetic variation from 1,092 human genomes. Nature 491(7422):5665.
  • Avramopoulos D. 2010. Genetics of psychiatric disorders methods: molecular approaches. Clin Lab Med 30(4):815827.
  • Awadalla P, Gauthier J, Myers RA, Casals F, Hamdan FF, Griffing AR, Côté M, Henrion E, Spiegelman D, Tarabeux J, et al. 2010. Direct measure of the de novo mutation rate in autism and schizophrenia cohorts. Am J Hum Genet 87(3):316324.
  • Bassett AS, Hodgkinson K, Chow EW, Correia S, Scutt LE, Weksberg R. 1998. 22q11 deletion syndrome in adults with schizophrenia. Am J Med Genet 81(4):328337.
  • Bassett AS, Chow EW, Weksberg R. 2000. Chromosomal abnormalities and schizophrenia. Am J Med Genet 97(1):4551.
  • Bassett AS, Costain G, Fung WL, Russell KJ, Pierce L, Kapadia R, Carter RF, Chow EW, Forsythe PJ. 2010a. Clinically detectable copy number variations in a Canadian catchment population of schizophrenia. J Psychiatr Res 44(15):10051009.
  • Bassett AS, Scherer SW, Brzustowicz LM. 2010b. Copy number variations in schizophrenia: critical review and new perspectives on concepts of genetics and disease. Am J Psychiatry 167(8):899914.
  • Bentley DR, Balasubramanian S, Swerdlow HP, Smith GP, Milton J, Brown CG, Hall KP, Evers DJ, Barnes CL, Bignell HR, et al. 2008. Accurate whole human genome sequencing using reversible terminator chemistry. Nature 456(7218):5359.
  • Berkel S, Marshall CR, Weiss B, Howe J, Roeth R, Moog U, Endris V, Roberts W, Szatmari P, Pinto D., et al., 2010. Mutations in the SHANK2 synaptic scaffolding gene in autism spectrum disorder and mental retardation. Nat Genet 42(6):489491.
  • Biesecker LG, Burke W, Kohane I, Plon SE, Zimmern R. 2012. Next-generation sequencing in the clinic: Are we ready? Nat Rev Genet 13(11):818824.
  • Bodmer W, Bonilla C. 2008. Common and rare variants in multifactorial susceptibility to common diseases. Nat Genet 40(6):695701.
  • Bras J, Guerreiro R, Hardy J. 2012. Use of next-generation sequencing and other whole-genome strategies to dissect neurological disease. Nat Rev Neurosci 13(7):453464.
  • Brown AS. 2006. Prenatal infection as a risk factor for schizophrenia. Schizophr Bull 32(2):200202.
  • Carroll LS, Owen MJ. 2009. Genetic overlap between autism, schizophrenia and bipolar disorder. Genome Med 1(10):102.
  • Cohen JC, Pertsemlidis A, Fahmi S, Esmail S, Vega GL, Grundy SM, Hobbs HH. 2006. Multiple rare variants in NPC1L1 associated with reduced sterol absorption and plasma low-density lipoprotein levels. Proceedings of the National Academy of Sciences of the United States of America 103(6):18101815.
  • Conrad DF, Keebler JE, DePristo MA, Lindsay SJ, Zhang Y, Casals F, Idaghdour Y, Hartl CL, Torroja C, Garimella KV, et al. 2011. Variation in genome-wide mutation rates within and between human families. Nat Genet 43(7):712714.
  • de Ligt J, Willemsen MH, van Bon BW, Kleefstra T, Yntema HG, Kroes T, Vulto-van Silfhout AT, Koolen DA, de Vries P, Gilissen C., et al. 2012. Diagnostic exome sequencing in persons with severe intellectual disability. N Engl J Med 367(20):19211929.
  • DePristo MA, Banks E, Poplin R, Garimella KV, Maguire JR, Hartl C, Philippakis AA, del Angel G, Rivas MA, Hanna M, and others. 2011. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat Genet 43(5):491498.
  • Faraone SV, Tsuang MT, Tsuang DW. 1999. Genetics of mental disorders : A guide for students, clinicians, and researchers. New York: Guilford Press. xvi p. 272.
  • Gejman PV, Sanders AR, Kendler KS. 2011. Genetics of schizophrenia: new findings and challenges. Annu Rev Genomics Hum Genet 12:121144.
  • Girard SL, Gauthier J, Noreau A, Xiong L, Zhou S, Jouan L, Dionne-Laporte A, Spiegelman D, Henrion E, Diallo O, et al. 2011. Increased exonic de novo mutation rate in individuals with schizophrenia. Nat Genet 43(9):860863.
  • Glatt SJ, Chandler SD, Bousman CA, Chana G, Lucero GR, Tatro E, May T, Lohr JB, Kremen WS, Everall IP, et al. 2009. Alternatively spliced genes as biomarkers for schizophrenia, bipolar disorder and psychosis: A blood-based spliceome-profiling exploratory study. Curr Pharmacogenomics Person Med 7(3):164188.
  • Goldstein DB, Chikhi L. 2002. Human migrations and population structure: What we know and why it matters. Annu Rev Genomics Hum Genet 3:129152.
  • Gorlov IP, Gorlova OY, Sunyaev SR, Spitz MR, Amos CI. 2008. Shifting paradigm of association studies: value of rare single-nucleotide polymorphisms. Am J Hum Genet 82(1):100112.
  • Gottesman II, Wolfgram DL. 1991. Schizophrenia genesis : The origins of madness. New York: Freeman. xiii, p. 296.
  • Hirschhorn JN, Daly MJ. 2005. Genome-wide association studies for common diseases and complex traits. Nat Rev Genet 6(2):95108.
  • International Schizophrenia Consortium. 2008. Rare chromosomal deletions and duplications increase risk of schizophrenia. Nature 455(7210):237241.
  • Iyengar SK, Elston RC. 2007. The genetic basis of complex traits: Rare variants or “common gene, common disease”? Methods Mol Biol 376:7184.
  • Karayiorgou M, Morris MA, Morrow B, Shprintzen RJ, Goldberg R, Borrow J, Gos A, Nestadt G, Wolyniec PS, Lasseter VK. 1995. Schizophrenia susceptibility associated with interstitial deletions of chromosome 22q11. Proc Natl Acad Sci USA 92(17):76127616.
  • Kim HG, Kishikawa S, Higgins AW, Seong IS, Donovan DJ, Shen Y, Lally E, Weiss LA, Najm J, Kutsche K, et al. 2008. Disruption of neurexin 1 associated with autism spectrum disorder. Am J Hum Genet 82(1):199207.
  • Kirov G, Rujescu D, Ingason A, Collier DA, O'Donovan MC, Owen MJ. 2009. Neurexin 1 (NRXN1) deletions in schizophrenia. Schizophr Bull 35(5):851854.
  • Kirov G, Pocklington AJ, Holmans P, Ivanov D, Ikeda M, Ruderfer D, Moran J, Chambert K, Toncheva D, Georgieva L, et al. 2012. De novo CNV analysis implicates specific abnormalities of postsynaptic signalling complexes in the pathogenesis of schizophrenia. Mol Psychiatry 17(2):142153.
  • Kong A, Frigge ML, Masson G, Besenbacher S, Sulem P, Magnusson G, Gudjonsson SA, Sigurdsson A, Jonasdottir A, Wong WS, et al. 2012. Rate of de novo mutations and the importance of father's age to disease risk. Nature 488(7412):471475.
  • Li H, Durbin R. 2009. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25(14):17541760.
  • Li H, Ruan J, Durbin R. 2008. Mapping short DNA sequencing reads and calling variants using mapping quality scores. Genome Res 18(11):18511858.
  • Marchani E, Chapman N, Cheung C, Ankenman K, Stanaway I, Coon H, Nickerson D, Bernier R, Brkanac Z, Wijsman E. In press. Identification of rare variants from exome sequence in a large pedigree with autism. Hum Hered.
  • McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, Garimella K, Altshuler D, Gabriel S, Daly M, and others. 2010. The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res 20(9):12971303.
  • Millar JK, Wilson-Annan JC, Anderson S, Christie S, Taylor MS, Semple CA, Devon RS, St Clair DM, Muir WJ, Blackwood DH, et al. 2000. Disruption of two novel genes by a translocation co-segregating with schizophrenia. Hum Mol Genet 9(9):14151423.
  • Müller N, Schwarz MJ. 2010. Immune system and schizophrenia. Curr Immunol Rev 6(3):213220.
  • Murphy KC, Jones LA, Owen MJ. 1999. High rates of schizophrenia in adults with velo-cardio-facial syndrome. Arch Gen Psychiatry 56(10):940945.
  • Neale BM, Kou Y, Liu L, Ma'ayan A, Samocha KE, Sabo A, Lin CF, Stevens C, Wang LS, Makarov V, et al. 2012. Patterns and rates of exonic de novo mutations in autism spectrum disorders. Nature 485(7397):242245.
  • Need AC, McEvoy JP, Gennarelli M, Heinzen EL, Ge D, Maia JM, Shianna KV, He M, Cirulli ET, Gumbs CE, et al. 2012. Exome sequencing followed by large-scale genotyping suggests a limited role for moderately rare risk factors of strong effect in schizophrenia. Am J Hum Genet 91(2):303312.
  • Ng MY, Levinson DF, Faraone SV, Suarez BK, DeLisi LE, Arinami T, Riley B, Paunio T, Pulver AE, Irmansyah X, et al. 2009a. Meta-analysis of 32 genome-wide linkage studies of schizophrenia. Mol Psychiatry 14(8):774785.
  • Ng SB, Turner EH, Robertson PD, Flygare SD, Bigham AW, Lee C, Shaffer T, Wong M, Bhattacharjee A, Eichler EE, et al. 2009b. Targeted capture and massively parallel sequencing of 12 human exomes. Nature 461(7261):272276.
  • O'Roak BJ, Deriziotis P, Lee C, Vives L, Schwartz JJ, Girirajan S, Karakoc E, Mackenzie AP, Ng SB, Baker C, et al. 2011. Exome sequencing in sporadic autism spectrum disorders identifies severe de novo mutations. Nat Genet 43(6):585589.
  • Pinto D, Pagnamenta AT, Klei L, Anney R, Merico D, Regan R, Conroy J, Magalhaes TR, Correia C, Abrahams BS, et al. 2010. Functional impact of global rare copy number variation in autism spectrum disorders. Nature 466(7304):368372.
  • Pulver AE, Nestadt G, Goldberg R, Shprintzen RJ, Lamacz M, Wolyniec PS, Morrow B, Karayiorgou M, Antonarakis SE, Housman D. 1994. Psychotic illness in patients diagnosed with velo-cardio-facial syndrome and their relatives. J Nerv Ment Dis 182(8):476478.
  • Reich DE, Lander ES. 2001. On the allelic spectrum of human disease. Trends Genet 17(9):502510.
  • Ripke S, Sanders AR, Kendler KS, Levinson DF, Sklar P, Holmans PA, Lin DY, Duan J, Ophoff RA, Andreassen OA, et al. 2011. Genome-wide association study identifies five new schizophrenia loci. Nat Genet 43(10):969976.
  • Romeo S, Pennacchio LA, Fu Y, Boerwinkle E, Tybjaerg-Hansen A, Hobbs HH, Cohen JC. 2007. Population-based resequencing of ANGPTL4 uncovers variations that reduce triglycerides and increase HDL. Nat Genet 39(4):513516.
  • Sanders SJ, Ercan-Sencicek AG, Hus V, Luo R, Murtha MT, Moreno-De-Luca D, Chu SH, Moreau MP, Gupta AR, Thomson SA, et al. 2011. Multiple recurrent de novo CNVs, including duplications of the 7q11.23 Williams syndrome region, are strongly associated with autism. Neuron 70(5):863885.
  • Sanders SJ, Murtha MT, Gupta AR, Murdoch JD, Raubeson MJ, Willsey AJ, Ercan-Sencicek AG, DiLullo NM, Parikshak NN, Stein JL, et al. 2012. De novo mutations revealed by whole-exome sequencing are strongly associated with autism. Nature 485(7397):237241.
  • Sebat J, Lakshmi B, Malhotra D, Troge J, Lese-Martin C, Walsh T, Yamrom B, Yoon S, Krasnitz A, Kendall J, et al. 2007. Strong association of de novo copy number mutations with autism. Science 316(5823):445449.
  • Shendure J. 2011. Next-generation human genetics. Genome Biol 12(9):408.
  • Slater E, Tsuang MT. 1968. Abnormality on paternal and maternal sides: observations in schizophrenia and manic-depression. J Med Genet 5(3):197199.
  • Smith DJ, Lusis AJ. 2002. The allelic structure of common disease. Hum Mol Genet 11(20):24552461.
  • St Clair D, Blackwood D, Muir W, Carothers A, Walker M, Spowart G, Gosden C, Evans HJ. 1990. Association within a family of a balanced autosomal translocation with major mental illness. Lancet 336(8706):1316.
  • Stefansson H, Ophoff RA, Steinberg S, Andreassen OA, Cichon S, Rujescu D, Werge T, Pietilainen OP, Mors O, Mortensen PB, et al. 2009. Common variants conferring risk of schizophrenia. Nature 460(7256):744747.
  • Sullivan PF, Magnusson C, Reichenberg A, Boman M, Dalman C, Davidson M, Fruchter E, Hultman CM, Lundberg M, Långström N, et al. 2012. Family history of schizophrenia and bipolar disorder as risk factors for autism. Arch Gen Psychiatry 69(11):10991110.
  • Tam GW, Redon R, Carter NP, Grant SG. 2009. The role of DNA copy number variation in schizophrenia. Biol Psychiatry 66(11):10051012.
  • Timms AE, Dorschner MO, Wechsler J, Choi KY, Kirkwood R, Girirajan S, Baker C, Eichler EE, Korvatska O, Roche KW, et al. In press. Support for the N-methyl-d-aspartate receptor hypofunction hypothesis of schizophrenia from exome sequencing in multiplex families. JAMA Psychiatry.
  • Tsuang MT. 1993. Genotypes, phenotypes, and the brain. A search for connections in schizophrenia. Br J Psychiatry 163:299307.
  • Tsuang MT. 1994. Genetics, epidemiology, and the search for causes of schizophrenia. Am J Psychiatry 151(1):36.
  • Tsuang MT, Faraone SV. 1995. The case for heterogeneity in the etiology of schizophrenia. Schizophr Res 17(2):161175.
  • Tsuang MT, Fowler RC, Cadoret RJ, Monnelly E. 1974. Schizophrenia among first-degree relatives of paranoid and nonparanoid schizophrenics. Compr Psychiatry 15(4):295302.
  • Vissers LE, de Ligt J, Gilissen C, Janssen I, Steehouwer M, de Vries P, van Lier B, Arts P, Wieskamp N, del Rosario M, et al. 2010. A de novo paradigm for mental retardation. Nat Genet 42(12):11091112.
  • Vorstman JA, Anney RJ, Derks EM, Gallagher L, Gill M, de Jonge MV, van Engeland H, Kahn RS, Ophoff RA. the Autism Genome Project ISC. 2012. No evidence that common genetic risk variation is shared between schizophrenia and autism. Am J Med Genet B 162(1):5560.
  • Walsh T, McClellan JM, McCarthy SE, Addington AM, Pierce SB, Cooper GM, Nord AS, Kusenda M, Malhotra D, Bhandari A, et al. 2008. Rare structural variants disrupt multiple genes in neurodevelopmental pathways in schizophrenia. Science 320(5875):539543.
  • Wijsman EM. 2012. The role of large pedigrees in an era of high-throughput sequencing. Hum Genet 131(10):15551563.
  • Xu B, Roos JL, Levy S, van Rensburg EJ, Gogos JA, Karayiorgou M. 2008. Strong association of de novo copy number mutations with sporadic schizophrenia. Nat Genet 40(7):880885.
  • Xu B, Roos JL, Dexheimer P, Boone B, Plummer B, Levy S, Gogos JA, Karayiorgou M. 2011. Exome sequencing supports a de novo mutational paradigm for schizophrenia. Nat Genet 43(9):864868.
  • Xu B, Ionita-Laza I, Roos JL, Boone B, Woodrick S, Sun Y, Levy S, Gogos JA, Karayiorgou M. 2012. De novo gene mutations highlight patterns of genetic and neural complexity in schizophrenia. Nat Genet 44(12):13651369.