New findings in the genetics of major psychoses

Dialogues Clin Neurosci. 2010;12(1):85-93.

Schizophrenia and bipolar disorder have a largely unknown pathophysiology and etiology, but they are highly heritable. Although linkage and association studies have identified a series of chromosomal regions likely to contain susceptibility genes, progress in identifying causative genes has been largely disappointing. However, rapid technological advances are beginning to lead to new insights. Systematic genome-wide association and follow-up studies have reported genome-wide significant association findings of common variants for schizophrenia and bipolar disorder. The risk conferred by individual variants is small, and some variants confer a risk for both disorders. In addition, recent studies have identified rare, large structural variants (copy number variants) that confer a greater risk for schizophrenia. This review summarizes recent developments in genetic research into schizophrenia and bipolar disorder, and discusses possible future directions in this field.

Author Affiliations: 
Department of Genomics, Life & Brain Centre and Instituteof Human Genetics, University of Bonn, Bonn, Germany (Markus M Nöthen, Sven Cichon); Division of Genetic Epidemiology in Psychiatry, Central Institute ofMental Health, Mannheim, Germany (Vanessa Nieratschker, Marcella Rietschel) 
Address for correspondence:  

Schizophrenia and bipolar affective disorder (bipolar disorder, manic depression) are major psychiatric disorders. They profoundly affect thought, perception, emotion, and behavior, and their symptoms cause significant social and/or occupational dysfunction. The World Health Organization ranks both disorders among the top 10 leading causes of the global burden of disease for the 15-to-44 age group.

Schizophrenia and bipolar disorder are illnesses with a largely unknown pathophysiology and etiology. However, genetic epidemiology has demonstrated that modern psychiatric diagnostic criteria define disorders that are highly heritable. Estimates of heritability range between 70% and 90% for schizophrenia [1] and 60% and 80% for bipolar disorder. [2] It is generally accepted that the inheritance of psychiatric disorders is complex. Multiple genetic and environmental factors contribute to the development of a disorder [3]-[9] and it is possible that gene-gene interactions also occur. [10],[11]

Extensive efforts have been made over the past 20 years to identify the susceptibility genes for psychiatric disorders on a molecular genetic level, although this has proven to be a far more difficult undertaking than was first anticipated. Until recently, the linkage approach and microscopic cytogenetic studies were the only available methods of systematically searching the genome. A disadvantage of these two methods is their low level of resolution. Linkage studies have identified a series of chromosomal regions that are likely to contain susceptibility genes, and highly promising association findings have been obtained for several genes in these regions (eg, neuregulin 1 [NRG1], G72/G30 locus, dystrobrevin-binding protein 1 [DTNBP1]). [12]-[14] However, it has not yet been possible to identify any genetic variant that confers a direct functional effect and which is consistently associated with disease across populations. Cytogenetic studies have also generated some highly promising candidate genes such as the disrupted-in-schizophrenia-1 gene (DISCI). [15] Subsequent studies have reported highly interesting findings regarding the function of these genes and their associated pathways. [16]

Recently, however, important advances have been made as a result of rapid developments in technologies that are able to decipher the variability of the human genome at high resolution, and which allow systematic investigation of the impact of such variability in large samples. This article summarizes these developments in genetic research into schizophrenia and bipolar disorder, and discusses possible future directions in this field.

Genome-wide association studies

The introduction of the genome-wide association study (GWAS) is the result of enormous technological advances. GWASs involve the use of arrays that simultaneously genotype several hundred thousand single nucleotide polymorphisms (SNPs) per individual. This enables a hypothesis-free search of every gene and most intergenic regions of the genome in samples of unrelated patients and controls. In this respect GWASs resemble genome-wide linkage studies (genome scans), but they have several major advantages: (i) they are not dependent on the recruitment of families; (ii) they have better resolution since (in contrast to linkage) they detect linkage disequilibrium with susceptibility variants, which usually extends over smaller genomic regions (in the range of a few ten thousand base pairs); and (iii) they have greater power to detect small genetic effects. In contrast to linkage studies, however, they are restricted to the investigation of common variants, since SNPs with low minor allele frequencies are poorly represented on currently available arrays. A serious difficulty in evaluating the results of GWASs is the issue of multiple testing. A large number of SNPs may be tested within the same study for their association with a disease, and this generates many nominally significant findings that are actually false positives. It is therefore necessary to correct for multiple testing to achieve the level of genomewide significance. This level is dependent upon the number of SNPs analyzed, and the threshold for currently available GWA chips is approximately 5 x 10-8 (660 000 to 1 000 000 SNPs). [17]-[19] This correction method is very conservative since the association findings of each SNP are considered to be independent, and the haplotype structure of the genome is not taken into account. Conservative correction for multiple testing reduces the risk of false-positive findings, but hampers the detection of true association signals that represent small effects on disease risk.

Following the publication of the first GWAS in agerelated macular degeneration, [20] successful GWASs have been conducted for a variety of common, complex diseases including type 2 diabetes, myocardial infarction, breast cancer, and Crohn's disease (for details of all published studies see


The first GWASs for schizophrenia have recently been published. [21]-[30] Three of these studies used pooled DNA samples. [21],[26],[27] The best supported variants in these three studies failed to achieve genome-wide significance [21],[26],[27] (Table I). This is a cost-effective method of performing GWASs and has proved to be effective in identifying disease genes (eg, refs 31,32). However, due to errors in DNA quantification, this method is less sensitive than individual genotyping and has less power. Furthermore, the evaluation of data is limited to the study of (estimated) allele frequencies at the level of individual SNPs. This method does not detect the effect of haplotypes, interactions between SNPs, or the effects of genotypes that do not show differences in allele frequencies. The first individual-genotyping-based GWAS of schizophrenia involved a very small sample of 178 cases and 144 controls. [29] The best hit was for a variant near the colony-stimulating factor-2 receptor alpha (CSF2RA) gene, but this did not achieve genome-wide significance. [29] The second GWAS of this type included 738 patients and 733 controls. Although a few signals coincided with genomic regions that had been implicated in previous linkage studies of schizophrenia, this study found no genome-wide significant association. [30] O'Donovan et al initially performed a GWAS using a moderately sized patient sample (n=479). They then performed a follow-up study of 12 markers with a P value ≤ 10-5 in a much larger sample to enhance the statistical power. [25] Strong evidence for replication was obtained for 3 of these 12 markers (P ≤ 5 x 10-4), although the best supported variant still failed to achieve genome-wide significance (Table I) . The highest-ranking SNP identified in this study is located in an intron of the zinc finger protein 804A gene (ZNF804A), a putative transcription factor which had never been implicated previously in the risk for schizophrenia. The case sample was then extended to include bipolar patients. The P value for the total sample surpassed the level of genome-wide significance (P=9 x 10-9). The association between ZNF804A and schizophrenia has recently been replicated by the International Schizophrenia Consortium, [24] and ZNF804A is therefore a promising susceptibility gene for schizophrenia. A recent imaging genetics study of ZNF804A risk genotypes has provided evidence in support of these genetic findings. This study demonstrated that healthy carriers of ZNF804A risk genotypes display pronounced genedosage-dependent alterations in functional coupling between the hippocampus and the dorsolateral prefrontal cortex (DLPFC) across the two hemispheres, which mirrors findings in patients. [33]

Three recent multicenter studies have provided important insights. The initial findings of these three studies failed to surpass the level of genome-wide significance. However, a meta-analysis was then performed using the best hits from the European data of these studies and data from a replication study by Stefansson et al. [22] This revealed a cluster of genome-wide significant SNPs in the major histocompatibility (MHC) region of chromosome 6p22.1 that were in substantial linkage disequilibrium. [22]-[24] These results provide evidence that the immunological system may play a role in the pathogenesis of schizophrenia. Furthermore, a variant upstream of neurogranin (NRGN; P=2.4 x 10-9) and a SNP in transcription factor 4 (TCF4; P= 4.1 x 10-9) achieved genomewide significance in Stefansson et al 's study [22] These studies demonstrate that GWASs of large samples can overcome limitations in power and detect common risk variants for complex psychiatric disorders.

In the study by the International Schizophrenia Consortium, it was demonstrated that possible risk variants may have been among the nominally significant SNPs that failed to reach genome-wide significance. Nominally significant SNPs were grouped into a “set of score alleles” and analyzed in an independent case-control sample, and it was shown that they distinguished cases from controls. [24] This study also demonstrated that this set of genes distinguished bipolar cases from controls, thus providing further evidence for a genetic overlap between schizophrenia and bipolar disorder. Although these SNPs explained only approximately 3% of the variance in schizophrenia risk, this may be regarded as a step towards molecular genetic evidence for the polygenic inheritance of schizophrenia.

Study SNPs analyzed Supported gene Supported variant Genomic region P value discovery N° samples discovery P value combined replication/ meta-analysis
Mah et al -(2006) ~ 25000 plexin A2 (PLXNA2) rs752016 1q32.2 0.006 320 cases 0.035 200 cases (EA)
325 controls 230 controls (EA)
Lencz et al (2007) ~ 500000 colony stimulating rs4129148 Xp22.33 3.7 x10-7 178 cases ND ND
factor receptor 2 Yp22.32 144 controls
alpha (CSF2RA)
Sullivan et al (2008) ~ 500000 nearest gene: rs4846033 1p36.22 4.4x10-6 738 cases ND ND
angiotensin II receptor- 733 controls
associated protein
O'Donovan et al (2008) ~ 500000 zinc finger protein rs1344706 2q32.1 1.8x10-6 479 cases 1.6 x10-7 7308 cases
804A (ZNF804A) 2937 controls 12834 controls
Shifman et al (2008) ~ 500000 reelin (RELN) rs7341475 7q22.1 2.9x10-5 745 cases 8.8 x10-7 2274 cases
(in females) 2644 controls (in females) 4401 controls
Kirov et al (2009) ~ 550000 coiled coiled domain rs11064768 12q24.23 1.2x10-6 574 trios ND
containing 60 (CCDC60)
Need et al (2009) ~ 550000 ADAMTS like 3 rs2135551 15q25.2 1.3x10-6 871 cases NR 1460 cases
(ADAMTSL3) 863 controls 12995 controls
Shi et al (2009) ~ 600000 ArfGAP with GTPase rs13025591 2q37.2 4.6 x10-7 2681 cases ND
domain, ankyrin repeat (in EA) 2653 controls
and PH domain 1 (EA)
v-erb-a erythroblastic rs1851196 2q34 2.1x10-6 1286 cases ND
leukemia viral oncogene (inAA) 973 controls
homolog 4 (avian) (AA)
major histocompatibility rs9272219 6p21.32 ND 6.9 x10-8 8008 cases (EA)
complex (MHC rs9272535 6p21.32 ND 8.9 x10-8 19077 controls (EA)
cluster of histone rs13194053 6p22.1 1.4x10-2 9.5 x10-9
protein genes (in EA)
The ~ 1000000 myosin XVIIIB rs5761163 22q12.1 3.4 x10-7 3322 cases ND 8008 cases
International (MYO18B) 3587 controls 9.5 x10-9 19077 controls
Schizophrenia major histocompatibility rs13194053 6p22.1 ND
Consortium (2009) complex (MHC)
Stefansson et al -(2009) ~ 300000 major histocompatibility 5 variants 6p21.3- 0.0027- 2663 cases 1.1x10-9 - 12945 cases
complex (MHC) 6p22.1 0.00023 13498 controls 1.4x10-12 34591 controls
neurogranin (NRGN) rs12807809 11q24.2 0.00045 2.4x10-9
transcription factor 4 rs9960767 18q21.1 0.0011 4.1 x 10-9
Table I Published genome-wide association studies (GWASs) for schizophrenia. [2]-[30] , [32] The number of variants investigated, the best associated single-nucleotide polymorphism(s)-SNP(s) - found and the gene(s) containing the SNP(s), the corresponding Pvalue(s), and the number of cases and controls in the discovery and the replication/meta-ana lysis sample are all given. Genome-wide significant findings are highlighted in bold EA, European Ancestry Individuals; AA, African-American Individuals; ND, no data available; NR, no replication

Bipolar disorder

Six GWASs have been published to date for bipolar dis­order [34]-[39] (Table II) including the landmark study by the Wellcome Trust Case Control Consortium (WTCCC) which investigated seven common disorders. [36] These studies were all based upon individual genotyping, with the exception of the study by Baum et al [39] which involved DNA pooling. Although there has been some inconsistency across studies in terms of their most asso­ciated genomic regions, [35]-[39] meta-analyses of some of these studies have revealed common association signals. A meta-analysis of the Baum et al [39] and the WTCCC [36] datasets found a consistent association between bipolar disorder and variants in the genes junction adhesion mol­ecule 3 (JAM3) (rs10791345, P=1 x 10-6), and solute car­rier family 39 (zinc transporter), member 3 (SLC39A3) (rs4806874, P=5 x 10-6). [40] A combined analysis of the Sklar et al [35] and WTCCC [36] studies, which included a total of 4387 patients and 6209 controls, identified the first genome-wide significant association signal for bipolar disorder for ankyrin 3, node of Ranvier (ANK3) (rs10994336, P=9.1 x 10-9). [34] The second most strongly associated region was marked rs1006737 in calcium channel, voltage-dependent, L type, alpha 1C subunit CACNA1C (P=7 x 10-8). Further independent support for ANK3 rs10994336 has recently been obtained by Schulze et al [41] in samples from Germany and the United States (US); this study also found evidence for allelic heterogeneity at the ANK3 locus.

Although GWASs of bipolar disorder have identified a number of potentially relevant genetic variants, the widely acknowledged formal threshold for genome-wide significance of P=5 x 10-8 has only been surpassed so far for variation in ANK3.

Future studies involving larger samples, the pooling of datasets, and higher statistical power are expected to identify additional specific risk factors for bipolar disorder and schizophrenia.

Study N° SNPs Supported Supported Genomic P value samples. P value N° samples.
analyzed gene variant region discovery discovery combined replication/
Baum et al ~ 550 000 diacylglycerol kinase rs1012053 13q14.11 0.0002 461 cases 1.5x10-8 772 cases
(2007) eta (DGKH) 563 controls 876 controls
Welcome Trust ~ 500 000 partner and localizer rs42059 7q21.3 6.3 x10-8 1868 cases ND ND
Case Control Of BRCA2 (PALB2) 2938 controls
(WTCCC; 2007)
Sklar et al ~ 400 000 tetraspanin-8 (TSPAN8) rs1705236 12q21.1 6.1x10-7 1461 cases NR
(2008) myosin5B (MYO5B) rs4939921 18q21.1 1.7 x10-7 2008 controls NR
voltage-dependent 3329 cases
calcium channel, L-type, rs1006737 12p13.33 8.8x10-4 3.1 x 10-6 4946 controls
alpha K subunit
Ferreira et al ~ 1 800 000 ankyrin G (ANK3) rs10994336 10q21.2 0.0002 1098 cases 9.1 x 10-9 4387 cases
(2008) (imputed) rs1938526 10q21.2 0.0002 1267 controls 1.3x10-8 6209 controls
~ 300 000 voltage-dependent rs1006737 7.0 x10-8
(genotyped) calcium channel, L-type, 12p13.33 0.0108
alpha K subunit
Scott et al ~ 550 000 inter-alpha (globulin) rs1042779 3p21.1 2076 cases 1.8 x10-7 3683 cases
(2009) inhibitor H1 (ITIH1) 1676 controls 14507 controls
multiple C2 domains, rs17418283 5q15 ND
transmembrane 1 1.3 x10-7
nuclear factor 1 A-type rs472913 1p32.1 2.0 x10-7
Smith et al ~ 700 000 nck-associated protein 5 rs10193871 2q21.2 9.8x10-6 1001 cases ND ND
(2009) (NAP5) 1033 controls
dpy-19-like 3 (DPY19L3) rs2111504 19q13.11 1.5x10-6 345 cases
670 controls
Table II Published genome-wide association studies (GWASs) for bipolar disorder. [34]-[39] The number of variants investigated, the best associated singlenucleotide polymorphism(s)-SNP(s) - found and the gene(s) containing that SNP(s), the corresponding Pvalue(s), and the number of cases and controls in the discovery and the replication/meta-analysis sample are all given. Genome-wide significant findings are highlighted in bold EA, European Ancestry Individuals; AA, African-American Individuals; ND, no data available; NR, no replication

Copy number variations

Small chromosomal aberrations (microdeletions and microduplications, collectively known as copy number variations, CNV) may confer a risk for schizophrenia, as

illustrated by the 22q11.2 deletion syndrome (22q11.2DS). This is a common microdeletion syndrome with congenital and late-onset features. Patients have a high risk for neuropsychiatric diseases including psychotic disorders and major depression. [42]-[44] It has not been possible to correlate the extent of the deletion with the occurrence of schizophrenia in these patients, and there is experimental evidence that increased susceptibility may require the altered expression of several genes within the 22q11.2 region. [45]-[46] This may explain why no replicable results have been obtained from attempts to implicate individual genes within the deletion region as susceptibility genes for schizophrenia. [47]


The application of new technologies such as comparative genomic hybridization (CGH) and SNP arrays in GWASs has enabled the identification of small chromosomal aberrations on a genome-wide scale. Initial studies reported an increased rate of aberrations in schizophrenia [48],[49] and subsequent studies have implicated specific chromosomal regions. [28],[50]-[54] Implicated aberrations include microdeletions in chromosomal regions 1q21.1, 2p16.3, 15q11.2, and 15q13.3, as well as microduplications in chromosomal regions 15q13.1 and 16p11.2. Although all of these variants are observed more frequently in patients than in controls (with odds ratios of >10 for some variants), the frequency of each individual variant in schizophrenia patients is low (<1%). Further studies are required to determine the penetrance and mutation rate of these aberrations, as well as their phenotypic spectrum. Research has shown that some variants also occur more frequently in patients with other central nervous system phenotypes such as autism, mental disability, and epilepsy. [55]-[58] The mechanisms that underlie the phenotypic outcome however, remain unknown. The fact that de novo mutations are found in a proportion of patients with CNVs supports the hypothesis that the negative effect on reproductive fitness observed in schizophrenia patients may be at least partly offset by the occurrence of new mutations.

Bipolar disorder

There have been few CNV studies of bipolar disorder. [59]-[61] Lachman et al investigated a mixed cohort of Caucasian patients (n=227) and controls (n=276) from the Czech Republic and the United States, and found that CNVs involving the gene glycogen synthase kinase 3 beta (GSK3beta) were significantly increased in patients compared with controls. [59] Using a European American sample of 1001 BD patients and 1034 controls, Zhang et al investigated singleton microdeletions (ie, those occurring only once in the total dataset of patients and controls) of more than 100 kb and found that they were overrepresented in patients. [60] The effect was strongest in a subgroup of patients with an early onset of mania (<8 years of age). A recent study of a three-generation Older Amish pedigree with segregating affective disorder [61] identified a set of 4 CNVs on chromosomes 6q27,9q21,12p13, and 15q11 that were enriched in affected family members and which altered the expression of neuronal genes.

No CNV with a genetic effect comparable to those identified for neuropsychiatric disorders such as schizophrenia or autism has yet been identified for bipolar disorder. In view of the limited number of studies performed, it is not possible to evaluate the influence of CNVs on disease development.


The first GWASs of schizophrenia and bipolar disorder have recently been published, and many more are in progress. Large international collaborations have been initiated to combine GWAS data sets in order to increase statistical power, the largest being the Psychiatric GWAS Consortium, which is expected to publish its first results in 2010 (The Psychiatric GWAS Consortium Steering Committee 2009). Currently available research findings suggest that the variants identified through GWASs confer only small individual risks. The major limitation of GWASs is that they are only able to investigate common variants. If a large fraction of the genetic contribution is conferred by rare variants, other approaches will be necessary to identify them. A successful first step in this direction has been the identification of associations between rare CNVs and psychiatric diseases, in particular schizophrenia. However, due to methodological constraints, this approach remains restricted to the investigation of aberrations of at least several thousand base pairs. Continuing technological developments will provide future studies with increasing resolution, and the availability of low-cost whole genome sequencing technology will ultimately make it possible to obtain the complete genomic sequences of large patient samples for comparison with controls. In principle, this will allow the systematic identification of rare variants that are associated with disease risk, although the existence of a myriad of rare variants in the human genome will render this a complex task. It is hoped that some rare variants confer a larger disease risk, as this will facilitate the detection of association in large case-control samples. Rare variants with small disease risk may be extremely difficult to detect, since prohibitively large sample sizes may be required to demonstrate any significant association.

It is likely, however, that even after the identification of all common and rare risk variants a substantial fraction of the familial clustering will remain unexplained. This “missing heritability” in complex diseases is the subject of intense debate and several potential explanations have been proposed, including epistasis and epigenetic mechanisms. [62]-[64] It will be necessary to apply specific research strategies to further investigate this issue, although these may require prohibitively large sample sizes or tissue samples that are difficult to access in human subjects.

It is not yet clear whether any of the association findings identified by GWASs represent causal variants. Systematic resequencing of the associated genomic regions will provide a comprehensive overview of such variants. In cases where association findings are due to linkage disequilibrium, it is possible that the causal variants have a stronger genetic effect than has been previously suspected. It is also theoretically possible that a given association finding is not attributable to a common causal variant. A simulation study has shown that the “synthetic” effect of multiple rare variants may be responsible for signals detected for common variants. It has also been shown that the location of these variants may be relatively far (up to 2 megabases) from the site identified in GWASs. [65] If this were the case for an associated locus, resequencing over large genomic distances in large samples would be required to identify the true causative variants. Ultimately, it is necessary to identify a direct functional effect for each potential causal variant, such as an effect on the function or expression of a gene.

GWASs performed to date have indicated that certain genes contribute to a susceptibility to both schizophrenia and bipolar disorder. It is clear that some of these genes convey a rather nonspecific susceptibility that overlaps diagnostic boundaries, and it is highly probable that this also overlaps with other psychiatric disorders. Other genes, however, convey specific effects. Future studies of the phenotypic dimensions that are most strongly associated with a specific gene will include analysis of clinical symptoms and endophenotypes. The latter may be particularly suited to guiding researchers in the selection of the most promising phenotypes for animal studies. [66]

The identification of disease-associated genes is likely to increase our knowledge of the underlying pathophysiology of psychiatric disorders in an as-yet unforeseen manner. The identification of biological pathways has the potential to revolutionize diagnostics and treatment.?

1. Sullivan PF, Kendler KS, Neale MC. Schizophrenia as a complex trait: evidence from a meta-analysis of twin studies Arch Gen Psychiatry 2003;60:1187-1192 [ Pub Med ]
2. Craddock N, Forty L. Genetics of affective (mood) disorders Eur J Hum Genet 2006;14:660-668 [ Pub Med ]
3. Gottesman , II , Hanson DR. Human development: biological and genetic processes Annu Rev Psychol 2005;56:263-286 [ Pub Med ]
4. Kato T, Kuratomi G, Kato N. Genetics of bipolar disorder Drugs Today (Barc) 2005;41:335-344 [ Pub Med ]
5. Hattori E, Liu C, Zhu H, Gershon ES. Genetic tests of biologic systems in affective disorders Mol Psychiatry 2005;10:719-740 [ Pub Med ]
6. Levinson DF. The genetics of depression: a review Biol Psychiatry 2006;60:84-92 [ Pub Med ]
7. McGuffin P. Nature and nurture interplay: schizophrenia Psychiatr Prax 2004;31 (suppl 2):S189-193 [ Pub Med ]
8. Abdolmaleky HM, Thiagalingam S, Wilcox M. Genetics and epigenetics in major psychiatric disorders: dilemmas, achievements, applications, and future scope Am J Pharmacogenomics 2005;5:149-160 [ Pub Med ]
9. Burmeister M, McInnis MG, Zollner S. Psychiatric genetics: progress amid controversy Nat Rev Genet 2008;9:527-540 [ Pub Med ]
10. Thapar A, Harold G, Rice F, Langley K, O'Donovan M. The contribution of gene-environment interaction to psychopathology Dev Psychopathol 2007;19:989-1004 [ Pub Med ]
11. Van Os J, Rutten BP, Poulton R. Gene-environment interactions in schizophrenia: review of epidemiological findings and future directions Schizophr Bull 2008;34:1066-1082 [ Pub Med ]
12. Chumakov I, Blumenfeld M, Guerassimenko O, et al. . Genetic and physiological data implicating the new human gene G72 and the gene for Damino acid oxidase in schizophrenia Proc Natl Acad S c i U S A 2002;99:13675-13680 [ Pub Med ]
13. Stefansson H, Sigurdsson E, Steinthorsdottir V, et al. . Neuregulin 1 and susceptibility to schizophrenia Am J Hum Genet 2002;71:877-892 [ Pub Med ]
14. Straub RE, Jiang Y, MacLean CJ, et al. . Genetic variation in the 6p22.3 gene DTNBP1, the human ortholog of the mouse dysbindin gene, is associated with schizophrenia Am J Hum Genet 2002;71:337-348 [ Pub Med ]
15. Millar JK, Wilson-Annan JC, Anderson S, et al. . Disruption of two novel genes by a translocation co-segregating with schizophrenia Hum Mol Genet 2000;9:1415-1423 [ Pub Med ]
16. Jaaro-Peled H, Hayashi-Takagi A, Seshadri S, Kamiya A, Brandon NJ, Sawa A. Neurodevelopmental mechanisms of schizophrenia: understanding disturbed postnatal brain maturation through neuregulin-1-ErbB4 and DISC1 Trends Neurosci 2009;32:485-495 [ Pub Med ]
17. Dudbridge F, Gusnanto A. Estimation of significance thresholds for genomewide association scans Genet Epidemiol 2008;32:227-234 [ Pub Med ]
18. Pe'er I, Yelensky R, Altshuler D, Daly MJ. Estimation of the multiple testing burden for genomewide association studies of nearly all common variants Genet Epidemiol 2008;32:381-385 [ Pub Med ]
19. Hoggart CJ, Clark TG, De Iorio M, Whittaker JC, Balding DJ. Genomewide significance for dense SNP and resequencing data Genet Epidemiol 2008;32:179-185 [ Pub Med ]
20. Klein RJ, Zeiss C, Chew EY, et al. . Complement factor H polymorphism in age-related macular degeneration Science 2005;308:385-389 [ Pub Med ]
21. Kirov G, Zaharieva I, Georgieva L, et al. . A genome-wide association study in 574 schizophrenia trios using DNA pooling Mol Psychiatry 2009;14:796-803 [ Pub Med ]
22. Stefansson H, Ophoff RA, Steinberg S, et al. . Common variants conferring risk of schizophrenia Nature 2009;460:744-747 [ Pub Med ]
23. Shi J, Levinson DF, Duan J, et al. . Common variants on chromosome 6p22.1 are associated with schizophrenia Nature 2009;460:753-757 [ Pub Med ]
24. Purcell SM, Wray NR, Stone JL, et al. . Common polygenic variation contributes to risk of schizophrenia and bipolar disorder Nature 2009;460:748-752 [ Pub Med ]
25. O'Donovan MC, Craddock N, Norton N, et al. . Identification of loci associated with schizophrenia by genome-wide association and follow-up Nat Genet 2008;40:1053-1055 [ Pub Med ]
26. Shifman S, Johannesson M, Bronstein M, et al. . Genome-wide association identifies a common variant in the reelin gene that increases the risk of schizophrenia only in women PLoS Genet 2008;4:e28 [ Pub Med ]
27. Mah S, Nelson MR, Delisi LE, et al. . Identification of the semaphorin receptor PLXNA2 as a candidate for susceptibility to schizophrenia Mol Psychiatry 2006;1 1:471-478 [ Pub Med ]
28. Need AC, Ge D, Weale ME, et al. . A genome-wide investigation of SNPs and CNVs in schizophrenia PLoS Genet 2009;5:e1000373 [ Pub Med ]
29. Lencz T, Morgan TV, Athanasiou M, et al. . Converging evidence for a pseudoautosomal cytokine receptor gene locus in schizophrenia Mol Psychiatry 2007;12:572-580 [ Pub Med ]
30. Sullivan PF, Lin D, Tzeng JY, et al. . Genomewide association for schizophrenia in the CATIE study: results of stage 1 Mol Psychiatry 2008;13:570-584 [ Pub Med ]
31. Johnson C, Drgon T, Liu QR, et al. . Pooled association genome scanning for alcohol dependence using 104,268 SNPs: validation and use to identify alcoholism vulnerability loci in unrelated individuals from the collaborative study on the genetics of alcoholism Am J Med Genet B Neuropsychiatr Genet 2006;141B:844-853 [ Pub Med ]
32. Liu QR, Drgon T, Walther D, et al. . Pooled association genome scanning: validation and use to identify addiction vulnerability loci in two samples Proc Natl Acad Sci U S A 2005;102:11864-11869 [ Pub Med ]
33. Esslinger C, Walter H, Kirsch P, et al. . Neural mechanisms of a genomewide supported psychosis variant Science 2009;324:605 [ Pub Med ]
34. Ferreira MA, O'Donovan MC, Meng YA, et al. . Collaborative genomewide association analysis supports a role for ANK3 and CACNA1C in bipolar disorder Nat Genet 2008;40:1056-1058 [ Pub Med ]
35. Sklar P, Smoller JW, Fan J, et al. . Whole-genome association study of bipolar disorder Mol Psychiatry 2008;13:558-569 [ Pub Med ]
36. WTCCC. Genome-wide association study of 14,000 cases of seven common disseases and 3,00 shared controls Nature 2007;447:661-678 [ Pub Med ]
37. Scott LJ, Muglia P, Kong XQ, et al. . Genome-wide association and metaanalysis of bipolar disorder in individuals of European ancestry Proc Natl Acad Sci U S A 2009;106:7501-7506 [ Pub Med ]
38. Smith EN, Bloss CS, Badner JA, et al. . Genome-wide association study of bipolar disorder in European American and African American individuals Mol Psychiatry 2009;14:755-763 [ Pub Med ]
39. Baum AE, Akula N, Cabanero M, et al. . A genome-wide association study implicates diacylglycerol kinase eta (DGKH) and several other genes in the etiology of bipolar disorder Mol Psychiatry 2008;13:197-207 [ Pub Med ]
40. Baum AE, Hamshere M, Green E, et al. . Meta-analysis of two genomewide association studies of bipolar disorder reveals important points of agreement Mol Psychiatry 2008;13:466-467 [ Pub Med ]
41. Schulze TG, Detera-Wadleigh SD, Akula N, et al. . Two variants in Ankyrin 3 (ANK3) are independent genetic risk factors for bipolar disorder Mol Psychiatry 2009;14:487-491 [ Pub Med ]
42. Green T, Gothelf D, Glaser B, et al. . Psychiatric disorders and intellectual functioning throughout development in velocardiofacial (22q11.2 deletion) syndrome J Am Acad Child Adolesc Psychiatry 2009;48:1060-1068 [ Pub Med ]
43. Karayiorgou M, Morris MA, Morrow B, et al. . Schizophrenia susceptibility associated with interstitial deletions of chromosome 22q1 1 Proc Natl Acad Sci U S A 1995;92:7612-7616 [ Pub Med ]
44. Bassett AS, Chow EW, Husted J, et al. . Clinical features of 78 adults with 22q11 Deletion Syndrome Am J Med Genet A 2005;138:307-313 [ Pub Med ]
45. Sivagnanasundaram S, Fletcher D, Hubank M, Illingworth E, Skuse D, Scambler P. Differential gene expression in the hippocampus of the Df1/+ mice: a model for 22q11.2 deletion syndrome and schizophrenia Brain Res 2007;1139:48-59 [ Pub Med ]
46. Meechan DW, Maynard TM, Gopalakrishna D, Wu Y, LaMantia AS. When half is not enough: gene expression and dosage in the 22q11 deletion syndrome Gene Expr 2007;13:299-310 [ Pub Med ]
47. Glaser B, Moskvina V, Kirov G, et al. . Analysis of ProDH, COMT and ZDHHC8 risk variants does not support individual or interactive effects on schizophrenia susceptibility Schizophr Res 2006;87:21-27 [ Pub Med ]
48. Walsh T, McClellan JM, McCarthy SE, et al. . Rare structural variants disrupt multiple genes in neurodevelopmental pathways in schizophrenia Science 2008;320:539-543 [ Pub Med ]
49. Xu B, Roos JL, Levy S, Van Rensburg EJ, Gogos JA, Karayiorgou M. Strong association of de novo copy number mutations with sporadic schizophrenia Nat Genet 2008;40:880-885 [ Pub Med ]
50. Rujescu D, Ingason A, Cichon S, et al. . Disruption of the neurexin 1 gene is associated with schizophrenia Hum Mol Genet 2009;18:988-996 [ Pub Med ]
51. Stefansson H, Rujescu D, Cichon S, et al. . Large recurrent microdeletions associated with schizophrenia Nature 2008;455:232-236 [ Pub Med ]
52. Kirov G, Gumus D, Chen W, et al. . Comparative genome hybridization suggests a role for NRXN1 and APBA2 in schizophrenia Hum Mol Genet 2008;17:458-465 [ Pub Med ]
53. International Schizophrenia Consortium. Rare chromosomal deletions and duplications increase risk of schizophrenia Nature 2008;455:237-241 [ Pub Med ]
54. McCarthy SE, Makarov V, Kirov G, et al. . Microduplications of 16p1 1.2 are associated with schizophrenia Nat Genet 2009;41:1223-1227 [ Pub Med ]
55. Mefford HC, Sharp AJ, Baker C, et al. . Recurrent rearrangements of chromosome 1q21.1 and variable pediatric phenotypes N Engl J Med 2008;359:1685-1699 [ Pub Med ]
56. Helbig I, Mefford HC, Sharp AJ, et al. . 15q13.3 microdeletions increase risk of idiopathic generalized epilepsy Nat Genet 2009;41:160-162 [ Pub Med ]
57. Ben-Shachar S, Lanpher B, German JR, et al. . Microdeletion 15q13.3: a locus with incomplete penetrance for autism, mental retardation, and psychiatric disorders J Med Genet 2009;46:382-388 [ Pub Med ]
58. Miller DT, Shen Y, Weiss LA, et al. . Microdeletion/duplication at 15q13.2q13.3 among individuals with features of autism and other neuropsychiatric disorders J Med Genet 2009;46:242-248 [ Pub Med ]
59. Lachman HM, Pedrosa E, Petruolo OA, et al. . Increase in GSK3beta gene copy number variation in bipolar disorder Am J Med Genet B Neuropsychiatr Genet 2007;144B:259-265 [ Pub Med ]
60. Zhang D, Cheng L, Qian Y, et al. . Singleton deletions throughout the genome increase risk of bipolar disorder Mol Psychiatry 2009;14:376-380 [ Pub Med ]
61. Yang S, Wang K, Gregory B, et al. . Genomic landscape of a three-generation pedigree segregating affective disorder PLoS ONE 2009;4:e4474 [ Pub Med ]
62. Maher B. Personal genomes: the case of the missing heritability Nature 2008;456:18-21 [ Pub Med ]
63. Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, Hunter DJ, et al. . Finding the missing heritability of complex diseases Nature 2009;461:747-753 [ Pub Med ]
64. Slatkin M. Epigenetic inheritance and the missing heritability problem Genetics 2009;182:845-850 [ Pub Med ]
65. Dickson SP, Wang K, Krantz I, Hakonarson H, Goldstein DB. Rare variants create synthetic genome-wide associations PLoS Biol ;8:e1000294 [ Pub Med ]
66. Gould TD, Gottesman . II. Psychiatric endophenotypes and the development of valid animal models Genes Brain Behav 2006;5:113-119 [ Pub Med ]