PERSPECTIVE
What have we learned from the Psychiatric Genomics Consortium MICHAEL C. O’DONOVAN Medical Research Council Centre for Psychiatric Genetics and Genomics, Institute of Psychological Medicine and Clinical Neurosciences, Cardiff University School of Medicine, Cardiff CF24 4HQ, UK
Decades of research in the pre-molecular genetics era firmly established that major psychiatric disorders are highly to moderately heritable, but only with the emergence of molecular genetic technology about 35 years ago was it possible to envisage identifying the specific pathogenic genes responsible. In psychiatry, the opportunity to probe pathophysiology using DNA seemed particularly attractive given that other biomedical approaches had been frustrated by the complexity of the brain, the challenges in obtaining access to fresh tissue, and the extensive potential for reverse causal associations due to the many environmental and behavioural consequences of the disorders. Simple in concept, translating heritability to pathophysiology has proven arduous. This paper provides a perspective on that process, how some of the obstacles have been overcome, and some of the implications of the current findings. The focus is the work of the Psychiatric Genomics Consortium (PGC), whose main impacts relate to common rather than rare genetic variation. This reflects the data available rather than any ideological position that rare genetic variation is unimportant in psychiatry.
FROM MENDELIAN TO MULTIFACTORIAL POLYGENIC INHERITANCE Early studies were predicated on the hypothesis of Mendelian transmission, where high penetrance mutations are sufficient to cause disease. With the exception of neurodegenerative diseases and some forms of autism, this proved a dead end in psychiatry. Although the possibility that Mendelian alleles act in a small proportion of cases cannot be excluded, the vast majority of psychiatric illness do not conform to this mode of inheritance. Accordingly, the focus switched to oligogenic, polygenic or multifactorial threshold models and the concept of “susceptibility alleles” which only modestly increase liability of disorder. How modestly was ultimately laid bare by studies using genome-wide association study (GWAS) technology, notably that of the Wellcome Trust Case Control Consortium (1), but also early studies of schizophrenia (2) and bipolar disorder (3). The conclusions were that common risk alleles typically confer effects with odds ratios (OR) less than 1.1 and that the sample sizes required to detect them were beyond those available to individual groups, or even existing psychiatric
consortia. These considerations led to the formation of the Psychiatric Genome Wide Association Consortium (4), now known as the Psychiatric Genomics Consortium (PGC).
THE PSYCHIATRIC GENOMICS CONSORTIUM Initially focussing on attention-deficit/hyperactivity disorder (ADHD), autism, bipolar disorder, major depression and schizophrenia, the PGC has expanded to include anorexia nervosa, obsessive-compulsive disorder/Tourette syndrome, post-traumatic stress disorders, and substance use disorder. With a dynamic membership currently comprising over 800 investigators from 36 countries, the PGC actively welcomes additional investigators (see http://pgc.unc.edu). From the perspective of genome-wide significant results, schizophrenia has enjoyed the greatest success (5), followed by bipolar disorder (6). ADHD, major depressive disorder, and autism spectrum disorder (ASD) are yet to leave the starting blocks, but studies by the PGC (and others) have shown that common risk alleles do indeed contribute to these disorders (7,8) and success likely reflects the relative sample sizes studied. The impact of numbers is clear. In 2011, with a schizophrenia discovery sample of 9,394 cases, the PGC reported only five novel findings (9), yet within three years, data from around 35,500 cases resulted in 128 independent genetic associations (5). Overall, the pattern was of minimal progress until a breakthrough threshold of about 13,000 cases was attained, after which the rate of new findings increased rapidly by about four independent associations per 1,000 new cases. Published sample sizes for the other founder PGC phenotypes are still below the breakthrough point for schizophrenia (for ADHD and ASD, less than 5000 cases; for major depressive disorder, less than 10,000), but inspired by schizophrenia, equivalent (or larger) samples will be available in the next couple of years. As yet unknown differences in the genetic architectures between disorders may mean that both the breakthrough threshold and subsequent ratio of discovery to sample size may differ across disorders. In particular, for major depressive disorder, early findings suggest that the population variance contributed by each individual allele may be particularly small, and that alternative approaches may be required for defining more homogeneous – and heritable – phenotypes (10). 291
PLEIOTROPY Pleiotropy denotes the influence of a genetic variant on multiple apparently unrelated phenotypes. Observed in prePGC GWAS studies (2,11), this phenomenon has been more fully explored by the PGC. Using novel methods that allow patterns of allele sharing across disorders to be estimated at a genome-wide level, and the degree of shared genetic risk to be quantified, the Cross Disorder Group of the PGC reported substantial overlaps between common alleles influencing risk of schizophrenia and bipolar disorder, and between those influencing risk of major depressive disorder and each of schizophrenia, bipolar disorder and, most surprising of all, ADHD (7). These findings complement studies of rare genetic variation showing that identical rare mutations can increase risk of schizophrenia, ASD, intellectual disability, and ADHD (12,13). Moreover, the recent PGC schizophrenia study (5) found that loci defined by common allelic associations were enriched for genes carrying rare mutations in intellectual disability and autism. Thus, pleiotropic effects in psychiatry appear to be the rule rather than the exception. An alternative view is that apparent pleiotropy merely reflects deficiencies in the pathophysiological validity of our classification system and that the distinctive phenomenological states enshrined in categorical diagnosis do not define discrete pathophysiological disorders (see 14). Together with analogous findings in non-genetic research, pleiotropy has provided much of the impetus for calls for psychiatric research to move beyond diagnostic categories and consider alternative measures such as domains of psychopathology, or other non-clinical features (e.g., cognitive measures), that might map better onto underlying biology (14).
PATHOPHYSIOLOGY AND THERAPEUTICS Genetics is still short of delivering clear insights into disease mechanisms. While each of the 128 independent genetic associations in schizophrenia have the potential to generate new insights into the disorder, achieving this requires associations to be linked to changes in the function of specific genes, a step not yet unequivocally taken for any common variant association. Nevertheless, some general clues about disorder-related biology are emerging, particularly when the common variant work of the PGC is considered together with findings from studies of rare genetic variation. At the most general level, schizophrenia associations are enriched at elements that regulate gene expression in brain, and possibly immune tissues (5). That schizophrenia is emerging as (largely) a brain disorder is in one sense trivial, but in the context of historically highly polarized opinions about its origins, such empirical findings are important. More specifically, there is increasing evidence that common (5) and rare variant associations (15-17) in schizophrenia show 292
a tendency to converge upon genes encoding functionally related proteins, for example multiple calcium channels and post-synaptic proteins complexes of glutamatergic synapses, including NMDA, AMPA and metabotropic receptors. In other disorders, the data are sparse, and the patterns less clear. Nevertheless, in bipolar disorder as in schizophrenia, the findings point to perturbation of function at calcium channels (6). PGC studies exploiting the genetic correlation between schizophrenia, major depressive disorder and bipolar disorder further suggest a shared involvement across these disorders for genes implicated in histone methylation, a process involved in regulating gene expression, and in immune pathways (18). These and other findings are finally allowing the development of new molecular models of psychiatric pathophysiology based upon, for example, synaptic plasticity (19). The models are crude and yet to be tested experimentally, and even if they do reflect aspects of pathophysiology, they are unlikely to represent the full picture. Nevertheless, the findings suggest that continued acquisition of genetic data will provide increasingly better insights into novel disease mechanisms, and in doing so, novel therapeutic options. This journey is long haul, but it has been argued (20) that associations in schizophrenia spanning genes encoding the dopamine D2 receptor (the target for all known effective antipsychotic drugs) and a number of glutamate receptors (existing targets of interest among pharmaceutical companies) suggest that other genes within GWAS associated regions may provide fast-track targets for developing treatments. One example is the kindling of interest in the application of calcium channel blockers in bipolar disorder.
RISK PROFILES AND PATIENT STRATIFICATION Using approaches introduced by the International Schizophrenia Consortium (11), the most recent PGC study (5) calculated that, in schizophrenia, a composite genetic risk profile score derived from all independent nominally significant associated alleles (p < 0.05) captures about 7% of total liability for the disorder in people of European ancestry, though somewhat less in people of non-European ancestry. Viewed from the perspective of effect size, those in the top decile of risk profile scores had approximately 4-5 fold higher risk than average. This degree of risk prediction is not clinically useful but, as more genetic variance is captured by larger studies, and risk profile scores are perhaps combined with other forms of data, it may become so in the future. Beyond risk prediction, the potential applications for using risk profile scores to stratifying patients are extensive. By way of illustration, studies are underway to test the possibility that high risk profile scores might predict chronicity or treatment resistance and the need for early introduction of clozapine. The availability of risk profile scores as a marker of trait liability is also providing a new, and increasing widely used, World Psychiatry 14:3 - October 2015
research tool. Applications include selecting individuals on the basis of those scores rather than affected status to investigate the neurobiological basis of schizophrenia liability. Furthermore, researchers with a developmental perspective have initiated epidemiological studies of children aiming to identify the cognitive and behavioural correlates of genetic risk that predate clinical disorders, and may even mediate the link between risk and disorder and be open to therapeutic intervention.
CONCLUSIONS Recent years have brought considerable advances in psychiatric genetics and, in the arena of common genetic variation, the PGC is now the major driving force. Funding aside, future success is critically dependent upon the continued donation of biological samples from individuals (almost half a million have done so already) and on the willingness of more researchers to contribute what is often their life’s work of data acquisition, frequently in the face of a perception of risk to self-interest. If this continues, given an increasingly global reach and expanding membership and sample base, there are reasons to expect that progress in schizophrenia will accelerate, and that the momentum achieved by that disorder will transmit to the full spectrum of psychiatric disorders. In doing so, we believe that the discipline will justify the faith the early genetics pioneers placed in it to provide the fundamental insights into aetiology that will fuel the accelerating phase of mechanistic research marking progress in other areas of medicine, thus potentially transforming the outlook for patients with these disorders.
Acknowledgements The author is funded by Medical Research Council (MRC) Centre (G0800509) and Program (G0801418) grants, and the European Community’s Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 279227.
References 1. Wellcome Trust Case Control Consortium. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 2007;447:661-78.
2. 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;40:1053-5. 3. Ferreira MA, O’Donovan MC, Meng YA et al. Collaborative genome-wide association analysis supports a role for ANK3 and CACNA1C in bipolar disorder. Nat Genet 2008;40:1056-8. 4. Sullivan PF. The psychiatric GWAS consortium: big science comes to psychiatry. Neuron 2010;68:182-6. 5. Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature 2014;511:421-7. 6. Psychiatric GWAS Consortium Bipolar Disorder Working Group. Large-scale genome-wide association analysis of bipolar disorder identifies a new susceptibility locus near ODZ4. Nat Genet 2011;43:977-83. 7. Cross-Disorder Group of the Psychiatric Genomics Consortium. Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs. Nat Genet 2013;45:984-94. 8. Cross-Disorder Group of the Psychiatric Genomics Consortium. Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis. Lancet 2013;381:1371-9. 9. Schizophrenia Psychiatric Genome-Wide Association Study (GWAS) Consortium. Genome-wide association study identifies five new schizophrenia loci. Nat Genet 2011;43:969-76. 10. Major Depressive Disorder Working Group of the Psychiatric GWAS Consortium. A mega-analysis of genome-wide association studies for major depressive disorder. Mol Psychiatry 2013;18:497511. 11. International Schizophrenia Consortium. Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature 2009;460:748-52. 12. Kirov G, Rees E, Walters JT et al. The penetrance of copy number variations for schizophrenia and developmental delay. Biol Psychiatry 2014;75:378-85. 13. Malhotra D, Sebat J. CNVs: harbingers of a rare variant revolution in psychiatric genetics. Cell 2012;148:1223-41. 14. Owen MJ. New approaches to psychiatric diagnostic classification. Neuron 2014;84:564-71. 15. Kirov G, Pocklington AJ, Holmans P et al. De novo CNV analysis implicates specific abnormalities of postsynaptic signalling complexes in the pathogenesis of schizophrenia. Mol Psychiatry 2012; 17:142-53. 16. Fromer M, Pocklington AJ, Kavanagh DH et al. De novo mutations in schizophrenia implicate synaptic networks. Nature 2014; 506:179-84. 17. Purcell SM, Moran JL, Fromer M et al. A polygenic burden of rare disruptive mutations in schizophrenia. Nature 2014;506:185-90. 18. Network and Pathway Analysis Subgroup of Psychiatric Genomics Consortium. Psychiatric genome-wide association study analyses implicate neuronal, immune and histone pathways. Nat Neurosci 2015;18:199-209. 19. Hall J, Trent S, Thomas KL et al. Genetic risk for schizophrenia: convergence on synaptic pathways involved in plasticity. Biol Psychiatry 2015;77:52-8. 20. Lencz T, Malhotra AK. Targeting the schizophrenia genome: a fast track strategy from GWAS to clinic. Mol Psychiatry 2015;20:820-6. DOI 10.1002/wps.20270
293
What have we learned from the Psychiatric Genomics Consortium MICHAEL C. O’DONOVAN Medical Research Council Centre for Psychiatric Genetics and Genomics, Institute of Psychological Medicine and Clinical Neurosciences, Cardiff University School of Medicine, Cardiff CF24 4HQ, UK
Decades of research in the pre-molecular genetics era firmly established that major psychiatric disorders are highly to moderately heritable, but only with the emergence of molecular genetic technology about 35 years ago was it possible to envisage identifying the specific pathogenic genes responsible. In psychiatry, the opportunity to probe pathophysiology using DNA seemed particularly attractive given that other biomedical approaches had been frustrated by the complexity of the brain, the challenges in obtaining access to fresh tissue, and the extensive potential for reverse causal associations due to the many environmental and behavioural consequences of the disorders. Simple in concept, translating heritability to pathophysiology has proven arduous. This paper provides a perspective on that process, how some of the obstacles have been overcome, and some of the implications of the current findings. The focus is the work of the Psychiatric Genomics Consortium (PGC), whose main impacts relate to common rather than rare genetic variation. This reflects the data available rather than any ideological position that rare genetic variation is unimportant in psychiatry.
FROM MENDELIAN TO MULTIFACTORIAL POLYGENIC INHERITANCE Early studies were predicated on the hypothesis of Mendelian transmission, where high penetrance mutations are sufficient to cause disease. With the exception of neurodegenerative diseases and some forms of autism, this proved a dead end in psychiatry. Although the possibility that Mendelian alleles act in a small proportion of cases cannot be excluded, the vast majority of psychiatric illness do not conform to this mode of inheritance. Accordingly, the focus switched to oligogenic, polygenic or multifactorial threshold models and the concept of “susceptibility alleles” which only modestly increase liability of disorder. How modestly was ultimately laid bare by studies using genome-wide association study (GWAS) technology, notably that of the Wellcome Trust Case Control Consortium (1), but also early studies of schizophrenia (2) and bipolar disorder (3). The conclusions were that common risk alleles typically confer effects with odds ratios (OR) less than 1.1 and that the sample sizes required to detect them were beyond those available to individual groups, or even existing psychiatric
consortia. These considerations led to the formation of the Psychiatric Genome Wide Association Consortium (4), now known as the Psychiatric Genomics Consortium (PGC).
THE PSYCHIATRIC GENOMICS CONSORTIUM Initially focussing on attention-deficit/hyperactivity disorder (ADHD), autism, bipolar disorder, major depression and schizophrenia, the PGC has expanded to include anorexia nervosa, obsessive-compulsive disorder/Tourette syndrome, post-traumatic stress disorders, and substance use disorder. With a dynamic membership currently comprising over 800 investigators from 36 countries, the PGC actively welcomes additional investigators (see http://pgc.unc.edu). From the perspective of genome-wide significant results, schizophrenia has enjoyed the greatest success (5), followed by bipolar disorder (6). ADHD, major depressive disorder, and autism spectrum disorder (ASD) are yet to leave the starting blocks, but studies by the PGC (and others) have shown that common risk alleles do indeed contribute to these disorders (7,8) and success likely reflects the relative sample sizes studied. The impact of numbers is clear. In 2011, with a schizophrenia discovery sample of 9,394 cases, the PGC reported only five novel findings (9), yet within three years, data from around 35,500 cases resulted in 128 independent genetic associations (5). Overall, the pattern was of minimal progress until a breakthrough threshold of about 13,000 cases was attained, after which the rate of new findings increased rapidly by about four independent associations per 1,000 new cases. Published sample sizes for the other founder PGC phenotypes are still below the breakthrough point for schizophrenia (for ADHD and ASD, less than 5000 cases; for major depressive disorder, less than 10,000), but inspired by schizophrenia, equivalent (or larger) samples will be available in the next couple of years. As yet unknown differences in the genetic architectures between disorders may mean that both the breakthrough threshold and subsequent ratio of discovery to sample size may differ across disorders. In particular, for major depressive disorder, early findings suggest that the population variance contributed by each individual allele may be particularly small, and that alternative approaches may be required for defining more homogeneous – and heritable – phenotypes (10). 291
PLEIOTROPY Pleiotropy denotes the influence of a genetic variant on multiple apparently unrelated phenotypes. Observed in prePGC GWAS studies (2,11), this phenomenon has been more fully explored by the PGC. Using novel methods that allow patterns of allele sharing across disorders to be estimated at a genome-wide level, and the degree of shared genetic risk to be quantified, the Cross Disorder Group of the PGC reported substantial overlaps between common alleles influencing risk of schizophrenia and bipolar disorder, and between those influencing risk of major depressive disorder and each of schizophrenia, bipolar disorder and, most surprising of all, ADHD (7). These findings complement studies of rare genetic variation showing that identical rare mutations can increase risk of schizophrenia, ASD, intellectual disability, and ADHD (12,13). Moreover, the recent PGC schizophrenia study (5) found that loci defined by common allelic associations were enriched for genes carrying rare mutations in intellectual disability and autism. Thus, pleiotropic effects in psychiatry appear to be the rule rather than the exception. An alternative view is that apparent pleiotropy merely reflects deficiencies in the pathophysiological validity of our classification system and that the distinctive phenomenological states enshrined in categorical diagnosis do not define discrete pathophysiological disorders (see 14). Together with analogous findings in non-genetic research, pleiotropy has provided much of the impetus for calls for psychiatric research to move beyond diagnostic categories and consider alternative measures such as domains of psychopathology, or other non-clinical features (e.g., cognitive measures), that might map better onto underlying biology (14).
PATHOPHYSIOLOGY AND THERAPEUTICS Genetics is still short of delivering clear insights into disease mechanisms. While each of the 128 independent genetic associations in schizophrenia have the potential to generate new insights into the disorder, achieving this requires associations to be linked to changes in the function of specific genes, a step not yet unequivocally taken for any common variant association. Nevertheless, some general clues about disorder-related biology are emerging, particularly when the common variant work of the PGC is considered together with findings from studies of rare genetic variation. At the most general level, schizophrenia associations are enriched at elements that regulate gene expression in brain, and possibly immune tissues (5). That schizophrenia is emerging as (largely) a brain disorder is in one sense trivial, but in the context of historically highly polarized opinions about its origins, such empirical findings are important. More specifically, there is increasing evidence that common (5) and rare variant associations (15-17) in schizophrenia show 292
a tendency to converge upon genes encoding functionally related proteins, for example multiple calcium channels and post-synaptic proteins complexes of glutamatergic synapses, including NMDA, AMPA and metabotropic receptors. In other disorders, the data are sparse, and the patterns less clear. Nevertheless, in bipolar disorder as in schizophrenia, the findings point to perturbation of function at calcium channels (6). PGC studies exploiting the genetic correlation between schizophrenia, major depressive disorder and bipolar disorder further suggest a shared involvement across these disorders for genes implicated in histone methylation, a process involved in regulating gene expression, and in immune pathways (18). These and other findings are finally allowing the development of new molecular models of psychiatric pathophysiology based upon, for example, synaptic plasticity (19). The models are crude and yet to be tested experimentally, and even if they do reflect aspects of pathophysiology, they are unlikely to represent the full picture. Nevertheless, the findings suggest that continued acquisition of genetic data will provide increasingly better insights into novel disease mechanisms, and in doing so, novel therapeutic options. This journey is long haul, but it has been argued (20) that associations in schizophrenia spanning genes encoding the dopamine D2 receptor (the target for all known effective antipsychotic drugs) and a number of glutamate receptors (existing targets of interest among pharmaceutical companies) suggest that other genes within GWAS associated regions may provide fast-track targets for developing treatments. One example is the kindling of interest in the application of calcium channel blockers in bipolar disorder.
RISK PROFILES AND PATIENT STRATIFICATION Using approaches introduced by the International Schizophrenia Consortium (11), the most recent PGC study (5) calculated that, in schizophrenia, a composite genetic risk profile score derived from all independent nominally significant associated alleles (p < 0.05) captures about 7% of total liability for the disorder in people of European ancestry, though somewhat less in people of non-European ancestry. Viewed from the perspective of effect size, those in the top decile of risk profile scores had approximately 4-5 fold higher risk than average. This degree of risk prediction is not clinically useful but, as more genetic variance is captured by larger studies, and risk profile scores are perhaps combined with other forms of data, it may become so in the future. Beyond risk prediction, the potential applications for using risk profile scores to stratifying patients are extensive. By way of illustration, studies are underway to test the possibility that high risk profile scores might predict chronicity or treatment resistance and the need for early introduction of clozapine. The availability of risk profile scores as a marker of trait liability is also providing a new, and increasing widely used, World Psychiatry 14:3 - October 2015
research tool. Applications include selecting individuals on the basis of those scores rather than affected status to investigate the neurobiological basis of schizophrenia liability. Furthermore, researchers with a developmental perspective have initiated epidemiological studies of children aiming to identify the cognitive and behavioural correlates of genetic risk that predate clinical disorders, and may even mediate the link between risk and disorder and be open to therapeutic intervention.
CONCLUSIONS Recent years have brought considerable advances in psychiatric genetics and, in the arena of common genetic variation, the PGC is now the major driving force. Funding aside, future success is critically dependent upon the continued donation of biological samples from individuals (almost half a million have done so already) and on the willingness of more researchers to contribute what is often their life’s work of data acquisition, frequently in the face of a perception of risk to self-interest. If this continues, given an increasingly global reach and expanding membership and sample base, there are reasons to expect that progress in schizophrenia will accelerate, and that the momentum achieved by that disorder will transmit to the full spectrum of psychiatric disorders. In doing so, we believe that the discipline will justify the faith the early genetics pioneers placed in it to provide the fundamental insights into aetiology that will fuel the accelerating phase of mechanistic research marking progress in other areas of medicine, thus potentially transforming the outlook for patients with these disorders.
Acknowledgements The author is funded by Medical Research Council (MRC) Centre (G0800509) and Program (G0801418) grants, and the European Community’s Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 279227.
References 1. Wellcome Trust Case Control Consortium. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 2007;447:661-78.
2. 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;40:1053-5. 3. Ferreira MA, O’Donovan MC, Meng YA et al. Collaborative genome-wide association analysis supports a role for ANK3 and CACNA1C in bipolar disorder. Nat Genet 2008;40:1056-8. 4. Sullivan PF. The psychiatric GWAS consortium: big science comes to psychiatry. Neuron 2010;68:182-6. 5. Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature 2014;511:421-7. 6. Psychiatric GWAS Consortium Bipolar Disorder Working Group. Large-scale genome-wide association analysis of bipolar disorder identifies a new susceptibility locus near ODZ4. Nat Genet 2011;43:977-83. 7. Cross-Disorder Group of the Psychiatric Genomics Consortium. Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs. Nat Genet 2013;45:984-94. 8. Cross-Disorder Group of the Psychiatric Genomics Consortium. Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis. Lancet 2013;381:1371-9. 9. Schizophrenia Psychiatric Genome-Wide Association Study (GWAS) Consortium. Genome-wide association study identifies five new schizophrenia loci. Nat Genet 2011;43:969-76. 10. Major Depressive Disorder Working Group of the Psychiatric GWAS Consortium. A mega-analysis of genome-wide association studies for major depressive disorder. Mol Psychiatry 2013;18:497511. 11. International Schizophrenia Consortium. Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature 2009;460:748-52. 12. Kirov G, Rees E, Walters JT et al. The penetrance of copy number variations for schizophrenia and developmental delay. Biol Psychiatry 2014;75:378-85. 13. Malhotra D, Sebat J. CNVs: harbingers of a rare variant revolution in psychiatric genetics. Cell 2012;148:1223-41. 14. Owen MJ. New approaches to psychiatric diagnostic classification. Neuron 2014;84:564-71. 15. Kirov G, Pocklington AJ, Holmans P et al. De novo CNV analysis implicates specific abnormalities of postsynaptic signalling complexes in the pathogenesis of schizophrenia. Mol Psychiatry 2012; 17:142-53. 16. Fromer M, Pocklington AJ, Kavanagh DH et al. De novo mutations in schizophrenia implicate synaptic networks. Nature 2014; 506:179-84. 17. Purcell SM, Moran JL, Fromer M et al. A polygenic burden of rare disruptive mutations in schizophrenia. Nature 2014;506:185-90. 18. Network and Pathway Analysis Subgroup of Psychiatric Genomics Consortium. Psychiatric genome-wide association study analyses implicate neuronal, immune and histone pathways. Nat Neurosci 2015;18:199-209. 19. Hall J, Trent S, Thomas KL et al. Genetic risk for schizophrenia: convergence on synaptic pathways involved in plasticity. Biol Psychiatry 2015;77:52-8. 20. Lencz T, Malhotra AK. Targeting the schizophrenia genome: a fast track strategy from GWAS to clinic. Mol Psychiatry 2015;20:820-6. DOI 10.1002/wps.20270
293
