Final Report Summary - PSYCHGENE (Copy Number Variation and Endophenotypes in Psychiatric Disorders)
Through the PSYCHGENE collaboration (see http://psych-gene.eu online) deCODE genetics, Sct Hans Hospital and the Technical University of Denmark (DTU) have associated both common variants conferring low risk and rare variants conferring high risk of psychiatric disorders. The consortium has studied contributions from the sequence variants to endophenotypes and through a systems biology approach searched for biological pathways at-risk in psychiatric disorders. The common variants identified by the consortium explain only a very small fraction of the heritability observed in psychiatric disorders which probably can be explained by reduced fecundity associated with many psychiatric disorders. The high-risk variants, recurrent copy number variations and mutations in coding sequence, explain more of the heritability. The high-risk variants are pleiotropic which challenges the current classification of psychiatric disorders. The high-risk variants, due to their pleiotropic effects, also provide opportunities to understand what diseases like schizophrenia, autism and mental retardation have in common. Systems biology approaches including the associated variants have implicated specific abnormalities of synaptic complexes in the pathogenesis of schizophrenia. This progress brings us closer to earlier intervention and new therapeutic targets.
The high heritability of psychiatric disorders has motivated the search for genetic variation conferring the risk. The availability of powerful technologies and samples of sufficient sizes have at last shed light on the very complex genetics of psychiatric disorders. The PSYCHGENE collaboration has resulted in identification of several sequence variants conferring risk of schizophrenia. Partners elaborated and expanded an older European Union (EU) funded collaboration, SGENE (see http://sgene.eu for details), which led to identification of common variants conferring risk of schizophrenia. In that study, three chromosomal regions were associated with schizophrenia, markers in; the HLA region, the Neurogranin- and the TCF4 genes1. All three have been replicated by other groups. A follow-up paper by PSYCHGENE associated a variant in the VRK2 gene and a second variant in the TCF4 gene with schizophrenia (2). These variants confer low or modest risk of schizophrenia and individually did not contribute strongly to endophenotypes. The PSYCHGENE collaboration also provided support for association of AHI1 markers with schizophrenia (3) and through a large meta-analyses including the PSYCHGENE and SGENE samples additional five genomic regions were associated with schizophrenia (1p21.3 2q32.3 8p23.2 8q21.3 and 10q24.32-q24.33) (4). In a joint analysis with a bipolar, three loci reached genome-wide significance in the large meta-analysis (4) while sparse evidence was found when sequence variants were tested for association with depression (5).
Several studies have supported associations between schizophrenia and metabolic syndrome (MS), glucose intolerance, cardiovascular disease, and type II diabetes. The PSYCHGENE consortium searched for an overlap in the genetics of schizophrenia and diabetes which led to association of a variant in the TCF7L2 gene with schizophrenia (6).
PSYCHGENE has contributed to identification of several rare variants conferring high-risk of schizophrenia and psychoses. Steinberg et al. expanded the range of variants associated with the ZNF804A gene by associating rare CNVs disrupting the gene with psychoses (7). Ingason et al. demonstrated that maternally derived microduplications at 15q11-q13 conferred risk of psychoses (8) and therefore showed that imprinted genes contribute to psychotic illness. Through collaboration with the SGENE consortium deletions in the neurexin 1 gene were associated with schizophrenia (9) and Ingason et al. associated a duplication of the chromosome 16p13.1 locus with schizophrenia (10). This locus was later also associated with ADHD11. Duong et al. furthermore showed that mutations in the neurexin 1 gene segregated with brain disorders in a multiply affected Danish family (12). In a search, led by Cardiff University, for biological pathways conferring risk of schizophrenia a system biology approach was used to demonstrate association with specific abnormalities of postsynaptic signaling complexes in the pathogenesis of schizophrenia (13).
In conclusion, it is likely that large fraction of the common variants conferring risk of psychiatric disorders have been uncovered while rare variant exploration will yield much more. The common variants have very modest impact on physiological function while the high risk variants disrupt function of genes through copy number variations or mutations in exons. Effective treatment for schizophrenia and other psychiatric disorders is still an unmet clinical need. The PSYCHGENE discoveries have contributed to current understanding of the basis of the pathology of psychiatric disorders which hopefully will become useful in drug discovery.
References
1. Stefansson, H. et al. Common variants conferring risk of schizophrenia. Nature 460, 744-747 (2009).
2. Steinberg, S. et al. Common Variants at VRK2 and TCF4 Conferring Risk of Schizophrenia. Hum Mol Genet.
3. Ingason, A. et al. Support for involvement of the AHI1 locus in schizophrenia. Eur J Hum Genet 15, 988-991 (2007).
4. Ripke, S. et al. Genome-wide association study identifies five new schizophrenia loci. Nature genetics 43, 969-976, doi:10.1038/ng.940 (2011).
5. Olsen, L. et al. Copy number variations in affective disorders and meta-analysis. Psychiatric genetics 21, 319-322, doi:10.1097/YPG.0b013e3283463deb (2011).
6. Hansen, T. et al. At-risk variant in TCF7L2 for type II diabetes increases risk of schizophrenia. Biol Psychiatry 70, 59-63, doi:10.1016/j.biopsych.2011.01.031 (2011).
7. Steinberg, S. et al. Expanding the range of ZNF804A variants conferring risk of psychosis. Mol Psychiatry 16, 59-66.
8. Ingason, A. et al. Maternally derived microduplications at 15q11-q13: implication of imprinted genes in psychotic illness. Am J Psychiatry 168, 408-417.
9. Rujescu, D. et al. Disruption of the neurexin 1 gene is associated with schizophrenia. Hum Mol Genet 18, 988-996 (2009).
10. Ingason, A. et al. Copy number variations of chromosome 16p13.1 region associated with schizophrenia. Mol Psychiatry 16, 17-25.
11. Williams, N. M. et al. Rare chromosomal deletions and duplications in attention-deficit hyperactivity disorder: a genome-wide analysis. Lancet 376, 1401-1408.
12. Duong, L. et al. Mutations in NRXN1 in a family multiply affected with brain disorders: NRXN1 mutations and brain disorders. American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics 159B, 354-358, doi:10.1002/ajmg.b.32036 (2012).
13. Kirov, G. et al. De novo CNV analysis implicates specific abnormalities of postsynaptic signalling complexes in the pathogenesis of schizophrenia. Mol Psychiatry 17, 142-153, doi:10.1038/mp.2011.154 (2012).
PSYCHGENE fellow training:
Seconded and recruited fellows were trained at deCODE and St Hans. All fellows were trained in calling copy number variations from chip arrays. All fellows were also trained in using Disease Miner, a software suit developed by deCODE, and used much both at deCODE and at St Hans. One fellow was furthermore trained in using the PennCNV software for calling large datasets. For analyzing and plotting results from the CNV data fellows were trained in using and writing scripts in the software R. A one week course was given at deCODE for training the fellows. One of the fellows was furthermore trained in using C++ for converting some of the R scripts to a software language more suitable for working on large datasets. At Sct Hans, fellows were trained in retrieving and working with demographic and phenotypic information from health registry databases maintained at the research institute, and introduced to the Electronic Patient Journal (EPJ) system used in the Sct Hans clinic.
The high heritability of psychiatric disorders has motivated the search for genetic variation conferring the risk. The availability of powerful technologies and samples of sufficient sizes have at last shed light on the very complex genetics of psychiatric disorders. The PSYCHGENE collaboration has resulted in identification of several sequence variants conferring risk of schizophrenia. Partners elaborated and expanded an older European Union (EU) funded collaboration, SGENE (see http://sgene.eu for details), which led to identification of common variants conferring risk of schizophrenia. In that study, three chromosomal regions were associated with schizophrenia, markers in; the HLA region, the Neurogranin- and the TCF4 genes1. All three have been replicated by other groups. A follow-up paper by PSYCHGENE associated a variant in the VRK2 gene and a second variant in the TCF4 gene with schizophrenia (2). These variants confer low or modest risk of schizophrenia and individually did not contribute strongly to endophenotypes. The PSYCHGENE collaboration also provided support for association of AHI1 markers with schizophrenia (3) and through a large meta-analyses including the PSYCHGENE and SGENE samples additional five genomic regions were associated with schizophrenia (1p21.3 2q32.3 8p23.2 8q21.3 and 10q24.32-q24.33) (4). In a joint analysis with a bipolar, three loci reached genome-wide significance in the large meta-analysis (4) while sparse evidence was found when sequence variants were tested for association with depression (5).
Several studies have supported associations between schizophrenia and metabolic syndrome (MS), glucose intolerance, cardiovascular disease, and type II diabetes. The PSYCHGENE consortium searched for an overlap in the genetics of schizophrenia and diabetes which led to association of a variant in the TCF7L2 gene with schizophrenia (6).
PSYCHGENE has contributed to identification of several rare variants conferring high-risk of schizophrenia and psychoses. Steinberg et al. expanded the range of variants associated with the ZNF804A gene by associating rare CNVs disrupting the gene with psychoses (7). Ingason et al. demonstrated that maternally derived microduplications at 15q11-q13 conferred risk of psychoses (8) and therefore showed that imprinted genes contribute to psychotic illness. Through collaboration with the SGENE consortium deletions in the neurexin 1 gene were associated with schizophrenia (9) and Ingason et al. associated a duplication of the chromosome 16p13.1 locus with schizophrenia (10). This locus was later also associated with ADHD11. Duong et al. furthermore showed that mutations in the neurexin 1 gene segregated with brain disorders in a multiply affected Danish family (12). In a search, led by Cardiff University, for biological pathways conferring risk of schizophrenia a system biology approach was used to demonstrate association with specific abnormalities of postsynaptic signaling complexes in the pathogenesis of schizophrenia (13).
In conclusion, it is likely that large fraction of the common variants conferring risk of psychiatric disorders have been uncovered while rare variant exploration will yield much more. The common variants have very modest impact on physiological function while the high risk variants disrupt function of genes through copy number variations or mutations in exons. Effective treatment for schizophrenia and other psychiatric disorders is still an unmet clinical need. The PSYCHGENE discoveries have contributed to current understanding of the basis of the pathology of psychiatric disorders which hopefully will become useful in drug discovery.
References
1. Stefansson, H. et al. Common variants conferring risk of schizophrenia. Nature 460, 744-747 (2009).
2. Steinberg, S. et al. Common Variants at VRK2 and TCF4 Conferring Risk of Schizophrenia. Hum Mol Genet.
3. Ingason, A. et al. Support for involvement of the AHI1 locus in schizophrenia. Eur J Hum Genet 15, 988-991 (2007).
4. Ripke, S. et al. Genome-wide association study identifies five new schizophrenia loci. Nature genetics 43, 969-976, doi:10.1038/ng.940 (2011).
5. Olsen, L. et al. Copy number variations in affective disorders and meta-analysis. Psychiatric genetics 21, 319-322, doi:10.1097/YPG.0b013e3283463deb (2011).
6. Hansen, T. et al. At-risk variant in TCF7L2 for type II diabetes increases risk of schizophrenia. Biol Psychiatry 70, 59-63, doi:10.1016/j.biopsych.2011.01.031 (2011).
7. Steinberg, S. et al. Expanding the range of ZNF804A variants conferring risk of psychosis. Mol Psychiatry 16, 59-66.
8. Ingason, A. et al. Maternally derived microduplications at 15q11-q13: implication of imprinted genes in psychotic illness. Am J Psychiatry 168, 408-417.
9. Rujescu, D. et al. Disruption of the neurexin 1 gene is associated with schizophrenia. Hum Mol Genet 18, 988-996 (2009).
10. Ingason, A. et al. Copy number variations of chromosome 16p13.1 region associated with schizophrenia. Mol Psychiatry 16, 17-25.
11. Williams, N. M. et al. Rare chromosomal deletions and duplications in attention-deficit hyperactivity disorder: a genome-wide analysis. Lancet 376, 1401-1408.
12. Duong, L. et al. Mutations in NRXN1 in a family multiply affected with brain disorders: NRXN1 mutations and brain disorders. American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics 159B, 354-358, doi:10.1002/ajmg.b.32036 (2012).
13. Kirov, G. et al. De novo CNV analysis implicates specific abnormalities of postsynaptic signalling complexes in the pathogenesis of schizophrenia. Mol Psychiatry 17, 142-153, doi:10.1038/mp.2011.154 (2012).
PSYCHGENE fellow training:
Seconded and recruited fellows were trained at deCODE and St Hans. All fellows were trained in calling copy number variations from chip arrays. All fellows were also trained in using Disease Miner, a software suit developed by deCODE, and used much both at deCODE and at St Hans. One fellow was furthermore trained in using the PennCNV software for calling large datasets. For analyzing and plotting results from the CNV data fellows were trained in using and writing scripts in the software R. A one week course was given at deCODE for training the fellows. One of the fellows was furthermore trained in using C++ for converting some of the R scripts to a software language more suitable for working on large datasets. At Sct Hans, fellows were trained in retrieving and working with demographic and phenotypic information from health registry databases maintained at the research institute, and introduced to the Electronic Patient Journal (EPJ) system used in the Sct Hans clinic.