The Psychiatric Diagnostic and Prevention Consortium (PsychDPC) consists of the following partners:


The consortium was funded by the European Union’s Seventh Framework Programme, under the Marie Curie Industry-Academia Partnerships and Pathways (IAPP), during 2012-2016.

The objectives of PsychDPC were to develop a framework for research into genetic and genomic epidemiology that would strengthen collaboration among European researchers working on schizophrenia. A further objective, building on this framework, was to characterize genomic disorders associating with schizophrenia and translate findings into the clinical arena. The project has resulted in the identification of common and rare variants conferring risk of schizophrenia(1, 2). Detailed phenotyping of carriers of at-risk variants has contributed to improving diagnosis and patient classification by uncovering associations between high-impact variants and various clinical determinants and endophenotypes. To this end, large samples of subjects, patients and controls carrying neuropsychiatric CNVs conferring high-risk of schizophrenia, have been assessed using a battery of cognitive tests (3). The results demonstrate that control carriers, who have not been diagnosed with a psychotic disorder or autism, have cognitive abnormalities that are akin to those encountered in schizophrenia(3). Thus, these neuropsychiatric CNVs can be used as instruments in the study of underlying cognitive features characterizing the disease. Carriers of these CNVs without a diagnosis of schizophrenia show cognitive abilities in between those of non-carriers among controls and patients with schizophrenia(3). This raises the possibility that the difference between the patients and the control carriers may not be due to a lack of penetrance but instead to variation in expressivity of the CNVs. It also shows that the cognitive abnormalities are not necessarily consequences of the disease, and that the risk of the disease may, at least in part, be mediated through the cognitive impairments. These approaches have allowed for studying the cognitive impairments associated with schizophrenia without the confounding effects of psychosis or medications.
The highly pleiotropic effects observed for neuropsychiatric CNVs challenge current classification of these disorders but also provide opportunities to understand their origins and the relationships between diseases and their comorbidities. PsychDPC has demonstrated that the 15q11.2(BP1-BP2) deletion carriers have enlarged corpus callosum and a smaller left fusiform gyrus. Furthermore, tailored functional magnetic resonance imaging experiments, using phonological lexical decision and multiplication verification tasks, have demonstrated altered activation in the left fusiform and the left angular gyri in carriers(4). Thus, by uncovering convergent evidence, from neuropsychological testing and structural and functional neuroimaging, PsychDPC has demonstrated that the 15q11.2(BP1-BP2) deletion affects cognitive, structural and functional correlates of dyslexia and dyscalculia(4) as wells as some of the known key features of schizophrenia(3). Dosage-sensitive genes at CNV loci can give rise to mirror effects on phenotypes for anthropometric loci. PsychDPC obtained evidence showing that dose dependent effects of CNVs not only affect risk of psychosis but also affect human brain structure directly(3). One of the missing pieces in our understanding of the pathogenesis of schizophrenia has been the nature of the physiologic function that is first perturbed in the disease or the perturbation of which leads to the disease. PsychDPC demonstrated that 15q11.2 deletion carriers who have not been diagnosed with autism, intellectual disability, or schizophrenia show intermediate phenotypes in brain structure that are congruent with observations from MRI studies of patients experiencing their first-episode psychosis(3). Hence, our research lends support to the notion that the cognitive abnormalities we have identified are fundamental defects in schizophrenia as they are manifested in carriers of CNVs conferring risk of the disease who do not suffer from the disease. We have, furthermore, used whole-genome sequencing in tandem with long-range phasing approaches to investigate large kindreds containing closely related schizophrenia patients. This effort has uncovered a loss-of-function variant in an RBM-gene conferring high-risk of schizophrenia(2). The RBM loss-of-function variant segregates with schizophrenia in two large pedigrees and although not fully penetrant for schizophrenia, compared to family members lacking the variant, carrier-controls have lower educational attainment, are more likely to receive disability benefits, and show impaired performance on a battery of cognitive tests. Hence, high-impact variants, CNVs or SNPs, impact cognition in carriers unaffected by the disease.
A polygenic risk score (PRS) is a sum of trait-associated alleles across an ensamble of genetic loci, typically weighted by effect sizes estimated from a genome-wide association study (GWAS). The application of PRSs has grown as their utility for detecting shared genetic aetiology among traits has become apparent; PRSs act as a biomarker for a phenotype and can thus also be used to establish the presence of a genetic signal in underpowered studies, infer the genetic architecture of a trait, and for screening into clinical trials. PsychDPC has contributed to the first dedicated PRS software, PRSice (‘precise’), for calculating, applying, evaluating and plotting the results of polygenic risk scores(5). Using PRSice, and similar approaches, PsychDPC has evaluated the impact of schizophrenia PRS on various traits. First, we tested whether PRS for schizophrenia and bipolar disorder would predict creativity. Higher scores were associated with membership in artistic societies or creative professions in both Icelandic and replication cohorts. Hence, creativity and psychosis share genetic roots. Second, PsychDPC has studied whether schizophrenia PRS associates with drug response or adverse drug reactions(6-9). Third, PsychDPC has contributed to research providing support for shared genetic influences between personality traits and psychiatric disorders and for the idea that personality traits and psychiatric disorders exist on a continuum in phenotypic and genomic space(10). Also, the persistence of common heritable psychiatric disorders, like schizophrenia that reduce reproductive success, is an evolutionary paradox. PsychDPC investigated the selection pressures acting on sequence variants conferring risk of schizophrenia. While the neuropsychiatric CNVs are associated with reproductive disadvantage the PRS for schizophrenia doesn’t impact fecundity in the same manner(11).
PsychDPC has thus contributed in several key areas to advance genomics and neuroscience leading to considerable progress in understanding schizophrenia and other psychiatric disorders. While results presented by PsychDPC and other scientific collaborators have clearly demonstrated the existence of a continuum of the variation in symptomatology and genetic overlap between psychiatric disorders, the categorical approach embodied in current classifications is not expected to change much in the near future. However, the maturing insights into the genetic architecture will more rapidly result in development of novel therapeutic strategies and personalized treatments.

1. Luo XJ, et al. Convergent lines of evidence support CAMKK2 as a schizophrenia susceptibility gene. Molecular psychiatry. 2014;19(7):774-83.
2. Steinberg S, et al. A truncating mutation in RBM cause familial psychosis. Manuscript. Manuscript in preparation.
3. Stefansson H, et al. CNVs conferring risk of autism or schizophrenia affect cognition in controls. Nature. 2014;505(7483):361-6.
4. Ulfarsson M, et al. 15q11.2 CNV affects cognitive, structural and functional correlates of dyslexia and dyscalculia. Translational Psychiatry. Under review.
5. Euesden J, Lewis CM, O’Reilly PF. PRSice: Polygenic Risk Score software. Bioinformatics. 2015;31(9):1466-8.
6. Iniesta R, et al. Combining clinical variables to optimize prediction of antidepressant treatment outcomes. Journal of psychiatric research. 2016;78:94-102.
7. Iniesta R, Stahl D, McGuffin P. Machine learning, statistical learning and the future of biological research in psychiatry. Psychological medicine. 2016;46(12):2455-65.
8. Verbelen M, et al. Establishing the characteristics of an effective pharmacogenetic test for clozapine-induced agranulocytosis. The pharmacogenomics journal. 2015;15(5):461-6.
9. Verbelen M, Lewis CM. How close are we to a pharmacogenomic test for clozapine-induced agranulocytosis? Pharmacogenomics. 2015;16(9):915-7.
10. Lo MT, et al. Genome-wide analyses for personality traits identify six genomic loci and show correlations with psychiatric disorders. Nature genetics. 2016.
11. Mullins N, et al. genetic risk for psychiatric disorders and reproductive sucess in the general population. Nature Communication. Under review.