O1.4. CAN PRS FOR SCHIZOPHRENIA, BIPOLAR DISORDER AND MAJOR DEPRESSION DISTINGUISH AFFECTIVE PSYCHOSIS DIAGNOSTIC CATEGORIES? THE EU-GEI STUDY. (9th April 2019)

• Record Type: Journal Article
• Authors: Rodriguez, Victoria; Quattrone, Diego; Tripoli, Giada; Alameda, Luis; Forti, Marta Di; Murray, Robin; Vassos, Evangelos
• Is Part Of: Schizophrenia bulletin. Volume 45(2019)Supplement 2
• Page Start: S160
• Page End: S160
• Publication Date: 2019-04-09
• Subjects: Schizophrenia -- Periodicals; Schizophrenia -- Research -- Periodicals; 616.898005
• DOI: 10.1093/schbul/sbz021.180
• Languages: English
• ISSNs: 0586-7614

Abstract

Abstract: Background: It is well established that Bipolar Disorder (BD) and Major Depression Disorder (MDD) run in families, and that this genetic susceptibility is due to many hundreds of common variants, each one conferring a fractional risk. Using the so-called polygenic risk score (PRS) method we can summarise the individual risks for an illness across those associated variants into a quantitative score. However, the variance explained by PRS for identifying case-control status is lower than expected, which can be partially explained by the heterogeneity within groups, but more importantly by the genetic overlap between disorders. Using data from the EUGEI case-control study of first-episode psychosis (FEP), we test whether PRS for schizophrenia (SZ), BP and MDD can distinguish between affective psychosis diagnosis beyond the genetic overlap. Methods: DNA was obtained from most participants at baseline (73.6% of 1130 cases and 78.5% of 1499 controls) among 16 European cities as part of the EUGEI case-control study. The sample was split based on ancestry and analyses were done on the subsample representing white population (n=1659). PRS for SZ, BD and MDD were built using las available data from the Psychiatric Genomic Consortium (PGC). DSM-IV based diagnoses were extracted utilizing the Operational Criteria Checklist at baseline and were grouped into diagnostic groups (Bipolar Disorder, Psychotic Depression, Affective psychosis, Non-affective psychosis) for clinical status comparisons. We built different logistic regression models using separately the different PRSs as predictors for each clinical comparison, adjusted by population stratification using the first 10 principal components (PC). A further model was built including all diagnostic categories as outcome (using control as reference group) and the three PRS as independent variables in a multinomial logistic regression, adjusting by 10 PC. Results: PRS SZ was the only able to distinguish between affective and non-affective psychosis, showing an association with non-affective psychosis (OR=0.36, 95% CI .19 - .67, p=0.001). When comparing bipolar disorder with psychotic depression PRS SZ was the only able to distinguish between categories, showing an association with bipolar disorder (OR=2.89 95% CI 1.01 – 8.24, p>0.001). In the multinomial regression, both PRS SZ (RRR=2.09 95% CI 1.0 4- 2.5, p<0.001) and PRS BD (RRR=1.23 95% CI 1.06 - 1.44, p=0.007) were significantly associated with non-affective psychosis; and with Bipolar Disorder (RRR=1.99 95% CI 1.37 – 2.9, p<0.001 for PRS SZ; and RRR=1.39 95% CI 1 - 1.94, p=0.048 for PRS BD). Interestingly, Psychotic Depression group was only significant associated with PRS BD (RRR=1.39 95% CI 1.06 - 1.85, p=0.019). Discussion: These results provide evidence of the potential use of PRS as a marker to help us understand the architecture of affective psychosis; for both positioning it on the spectrum of psychotic or mood disorders and delineating diagnostic boundaries once the explained variance becomes more significant. This potential use in High Risk or FEP settings may imply important differences in treatment and prognosis prediction. The development of more integrative models including other potential etiopathogenic factors are warranted.