Proteomics for blood biomarker exploration of severe mental illness: pitfalls of the past and potential for the future

Recent improvements in high-throughput proteomic approaches are likely to constitute an essential advance in biomarker discovery, holding promise for improved personalized care and drug development. These methodologies have been applied to study multivariate protein patterns and provide valuable data of peripheral tissues. To highlight findings of the last decade for three of the most common psychiatric disorders, namely schizophrenia (SZ), bipolar disorder (BD), and major depressive disorder (MDD), we queried PubMed. Here we delve into the findings from thirty studies, which used proteomics and multiplex immunoassay approaches for peripheral blood biomarker exploration. In an explorative approach, we ran enrichment analyses in peripheral blood according to these results and ascertained the overlap between proteomic findings and genetic loci identified in genome-wide association studies (GWAS). The studies we appraised demonstrate that proteomics for psychiatric research has been heterogeneous in aims and methods and limited by insufficient sample sizes, poorly defined case definitions, methodological inhomogeneity, and confounding results constraining the conclusions that can be extracted from them. Here, we discuss possibilities for overcoming methodological challenges for the implementation of proteomic signatures in psychiatric diagnosis and offer an outlook for future investigations. To fulfill the promise of proteomics in mental disease diagnostics, future research will need large, well-defined cohorts in combination with state-of-the-art technologies

A genome-wide association study of the longitudinal course of executive functions

Executive functions are metacognitive capabilities that control and coordinate mental processes. In the transdiagnostic PsyCourse Study, comprising patients of the affective-to-psychotic spectrum and controls, we investigated the genetic basis of the time course of two core executive subfunctions: set-shifting (Trail Making Test, part B (TMT-B)) and updating (Verbal Digit Span backwards) in 1338 genotyped individuals. Time course was assessed with four measurement points, each 6 months apart. Compared to the initial assessment, executive performance improved across diagnostic groups. We performed a genome-wide association study to identify single nucleotide polymorphisms (SNPs) associated with performance change over time by testing for SNP-by-time interactions using linear mixed models. We identified nine genome-wide significant SNPs for TMT-B in strong linkage disequilibrium with each other on chromosome 5. These were associated with decreased performance on the continuous TMT-B score across time. Variant rs150547358 had the lowest P value = 7.2 × 10(−10) with effect estimate beta = 1.16 (95% c.i.: 1.11, 1.22). Implementing data of the FOR2107 consortium (1795 individuals), we replicated these findings for the SNP rs150547358 (P value = 0.015), analyzing the difference of the two available measurement points two years apart. In the replication study, rs150547358 exhibited a similar effect estimate beta = 0.85 (95% c.i.: 0.74, 0.97). Our study demonstrates that longitudinally measured phenotypes have the potential to unmask novel associations, adding time as a dimension to the effects of genomics

Polygenic risk scores across the extended psychosis spectrum

As early detection of symptoms in the subclinical to clinical psychosis spectrum may improve health outcomes, knowing the probabilistic susceptibility of developing a disorder could guide mitigation measures and clinical intervention. In this context, polygenic risk scores (PRSs) quantifying the additive effects of multiple common genetic variants hold the potential to predict complex diseases and index severity gradients. PRSs for schizophrenia (SZ) and bipolar disorder (BD) were computed using Bayesian regression and continuous shrinkage priors based on the latest SZ and BD genome-wide association studies (Psychiatric Genomics Consortium, third release). Eight well-phenotyped groups (n = 1580; 56% males) were assessed: control (n = 305), lower (n = 117) and higher (n = 113) schizotypy (both groups of healthy individuals), at-risk for psychosis (n = 120), BD type-I (n = 359), BD type-II (n = 96), schizoaffective disorder (n = 86), and SZ groups (n = 384). PRS differences were investigated for binary traits and the quantitative Positive and Negative Syndrome Scale. Both BD-PRS and SZ-PRS significantly differentiated controls from at-risk and clinical groups (Nagelkerke's pseudo-R$^{2}$: 1.3-7.7%), except for BD type-II for SZ-PRS. Out of 28 pairwise comparisons for SZ-PRS and BD-PRS, 9 and 12, respectively, reached the Bonferroni-corrected significance. BD-PRS differed between control and at-risk groups, but not between at-risk and BD type-I groups. There was no difference between controls and schizotypy. SZ-PRSs, but not BD-PRSs, were positively associated with transdiagnostic symptomology. Overall, PRSs support the continuum model across the psychosis spectrum at the genomic level with possible irregularities for schizotypy. The at-risk state demands heightened clinical attention and research addressing symptom course specifiers. Continued efforts are needed to refine the diagnostic and prognostic accuracy of PRSs in mental healthcare

The PsyCourse Codebook, Version 6.0

The metadata contained in this publication describe most of the data that were collected in the longitudinal PsyCourse study (doi.org/10.1002/ajmg.b.32639). The data can be accessed by submitting a short research proposal: psycourse.de/openscience-en.html. Both the dataset and the corresponding metadata are versioned. Please use the latest available version for your research. Known errors in this version of the codebook and the corresponding dataset are uploaded to this repository

The PsyCourse Codebook, Version 4.1

The metadata contained in this publication describe most of the data that were collected in the longitudinal PsyCourse study (doi.org/10.1002/ajmg.b.32639). The data can be accessed by submitting a short research proposal: psycourse.de/openscience-en.html. Both the dataset and the corresponding metadata are versioned. Please use the latest available version for your research. Known errors in this version of the codebook and the corresponding dataset are uploaded to this repository

Polygenic risk scores across the extended psychosis spectrum

As early detection of symptoms in the subclinical to clinical psychosis spectrum may improve health outcomes, knowing the probabilistic susceptibility of developing a disorder could guide mitigation measures and clinical intervention. In this context, polygenic risk scores (PRSs) quantifying the additive effects of multiple common genetic variants hold the potential to predict complex diseases and index severity gradients. PRSs for schizophrenia (SZ) and bipolar disorder (BD) were computed using Bayesian regression and continuous shrinkage priors based on the latest SZ and BD genome-wide association studies (Psychiatric Genomics Consortium, third release). Eight well-phenotyped groups (n = 1580; 56% males) were assessed: control (n = 305), lower (n = 117) and higher (n = 113) schizotypy (both groups of healthy individuals), at-risk for psychosis (n = 120), BD type-I (n = 359), BD type-II (n = 96), schizoaffective disorder (n = 86), and SZ groups (n = 384). PRS differences were investigated for binary traits and the quantitative Positive and Negative Syndrome Scale. Both BD-PRS and SZ-PRS significantly differentiated controls from at-risk and clinical groups (Nagelkerke's pseudo-R-2: 1.3-7.7%), except for BD type-II for SZ-PRS. Out of 28 pairwise comparisons for SZ-PRS and BD-PRS, 9 and 12, respectively, reached the Bonferroni-corrected significance. BD-PRS differed between control and at-risk groups, but not between at-risk and BD type-I groups. There was no difference between controls and schizotypy. SZ-PRSs, but not BD-PRSs, were positively associated with transdiagnostic symptomology. Overall, PRSs support the continuum model across the psychosis spectrum at the genomic level with possible irregularities for schizotypy. The at-risk state demands heightened clinical attention and research addressing symptom course specifiers. Continued efforts are needed to refine the diagnostic and prognostic accuracy of PRSs in mental healthcare.ISSN:2158-318