Effects of MiR-137 genetic risk score on brain volume and cortical measures in patients with schizophrenia and controls

Association of neurotransmitter pathway polygenic risk with specific symptom profiles in psychosis

A primary goal of psychiatry is to better understand the pathways that link genetic risk to psychiatric symptoms. Here, we tested association of diagnosis and endophenotypes with overall and neurotransmitter pathway-specific polygenic risk in patients with early-stage psychosis. Subjects included 205 demographically diverse cases with a psychotic disorder who underwent comprehensive psychiatric and neurological phenotyping and 115 matched controls. Following genotyping, we calculated polygenic scores (PGSs) for schizophrenia (SZ) and bipolar disorder (BP) using Psychiatric Genomics Consortium GWAS summary statistics. To test if overall genetic risk can be partitioned into affected neurotransmitter pathways, we calculated pathway PGSs (pPGSs) for SZ risk affecting each of four major neurotransmitter systems: glutamate, GABA, dopamine, and serotonin. Psychosis subjects had elevated SZ PGS versus controls; cases with SZ or BP diagnoses had stronger SZ or BP risk, respectively. There was no significant association within psychosis cases between individual symptom measures and overall PGS. However, neurotransmitter-specific pPGSs were moderately associated with specific endophenotypes; notably, glutamate was associated with SZ diagnosis and with deficits in cognitive control during task-based fMRI, while dopamine was associated with global functioning. Finally, unbiased endophenotype-driven clustering identified three diagnostically mixed case groups that separated on primary deficits of positive symptoms, negative symptoms, global functioning, and cognitive control. All clusters showed strong genome-wide risk. Cluster 2, characterized by deficits in cognitive control and negative symptoms, additionally showed specific risk concentrated in glutamatergic and GABAergic pathways. Due to the intensive characterization of our subjects, the present study was limited to a relatively small cohort. As such, results should be followed up with additional research at the population and mechanism level. Our study suggests pathway-based PGS analysis may be a powerful path forward to study genetic mechanisms driving psychiatric endophenotypes.

MIR137 polygenic risk for schizophrenia and ephrin-regulated pathway: Role in lateral ventricles and corpus callosum volume

Background/Objective. Enlarged lateral ventricle (LV) volume and decreased volume in the corpus callosum (CC) are hallmarks of schizophrenia (SZ). We previously showed an inverse correlation between LV and CC volumes in SZ, with global functioning decreasing with increased LV volume. This study investigates the relationship between LV volume, CC abnormalities, and the microRNA MIR137 and its regulated genes in SZ, because of MIR137's essential role in neurodevelopment. Methods. Participants were 1224 SZ probands and 1466 unaffected controls from the GENUS Consortium. Brain MRI scans, genotype, and clinical data were harmonized across cohorts and employed in the analyses. Results. Increased LV volumes and decreased CC central, mid-anterior, and mid-posterior volumes were observed in SZ probands. The MIR137-regulated ephrin pathway was significantly associated with CC:LV ratio, explaining a significant proportion (3.42 %) of CC:LV variance, and more than for LV and CC separately. Other pathways explained variance in either CC or LV, but not both. CC:LV ratio was also positively correlated with Global Assessment of Functioning, supporting previous subsample findings. SNP-based heritability estimates were higher for CC central:LV ratio (0.79) compared to CC or LV separately. Discussion. Our results indicate that the CC:LV ratio is highly heritable, influenced in part by variation in the MIR137-regulated ephrin pathway. Findings suggest that the CC:LV ratio may be a risk indicator in SZ that correlates with global functioning.

Association of neurotransmitter pathway polygenic risk with specific symptom profiles in psychosis

A primary goal of psychiatry is to better understand the pathways that link genetic risk to psychiatric symptoms. Here, we tested association of diagnosis and endophenotypes with overall and neurotransmitter pathway-specific polygenic risk in patients with early-stage psychosis. Subjects included 206 demographically diverse cases with a psychotic disorder who underwent comprehensive psychiatric and neurological phenotyping and 115 matched controls. Following genotyping, we calculated polygenic scores (PGSs) for schizophrenia (SZ) and bipolar disorder (BP) using Psychiatric Genomics Consortium GWAS summary statistics. To test if overall genetic risk can be partitioned into affected neurotransmitter pathways, we calculated pathway PGSs (pPGSs) for SZ risk affecting each of four major neurotransmitter systems: glutamate, GABA, dopamine, and serotonin. Psychosis subjects had elevated SZ PGS versus controls; cases with SZ or BP diagnoses had stronger SZ or BP risk, respectively. There was no significant association within psychosis cases between individual symptom measures and overall PGS. However, neurotransmitter-specific pPGSs were moderately associated with specific endophenotypes; notably, glutamate was associated with SZ diagnosis and with deficits in cognitive control during task-based fMRI, while dopamine was associated with global functioning. Finally, unbiased endophenotype-driven clustering identified three diagnostically mixed case groups that separated on primary deficits of positive symptoms, negative symptoms, global functioning, and cognitive control. All clusters showed strong genome-wide risk. Cluster 2, characterized by deficits in cognitive control and negative symptoms, additionally showed specific risk concentrated in glutamatergic and GABAergic pathways. Due to the intensive characterization of our subjects, the present study was limited to a relatively small cohort. As such, results should be followed up with additional research at the population and mechanism level. Our study suggests pathway-based PGS analysis may be a powerful path forward to study genetic mechanisms driving psychiatric endophenotypes.

Pathway-Specific Polygenic Scores Improve Cross-Ancestry Prediction of Psychosis and Clinical Outcomes

Psychotic disorders are debilitating conditions with disproportionately high public health burden. Genetic studies indicate high heritability, but current polygenic scores (PGS) account for only a fraction of variance in psychosis risk. PGS often show poor portability across ancestries, performing significantly worse in non-European populations. Pathway-specific PGS (pPGS), which restrict PGS to genomic locations within distinct biological units, could lead to increased mechanistic understanding of pathways that lead to risk and improve cross-ancestry prediction by reducing noise in genetic predictors. This study examined the predictive power of genome-wide PGS and nine pathway-specific pPGS in a unique Chinese-ancestry sample of deeply-phenotyped psychosis patients and non-psychiatric controls. We found strong evidence for the involvement of schizophrenia-associated risk variants within "nervous system development" (p=2.5e-4) and "regulation of neuron differentiation" pathways (p=3.0e-4) in predicting risk for psychosis. We also found the "ion channel complex" pPGS, with weights derived from GWAS of bipolar disorder, to be strongly associated with the number of inpatient psychiatry admissions a patient experiences (p=1.5e-3) and account for a majority of the signal in the overall bipolar PGS. Importantly, although the schizophrenia genome-wide PGS alone explained only 3.7% of the variance in liability to psychosis in this Chinese ancestry sample, the addition of the schizophrenia-weighted pPGS for "nervous system development" and "regulation of neuron differentiation" increased the variance explained to 6.9%, which is on-par with the predictive power of PGS in European ancestry samples. Thus, not only can pPGS provide greater insight into mechanisms underlying genetic risk for disease and clinical outcomes, but may also improve cross-ancestry risk prediction accuracy.

Lessons Learned From Parsing Genetic Risk for Schizophrenia Into Biological Pathways

The clinically heterogeneous presentation of schizophrenia is compounded by the heterogeneity of risk factors and neurobiological correlates of the disorder. Genome-wide association studies in schizophrenia have uncovered a remarkably high number of genetic variants, but the biological pathways they impact upon remain largely unidentified. Among the diverse methodological approaches employed to provide a more granular understanding of genetic risk for schizophrenia, the use of biological labels, such as gene ontologies, regulome approaches, and gene coexpression have all provided novel perspectives into how genetic risk translates into the neurobiology of schizophrenia. Here, we review the salient aspects of parsing polygenic risk for schizophrenia into biological pathways. We argue that parsed scores, compared to standard polygenic risk scores, may afford a more biologically plausible and accurate physiological modeling of the different dimensions involved in translating genetic risk into brain mechanisms, including multiple brain regions, cell types, and maturation stages. We discuss caveats, opportunities, and pitfalls inherent in the parsed risk approach.

Microglial-expressed genetic risk variants, cognitive function and brain volume in patients with schizophrenia and healthy controls

Changes in immune function are associated with variance in cognitive functioning in schizophrenia. Given that microglia are the primary innate immune cells in the brain, we examined whether schizophrenia risk-associated microglial genes (measured via polygenic score analysis) explained variation in cognition in patients with schizophrenia and controls (n = 1,238) and tested whether grey matter mediated this association. We further sought to replicate these associations in an independent sample of UK Biobank participants (n = 134,827). We then compared the strength of these microglial associations to that of neuronal and astroglial (i.e., other brain-expressed genes) polygenic scores, and used MAGMA to test for enrichment of these gene-sets with schizophrenia risk. Increased microglial schizophrenia polygenic risk was associated with significantly lower performance across several measures of cognitive functioning in both samples; associations which were then found to be mediated via total grey matter volume in the UK Biobank. Unlike neuronal genes which did show evidence of enrichment, the microglial gene-set was not significantly enriched for schizophrenia, suggesting that the relevance of microglia may be for neurodevelopmental processes related more generally to cognition. Further, the microglial polygenic score was associated with performance on a range of cognitive measures in a manner comparable to the neuronal schizophrenia polygenic score, with fewer cognitive associations observed for the astroglial score. In conclusion, our study supports the growing evidence of the importance of immune processes to understanding cognition and brain structure in both patients and in the healthy population.

Cell type-specific and cross-population polygenic risk score analyses of MIR137 gene pathway in schizophrenia

Cell type-specific pathway-based polygenic risk scores (PRSs) may better inform disease biology and improve the precision of PRS-based clinical prediction. For microRNA-137 (MIR137), a leading neuropsychiatric risk gene and a post-transcriptional master regulator, we conducted a cell type-specific gene set PRS analysis in both European and Han Chinese schizophrenia (SZ) samples. We found that the PRS of neuronal MIR137-target genes better explains SZ risk than PRS derived from MIR137-target genes in iPSC or from the reported gene sets showing MIR137-altered expression. Compared with the PRS derived from the whole genome or the target genes of TCF4, the PRS of neuronal MIR137-target genes explained a disproportionally larger (relative to SNP number) SZ risk in the European sample, but with a more modest advantage in the Han Chinese sample. Our study demonstrated a cell type-specific polygenic contribution of MIR137-target genes to SZ risk, highlighting the value of cell type-specific pathway-based PRS analysis for uncovering disease-relevant biological features.

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PRS of neural MIR137 target genes better explains schizophrenia (SZ) risk variance

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SZ risk and SNP heritability explained by MIR137 target genes is cell type-specific

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MIR137 target genes explain a disproportionally larger SZ risk than genomic control

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PRS of MIR137 target genes better explains SZ risk in Europeans than in Han Chinese

Effects of complement gene-set polygenic risk score on brain volume and cortical measures in patients with psychotic disorders and healthy controls

Multiple genome-wide association studies of schizophrenia have reported associations between genetic variants within the MHC region and disease risk, an association that has been partially accounted for by alleles of the complement component 4 (C4) gene. Following on previous findings of association between both C4 and other complement-related variants and memory function, we tested the hypothesis that polygenic scores calculated based on identified schizophrenia risk alleles within the "complement" system would be broadly associated with memory function and associated brain structure. We tested this using a polygenic risk score (PRS) calculated for complement genes, but excluding C4 variants. Higher complement-based PRS scores were observed to be associated with lower memory scores for the sample as a whole (N = 620, F change = 8.25; p = .004). A significant association between higher PRS and lower hippocampal volume was also observed (N = 216, R² change = 0.016, p = .015). However, after correcting for further testing of association with the more general indices of cortical thickness, surface area or total brain volume, none of which were associated with complement, the association with hippocampal volume became non-significant. A post-hoc analysis of hippocampal subfields suggested an association between complement PRS and several hippocampal subfields, findings that appeared to be particularly driven by the patient sample. In conclusion, our study yielded suggestive evidence of association between complement-based schizophrenia PRS and variation in memory function and hippocampal volume.

Polygenic risk for schizophrenia and subcortical brain anatomy in the UK Biobank cohort

Research has shown differences in subcortical brain volumes between participants with schizophrenia and healthy controls. However, none of these differences have been found to associate with schizophrenia polygenic risk. Here, in a large sample (n = 14,701) of unaffected participants from the UK Biobank, we test whether schizophrenia polygenic risk scores (PRS) limited to specific gene-sets predict subcortical brain volumes. We compare associations with schizophrenia PRS at the whole genome level ('genomic', including all SNPs associated with the disorder at a p-value threshold < 0.05) with 'genic' PRS (based on SNPs in the vicinity of known genes), 'intergenic' PRS (based on the remaining SNPs), and genic PRS limited to SNPs within 7 gene-sets previously found to be enriched for genetic association with schizophrenia ('abnormal behaviour,' 'abnormal long-term potentiation,' 'abnormal nervous system electrophysiology,' 'FMRP targets,' '5HT2C channels,' 'CaV2 channels' and 'loss-of-function intolerant genes'). We observe a negative association between the 'abnormal behaviour' gene-set PRS and volume of the right thalamus that survived correction for multiple testing (ß = -0.031, pFDR = 0.005) and was robust to different schizophrenia PRS p-value thresholds. In contrast, the only association with genomic PRS surviving correction for multiple testing was for right pallidum, which was observed using a schizophrenia PRS p-value threshold < 0.01 (ß = -0.032, p = 0.0003, pFDR = 0.02), but not when using other PRS P-value thresholds. We conclude that schizophrenia PRS limited to functional gene sets may provide a better means of capturing differences in subcortical brain volume than whole genome PRS approaches.

MIR137 polygenic risk is associated with schizophrenia and affects functional connectivity of the dorsolateral prefrontal cortex

BACKGROUND

Genome-wide association studies (GWAS) have consistently revealed that a variant of microRNA 137 (MIR137) shows a quite significant association with schizophrenia. Identifying the network of genes regulated by MIR137 could provide insights into the biological processes underlying schizophrenia. In addition, DLPFC functional connectivity, a robust correlate of MIR137, may provide plausible endophenotypes. However, the regulatory role of the MIR137 gene network in the disrupted functional connectivity remains unclear. Here, we tested the effects of the MIR137 regulated genes on the risk for schizophrenia and DLPFC functional connectivity.

METHODS

To evaluate the additive effects of the MIR137 regulated genes (N = 1274), we calculated a MIR137 polygenic risk score (PRS) for schizophrenia and tested its association with the risk for schizophrenia in the genomic data of a Han Chinese population that included schizophrenia patients (N = 589) and normal controls (N = 575). We then investigated the association between MIR137 PRS and DLPFC functional connectivity in two independent young healthy cohorts (N = 356 and N = 314).

RESULTS

We found that the MIR137 PRS successfully captured the differences in genetic structure between the patients and controls, but the single gene MIR137 did not. We then consistently found that a higher MIR137 PRS was correlated with lower functional connectivities between the DLPFC and both the superior parietal cortex and the inferior temporal cortex in two independent cohorts.

CONCLUSION

The findings suggested that these two functional connectivities of the DLPFC could be important endophenotypes linking the MIR137-regulated genetic structure to schizophrenia.

Genomic and Imaging Biomarkers in Schizophrenia

Recent large-scale genomic studies have confirmed that schizophrenia is a polygenic syndrome and have implicated a number of biological pathways in its aetiology. Both common variants individually of small effect and rarer but more penetrant genetic variants have been shown to play a role in the pathogenesis of the disorder. No simple Mendelian forms of the condition have been identified, but progress has been made in stratifying risk on the basis of the polygenic burden of common variants individually of small effect, and the contribution of rarer variants of larger effect such as Copy Number Variants (CNVs). Pathway analysis of risk-associated variants has begun to identify specific biological processes implicated in risk for the disorder, including elements of the glutamatergic NMDA receptor complex and post synaptic density, voltage-gated calcium channels, targets of the Fragile X Mental Retardation Protein (FMRP targets) and immune pathways. Genetic studies have also been used to drive genomic imaging approaches to the investigation of brain markers associated with risk for the disorder. Genomic imaging approaches have been applied both to investigate the effect of polygenic risk and to study the impact of individual higher-penetrance variants such as CNVs. Both genomic and genomic imaging approaches offer potential for the stratification of patients and at-risk groups and the development of better biomarkers of risk and treatment response; however, further research is needed to integrate this work and realise the full potential of these approaches.