Shared genetic architecture between schizophrenia and subcortical brain volumes implicates early neurodevelopmental processes and brain development in childhood

Genetic architecture of brain morphology and overlap with neuropsychiatric traits

Uncovering the genetic architectures of brain morphology offers valuable insights into brain development and disease. Genetic association studies of brain morphological phenotypes have discovered thousands of loci. However, interpretation of these loci presents a significant challenge. One potential solution is exploring the genetic overlap between brain morphology and disorders, which can improve our understanding of their complex relationships, ultimately aiding in clinical applications. In this review, we examine current evidence on the genetic associations between brain morphology and neuropsychiatric traits. We discuss the impact of these associations on the diagnosis, prediction, and treatment of neuropsychiatric diseases, along with suggestions for future research directions.

Abundant pleiotropy across neuroimaging modalities identified through a multivariate genome-wide association study

Genetic pleiotropy is abundant across spatially distributed brain characteristics derived from one neuroimaging modality (e.g. structural, functional or diffusion magnetic resonance imaging [MRI]). A better understanding of pleiotropy across modalities could inform us on the integration of brain function, micro- and macrostructure. Here we show extensive genetic overlap across neuroimaging modalities at a locus and gene level in the UK Biobank (N = 34,029) and ABCD Study (N = 8607). When jointly analysing phenotypes derived from structural, functional and diffusion MRI in a genome-wide association study (GWAS) with the Multivariate Omnibus Statistical Test (MOSTest), we boost the discovery of loci and genes beyond previously identified effects for each modality individually. Cross-modality genes are involved in fundamental biological processes and predominantly expressed during prenatal brain development. We additionally boost prediction of psychiatric disorders by conditioning independent GWAS on our multimodal multivariate GWAS. These findings shed light on the shared genetic mechanisms underlying variation in brain morphology, functional connectivity, and tissue composition.

Temporal changes in brain morphology related to inflammation and schizophrenia: an omnigenic Mendelian randomization study

BACKGROUND

Over the past several decades, more research focuses have been made on the inflammation/immune hypothesis of schizophrenia. Building upon synaptic plasticity hypothesis, inflammation may contribute the underlying pathophysiology of schizophrenia. Yet, pinpointing the specific inflammatory agents responsible for schizophrenia remains a complex challenge, mainly due to medication and metabolic status. Multiple lines of evidence point to a wide-spread genetic association across genome underlying the phenotypic variations of schizophrenia.

METHOD

We collected the latest genome-wide association analysis (GWAS) summary data of schizophrenia, cytokines, and longitudinal change of brain. We utilized the omnigenic model which takes into account all genomic SNPs included in the GWAS of trait, instead of traditional Mendelian randomization (MR) methods. We conducted two round MR to investigate the inflammatory triggers of schizophrenia and the resulting longitudinal changes in the brain.

RESULTS

We identified seven inflammation markers linked to schizophrenia onset, which all passed the Bonferroni correction for multiple comparisons (bNGF, GROA(CXCL1), IL-8, M-CSF, MCP-3 (CCL7), TNF-β, CRP). Moreover, CRP were found to significantly influence the linear rate of brain morphology changes, predominantly in the white matter of the cerebrum and cerebellum.

CONCLUSION

With an omnigenic approach, our study sheds light on the immune pathology of schizophrenia. Although these findings need confirmation from future studies employing different methodologies, our work provides substantial evidence that pervasive, low-level neuroinflammation may play a pivotal role in schizophrenia, potentially leading to notable longitudinal changes in brain morphology.

Parkinson's disease and schizophrenia interactomes contain temporally distinct gene clusters underlying comorbid mechanisms and unique disease processes

Genome-wide association studies suggest significant overlaps in Parkinson's disease (PD) and schizophrenia (SZ) risks, but the underlying mechanisms remain elusive. The protein-protein interaction network ('interactome') plays a crucial role in PD and SZ and can incorporate their spatiotemporal specificities. Therefore, to study the linked biology of PD and SZ, we compiled PD- and SZ-associated genes from the DisGeNET database, and constructed their interactomes using BioGRID and HPRD. We examined the interactomes using clustering and enrichment analyses, in conjunction with the transcriptomic data of 26 brain regions spanning foetal stages to adulthood available in the BrainSpan Atlas. PD and SZ interactomes formed four gene clusters with distinct temporal identities (Disease Gene Networks or 'DGNs'1-4). DGN1 had unique SZ interactome genes highly expressed across developmental stages, corresponding to a neurodevelopmental SZ subtype. DGN2, containing unique SZ interactome genes expressed from early infancy to adulthood, correlated with an inflammation-driven SZ subtype and adult SZ risk. DGN3 contained unique PD interactome genes expressed in late infancy, early and late childhood, and adulthood, and involved in mitochondrial pathways. DGN4, containing prenatally-expressed genes common to both the interactomes, involved in stem cell pluripotency and overlapping with the interactome of 22q11 deletion syndrome (comorbid psychosis and Parkinsonism), potentially regulates neurodevelopmental mechanisms in PD-SZ comorbidity. Our findings suggest that disrupted neurodevelopment (regulated by DGN4) could expose risk windows in PD and SZ, later elevating disease risk through inflammation (DGN2). Alternatively, variant clustering in DGNs may produce disease subtypes, e.g., PD-SZ comorbidity with DGN4, and early/late-onset SZ with DGN1/DGN2.

Shared Genetic Architecture between Parkinson's Disease and Brain Structural Phenotypes

BACKGROUND

Patients with Parkinson's disease (PD) have consistently demonstrated brain structure abnormalities, indicating the presence of shared etiological and pathological processes between PD and brain structures; however, the genetic relationship remains poorly understood.

OBJECTIVE

The aim of this study was to investigate the extent of shared genetic architecture between PD and brain structural phenotypes (BSPs) and to identify shared genomic loci.

METHODS

We used the summary statistics from genome-wide association studies to conduct MiXeR and conditional/conjunctional false discovery rate analyses to investigate the shared genetic signatures between PD and BSPs. Subsequent expression quantitative trait loci mapping in the human brain and enrichment analyses were also performed.

RESULTS

MiXeR analysis identified genetic overlap between PD and various BSPs, including total cortical surface area, average cortical thickness, and specific brain volumetric structures. Further analysis using conditional false discovery rate (FDR) identified 21 novel PD risk loci on associations with BSPs at conditional FDR < 0.01, and the conjunctional FDR analysis demonstrated that PD shared several genomic loci with certain BSPs at conjunctional FDR < 0.05. Among the shared loci, 16 credible mapped genes showed high expression in the brain tissues and were primarily associated with immune function-related biological processes.

CONCLUSIONS

We confirmed the polygenic overlap with mixed directions of allelic effects between PD and BSPs and identified multiple shared genomic loci and risk genes, which are likely related to immune-related biological processes. These findings provide insight into the complex genetic architecture associated with PD. © 2023 International Parkinson and Movement Disorder Society.

The genetic relationships between brain structure and schizophrenia

Genetic risks for schizophrenia are theoretically mediated by genetic effects on brain structure but it has been unclear which genes are associated with both schizophrenia and cortical phenotypes. We accessed genome-wide association studies (GWAS) of schizophrenia (N = 69,369 cases; 236,642 controls), and of three magnetic resonance imaging (MRI) metrics (surface area, cortical thickness, neurite density index) measured at 180 cortical areas (N = 36,843, UK Biobank). Using Hi-C-coupled MAGMA, 61 genes were significantly associated with both schizophrenia and one or more MRI metrics. Whole genome analysis with partial least squares demonstrated significant genetic covariation between schizophrenia and area or thickness of most cortical regions. Genetic similarity between cortical areas was strongly coupled to their phenotypic covariance, and genetic covariation between schizophrenia and brain phenotypes was strongest in the hubs of structural covariance networks. Pleiotropically associated genes were enriched for neurodevelopmental processes and positionally concentrated in chromosomes 3p21, 17q21 and 11p11. Mendelian randomization analysis indicated that genetically determined variation in a posterior cingulate cortical area could be causal for schizophrenia. Parallel analyses of GWAS on bipolar disorder, Alzheimer's disease and height showed that pleiotropic association with MRI metrics was stronger for schizophrenia compared to other disorders.

Shared Genetic Loci Between Schizophrenia and White Blood Cell Counts Suggest Genetically Determined Systemic Immune Abnormalities

BACKGROUND

Immune mechanisms are indicated in schizophrenia (SCZ). Recent genome-wide association studies (GWAS) have identified genetic variants associated with SCZ and immune-related phenotypes. Here, we use cutting edge statistical tools to identify shared genetic variants between SCZ and white blood cell (WBC) counts and further understand the role of the immune system in SCZ.

STUDY DESIGN

GWAS results from SCZ (patients, n = 53 386; controls, n = 77 258) and WBC counts (n = 56 3085) were analyzed. We applied linkage disequilibrium score regression, the conditional false discovery rate method and the bivariate causal mixture model for analyses of genetic associations and overlap, and 2 sample Mendelian randomization to estimate causal effects.

STUDY RESULTS

The polygenicity for SCZ was 7.5 times higher than for WBC count and constituted 32%-59% of WBC count genetic loci. While there was a significant but weak positive genetic correlation between SCZ and lymphocytes (rg = 0.05), the conditional false discovery rate method identified 383 shared genetic loci (53% concordant effect directions), with shared variants encompassing all investigated WBC subtypes: lymphocytes, n = 215 (56% concordant); neutrophils, n = 158 (49% concordant); monocytes, n = 146 (47% concordant); eosinophils, n = 135 (56% concordant); and basophils, n = 64 (53% concordant). A few causal effects were suggested, but consensus was lacking across different Mendelian randomization methods. Functional analyses indicated cellular functioning and regulation of translation as overlapping mechanisms.

CONCLUSIONS

Our results suggest that genetic factors involved in WBC counts are associated with the risk of SCZ, indicating a role of immune mechanisms in subgroups of SCZ with potential for stratification of patients for immune targeted treatment.

The relationship between cannabis use, schizophrenia, and bipolar disorder: a genetically informed study

BACKGROUND

The relationship between psychotic disorders and cannabis use is heavily debated. Shared underlying genetic risk is one potential explanation. We investigated the genetic association between psychotic disorders (schizophrenia and bipolar disorder) and cannabis phenotypes (lifetime cannabis use and cannabis use disorder).

METHODS

We used genome-wide association summary statistics from individuals with European ancestry from the Psychiatric Genomics Consortium, UK Biobank, and International Cannabis Consortium. We estimated heritability, polygenicity, and discoverability of each phenotype. We performed genome-wide and local genetic correlations. Shared loci were identified and mapped to genes, which were tested for functional enrichment. Shared genetic liabilities to psychotic disorders and cannabis phenotypes were explored using causal analyses and polygenic scores, using the Norwegian Thematically Organized Psychosis cohort.

FINDINGS

Psychotic disorders were more heritable than cannabis phenotypes and more polygenic than cannabis use disorder. We observed positive genome-wide genetic correlations between psychotic disorders and cannabis phenotypes (range 0·22-0·35) with a mixture of positive and negative local genetic correlations. Three to 27 shared loci were identified for the psychotic disorder and cannabis phenotype pairs. Enrichment of mapped genes implicated neuronal and olfactory cells as well as drug-gene targets for nicotine, alcohol, and duloxetine. Psychotic disorders showed a causal effect on cannabis phenotypes, and lifetime cannabis use had a causal effect on bipolar disorder. Of 2181 European participants from the Norwegian Thematically Organized Psychosis cohort applied in polygenic risk score analyses, 1060 (48·6%) were females and 1121 (51·4%) were males (mean age 33·1 years [SD 11·8]). 400 participants had bipolar disorder, 697 had schizophrenia, and 1044 were healthy controls. Within this sample, polygenic scores for cannabis phenotypes predicted psychotic disorders independently and improved prediction beyond the polygenic score for the psychotic disorders.

INTERPRETATION

A subgroup of individuals might have a high genetic risk of developing a psychotic disorder and using cannabis. This finding supports public health efforts to reduce cannabis use, particularly in individuals at high risk or patients with psychotic disorders. Identified shared loci and their functional implications could facilitate development of novel treatments.

FUNDING

US National Institutes of Health, the Research Council Norway, the South-East Regional Health Authority, Stiftelsen Kristian Gerhard Jebsen, EEA-RO-NO-2018-0535, European Union's Horizon 2020 Research and Innovation Programme, the Marie Skłodowska-Curie Actions, and University of Oslo Life Science.

Genome-wide association study of cerebellar white matter microstructure and genetic overlap with common brain disorders

BACKGROUND

The cerebellum is recognized as being involved in neurocognitive and motor functions with communication with extra-cerebellar regions relying on the white matter integrity of the cerebellar peduncles. However, the genetic determinants of cerebellar white matter integrity remain largely unknown.

METHODS

We conducted a genome-wide association analysis of cerebellar white matter microstructure using diffusion tensor imaging data from 25,415 individuals from UK Biobank. The integrity of cerebellar white matter microstructure was measured as fractional anisotropy (FA) and mean diffusivity (MD). Identification of independent genomic loci, functional annotation, and tissue and cell-type analysis were conducted with FUMA. The linkage disequilibrium score regression (LDSC) was used to calculate genetic correlations between cerebellar white matter microstructure and regional brain volumes and brain-related traits. Furthermore, the conditional/conjunctional false discovery rate (condFDR/conjFDR) framework was employed to identify the shared genetic basis between cerebellar white matter microstructure and common brain disorders.

RESULTS

We identified 11 genetic loci (P < 8.3 × 10^-9) and 86 genes associated with cerebellar white matter microstructure. Further functional enrichment analysis implicated the involvement of GABAergic neurons and cholinergic pathways. Significant polygenetic overlap between cerebellar white matter tracts and their anatomically connected or adjacent brain regions was detected. In addition, we report the overall genetic correlation and specific loci shared between cerebellar white matter microstructural integrity and brain-related traits, including movement, cognitive, psychiatric, and cerebrovascular categories.

CONCLUSIONS

Collectively, this study represents a step forward in understanding the genetics of cerebellar white matter microstructure and its shared genetic etiology with common brain disorders.