Rolf Adolfsson

Fluid biomarkers detectable in cerebrospinal fluid (CSF) and blood offer critical insights into neurodegenerative disorders ^1-5 , such as Alzheimer’s disease, frontotemporal dementia, and dementia with Lewy bodies ^6-9 . Previous proximity-extension assay (PEA) studies have discovered 42 CSF biomarkers for distinguishing the aforementioned dementias ^6-9 ; however, their disease- and matrix-specificity remain understudied. We developed and employed a federated meta-analysis framework to explore these biomarkers by analysing results from 65 case-control cohorts, including 20,576 samples (CSF n = 8,766, blood n = 11,810) covering 41 neurological and psychiatric conditions. Our comprehensive analysis identified both distinct and shared patterns of protein alterations across the spectrum of brain disorders, with marked differences observed between CSF and blood profiles. While several biomarkers demonstrated significant changes within their primary disease contexts, we also observed convergent dysregulation of proteins related to neuroinflammation and tau pathology across multiple conditions. These findings highlight the importance of fluid-specific biomarker assessment and provide a resource to inform the development and contextualization of novel biomarkers for brain disorders.

Most genetic variants associated with complex heritability phenotypes lie in non-coding regions and are thought to influence disease risk by regulating gene expression. However, most transcriptome-wide association approaches primarily model local ( cis ) genetic effects, leaving much of gene regulation unexplained. Here, we show that incorporating distal ( trans ) regulatory effects improves the prediction of gene expression and the identification of disease-associated genes. Using RNA sequencing data from six human post-mortem brain regions, we developed INGENE and MODULE, two models capturing the combined influence of candidate trans- acting variants within gene coexpression networks. Integrating these models with conventional cis -based predictors improved gene expression imputation (maximum likelihood estimation, α = 0.05) for 18,744 genes across regions. Applying this framework to Psychiatric Genomics Consortium wave 3 genotypes identified 766 genes associated with schizophrenia ( P _FDR < 0.01), including 641 not previously reported by transcriptome-wide analyses. These findings highlight the contribution of distal regulatory mechanisms and gene network interactions to schizophrenia risk.

Non-coding genetic variants statistically associated with complex heritability phenotypes are thought to act primarily through transcriptome regulatory mechanisms. Predictions of gene expression in tissue like the human brain traditionally rely primarily on cis -eQTLs. Here, we introduce INGENE and MODULE, trans -eQTLs models designed to enhance the prediction of gene expression by capturing the collective impact of candidate trans -eQTLs acting within co-expression networks. Exploiting RNA-seq data in six post-mortem brain regions (amygdala, caudate nucleus, dorsal/subgenual anterior cingulate cortex, dorsolateral prefrontal cortex, and hippocampus), we validate our models on two testing datasets, demonstrating increased gene predictability compared to both an original cis -based model and to EpiXcan, the leading benchmark in cis -model performance. Integration of cis - and trans -predictions significantly improves gene-level expression imputation (MLE α= 0.05) for 18,744 genes across the six brain regions considered. Applying cis and trans models to PGC wave 3 genotypes identifies 766 SCZ-associated genes across brain regions (pFDR < .01), emphasizing the complementary nature of cis and trans predictions in trait association discovery. Of these genes, 641 represent novel transcriptome-wide associations with schizophrenia, highlighting the role of trans -heritability and genetic interactions underlying risk for this disorder, in addition to further supporting 125 previous candidates.

Psychiatric conditions share common genes, but mechanisms that differentiate diagnoses remain unclear. We present a multidimensional framework for functional analysis of rare copy number variants (CNVs) across 6 diagnostic categories, including schizophrenia (SCZ), autism (ASD), bipolar disorder (BD), depression (MDD), PTSD, and ADHD (N = 574,965). Using gene-set burden analysis (GSBA), we tested duplication (DUP) and deletion (DEL) burden across 2,645 functional gene sets defined by the intersections of pathways, cell types, and cortical regions. While diagnoses converge on shared pathways, mixed-effects modeling revealed divergence of pathway effects by cell type, brain region, and gene dosage. Factor analysis identified latent dimensions aligned with clinical axes. A primary factor (F1) captured reciprocal dose-dependent effects of DUP and DEL in SCZ reflecting positive and negative effects in excitatory versus inhibitory neurons and association versus sensory cortex. SCZ and ASD were both strongly aligned with F1 but with opposing directionalities. Orthogonal factors highlighted neuronal versus non-neuronal effects in mood disorders (F2) and differential spatial distributions of DEL effects in ADHD and MDD (F3). High-impact CNVs at 16p11.2 and 22q11.2 were enriched for combinations of cell-type-specific genes involved in pathways consistent with our broader findings. These results reveal molecular and cellular mechanisms that are broadly shared across psychiatric traits but differ between diagnostic categories in context and directionality.

Bipolar disorder is a heritable mental illness with complex etiology. While the largest published genome-wide association study identified 64 bipolar disorder risk loci, the causal SNPs and genes within these loci remain unknown. We applied a suite of statistical and functional fine-mapping methods to these loci and prioritized 17 likely causal SNPs for bipolar disorder. We mapped these SNPs to genes and investigated their likely functional consequences by integrating variant annotations, brain cell-type epigenomic annotations, brain quantitative trait loci and results from rare variant exome sequencing in bipolar disorder. Convergent lines of evidence supported the roles of genes involved in neurotransmission and neurodevelopment, including SCN2A, TRANK1, DCLK3, INSYN2B, SYNE1, THSD7A, CACNA1B, TUBBP5, FKBP2, RASGRP1, FURIN, FES, MED24 and THRA among others in bipolar disorder. These represent promising candidates for functional experiments to understand biological mechanisms and therapeutic potential. Additionally, we demonstrated that fine-mapping effect sizes can improve performance of bipolar disorder polygenic risk scores across diverse populations and present a high-throughput fine-mapping pipeline.

Air pollution is increasingly discussed as a risk factor for dementia, but the biological mechanisms are not yet fully understood. Cellular integrity markers like telomere length are relevant to investigate in relation to air pollution exposure in this context, as they are associated with aging and dementia. Previous studies on air pollution and telomere length have somewhat mixed results, which may partly be due to differences in air pollution modelling, composition, and concentrations. The study aimed to investigate the relationship between source-specific air pollution exposure and telomere length in a low-level air pollution area. Data were obtained from the Betula project, a longitudinal study in Northern Sweden dedicated to studying aging, memory and dementia. A total of 519 participants who were recruited between 1988 and 1995 were included, whose relative telomere length was measured, and who were followed-up with respect to dementia for more than 20 years. To estimate annual mean air pollution concentrations, a dispersion model linked to each participant’s residential address was employed. We conducted linear regression to explore the associations between annual mean air pollution concentrations at participants’ residences and relative leukocyte telomere length. In the whole sample, there was no support for air pollution to affect telomere length, with regression slope estimates close to zero and p-values >0.10. There were tendencies for a positive association of longer telomere length and higher exposure to air pollution among individuals who were later diagnosed with dementia (N = 74), but these findings were not conclusive (p-values >0.10). The findings raise questions about susceptibility to air pollution and the state of the inflammatory response in individuals who later will develop dementia. Given the imprecise nature of these estimates, caution is advised in their interpretation however, and validation in other studies is essential.

Bipolar disorder (BD) is a heritable mental illness with complex etiology. While the largest published genome-wide association study identified 64 BD risk loci, the causal SNPs and genes within these loci remain unknown. We applied a suite of statistical and functional fine-mapping methods to these loci, and prioritized 17 likely causal SNPs for BD. We mapped these SNPs to genes, and investigated their likely functional consequences by integrating variant annotations, brain cell-type epigenomic annotations, brain quantitative trait loci, and results from rare variant exome sequencing in BD. Convergent lines of evidence supported the roles of genes involved in neurotransmission and neurodevelopment including SCN2A, TRANK1, DCLK3, INSYN2B, SYNE1, THSD7A, CACNA1B, TUBBP5, PLCB3, PRDX5, KCNK4, CRTC3, AP001453 . 3, TRPT1, FKBP2, DNAJC4, RASGRP1, FURIN, FES, DPH1, GSDMB, MED24 and THRA in BD. These represent promising candidates for functional experiments to understand biological mechanisms and therapeutic potential. Additionally, we demonstrated that fine-mapping effect sizes can improve performance of BD polygenic risk scores across diverse populations, and present a high-throughput fine-mapping pipeline ( https://github.com/mkoromina/SAFFARI ).

Bipolar disorder is associated with a long range of medical comorbidities, including migraine, diabetes, and cardiovascular disease. Bipolar disorder has also been associated with an increased risk of bone fractures. Osteoporosis is a reduction in bone mineral density, which leads to an increased risk for fragility fractures. Currently there is limited research on the association between bipolar disorder and osteoporosis. We aimed to study the association between high and low bone mineral density in relation to disease and treatment history in a sample of bipolar patients. We found that bipolar patients with high bone mineral density were more often on lithium medication, had a more active lifestyle and expressed lower current disease burden. Low mineral density was not associated with any of the addressed aspects of disease and treatment history. In conclusion our results support that patients on lithium treatment have higher bone mineral density; further studies are needed to address if lithium medication causes an increase in bone mineral density, and lowers the risk of bone fractures in bipolar disorder.