Friederike S. David

Resilience, the ability to adapt positively in the face of adversity, is shaped by combined influences of risk and protective factors. Previous neuroimaging studies on resilience have predominantly focused on single factors, often operationalizing resilience dichotomously as the absence of psychiatric disorders despite adversity. In this prospective magnetic resonance imaging study, we defined resilience as “better-than-expected” depressive symptom severity (Hamilton Depression Rating Scale) relative to cumulative risk across 22 risk and protective variables. Using ridge-regularized regression in N = 1804 participants (955 healthy, 849 depressed) from the Marburg-Münster Affective Disorders Cohort Study, we predicted symptom severity and derived residuals as measures of resilience. Residuals were then used to predict gray matter volume (GMV) and cortical thickness at baseline (T1) and two-year follow-up (T2; N = 808). This approach was complemented by extreme-group comparisons of resilient (better-than-expected outcome) and vulnerable (worse-than-expected outcome) individuals. Cumulative risk explained 49.6% of variance in depressive symptoms at T1 and 40.1% at T2. Residual scores showed moderate temporal stability ( r = 0.32, p < 0.001). Region-of-interest and whole-brain analyses revealed no morphometric associations with resilience at T1. In contrast, higher resilience at T1 predicted lower GMV in the left inferior orbitofrontal gyrus (IOFG) and temporal pole at T2 (ROI, p _FWE(peak) r _partial = 0.18), with no changes in cortical thickness. Taken together, resilience to cumulative risk, defined as better-than-expected depressive symptom severity, was not associated with immediate brain structural differences. However, prospective analyses revealed smaller GMV in the IOFG and temporal pole over time, potentially reflecting greater neural efficiency or delayed biological costs.

Social anxiety disorder (SAD) is a common anxiety disorder (ANX) with moderate heritability that often co-occurs with other mental disorders. Until now, sample sizes in genetic analyses of SAD have been limited, so that the genetic basis of SAD and its subphenotypes is still largely unknown. In a large cohort comprising n = 1,194 SAD patients derived from five German cohorts and n = 3,409 controls from the Heinz Nixdorf Recall Study, we computed polygenic risk scores (PRS) at six p -thresholds using PRSice-2 based on large-scale genome-wide association studies for depression, major depressive disorder (MDD), ANX, schizophrenia (SCZ), bipolar disorder (BD), attention-deficit/hyperactivity disorder (ADHD), anorexia nervosa (AN), autism spectrum disorder (ASD), and alcohol dependence (AD). We used general linear models to examine the association between the PRS and SAD status. In SAD subsamples, we investigated whether the PRS are associated with SAD subphenotypes (i.e., SAD severity, current depressive symptoms, comorbid MDD) using correlation analyses and a general linear model. Results were corrected for multiple testing. The SAD status was significantly associated with PRS for depression, MDD, ANX, SCZ, BD, AN, and ASD ( p _BH -adjusted0.05). Our results support that SAD seems to be genetically highly overlapping with other mental disorders, which might underline a common psychopathological factor. No significant association was found with SAD severity, current depressive symptoms or comorbid MDD. A better understanding of the genetic architecture of SAD may help to develop new diagnostic and treatment approaches.

Resilience, the ability to adapt positively in the face of adversity, is shaped by combined influences of risk and protective factors. Previous neuroimaging studies on resilience have predominantly focused on single factors, often operationalizing resilience dichotomously as the absence of psychiatric disorders despite adversity. In this prospective magnetic resonance imaging study, we defined resilience as “better-than-expected” depressive symptom severity (Hamilton Depression Rating Scale) relative to cumulative risk across 22 risk and protective variables. Using ridge-regularized regression in N = 1804 participants (955 healthy, 849 depressed) from the Marburg-Münster Affective Disorders Cohort Study, we predicted symptom severity and derived residuals as measures of resilience. Residuals were then used to predict gray matter volume (GMV) and cortical thickness at baseline (T1) and two-year follow-up (T2; N = 808). This approach was complemented by extreme-group comparisons of resilient (better-than-expected outcome) and vulnerable (worse-than-expected outcome) individuals. Cumulative risk explained 49.6% of variance in depressive symptoms at T1 and 40.1% at T2. Residual scores showed moderate temporal stability ( r = 0.32, p < 0.001). Region-of-interest and whole-brain analyses revealed no morphometric associations with resilience at T1. In contrast, higher resilience at T1 predicted lower GMV in the left inferior orbitofrontal gyrus (IOFG) and temporal pole at T2 (ROI, p _FWE(peak) < 0.001, r _partial =0.18), with no changes in cortical thickness. Taken together, resilience to cumulative risk, defined as better-than-expected depressive symptom severity, was not associated with immediate brain structural differences. However, prospective analyses revealed smaller GMV in the IOFG and temporal pole over time, potentially reflecting greater neural efficiency or delayed biological costs.

Polygenic scores are nowadays common tools in psychiatric genetic research and are also increasingly applied in clinical settings. In this article, we give an overview of polygenic scores in psychiatric research, focusing on recent trends and novel applications such as using polygenic scores in family studies. Furthermore, we discuss use cases of polygenic scores in clinical studies and clinical practice, for instance using polygenic scores to predict treatment response to medication. While some challenges of polygenic scores are not unique to psychiatry (e.g. limited reliability or transferability across ancestries), we also highlight the specialties of using polygenic scores in psychiatric research and clinical practice (e.g. clinical and biological heterogeneity across and within psychiatric conditions), as well as limitations of other applications beyond research, e.g. using polygenic scores for psychiatric conditions for embryo selection. We further make the case for careful communication of genetics to patients and the general public. We conclude by providing an outlook which includes future directions and applications of polygenic scores in psychiatric research and clinical practice.

Environmental exposures influence the risk of psychiatric disorders, yet the biological mechanisms by which such experiences become embedded in brain structure remain poorly understood. The human cerebral cortex is crucial for cognition and emotional regulation, and variation in cortical thickness (CT) and surface area (SA) is linked to various behavioural and psychiatric traits. Here, we present a large-scale epigenome-wide association study that combines peripheral blood DNA methylation (DNAm) with MRI-derived cortical measures in over 7,400 individuals across 20 cohorts within the ENIGMA consortium. We identify mostly non-overlapping DNAm signatures associated with CT and SA, consistent with their distinct developmental and regulatory architectures. CT-associated CpGs are replicated across independent cohorts and are enriched for environmentally responsive regulatory elements and pathways associated with stress, metabolism, and immune signalling. In contrast, SA-associated CpGs cluster within chromatin-regulatory regions involved in early cortical development. Phenome-wide and Mendelian randomisation analyses reveal pleiotropic associations between DNAm, cortical structure, and psychiatric and cognitive traits. These findings suggest that peripheral DNAm captures environmentally sensitive biological processes that link exposure, cortical organisation, and behavioural vulnerability.

Epstein–Barr virus (EBV) infects approximately 90–95% of the global population ^1,2 and persists in B cells as a lifelong infection ³ . Previous EBV infection is associated with autoimmune and neoplastic disease ⁴ . Still, the biological basis of host control during EBV persistence remains unclear. Here we report the identification of non-genetic and genetic factors that are associated with EBV control during persistent infection. Using blood-based genome sequence data from 486,315 UK Biobank and 336,123 All of Us participants, we identified short-read pairs mapping to the EBV genome in 16.2% and 21.8% of individuals, respectively. EBV read detection (EBVread ⁺ ) reflects increased viral load in blood cells, as shown by orthogonal measurements, and was associated with HIV infection, immunosuppressive drug intake and current smoking. Genome-wide analyses of EBVread ⁺ identified strong associations at the major histocompatibility complex (MHC), including 54 independent human leukocyte antigen (HLA) alleles of MHC classes I and II, and at 27 genomic regions outside MHC. Epistasis with distinct HLA alleles of MHC class I was observed at the ERAP2 locus. Analysis of individuals with EBV-associated diseases ⁴ revealed a higher polygenic burden of EBVread ⁺ for HLA alleles at MHC class I in multiple sclerosis (driven by HLA-A*02:01) and at MHC class II in rheumatoid arthritis. Phenome-wide analyses identified a polygenic overlap of EBVread ⁺ with inflammatory bowel disease, hypothyroidism and type 1 diabetes. Our study establishes by-products of human genome sequencing as a surrogate marker of EBV viral load. This will facilitate investigation and treatment for EBV and other persistent viral infections.

Background: Resilience, the ability to adapt positively in the face of adversity, is shaped by the interplay of risk and protective factors. Previous magnetic resonance imaging (MRI) studies on resilience have predominantly focused on single factors, often operationalizing resilience dichotomously as the absence of psychiatric disorders despite adversity. Methods: In this prospective MRI study, we defined resilience as “better-than-expected” depressive symptom severity (Hamilton Depression Rating Scale) relative to cumulative risk. Using ridge-regularized regression in N=1,804 participants (955 healthy, 849 depressed) from the Marburg-Münster Affective Disorders Cohort Study, we predicted symptom severity and derived residuals as measures of resilience. Residuals were then used to predict gray matter volume (GMV) and cortical thickness at baseline (T1) and two-year follow-up (T2; N=808), using voxel- and surface-based analyses. This approach was complemented by extreme-group comparisons of resilient (better-than-expected outcome) and vulnerable (worse-than-expected outcome) individuals. Results: Cumulative risk explained 51.4% of variance in depressive symptoms at T1 and 44.2% at T2. Residual scores showed moderate stability over time ( r =0.31, p p _FWE(peak) =0.002, partial r =0.18), with no changes in cortical thickness. Conclusion: Resilience to cumulative risk, defined as better-than-expected depressive symptom severity, was not associated with immediate brain structural differences. However, prospective analyses revealed smaller GMV in the IOFG and temporal pole over time, potentially reflecting greater neural efficiency or delayed biological costs.

Major depression (MD) is a leading cause of global disease burden, and both experimental and population-based studies suggest that differences in DNA methylation may be associated with the condition. However, previous DNA methylation studies have, so far, not been widely replicated, suggesting a need for larger meta-analysis studies. Here we conducted a meta-analysis of methylome-wide association analysis for lifetime MD across 18 studies of 24,754 European-ancestry participants (5,443 MD cases) and an East Asian sample (243 cases, 1,846 controls). We identified 15 CpG sites associated with lifetime MD with methylome-wide significance. The methylation score created using the methylome-wide association analysis summary statistics was significantly associated with MD status in an out-of-sample classification analysis (area under the curve 0.53). Methylation score was also associated with five inflammatory markers, with the strongest association found with tumor necrosis factor beta. Mendelian randomization analysis revealed 23 CpG sites potentially causally linked to MD, with 7 replicated in an independent dataset. Our study provides evidence that variations in DNA methylation are associated with MD, and further evidence supporting involvement of the immune system.

Epstein-Barr virus (EBV) is a herpes virus that infects around 90-95% of the global population, and is associated with numerous autoimmune and neoplastic diseases. EBV persists in B cells as a life-long latent infection. Despite its relevance to disease development, the biological basis of host control during EBV latent infection remains unknown. Here, we report the identification of genetic and non-genetic factors that contribute to latent EBV infection control. In the genome sequence (GS) data of blood from 486,315 UK Biobank and 336,123 All of Us participants, we identified short-reads mapping to the EBV genome in 16.2% and 21.8% of individuals, respectively. The detection of EBV-reads (EBVread+) reflected increased viral load, and was associated with HIV infection, intake of immunosuppressive drugs, and current, but not former, smoking. Genome-wide association analyses identified strong associations at the Major Histocompatibility Complex (MHC), including 54 independent HLA-alleles of MHC class I and II, and at 27 genomic regions outside MHC (e.g., loci encompassing ERAP2, CTLA4). Associated genes included genes underlying monogenic susceptibility to EBV infection (e.g., CD70), and novel candidates thereof (e.g., CD226). In an analysis of individuals with EBV-associated diseases, we observed a higher polygenic burden of EBVread+ for HLA-alleles at MHC class I in multiple sclerosis (driven by HLA-A*02:01), and at MHC class II in rheumatoid arthritis (RA). A phenome-wide association analysis identified a polygenic overlap of EBVread+ with inflammatory bowel disease, hypothyroidism, and type I diabetes (T1D). Mendelian randomization analyses suggested potential causal effects of EBVread+ on RA and T1D. Our results will inform mechanistic studies and therapeutic approaches in EBV-associated diseases. More broadly, our study illustrates how by-products of human GS data can be used to investigate persistent viral infections.