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The genetic make-up of an individual contributes to the susceptibility and response to viral infection. Although environmental, clinical and social factors have a role in the chance of exposure to SARS-CoV-2 and the severity of COVID-191,2, host genetics may also be important. Identifying host-specific genetic factors may reveal biological mechanisms of therapeutic relevance and clarify causal relationships of modifiable environmental risk factors for SARS-CoV-2 infection and outcomes. We formed a global network of researchers to investigate the role of human genetics in SARS-CoV-2 infection and COVID-19 severity. Here we describe the results of three genome-wide association meta-analyses that consist of up to 49,562 patients with COVID-19 from 46 studies across 19 countries. We report 13 genome-wide significant loci that are associated with SARS-CoV-2 infection or severe manifestations of COVID-19. Several of these loci correspond to previously documented associations to lung or autoimmune and inflammatory diseases3,4,5,6,7. They also represent potentially actionable mechanisms in response to infection. Mendelian randomization analyses support a causal role for smoking and body-mass index for severe COVID-19 although not for type II diabetes. The identification of novel host genetic factors associated with COVID-19 was made possible by the community of human genetics researchers coming together to prioritize the sharing of data, results, resources and analytical frameworks. This working model of international collaboration underscores what is possible for future genetic discoveries in emerging pandemics, or indeed for any complex human disease.
Genetic generalised epilepsy (GGE) is the most common form of genetic epilepsy, accounting for 20% of all epilepsies. Genomic copy number variations (CNVs) constitute important genetic risk factors of common GGE syndromes. In our present genome-wide burden analysis, large (≥ 400 kb) and rare (< 1%) autosomal microdeletions with high calling confidence (≥ 200 markers) were assessed by the Affymetrix SNP 6.0 array in European case-control cohorts of 1,366 GGE patients and 5,234 ancestry-matched controls. We aimed to: 1) assess the microdeletion burden in common GGE syndromes, 2) estimate the relative contribution of recurrent microdeletions at genomic rearrangement hotspots and non-recurrent microdeletions, and 3) identify potential candidate genes for GGE. We found a significant excess of microdeletions in 7.3% of GGE patients compared to 4.0% in controls (P = 1.8 x 10-7; OR = 1.9). Recurrent microdeletions at seven known genomic hotspots accounted for 36.9% of all microdeletions identified in the GGE cohort and showed a 7.5-fold increased burden (P = 2.6 x 10-17) relative to controls. Microdeletions affecting either a gene previously implicated in neurodevelopmental disorders (P = 8.0 x 10-18, OR = 4.6) or an evolutionarily conserved brain-expressed gene related to autism spectrum disorder (P = 1.3 x 10-12, OR = 4.1) were significantly enriched in the GGE patients. Microdeletions found only in GGE patients harboured a high proportion of genes previously associated with epilepsy and neuropsychiatric disorders (NRXN1, RBFOX1, PCDH7, KCNA2, EPM2A, RORB, PLCB1). Our results demonstrate that the significantly increased burden of large and rare microdeletions in GGE patients is largely confined to recurrent hotspot microdeletions and microdeletions affecting neurodevelopmental genes, suggesting a strong impact of fundamental neurodevelopmental processes in the pathogenesis of common GGE syndromes.
Sleep impairments are a hallmark of acute bipolar disorder (BD) episodes and are present even in the euthymic state. Studying healthy subjects who are vulnerable to BD can improve our understanding of whether sleep impairment is a predisposing factor. Therefore, we investigated whether vulnerability to BD, dimensionally assessed by the hypomanic personality scale (HPS), is associated with sleep disturbances in healthy subjects. We analyzed participants from a population-based cohort who had completed the HPS and had either a 7-day actigraphy recording or a Pittsburgh sleep quality index (PSQI) assessment. In addition, subjects had to be free of confounding diseases or medications. This resulted in 771 subjects for actigraphy and 1766 for PSQI analyses. We found strong evidence that higher HPS scores are associated with greater intraindividual sleep variability, more disturbed sleep and more daytime sleepiness. In addition, factor analyses revealed that core hypomanic features were especially associated with self-reported sleep impairments. Results support the assumption of disturbed sleep as a possibly predisposing factor for BD and suggest sleep improvement as a potential early prevention target.
Background: The COVID-19 pandemic has spurred large-scale, inter-institutional research efforts. To enable these efforts, the German Corona Consensus (GECCO) dataset has been developed previously as a harmonized, interoperable collection of the most relevant data elements for COVID-19-related patient research. As GECCO has been developed as a compact core dataset across all medical fields, the focused research within particular medical domains demanded the definition of extension modules that include those data elements that are most relevant to the research performed in these individual medical specialties.
Main body: We created GECCO extension modules for the immunization, pediatrics, and cardiology domains with respect to the pandemic requests. The data elements included in each of these modules were selected in a consensus-based process by working groups of medical experts from the respective specialty to ensure that the contents are aligned with the research needs of the specialty. The selected data elements were mapped to international standardized vocabularies and data exchange specifications were created using HL7 FHIR profiles on the appropriate resources. All steps were performed in close interdisciplinary collaboration between medical domain experts, medical information scientists and FHIR developers. The profiles and vocabulary mappings were syntactically and semantically validated in a two-stage process. In that way, we defined dataset specifications for a total number of 23 (immunization), 59 (pediatrics), and 50 (cardiology) data elements that augment the GECCO core dataset. We created and published implementation guides and example implementations as well as dataset annotations for each extension module.
Conclusions: We here present extension modules for the GECCO core dataset that contain data elements most relevant to COVID-19-related patient research in immunization, pediatrics and cardiology. These extension modules were defined in an interdisciplinary, iterative, consensus-based approach that may serve as a blueprint for the development of further dataset definitions and GECCO extension modules. The here developed GECCO extension modules provide a standardized and harmonized definition of specialty-related datasets that can help to enable inter-institutional and cross-country COVID-19 research in these specialties.
Background The COVID-19 pandemic has spurred large-scale, inter-institutional research efforts. To enable these efforts, researchers must agree on dataset definitions that not only cover all elements relevant to the respective medical specialty but that are also syntactically and semantically interoperable. Following such an effort, the German Corona Consensus (GECCO) dataset has been developed previously as a harmonized, interoperable collection of the most relevant data elements for COVID-19-related patient research. As GECCO has been developed as a compact core dataset across all medical fields, the focused research within particular medical domains demands the definition of extension modules that include those data elements that are most relevant to the research performed in these individual medical specialties.
Objective To (i) specify a workflow for the development of interoperable dataset definitions that involves a close collaboration between medical experts and information scientists and to (ii) apply the workflow to develop dataset definitions that include data elements most relevant to COVID-19-related patient research in immunization, pediatrics, and cardiology.
Methods We developed a workflow to create dataset definitions that are (i) content-wise as relevant as possible to a specific field of study and (ii) universally usable across computer systems, institutions, and countries, i.e., interoperable. We then gathered medical experts from three specialties (immunization, pediatrics, and cardiology) to the select data elements most relevant to COVID-19-related patient research in the respective specialty. We mapped the data elements to international standardized vocabularies and created data exchange specifications using HL7 FHIR. All steps were performed in close interdisciplinary collaboration between medical domain experts and medical information scientists. The profiles and vocabulary mappings were syntactically and semantically validated in a two-stage process.
Results We created GECCO extension modules for the immunization, pediatrics, and cardiology domains with respect to the pandemic requests. The data elements included in each of these modules were selected according to the here developed consensus-based workflow by medical experts from the respective specialty to ensure that the contents are aligned with the respective research needs. We defined dataset specifications for a total number of 48 (immunization), 150 (pediatrics), and 52 (cardiology) data elements that complement the GECCO core dataset. We created and published implementation guides and example implementations as well as dataset annotations for each extension module.
Conclusions These here presented GECCO extension modules, which contain data elements most relevant to COVID-19-related patient research in immunization, pediatrics and cardiology, were defined in an interdisciplinary, iterative, consensus-based workflow that may serve as a blueprint for the development of further dataset definitions. The GECCO extension modules provide a standardized and harmonized definition of specialty-related datasets that can help to enable inter-institutional and cross-country COVID-19 research in these specialties.
Based on Eysenck’s pioneering work, CNS arousal has long been considered an encouraging biological candidate that may explain individual differences in human personality. Yet, results from empirical studies remained inconclusive. Notably, the vast majority of published results have been derived from small samples, and EEG alpha power has usually served as exclusive indicator for CNS arousal. In this study, we selected N = 468 individuals of the LIFE-Adult cohort and investigated the associations between the Big Five personality traits and CNS arousal by using the low-resolution electromagnetic tomography-based analysis tool VIGALL. Our analyses revealed that subjects who reported higher levels of extraversion and openness to experience, respectively, exhibited lower levels of CNS arousal in the resting state. Bayesian and frequentist analysis results were especially convincing for openness to experience. Among the lower-order personality traits, we obtained strongest evidence for neuroticism facet ‘impulsivity’ and reduced CNS arousal. We regard these findings as well in line with the postulations of Eysenck and Zuckerman and consistent with the assumptions of the ‘arousal regulation model’. Our results also agree with meta-analytically derived effect sizes in the field of individual differences research, highlighting the need for large studies with at least several hundreds of subjects.
Relationship between regional white matter hyperintensities and alpha oscillations in older adults
(2021)
Aging is associated with increased white matter hyperintensities (WMHs) and with the alterations of alpha oscillations (7–13 Hz). However, a crucial question remains, whether changes in alpha oscillations relate to aging per se or whether this relationship is mediated by age-related neuropathology like WMHs. Using a large cohort of cognitively healthy older adults (N=907, 60-80 years), we assessed relative alpha power, alpha peak frequency, and long-range temporal correlations (LRTC) from resting-state EEG. We further associated these parameters with voxel-wise WMHs from 3T MRI. We found that a higher prevalence of WMHs in the superior and posterior corona radiata as well as in the thalamic radiation was related to elevated alpha power, with the strongest association in the bilateral occipital cortex. In contrast, we observed no significant relation of the WMHs probability with alpha peak frequency and LRTC. Finally, higher age was associated with elevated alpha power via total WMH volume. Although an increase in alpha oscillations due to WMH can have a compensatory nature, we rather suggest that an elevated alpha power is a consequence of WMH affecting a spatial organization of alpha sources.
Background: Cytokines are mediators of inflammation that contribute to a low-grade inflammation in different disorders like major depression and obesity. It still remains unclear which psychological and medical factors interact with cytokine regulation. In the current investigation, the association between levels of pro-and anti-inflammatory cytokines and anthropometrics, mood state (depressiveness), physical activity and sleep were investigated in a sample of community-dwelled adults.
Methods: Forty-nine subjects met the inclusion criteria for analyses and were assessed at two time-points (baseline (T1) and follow-up (T2), average T1-T2-interval = 215 days). Serum cytokine measures included the pro-inflammatory cytokines interleukin (IL)-2, IL-12, IFN-γ and TNF-α, the anti-inflammatory cytokines IL-4, IL-5, IL-10 and IL-13 and the granulocyte-macrophage colony-stimulating factor (GM-CSF); anthropometrics were assessed via physical examination, depressiveness was assessed via Beck Depression Inventory (BDI)2, parameters of physical activity (steps, METs) and sleep (night/total sleep duration) were measured via a 1-week actigraphy.
Results: Correlation analyses showed low-to moderate significant relationships between the majority of cytokines and the BDI2 at T1, positive correlation with weight and BMI at T1 and T2, and negative correlations with the number of steps and METs at T2 and T2. Regression analyses for T1 revealed that the BDI2 score was the best positive predictor for the concentrations of all nine cytokines, followed by the number of steps and the nightsleep duration as negative predictors. At T2, the amount of steps was found to be negatively associated with IL-4, IL5, IL-10, GM-CSF, IFN-γ, and TNF-α, whereas the BMI could significantly predict IL-12 and IL-13. The BDI2-score was not significantly associated with any of the cytokines. No associations could be found between dynamics in cytokines from T1 and T2 and changes in any of the variables.
Discussion: The present results indicate an influence of physical activity, subjective well-being and body composition on inflammatory mediators. Since there was no standardized intervention targeting the independent variables between T1 and T2, no assumptions on causality can be drawn from the association results.
Background: Internet- and mobile-based interventions are most efficacious in the treatment of depression when they involve some form of guidance, but providing guidance requires resources such as trained personnel, who might not always be available (eg, during lockdowns to contain the COVID-19 pandemic).
Objective: The current analysis focuses on changes in symptoms of depression in a guided sample of patients with depression who registered for an internet-based intervention, the iFightDepression tool, as well as the extent of intervention use, compared to an unguided sample. The objective is to further understand the effects of guidance and adherence on the intervention’s potential to induce symptom change.
Methods: Log data from two convenience samples in German routine care were used to assess symptom change after 6-9 weeks of intervention as well as minimal dose (finishing at least two workshops). A linear regression model with changes in Patient Health Questionnaire (PHQ-9) score as a dependent variable and guidance and minimal dose as well as their interaction as independent variables was specified.
Results: Data from 1423 people with symptoms of depression (n=940 unguided, 66.1%) were included in the current analysis. In the linear regression model predicting symptom change, a significant interaction of guidance and minimal dose revealed a specifically greater improvement for patients who received guidance and also worked with the intervention content (β=–1.75, t=–2.37, P=.02), while there was little difference in symptom change due to guidance in the group that did not use the intervention. In this model, the main effect of guidance was only marginally significant (β=–.53, t=–1.78, P=.08).
Conclusions: Guidance in internet-based interventions for depression is not only an important factor to facilitate adherence, but also seems to further improve results for patients adhering to the intervention compared to those who do the same but without guidance.
Background and purpose: The ENIGMA-EEG working group was established to enable large-scale international collaborations among cohorts that investigate the genetics of brain function measured with electroencephalography (EEG). In this perspective, we will discuss why analyzing the genetics of functional brain activity may be crucial for understanding how neurological and psychiatric liability genes affect the brain. Methods: We summarize how we have performed our currently largest genome-wide association study of oscillatory brain activity in EEG recordings by meta-analyzing the results across five participating cohorts, resulting in the first genome-wide significant hits for oscillatory brain function located in/near genes that were previously associated with psychiatric disorders. We describe how we have tackled methodological issues surrounding genetic meta-analysis of EEG features. We discuss the importance of harmonizing EEG signal processing, cleaning, and feature extraction. Finally, we explain our selection of EEG features currently being investigated, including the temporal dynamics of oscillations and the connectivity network based on synchronization of oscillations. Results: We present data that show how to perform systematic quality control and evaluate how choices in reference electrode and montage affect individual differences in EEG parameters. Conclusion: The long list of potential challenges to our large-scale meta-analytic approach requires extensive effort and organization between participating cohorts; however, our perspective shows that these challenges are surmountable. Our perspective argues that elucidating the genetic of EEG oscillatory activity is a worthwhile effort in order to elucidate the pathway from gene to disease liability.
