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There is increasing evidence that endogenous retroviruses (ERVs) play a significant role in central nervous system diseases, including amyotrophic lateral sclerosis (ALS). Studies of ALS have consistently identified retroviral enzyme reverse transcriptase activity in patients. Evidence indicates that ERVs are the cause of reverse transcriptase activity in ALS, but it is currently unclear whether this is due to a specific ERV locus or a family of ERVs. We employed a combination of bioinformatic methods to identify whether specific ERVs or ERV families are associated with ALS. Using the largest post-mortem RNA-sequence datasets available we selectively identified ERVs that closely resembled full-length proviruses. In the discovery dataset there was one ERV locus (HML6_3p21.31c) that showed significant increased expression in post-mortem motor cortex tissue after multiple-testing correction. Using six replication post-mortem datasets we found HML6_3p21.31c was consistently upregulated in ALS in motor cortex and cerebellum tissue. In addition, HML6_3p21.31c showed significant co-expression with cytokine binding and genes involved in EBV, HTLV-1 and HIV type-1 infections. There were no significant differences in ERV family expression between ALS and controls. Our results support the hypothesis that specific ERV loci are involved in ALS pathology.

Background: The COVID-19 pandemic is a novel population-level stressor. As such, it is important to examine pandemic-related changes in mental health and to identify which individuals are at greatest risk of worsening symptoms.Methods: Online questionnaires were administered to 34,465 individuals in the UK, recruited from existing cohorts or via social media. Around one third (n = 12,718) with prior diagnoses of depression or anxiety completed pre-pandemic mental health assessments, allowing prospective investigation of symptom change. We examined changes in depression, anxiety and PTSD symptoms using prospective, retrospective and global ratings of change assessments. We also examined the effect of key risk factors on changes in symptoms. Research in contextEvidence before this study We conducted a literature search (PubMed, Scopus) with the terms "mental*" or "psychiatr*" and "covid*" or "coronavirus" published before 8th February 2021. This resulted in 4,573 unique references, but only 15 longitudinal studies examining changes in symptoms of mental health conditions from before to during the COVID-19 pandemic. Results to date are mixed. Some studies found increases in mental distress, some found increases in either depression or anxiety, and others saw no observable change in symptoms.Examining individual-level risk factors, heightened vulnerability to worsening mental health during the pandemic has been demonstrated among young people, females, individuals with lower incomes/financial problems and among health care or key workers. Only one previous study used a large sample with prior mental health diagnoses to examine changes in anxiety and depression. This study showed that having a prior mental health diagnosis was associated with higher levels of perceived worsening of mental health but, when examining actual prospectively measured symptoms, a prior mental health diagnosis was actually associated with a lower likelihood of symptom worsening, compared to no prior diagnosis. This discrepancy across measures requires further investigation in order to understand the nature of changing mental health during the COVID-19 pandemic. Added value of this studyThis study prospectively examined changes in symptoms of depression, anxiety and PTSD in a large UK-based sample of individuals with prior depression or anxiety. Analyses were supplemented with data from additional cohorts to examine individual difference risk factors with greater statistical power. Inclusion of both prospectively measured and retrospectively estimated changes in symptoms, as well as ratings of perceived change in mental health, allowed closer examination of discrepancies in subjective experience versus actual objective change in symptoms.people who are students or are unemployed. Additionally, discrepancies in estimated symptom change across prospective and retrospective measures highlight the importance of using prospectively collected data to examine longitudinal changes.

Background: The COVID-19 pandemic is a novel population-level stressor. As such, it is important to examine pandemic-related changes in mental health and to identify which individuals are at greatest risk of worsening symptoms.Methods: Online questionnaires were administered to 34,465 individuals in the UK, recruited from existing cohorts or via social media. Around one third (n = 12,718) with prior diagnoses of depression or anxiety completed pre-pandemic mental health assessments, allowing prospective investigation of symptom change. We examined changes in depression, anxiety and PTSD symptoms using prospective, retrospective and global ratings of change assessments. We also examined the effect of key risk factors on changes in symptoms.Outcomes: Prospective analyses showed small decreases in depression (PHQ-9: - .43 points) and anxiety symptoms (GAD-7: -.33 points), and increases in PTSD symptoms (PCL-6: .22 points). Conversely, retrospective analyses demonstrated large significant increases in depression (2.40 points) and anxiety symptoms (1.97 points) and 55% reported worsening mental health since the beginning of the pandemic on a global change rating. Using both prospective and retrospective symptom measures, regression analyses demonstrated that worsening depression, anxiety and PTSD symptoms were associated with i) prior mental health diagnoses, ii) female gender; iii) young age, and iv) unemployed or student status.Interpretation: We highlight the effect of prior mental health diagnoses on worsening mental health during the pandemic and confirm previously-reported sociodemographic risk factors. Discrepancies between prospective and retrospective measures of changes in mental health may be related to recall bias underestimating prior symptom severity.

Background: Generalized anxiety and depression are extremely prevalent and debilitating. There is evidence for age and sex variability in symptoms of depression, but despite comorbidity it is unclear whether this extends to anxiety symptomatology. Studies using questionnaire sum scores typically fail to address this phenotypic complexity. Method: We conducted exploratory and confirmatory factor analyses on Generalized Anxiety Disorder (GAD-7) and Patient Health Questionnaire (PHQ-9) items to identify latent factors of anxiety and depression in participants from the Genetic Links to Anxiety and Depression Study (N = 35,637; 16-93 years). We assessed ageand sex-related variability in latent factors and individual symptoms using multiple logistic regression. Results: Four factors of mood, worry, motor, and somatic symptoms were identified (comparative fit index [CFI] = 0.99, Tucker-Lewis Index [TLI] = 0.99, root mean square error of approximation [RMSEA] = 0.07, standardized root mean square residuals [SRMR] = 0.04). Symptoms of irritability (odds ratio [OR] = 0.81) were most strongly associated with younger age, and sleep change (OR = 1.14) with older age.Males were more likely to report mood and motor symptoms (p < .001) and females to report somatic symptoms (p < .001). Conclusion:Significant age and sex variability suggest that classic diagnostic criteria reflect the presentation most commonly seen in younger males. This study provides avenues for diagnostic adaptation and factor-specific interventions.

The Mood Disorder Questionnaire (MDQ) is a common screening tool for bipolar disorder that assesses manic symptoms. Its utility for genetic studies of mania or bipolar traits has not been fully examined. We psychometrically compared the MDQ to self‐reported bipolar disorder in participants from the United Kingdom National Institute of Health and Care Research Mental Health BioResource. We conducted genome‐wide association studies of manic symptom quantitative traits and symptom subgroups, derived from the MDQ items (N = 11,568–19,859). We calculated genetic correlations with bipolar disorder and other psychiatric and behavioral traits. The MDQ screener showed low positive predictive value (0.29) for self‐reported bipolar disorder. Neither concurrent nor lifetime manic symptoms were genetically correlated with bipolar disorder. Lifetime manic symptoms had a highest genetic correlation (rg = 1.0) with posttraumatic stress disorder although this was not confirmed by within‐cohort phenotypic correlations (rp = 0.41). Other significant genetic correlations included attention deficit hyperactivity disorder (rg = 0.69), insomnia (rg = 0.55), and major depressive disorder (rg = 0.42). Our study adds to existing literature questioning the MDQ's validity and suggests it may capture symptoms of general distress or psychopathology, rather than hypomania/mania specifically, in at‐risk populations.

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BackgroundResearch to understand the complex aetiology of depressive and anxiety disorders often requires large sample sizes, but this comes at a cost. Large-scale studies are typically unable to utilise “gold standard” phenotyping methods, instead relying on remote, self-report measures to ascertain phenotypes.AimsTo assess the comparability of two commonly used phenotyping methods for depression and anxiety disorders.MethodParticipants from the Genetic Links to Anxiety and Depression (GLAD) Study (N = 37,419) completed an online questionnaire including detailed symptom reports. They received a lifetime algorithm-based diagnosis based on DSM-5 criteria for major depressive disorder (MDD), generalised anxiety disorder (GAD), specific phobia, social anxiety disorder, panic disorder, and agoraphobia. Any anxiety disorder included participants with at least one anxiety disorder. Participants also responded to single-item questions asking whether they had ever been diagnosed with these disorders by health professionals.ResultsAgreement for algorithm-based and single-item diagnoses was high for MDD and any anxiety disorder but low for the individual anxiety disorders. For GAD, many participants with a single-item diagnosis did not receive an algorithm-based diagnosis. In contrast, algorithm-based diagnoses of the other anxiety disorders were more common than the single-item diagnoses.ConclusionsThe two phenotyping methods were comparable for MDD and any anxiety disorder cases. However, frequencies of specific anxiety disorders varied depending on the method. Single-item diagnoses classified most participants as having GAD whereas algorithm-based diagnoses were more evenly distributed across the anxiety disorders. Future investigations of specific anxiety disorders should use algorithm-based or other robust phenotyping methods.