Daniel L. McCartney

Background: Pre-existing psychiatric disorders have been associated with the severity of acute respiratory infections, including COVID-19, particularly in hospitalized populations. However, the underlying mechanisms, especially in community-based populations, remain unknown, limiting preparedness for future pandemics. We investigated the role of genetic liability for psychiatric disorders and related traits in COVID-19 and other respiratory infection severity among individuals reporting SARS-CoV-2 testing and available respiratory symptom data. Methods: We included population-based cohort data from Denmark, Estonia, Iceland, Norway, and the United Kingdom (N = 78,507; 62% female; 41% SARS-CoV-2-positive; May 2020—March 2023). Respiratory infection severity was defined by self-reported days bedridden and an eleven-symptom composite score during the acute illness phase. Polygenic scores (PGSs) for eight psychiatric disorders and the personality trait neuroticism indexed genetic liability. Meta-analysed estimates from stepwise-adjusted regression analyses controlling for education, lifestyle, and psychiatric and somatic diagnosis history were used to assess associations between PGSs and acute respiratory infection severity, stratified by COVID-19 status. Results: Higher genetic liability to insomnia, major depression (MD), and neuroticism was consistently associated with longer time bedridden (relative risk ratios per 1 SD change in PGS: 1.07–1.14, 95% confidence intervals [CIs]: 1.03–1.21) and more symptoms (incident rate ratios [IRRs]: 1.02–1.04, 95% CIs: 1.01–1.05) due to COVID-19. PGSs for insomnia, MD, neuroticism, anxiety disorder, bipolar disorder, and schizophrenia were also linked to higher non-COVID-19 symptom count (IRRs: 1.01–1.05, 95% CIs: 1.00–1.07). These associations remained robust after adjustment for lifestyle, education and medical comorbidities. Individuals in the highest MD PGS decile reported 20% more symptoms in non-COVID-19 illness and 10% more in COVID-19 compared with the bottom decile. No consistent associations were detected with attention-deficit/hyperactivity disorder, alcohol dependence and post-traumatic stress disorder. No genetic link was observed with ischaemic heart disease or type 2 diabetes. Conclusions: Shared genetics may contribute to the link between psychiatric conditions and respiratory infection severity in community settings. Our results underscore the role of psychiatric genetic liability, beyond diagnosed psychiatric disorders, in contributing to both COVID-19 and other respiratory infection severity. These findings provide insights that may improve future risk stratification and public health strategies targeting respiratory viruses.

Genetic and environmental factors contribute to depression. Among the latter, early life adversity and immune dysregulation have been consistently linked with depression. Childhood maltreatment (CM) is believed to induce immune dysregulation later in life. However, it is not known how CM might interact with genetic immune factors to contribute to the occurrence of depression. We investigated how genetic variability in 2370 genes from 20 immune pathways associates with a broadly-defined lifetime depression phenotype at gene- and pathway-level, and how this variability interacts with CM. Depression analysis was carried out in 13,309 individuals (1867 cases) from Generation Scotland (GS). CM interaction analysis was carried out in a subset of 749 individuals (99 cases) from GS and an independent sample of 509 individuals (96 cases) from the German BiDirect (BD) Study for which both genetic and CM data was available. Interactions with different types of CM were tested using the subscales of the childhood trauma questionnaire (CTQ). These results were meta-analyzed to obtain general gene-CM interactions. We found association of the GHR gene (false discovery rate –FDR– = 0.03, z = 4.2) and Reactome “RUNX1-regulated transcription of genes involved in myeloid cell differentiation pathway” (FDR = 0.016, beta = 1.2) with depression in GS. After meta-analysis, 56 immune gene-CM interactions were associated with depression (FDR < 0.05) in both GS and BD. These exert functions in hematopoiesis, pathogen recognition and stress responses, among others. Network analysis suggested macrophages as main expressing cell types. Our results underscore the involvement of hematopoietic alterations and immune gene-CM interactions in the development of depression.

Recent epigenome-wide studies have identified a large number of genomic regions that consistently exhibit changes in their methylation status with aging across diverse populations, but the functional consequences of these changes are largely unknown. On the other hand, transcriptomic changes are more easily interpreted than epigenetic alterations, but previously identified age-related gene expression changes have shown limited replicability across populations. Here, we develop an approach that leverages high-resolution multi-omic data for an integrative analysis of epigenetic and transcriptomic age-related changes and identify genomic regions associated with both epigenetic and transcriptomic age-dependent changes in blood. Our results show that these multi-omic aging genes in blood are enriched for adaptive immune functions, replicate more robustly across diverse populations and are more strongly associated with aging-related outcomes compared to the genes identified using epigenetic or transcriptomic data alone. These multi-omic aging genes may serve as targets for epigenetic editing to facilitate cellular rejuvenation.

Background: Recruitment to population-based health studies remains challenging, with difficulties meeting target participant numbers, biosample returns, and achieving a representative sample. Few studies provide evaluations of traditional and web-based recruitment methods particularly for studies with broad inclusion criteria and extended recruitment periods. Generation Scotland (GS) is a family-based cohort study that initiated a new wave of recruitment in 2022 using web-based data collection and remote saliva sampling (for genotyping). Here, we provide an overview of recruitment strategies used by GS over the first 18 months of new recruitment, highlighting which proved most effective and cost-efficient in order to inform future research. Objective: This study evaluated recruitment strategies using four main outcomes: (1) absolute recruitment numbers, (2) sociodemographic representativeness, (3) biosample return rate, and (4) cost per participant. Methods: Between May 2022 and December 2023, recruitment was undertaken via snowball recruitment (through friends and family of existing volunteers), invitations to those who participated in a previous survey (CovidLife: the GS COVID-19 impact survey), and Scotland-wide recruitment through social media (including sponsored Meta-advertisements), news media, and TV advertisement. The method of recruitment was self-reported in the baseline questionnaire. We present absolute recruitment numbers and sociodemographic characteristics by recruitment method and evaluate the saliva sample return rate by recruitment strategy using chi-square tests. The overall cost and cost per participant were calculated for each method. Results: In total, 7889 new participants joined the cohort over this period. Recruitment sources by contribution were social media (n=2436, 30.9%), survey responder invitations (n=2049, 26.0%), TV advertising (n=367, 17.3%), snowball (n=891, 11.3%), news media (n=747, 9.5%), and other methods or unknown (n=399, 5.0%). More females signed up than males (5570/7889, 70.5% female). To date, 83.5% (6543/7836) of participants returned their postal saliva sample, which also varied by demographic factors (3485/3851, 90.5% older than 60 years vs 471/662, 71.1% aged 16‐34 years). Average cost per participant across all recruitment strategies was £13.52 (US $16.82). Previous survey recontacting was the most cost-effective (£0.37 [US $0.46]), followed by social media (£14.78 [US $18.39]), while TV advertisement recruitment was the most expensive per recruit (£33.67 [US $41.89]). Conclusions: This study highlights both the challenges and the opportunities in large web-based cohort recruitment. Overall, social media advertising has been the most cost-effective and easily sustained strategy for recruitment over the reported recruitment period. We note that different strategies resulted in successful recruitment over varying timescales (eg, consistent sustained recruitment for social media and large spikes for news media and TV advertising), which may be informative for future studies with different requirements of recruitment periods. Limitations include self-reported methods of recruitment and difficulties in evaluating multilayered recruitment. Overall, these data demonstrate the potential cost requirements and effectiveness of different strategies that could be applied to future research studies.

Understanding disease progression is of high biological and clinical interest. Unlike disease susceptibility, whose genetic basis has been abundantly studied, less is known about the genetics of disease progression and its overlap with disease susceptibility. Considering nine common diseases (n _cases ranging from 11,980 to 124,682) across seven biobanks, we systematically compared genetic architectures of susceptibility and progression, defined as disease-specific mortality. We identified only one locus substantially associated with disease-specific mortality and showed that, at a similar sample size, more genome-wide significant loci can be identified in a genome-wide association study of disease susceptibility. Variants substantially affecting disease susceptibility were weakly or not associated with disease-specific mortality. Moreover, susceptibility polygenic scores (PGSs) were weak predictors of disease-specific mortality, while a PGS for general lifespan was substantially associated with disease-specific mortality for seven of nine diseases. We explored alternative definitions of disease progression and found that genetic signals for macrovascular complications in type 2 diabetes overlap with similar phenotypes in the general population; however, these effects are attenuated. Overall, our findings indicate limited similarity in genetic effects between disease susceptibility and disease-specific mortality, suggesting that larger sample sizes, different measures of progression, or the integration of related phenotypes from the general population is needed to identify the genetic underpinnings of disease progression.

Both socioeconomic circumstances and genetic predisposition shape disease risk, yet their joint contribution across diseases has not been systematically examined. We studied 19 high-burden diseases in 743,194 participants (729,928 European; 13,266 non-European ancestry) from FinnGen, the UK Biobank, and Generation Scotland. Higher educational attainment was associated with lower risk of most conditions, but with higher risk of most common cancers. These associations were largely independent of disease-specific polygenic scores (PGSs). For seven out of 19 diseases, PGSs showed stronger effects among individuals with high education. Joint inclusion of education and PGSs modestly improved prediction for 14 and 10 out of 19 diseases in FinnGen and the UK Biobank, respectively. PGS associations were consistent across ancestries, whereas education effects were less stable; results using an alternative socioeconomic measure were directionally similar but smaller. Our findings highlight the distinct and partly interacting contributions of socioeconomic and genetic factors to disease risk.

Background: Multiple environmental and genetic factors play a role in the pathogenesis of atopic eczema ( AE ). We aimed to investigate gene–environment interactions (G × E) to improve understanding of the pathophysiology. Methods: We analysed data from 16 European studies to test for interaction between the 24 most significant AE‐associated loci identified from genome‐wide association studies and 18 early‐life environmental factors. We tested for replication using a further 10 studies and in vitro modeling to independently assess findings. Results: The discovery analysis (including 25,339 individuals) showed suggestive evidence for interaction ( p < 0.05) between seven environmental factors (antibiotic use, cat ownership, dog ownership, breastfeeding, elder sibling, smoking and washing practices) and at least one established variant for AE, 14 interactions in total. In the replication analysis (254,532 individuals) dog exposure × rs10214237 (on chromosome 5p13.2 near IL7R ) was nominally significant (OR _interaction = 0.91 [0.83–0.99] p = 0.025), with a risk effect of the T allele observed only in those not exposed to dogs. A similar interaction with rs10214237 was observed for siblings in the discovery analysis (OR _interaction = 0.84 [0.75–0.94] p = 0.003), but replication analysis was under‐powered (OR _interaction = 1.09 [0.82–1.46]). rs10214237 homozygous risk genotype is associated with lower IL‐7R expression in human keratinocytes, and dog exposure modelled in vitro showed a differential response according to rs10214237 genotype. Conclusion: Interaction analysis and functional assessment provide preliminary evidence that early‐life dog exposure may modify the genetic effect of rs10214237 on AE via IL7R , supporting observational epidemiology showing a protective effect for dog ownership. The lack of evidence for other G × E studied here implies only weak effects are likely to occur.

DNA methylation offers an objective method to assess the impact of smoking. In this work, we conduct a Bayesian EWAS of smoking pack years (n = 17,865, ~850k sites, Illumina EPIC array) and extend it by analysing whole genome data of smokers and non-smokers from Generation Scotland (n = 46, ~4–21 million sites via TWIST and Oxford Nanopore sequencing). We develop mCigarette, an epigenetic biomarker of smoking, and test it in two British cohorts. Results of brain- and blood-based EWAS (n_brain=14, n_blood = 882, >450k sites, Illumina arrays) reveal several loci with near-perfect discrimination of smoking status, but which do not overlap across tissues. Furthermore, we perform a GWAS of epigenetic smoking, identifying several smoking-related loci. Overall, we improve smoking-related biomarker accuracy and enhance the understanding of the effects of smoking by integrating DNA methylation data from multiple tissues and cohorts.

BACKGROUND Generation Scotland (GS) is a genetic family health cohort study established in 2006 (N~24,000). A new wave of recruitment was initiated in 2022 aimed at adding a further 20,000 new participants to the cohort using online data collection and remote saliva sampling (for genotyping and DNA methylation profiling). Eligible individuals included anyone living in Scotland aged over 12 years. New participants give consent for linkage to their medical and administrative records, and to provide a saliva sample for DNA. The current study evaluates the different strategies employed to recruit new participants to the GS cohort for those aged 16+ (recruitment of ages 12-15 will be presented separately due to additional strategies). OBJECTIVE This study aimed to evaluate recruitment strategies employed to recruit new participants to the GS cohort. Recruitment strategies were compared in terms of overall numbers as well as sociodemographic characteristics of the recruits, sample return rates and cost-effectiveness. METHODS From May 2022 to the end of December 2023 recruitment was undertaken by the following methods: snowball recruitment (through friends and family of existing volunteers), invitations to those who participated in a previous survey during the pandemic (CovidLife: the GS COVID-19 impact survey) and Scotland-wide recruitment through social media (including sponsored Meta advertisements), news media and TV advertisement. Method of recruitment was self-reported by participants in the baseline questionnaire. RESULTS Over the above period, 7,889 new participants were recruited to the cohort. According to the different strategies, this included, in order: social media (N=2,436, 30.9%), CovidLife survey responder invitations (N=2,049, 26.0%), TV advertising (N=1,367, 17.3%), snowball (N=891, 11.3%), news media (N=747, 9.5%) and other methods/unknown (N=399, 5.0%). More females signed up than males (70.5% female participants). To date, 83.5% of participants have returned their postal saliva sample. Sample return varied between demographic factors (>60 years 90.5% vs 16-34 years 71.1%). The average cost per participant across all recruitment strategies was £13.52. Past survey invitations (CovidLife) were most cost-effective at £0.37 per recruit, social media cost £14.78 per recruit, whilst TV advertisement recruitment was the most expensive at £33.67. CONCLUSIONS We present the challenges and successes of recruitment of new participants to a large ongoing cohort using remote assessment. Besides targeting existing survey responders, social media advertising has been the most cost-effective and easily sustained strategy for recruitment. We note different strategies resulted in successful recruitment over varying timescales (e.g. consistent sustained recruitment for social media, and large spikes for news media and TV advertising) which may be informative for future studies with different requirements of recruitment periods. Limitations include self-reported methods of recruitment, and difficulties in capturing multi-layered recruitment. Overall, these data demonstrate the potential cost requirements and effectiveness of different strategies that could be applied to future research studies. Future work will report success and challenges of recruitment activities aimed at younger individuals, under 16 years.

This study tests the association of whole-blood DNA methylation and antidepressant exposure in 16,531 individuals from Generation Scotland (GS), using self-report and prescription-derived measures. We identify 8 associations and a high concordance of results between self-report and prescription-derived measures. Sex-stratified analyses observe nominally significant increased effect estimates in females for four CpGs. There is observed enrichment for genes expressed in the Amygdala and annotated to synaptic vesicle membrane ontology. Two CpGs (cg15071067; DGUOK-AS1 and cg26277237; KANK1) show correlation between DNA methylation with the time in treatment. There is a significant overlap in the top 1% of CpGs with another independent methylome-wide association study of antidepressant exposure. Finally, a methylation profile score trained on this sample shows a significant association with antidepressant exposure in a meta-analysis of eight independent external datasets. In this large investigation of antidepressant exposure and DNA methylation, we demonstrate robust associations which warrant further investigation to inform on the design of more effective and tolerated treatments for depression.