GCAT|Genomes for life: a prospective cohort study of the genomes of Catalonia

Decoding depression by exploring the exposome-genome edge amidst COVID-19 lockdown

Risk of depression increased in the general population after the COVID-19 pandemic outbreak. By examining the interplay between genetics and individual environmental exposures during the COVID-19 lockdown, we have been able to gain an insight as to why some individuals are more vulnerable to depression, while others are more resilient. This study, conducted on a Spanish cohort of 9218 individuals (COVICAT), includes a comprehensive non-genetic risk analysis, the exposome, complemented by a genomics analysis in a subset of 2442 participants. Depression levels were evaluated using the Hospital Anxiety and Depression Scale. Together with Polygenic Risk Scores (PRS), we introduced a novel score; Poly-Environmental Risk Scores (PERS) for non-genetic risks to estimate the effect of each cumulative score and gene-environment interaction. We found significant positive associations for PERSSoc (Social and Household), PERSLife (Lifestyle and Behaviour), and PERSEnv (Wider Environment and Health) scores across all levels of depression severity, and for PRSB (Broad depression) only for moderate depression (OR 1.2, 95% CI 1.03-1.40). On average OR increased 1.2-fold for PERSEnv and 1.6-fold for PERLife and PERSoc from mild to severe depression level. The complete adjusted model explained 16.9% of the variance. We further observed an interaction between PERSEnv and PRSB showing a potential mitigating effect. In summary, stressors within the social and behavioral domains emerged as the primary drivers of depression risk in this population, unveiling a mitigating interaction effect that should be interpreted with caution.

Biological basis of extensive pleiotropy between blood traits and cancer risk

BACKGROUND

The immune system has a central role in preventing carcinogenesis. Alteration of systemic immune cell levels may increase cancer risk. However, the extent to which common genetic variation influences blood traits and cancer risk remains largely undetermined. Here, we identify pleiotropic variants and predict their underlying molecular and cellular alterations.

METHODS

Multivariate Cox regression was used to evaluate associations between blood traits and cancer diagnosis in cases in the UK Biobank. Shared genetic variants were identified from the summary statistics of the genome-wide association studies of 27 blood traits and 27 cancer types and subtypes, applying the conditional/conjunctional false-discovery rate approach. Analysis of genomic positions, expression quantitative trait loci, enhancers, regulatory marks, functionally defined gene sets, and bulk- and single-cell expression profiles predicted the biological impact of pleiotropic variants. Plasma small RNAs were sequenced to assess association with cancer diagnosis.

RESULTS

The study identified 4093 common genetic variants, involving 1248 gene loci, that contributed to blood-cancer pleiotropism. Genomic hotspots of pleiotropism include chromosomal regions 5p15-TERT and 6p21-HLA. Genes whose products are involved in regulating telomere length are found to be enriched in pleiotropic variants. Pleiotropic gene candidates are frequently linked to transcriptional programs that regulate hematopoiesis and define progenitor cell states of immune system development. Perturbation of the myeloid lineage is indicated by pleiotropic associations with defined master regulators and cell alterations. Eosinophil count is inversely associated with cancer risk. A high frequency of pleiotropic associations is also centered on the regulation of small noncoding Y-RNAs. Predicted pleiotropic Y-RNAs show specific regulatory marks and are overabundant in the normal tissue and blood of cancer patients. Analysis of plasma small RNAs in women who developed breast cancer indicates there is an overabundance of Y-RNA preceding neoplasm diagnosis.

CONCLUSIONS

This study reveals extensive pleiotropism between blood traits and cancer risk. Pleiotropism is linked to factors and processes involved in hematopoietic development and immune system function, including components of the major histocompatibility complexes, and regulators of telomere length and myeloid lineage. Deregulation of Y-RNAs is also associated with pleiotropism. Overexpression of these elements might indicate increased cancer risk.

Towards the Future of Public Health: Roadmapping Trends and Scenarios in the Post-COVID Healthcare Era

The COVID-19 pandemic, a transformative event in modern society, has disrupted routine, work, behavior, and human relationships. Organizations, amidst the chaos, have innovatively adapted to the evolving situation. However, many countries were unprepared for the magnitude of the challenge, revealing the fragility of health responses due to inadequate leadership, insufficient resources, and poor information system integration. Structural changes in health systems are imperative, particularly in leadership, governance, human resources, financing, information systems, technology, and health service provision. This research utilizes the Technological Roadmapping method to analyze the health sector, focusing on public health, drawing on articles from SCOPUS and PubMed databases, and creating a roadmap extending to 2050. The research presents three long-term scenarios based on the literature-derived roadmap and explores various alternatives, including integrated care, telemedicine, Big Data utilization, nanotechnology, and Big Tech's AI services. The results underscore the anticipation of post-pandemic public health with high expectations, emphasizing the importance of integrating health history access, encouraging self-care, and leveraging technology for streamlined treatment. Practical implications include insights for decision makers and stakeholders to inform strategic planning and adapt to evolving industry demands, recognizing the significance of preventive services and the humanizing potential of technology.

The Cantabria Cohort, a protocol for a population-based cohort in northern Spain

Cantabria Cohort stems from a research and action initiative lead by researchers from Valdecilla Research Institute (IDIVAL), Marqués de Valdecilla University Hospital and University of Cantabria, supported by the regional Goverment. Its aim is to identify and follow up a cohort that would provide information to improve the understanding of the etiology and prognosis of different acute and chronic diseases. The Cantabria Cohort will recruit between 40,000-50,000 residents aged 40-69 years at baseline, representing 10-20% of the target population. Currently, more than 30,000 volunteers have been enrolled. All participants will be invited for a re-assessment every three years, while the overall duration is planned for twenty years. The repeated collection of biomaterials combined with broad information from participant questionnaires, medical examinations, actual health system records and other secondary public data sources is a major strength of its design, which will make it possible to address biological pathways of disease development, identify new factors involved in health and disease, design new strategies for disease prevention, and advance precision medicine. It is conceived to allow access to a large number of researchers worldwide to boost collaboration and medical research.

Identification of intergenerational epigenetic inheritance by whole genome DNA methylation analysis in trios

Genome-wide association studies have identified thousands of loci associated with common diseases and traits. However, a large fraction of heritability remains unexplained. Epigenetic modifications, such as the observed in DNA methylation have been proposed as a mechanism of intergenerational inheritance. To investigate the potential contribution of DNA methylation to the missing heritability, we analysed the methylomes of four healthy trios (two parents and one offspring) using whole genome bisulphite sequencing. Of the 1.5 million CpGs (19%) with over 20% variability between parents in at least one family and compatible with a Mendelian inheritance pattern, only 3488 CpGs (0.2%) lacked correlation with any SNP in the genome, marking them as potential sites for intergenerational epigenetic inheritance. These markers were distributed genome-wide, with some preference to be located in promoters. They displayed a bimodal distribution, being either fully methylated or unmethylated, and were often found at the boundaries of genomic regions with high/low GC content. This analysis provides a starting point for future investigations into the missing heritability of simple and complex traits.

Molecular bases of comorbidities: present and future perspectives

Co-occurrence of diseases decreases patient quality of life, complicates treatment choices, and increases mortality. Analyses of electronic health records present a complex scenario of comorbidity relationships that vary by age, sex, and cohort under study. The study of similarities between diseases using 'omics data, such as genes altered in diseases, gene expression, proteome, and microbiome, are fundamental to uncovering the origin of, and potential treatment for, comorbidities. Recent studies have produced a first generation of genetic interpretations for as much as 46% of the comorbidities described in large cohorts. Integrating different sources of molecular information and using artificial intelligence (AI) methods are promising approaches for the study of comorbidities. They may help to improve the treatment of comorbidities, including the potential repositioning of drugs.

Long-Term Exposure to Air Pollution and COVID-19 Vaccine Antibody Response in a General Population Cohort (COVICAT Study, Catalonia)

BACKGROUND

Ambient air pollution has been associated with COVID-19 disease severity and antibody response induced by infection.

OBJECTIVES

We examined the association between long-term exposure to air pollution and vaccine-induced antibody response.

METHODS

This study was nested in an ongoing population-based cohort, COVICAT, the GCAT-Genomes for Life cohort, in Catalonia, Spain, with multiple follow-ups. We drew blood samples in 2021 from 1,090 participants of 2,404 who provided samples in 2020, and we included 927 participants in this analysis. We measured immunoglobulin M (IgM), IgG, and IgA antibodies against five viral-target antigens, including receptor-binding domain (RBD), spike-protein (S), and segment spike-protein (S2) triggered by vaccines available in Spain. We estimated prepandemic (2018-2019) exposure to fine particulate matter [PM ≤2.5μm in aerodynamic diameter (PM2.5)], nitrogen dioxide (NO2), black carbon (BC), and ozone (O3) using Effects of Low-Level Air Pollution: A Study in Europe (ELAPSE) models. We adjusted estimates for individual- and area-level covariates, time since vaccination, and vaccine doses and type and stratified by infection status. We used generalized additive models to explore the relationship between air pollution and antibodies according to days since vaccination.

RESULTS

Among vaccinated persons not infected by SARS-CoV-2 (n=632), higher prepandemic air pollution levels were associated with a lower vaccine antibody response for IgM (1 month post vaccination) and IgG. Percentage change in geometric mean IgG levels per interquartile range of PM2.5 (1.7 μg/m3) were -8.1 (95% CI: -15.9, 0.4) for RBD, -9.9 (-16.2, -3.1) for S, and -8.4 (-13.5, -3.0) for S2. We observed a similar pattern for NO2 and BC and an inverse pattern for O3. Differences in IgG levels by air pollution levels persisted with time since vaccination. We did not observe an association of air pollution with vaccine antibody response among participants with prior infection (n=295).

DISCUSSION

Exposure to air pollution was associated with lower COVID-19 vaccine antibody response. The implications of this association on the risk of breakthrough infections require further investigation. https://doi.org/10.1289/EHP11989.

Resilience characterized and quantified from physical activity data: A tutorial in R

Consistent physical activity is key for health and well-being, but it is vulnerable to stressors. The process of recovering from such stressors and bouncing back to the previous state of physical activity can be referred to as resilience. Quantifying resilience is fundamental to assess and manage the impact of stressors on consistent physical activity. In this tutorial, we present a method to quantify the resilience process from physical activity data. We leverage the prior operationalization of resilience, as used in various psychological domains, as area under the curve and expand it to suit the characteristics of physical activity time series. As use case to illustrate the methodology, we quantified resilience in step count time series (length = 366 observations) for eight participants following the first COVID-19 lockdown as a stressor. Steps were assessed daily using wrist-worn devices. The methodology is implemented in R and all coding details are included. For each person's time series, we fitted multiple growth models and identified the best one using the Root Mean Squared Error (RMSE). Then, we used the predicted values from the selected model to identify the point in time when the participant recovered from the stressor and quantified the resulting area under the curve as a measure of resilience for step count. Further resilience features were extracted to capture the different aspects of the process. By developing a methodological guide with a step-by-step implementation, we aimed at fostering increased awareness about the concept of resilience for physical activity and facilitate the implementation of related research.

Skin Phototype and Disease: A Comprehensive Genetic Approach to Pigmentary Traits Pleiotropy Using PRS in the GCAT Cohort

Human pigmentation has largely been associated with different disease prevalence among populations, but most of these studies are observational and inconclusive. Known to be genetically determined, pigmentary traits have largely been studied by Genome-Wide Association Study (GWAS), mostly in Caucasian ancestry cohorts from North Europe, identifying robustly, several loci involved in many of the pigmentary traits. Here, we conduct a detailed analysis by GWAS and Polygenic Risk Score (PRS) of 13 pigmentary-related traits in a South European cohort of Caucasian ancestry (n = 20,000). We observed fair phototype strongly associated with non-melanoma skin cancer and other dermatoses and confirmed by PRS-approach the shared genetic basis with skin and eye diseases, such as melanoma (OR = 0.95), non-melanoma skin cancer (OR = 0.93), basal cell carcinoma (OR = 0.97) and darker phototype with vitiligo (OR = 1.02), cataracts (OR = 1.04). Detailed genetic analyses revealed 37 risk loci associated with 10 out of 13 analyzed traits, and 16 genes significantly associated with at least two pigmentary traits. Some of them have been widely reported, such as MC1R, HERC2, OCA2, TYR, TYRP1, SLC45A2, and some novel candidate genes C1QTNF3, LINC02876, and C1QTNF3-AMACR have not been reported in the GWAS Catalog, with regulatory potential. These results highlight the importance of the assess phototype as a genetic proxy of skin functionality and disease when evaluating open mixed populations.

de Pancorbo M, Calafell F. Y-chromosome target enrichment reveals rapid expansion of haplogroup R1b-DF27 in Iberia during the Bronze Age transition

The Y chromosome can yield a unique perspective into the study of human demographic history. However, due to the repetitive nature of part of its sequence, only a small set of regions are suitable for variant calling and discovery from short-read sequencing data. These regions combined represent 8.9 Mbp or 0.14% of a diploid human genome. Consequently, investing in whole-genome sequencing to resolve Y-chromosome questions is poorly efficient. Here we use, as an alternative, target enrichment technology to greatly increase sequencing effectiveness, validating and applying the technique to 181 males, for 162 of whom we obtained a positive result. Additionally, 75 samples sequenced for the whole genome were also included, for a total sample size of 237. These samples were chosen for their Y chromosome haplogroup: R1b-DF27. In the context of European populations, and particularly in Iberia, this haplogroup stands out for its high frequency and its demographic history. Current evidence indicates that the diffusion of this haplogroup is related to the population movements that mark the cultural Bronze Age transition, making it remarkably interesting for population geneticists. The results of this study show the effects of the rapid radiation of the haplogroup in Spain, as even with the higher discriminating power of whole sequences, most haplotypes still fall within the R1b-DF27* paragroup rather than in the main derived branches. However, we were able to refine the ISOGG 2019-2020 phylogeny, and its two main subbranches, namely L176.2 and Z272, which present geographical differentiation between the Atlantic and Mediterranean coasts of Iberia.

Changes in Population Health-Related Behaviors During a COVID-19 Surge: A Natural Experiment

Background

The study of impact of lockdowns on individual health-related behaviors has produced divergent results.

Purpose

To identify patterns of change in multiple health-related behaviors analyzed as a whole, and their individual determinants.

Methods

Between March and August 2020, we collected data on smoking, alcohol, physical activity, weight, and sleep in a population-based cohort from Catalonia who had available pre-pandemic data. We performed multiple correspondence and cluster analyses to identify patterns of change in health-related behaviors and built multivariable multinomial logistic regressions to identify determinants of behavioral change.

Results

In 10,032 participants (59% female, mean (SD) age 55 (8) years), 8,606 individuals (86%) modified their behavior during the lockdown. We identified five patterns of behavioral change that were heterogeneous and directed both towards worsening and improvement in diverse combinations. Patterns ranged from “global worsening” (2,063 participants, 21%) characterized by increases in smoking, alcohol consumption, and weight, and decreases in physical activity levels and sleep time, to “improvement” (2,548 participants, 25%) characterized by increases in physical activity levels, decreases in weight and alcohol consumption, and both increases and decreases in sleep time. Being female, of older age, teleworking, having a higher education level, assuming caregiving responsibilities, and being more exposed to pandemic news were associated with changing behavior (all p < .05), but did not discriminate between favorable or unfavorable changes.

Conclusions

Most of the population experienced changes in health-related behavior during lockdowns. Determinants of behavior modification were not explicitly associated with the direction of changes but allowed the identification of older, teleworking, and highly educated women who assumed caregiving responsibilities at home as susceptible population groups more vulnerable to lockdowns.

SARS-CoV-2 infection, vaccination, and antibody response trajectories in adults: a cohort study in Catalonia

BACKGROUND

Heterogeneity of the population in relation to infection, COVID-19 vaccination, and host characteristics is likely reflected in the underlying SARS-CoV-2 antibody responses.

METHODS

We measured IgM, IgA, and IgG levels against SARS-CoV-2 spike and nucleocapsid antigens in 1076 adults of a cohort study in Catalonia between June and November 2020 and a second time between May and July 2021. Questionnaire data and electronic health records on vaccination and COVID-19 testing were available in both periods. Data on several lifestyle, health-related, and sociodemographic characteristics were also available.

RESULTS

Antibody seroreversion occurred in 35.8% of the 64 participants non-vaccinated and infected almost a year ago and was related to asymptomatic infection, age above 60 years, and smoking. Moreover, the analysis on kinetics revealed that among all responses, IgG RBD, IgA RBD, and IgG S2 decreased less within 1 year after infection. Among vaccinated, 2.1% did not present antibodies at the time of testing and approximately 1% had breakthrough infections post-vaccination. In the post-vaccination era, IgM responses and those against nucleoprotein were much less prevalent. In previously infected individuals, vaccination boosted the immune response and there was a slight but statistically significant increase in responses after a 2nd compared to the 1st dose. Infected vaccinated participants had superior antibody levels across time compared to naïve-vaccinated people. mRNA vaccines and, particularly the Spikevax, induced higher antibodies after 1st and 2nd doses compared to Vaxzevria or Janssen COVID-19 vaccines. In multivariable regression analyses, antibody responses after vaccination were predicted by the type of vaccine, infection age, sex, smoking, and mental and cardiovascular diseases.

CONCLUSIONS

Our data support that infected people would benefit from vaccination. Results also indicate that hybrid immunity results in superior antibody responses and infection-naïve people would need a booster dose earlier than previously infected people. Mental diseases are associated with less efficient responses to vaccination.

GCAT|Panel, a comprehensive structural variant haplotype map of the Iberian population from high-coverage whole-genome sequencing

The combined analysis of haplotype panels with phenotype clinical cohorts is a common approach to explore the genetic architecture of human diseases. However, genetic studies are mainly based on single nucleotide variants (SNVs) and small insertions and deletions (indels). Here, we contribute to fill this gap by generating a dense haplotype map focused on the identification, characterization, and phasing of structural variants (SVs). By integrating multiple variant identification methods and Logistic Regression Models (LRMs), we present a catalogue of 35 431 441 variants, including 89 178 SVs (≥50 bp), 30 325 064 SNVs and 5 017 199 indels, across 785 Illumina high coverage (30x) whole-genomes from the Iberian GCAT Cohort, containing a median of 3.52M SNVs, 606 336 indels and 6393 SVs per individual. The haplotype panel is able to impute up to 14 360 728 SNVs/indels and 23 179 SVs, showing a 2.7-fold increase for SVs compared with available genetic variation panels. The value of this panel for SVs analysis is shown through an imputed rare Alu element located in a new locus associated with Mononeuritis of lower limb, a rare neuromuscular disease. This study represents the first deep characterization of genetic variation within the Iberian population and the first operational haplotype panel to systematically include the SVs into genome-wide genetic studies.

Mental health and COVID-19 in a general population cohort in Spain (COVICAT study)

PURPOSE

Mental health conditions may affect outcome of COVID-19 disease, while exposure to stressors during the pandemic may impact mental health. The purpose of this study was to examine these factors in relation to ocurrence of depression and anxiety after the first outbreak in Spain.

METHODS

We contacted 9515 participants from a population-based cohort study in Catalonia between May and October 2020. We drew blood samples to establish infection to the virus. Pre-pandemic mental health conditions were confirmed through Electronic Health Registries. We used the Hospital Anxiety and Depression Scale to assess severe depression and anxiety post-pandemic. Exposure to proximal, financial and wider environment stressors during the lockdown were collected. We calculated Relative Risks (RR), adjusting for individual- and contextual covariates.

RESULTS

Pre-pandemic mental health disorders were not associated with SARS-CoV-2 infection , but were associated with severity of COVID-19 disease. People with pre-existing mental health disorders showed higher prevalence of severe depression (25.4%) and anxiety (37.8%) than those without prior mental disorders (4.9% and 10.1%). Living alone was a strong predictor of severe depression among mental health patients (RR = 1.6, 95% CI 1.2-2.2). Among those without prior mental health disorders, post-lockdown depression and anxiety were associated with household interpersonal conflicts (RR = 2.6, 95% CI 2.1-3.1; RR = 2.1, 95% CI 1.9-2.4) and financial instability (RR = 2.2, 95% CI 1.8-2.9; 1.9, 95% CI 1.6-2.2).

CONCLUSIONS

The COVID-19 pandemic and the lockdown were associated with increased post-lockdown depression and anxiety. Patients with pre-existing mental health conditions are a vulnerable group for severe COVID-19 disease.

RNA assay identifies a previous misclassification of BARD1 c.1977A>G variant

Case–control studies have shown an association of BARD1 with hereditary breast and/or ovarian cancer (HBOC) predisposition. BARD1 alternatively spliced isoforms are abundant and some are highly expressed in different cancer types. In addition, a number of BARD1 germline pathogenic variants have been reported among HBOC patients. In previous reports, BARD1 c.1977A>G variant has been classified as pathogenic since it produces a frameshift transcript lacking exons 2 to 9. In the present study, we sought to validate the mRNA splicing results previously published and to contribute with new evidence to refine the classification of this substitution according to ACMG/AMP guidelines. The presence of the variant was screened in patients and controls. RT-PCR was performed in order to compare the transcriptional profiles of two variant carriers and ten non-carrier controls. In addition, allele-specific expression was assessed. No differences in variant frequency were detected between patients and controls. The RNA assay confirmed the presence of the shorter transcript lacking exons 2–9, but it was detected both in carriers and non-carriers. Furthermore, allelic imbalance was discarded and no significant differences in the proportion of full-length and shorter transcript were detected between carriers and controls. The shorter transcript detected corresponds to BARD1 isoform η, constituted by exons 1, 10 and 11. Our results support that this transcript is a constitutive splicing product rather than an aberrant transcript caused by BARD1 c.1977A>G variant, and for this reason this variant should be considered as likely benign following ACMG/AMP guidelines.

Infection induced SARS-CoV-2 seroprevalence and heterogeneity of antibody responses in a general population cohort study in Catalonia Spain

Sparse data exist on the complex natural immunity to SARS-CoV-2 at the population level. We applied a well-validated multiplex serology test in 5000 participants of a general population study in Catalonia in blood samples collected from end June to mid November 2020. Based on responses to fifteen isotype-antigen combinations, we detected a seroprevalence of 18.1% in adults (n = 4740), and modeled extrapolation to the general population of Catalonia indicated a 15.3% seroprevalence. Antibodies persisted up to 9 months after infection. Immune profiling of infected individuals revealed that with increasing severity of infection (asymptomatic, 1-3 symptoms, ≥ 4 symptoms, admitted to hospital/ICU), seroresponses were more robust and rich with a shift towards IgG over IgA and anti-spike over anti-nucleocapsid responses. Among seropositive participants, lower antibody levels were observed for those ≥ 60 years vs < 60 years old and smokers vs non-smokers. Overweight/obese participants vs normal weight had higher antibody levels. Adolescents (13-15 years old) (n = 260) showed a seroprevalence of 11.5%, were less likely to be tested seropositive compared to their parents and had dominant anti-spike rather than anti-nucleocapsid IgG responses. Our study provides an unbiased estimate of SARS-CoV-2 seroprevalence in Catalonia and new evidence on the durability and heterogeneity of post-infection immunity.

Ambient Air Pollution in Relation to SARS-CoV-2 Infection, Antibody Response, and COVID-19 Disease: A Cohort Study in Catalonia, Spain (COVICAT Study)

BACKGROUND

Emerging evidence links ambient air pollution with coronavirus 2019 (COVID-19) disease, an association that is methodologically challenging to investigate.

OBJECTIVES

We examined the association between long-term exposure to air pollution with SARS-CoV-2 infection measured through antibody response, level of antibody response among those infected, and COVID-19 disease.

METHODS

We contacted 9,605 adult participants from a population-based cohort study in Catalonia between June and November 2020; most participants were between 40 and 65 years of age. We drew blood samples from 4,103 participants and measured immunoglobulin M (IgM), IgA, and IgG antibodies against five viral target antigens to establish infection to the virus and levels of antibody response among those infected. We defined COVID-19 disease using self-reported hospital admission, prior positive diagnostic test, or more than three self-reported COVID-19 symptoms after contact with a COVID-19 case. We estimated prepandemic (2018-2019) exposure to fine particulate matter [PM with an aerodynamic diameter of ≤ 2.5 μ m (PM 2.5 )], nitrogen dioxide (NO 2 ), black carbon (BC), and ozone (O 3 ) at the residential address using hybrid land-use regression models. We calculated log-binomial risk ratios (RRs), adjusting for individual- and area-level covariates.

RESULTS

Among those tested for SARS-CoV-2 antibodies, 743 (18.1%) were seropositive. Air pollution levels were not statistically significantly associated with SARS-CoV-2 infection: Adjusted RRs per interquartile range were 1.07 (95% CI: 0.97, 1.18) for NO 2 , 1.04 (95% CI: 0.94, 1.14) for PM 2.5 , 1.00 (95% CI: 0.92, 1.09) for BC, and 0.97 (95% CI: 0.89, 1.06) for O 3 . Among infected participants, exposure to NO 2 and PM 2.5 were positively associated with IgG levels for all viral target antigens. Among all participants, 481 (5.0%) had COVID-19 disease. Air pollution levels were associated with COVID-19 disease: adjusted RRs = 1.14 (95% CI: 1.00, 1.29) for NO 2 and 1.17 (95% CI: 1.03, 1.32) for PM 2.5 . Exposure to O 3 was associated with a slightly decreased risk (RR = 0.92 ; 95% CI: 0.83, 1.03). Associations of air pollution with COVID-19 disease were more pronounced for severe COVID-19, with RRs = 1.26 (95% CI: 0.89, 1.79) for NO 2 and 1.51 (95% CI: 1.06, 2.16) for PM 2.5 .

DISCUSSION

Exposure to air pollution was associated with a higher risk of COVID-19 disease and level of antibody response among infected but not with SARS-CoV-2 infection. https://doi.org/10.1289/EHP9726.

BARD1 Pathogenic Variants are Associated with Triple-Negative Breast Cancer in a Spanish Hereditary Breast and Ovarian Cancer Cohort

Only a small fraction of hereditary breast and/or ovarian cancer (HBOC) cases are caused by germline variants in the high-penetrance breast cancer 1 and 2 genes (BRCA1 and BRCA2). BRCA1-associated ring domain 1 (BARD1), nuclear partner of BRCA1, has been suggested as a potential HBOC risk gene, although its prevalence and penetrance are variable according to populations and type of tumor. We aimed to investigate the prevalence of BARD1 truncating variants in a cohort of patients with clinical suspicion of HBOC. A comprehensive BARD1 screening by multigene panel analysis was performed in 4015 unrelated patients according to our regional guidelines for genetic testing in hereditary cancer. In addition, 51,202 Genome Aggregation Database (gnomAD) non-Finnish, non-cancer European individuals were used as a control population. In our patient cohort, we identified 19 patients with heterozygous BARD1 truncating variants (0.47%), whereas the frequency observed in the gnomAD controls was 0.12%. We found a statistically significant association of truncating BARD1 variants with overall risk (odds ratio (OR) = 3.78; CI = 2.10–6.48; p = 1.16 × 10⁻⁵). This association remained significant in the hereditary breast cancer (HBC) group (OR = 4.18; CI = 2.10–7.70; p = 5.45 × 10⁻⁵). Furthermore, deleterious BARD1 variants were enriched among triple-negative BC patients (OR = 5.40; CI = 1.77–18.15; p = 0.001) compared to other BC subtypes. Our results support the role of BARD1 as a moderate penetrance BC predisposing gene and highlight a stronger association with triple-negative tumors.

A likelihood ratio approach for identifying three-quarter siblings in genetic databases

The detection of family relationships in genetic databases is of interest in various scientific disciplines such as genetic epidemiology, population and conservation genetics, forensic science, and genealogical research. Nowadays, screening genetic databases for related individuals forms an important aspect of standard quality control procedures. Relatedness research is usually based on an allele sharing analysis of identity by state (IBS) or identity by descent (IBD) alleles. Existing IBS/IBD methods mainly aim to identify first-degree relationships (parent-offspring or full siblings) and second degree (half-siblings, avuncular, or grandparent-grandchild) pairs. Little attention has been paid to the detection of in-between first and second-degree relationships such as three-quarter siblings (3/4S) who share fewer alleles than first-degree relationships but more alleles than second-degree relationships. With the progressively increasing sample sizes used in genetic research, it becomes more likely that such relationships are present in the database under study. In this paper, we extend existing likelihood ratio (LR) methodology to accurately infer the existence of 3/4S, distinguishing them from full siblings and second-degree relatives. We use bootstrap confidence intervals to express uncertainty in the LRs. Our proposal accounts for linkage disequilibrium (LD) by using marker pruning, and we validate our methodology with a pedigree-based simulation study accounting for both LD and recombination. An empirical genome-wide array data set from the GCAT Genomes for Life cohort project is used to illustrate the method.

Genomewide Association Study of Severe Covid-19 with Respiratory Failure

BACKGROUND

There is considerable variation in disease behavior among patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes coronavirus disease 2019 (Covid-19). Genomewide association analysis may allow for the identification of potential genetic factors involved in the development of Covid-19.

METHODS

We conducted a genomewide association study involving 1980 patients with Covid-19 and severe disease (defined as respiratory failure) at seven hospitals in the Italian and Spanish epicenters of the SARS-CoV-2 pandemic in Europe. After quality control and the exclusion of population outliers, 835 patients and 1255 control participants from Italy and 775 patients and 950 control participants from Spain were included in the final analysis. In total, we analyzed 8,582,968 single-nucleotide polymorphisms and conducted a meta-analysis of the two case-control panels.

RESULTS

We detected cross-replicating associations with rs11385942 at locus 3p21.31 and with rs657152 at locus 9q34.2, which were significant at the genomewide level (P<5×10-8) in the meta-analysis of the two case-control panels (odds ratio, 1.77; 95% confidence interval [CI], 1.48 to 2.11; P = 1.15×10-10; and odds ratio, 1.32; 95% CI, 1.20 to 1.47; P = 4.95×10-8, respectively). At locus 3p21.31, the association signal spanned the genes SLC6A20, LZTFL1, CCR9, FYCO1, CXCR6 and XCR1. The association signal at locus 9q34.2 coincided with the ABO blood group locus; in this cohort, a blood-group-specific analysis showed a higher risk in blood group A than in other blood groups (odds ratio, 1.45; 95% CI, 1.20 to 1.75; P = 1.48×10-4) and a protective effect in blood group O as compared with other blood groups (odds ratio, 0.65; 95% CI, 0.53 to 0.79; P = 1.06×10-5).

CONCLUSIONS

We identified a 3p21.31 gene cluster as a genetic susceptibility locus in patients with Covid-19 with respiratory failure and confirmed a potential involvement of the ABO blood-group system. (Funded by Stein Erik Hagen and others.).

Exploring the Role of Mutations in Fanconi Anemia Genes in Hereditary Cancer Patients

Fanconi anemia (FA) is caused by biallelic mutations in FA genes. Monoallelic mutations in five of these genes (BRCA1, BRCA2, PALB2, BRIP1 and RAD51C) increase the susceptibility to breast/ovarian cancer and are used in clinical diagnostics as bona-fide hereditary cancer genes. Increasing evidence suggests that monoallelic mutations in other FA genes could predispose to tumor development, especially breast cancer. The objective of this study is to assess the mutational spectrum of 14 additional FA genes (FANCA, FANCB, FANCC, FANCD2, FANCE, FANCF, FANCG, FANCI, FANCL, FANCM, FANCP, FANCQ, FANCR and FANCU) in a cohort of hereditary cancer patients, to compare with local cancer-free controls as well as GnomAD. A total of 1021 hereditary cancer patients and 194 controls were analyzed using our next generation custom sequencing panel. We identified 35 pathogenic variants in eight genes. A significant association with the risk of breast cancer/breast and ovarian cancer was found for carriers of FANCA mutations (odds ratio (OR) = 3.14 95% confidence interval (CI) 1.4–6.17, p = 0.003). Two patients with early-onset cancer showed a pathogenic FA variant in addition to another germline mutation, suggesting a modifier role for FA variants. Our results encourage a comprehensive analysis of FA genes in larger studies to better assess their role in cancer risk.

A Log-Ratio Biplot Approach for Exploring Genetic Relatedness Based on Identity by State

The detection of cryptic relatedness in large population-based cohorts is of great importance in genome research. The usual approach for detecting closely related individuals is to plot allele sharing statistics, based on identity-by-state or identity-by-descent, in a two-dimensional scatterplot. This approach ignores that allele sharing data across individuals has in reality a higher dimensionality, and neither regards the compositional nature of the underlying counts of shared genotypes. In this paper we develop biplot methodology based on log-ratio principal component analysis that overcomes these restrictions. This leads to entirely new graphics that are essentially useful for exploring relatedness in genetic databases from homogeneous populations. The proposed method can be applied in an iterative manner, acting as a looking glass for more remote relationships that are harder to classify. Datasets from the 1,000 Genomes Project and the Genomes For Life-GCAT Project are used to illustrate the proposed method. The discriminatory power of the log-ratio biplot approach is compared with the classical plots in a simulation study. In a non-inbred homogeneous population the classification rate of the log-ratio principal component approach outperforms the classical graphics across the whole allele frequency spectrum, using only identity by state. In these circumstances, simulations show that with 35,000 independent bi-allelic variants, log-ratio principal component analysis, combined with discriminant analysis, can correctly classify relationships up to and including the fourth degree.

Disease networks identify specific conditions and pleiotropy influencing multimorbidity in the general population

Multimorbidity is an emerging topic in public health policy because of its increasing prevalence and socio-economic impact. However, the age- and gender-dependent trends of disease associations at fine resolution, and the underlying genetic factors, remain incompletely understood. Here, by analyzing disease networks from electronic medical records of primary health care, we identify key conditions and shared genetic factors influencing multimorbidity. Three types of diseases are outlined: "central", which include chronic and non-chronic conditions, have higher cumulative risks of disease associations; "community roots" have lower cumulative risks, but inform on continuing clustered disease associations with age; and "seeds of bursts", which most are chronic, reveal outbreaks of disease associations leading to multimorbidity. The diseases with a major impact on multimorbidity are caused by genes that occupy central positions in the network of human disease genes. Alteration of lipid metabolism connects breast cancer, diabetic neuropathy and nutritional anemia. Evaluation of key disease associations by a genome-wide association study identifies shared genetic factors and further supports causal commonalities between nervous system diseases and nutritional anemias. This study also reveals many shared genetic signals with other diseases. Collectively, our results depict novel population-based multimorbidity patterns, identify key diseases within them, and highlight pleiotropy influencing multimorbidity.

Multitrait genome association analysis identifies new susceptibility genes for human anthropometric variation in the GCAT cohort

Background

Heritability estimates have revealed an important contribution of SNP variants for most common traits; however, SNP analysis by single-trait genome-wide association studies (GWAS) has failed to uncover their impact. In this study, we applied a multitrait GWAS approach to discover additional factor of the missing heritability of human anthropometric variation.

Methods

We analysed 205 traits, including diseases identified at baseline in the GCAT cohort (Genomes For Life- Cohort study of the Genomes of Catalonia) (n=4988), a Mediterranean adult population-based cohort study from the south of Europe. We estimated SNP heritability contribution and single-trait GWAS for all traits from 15 million SNP variants. Then, we applied a multitrait-related approach to study genome-wide association to anthropometric measures in a two-stage meta-analysis with the UK Biobank cohort (n=336 107).

Results

Heritability estimates (eg, skin colour, alcohol consumption, smoking habit, body mass index, educational level or height) revealed an important contribution of SNP variants, ranging from 18% to 77%. Single-trait analysis identified 1785 SNPs with genome-wide significance threshold. From these, several previously reported single-trait hits were confirmed in our sample with LINC01432 (p=1.9×10⁻⁹) variants associated with male baldness, LDLR variants with hyperlipidaemia (ICD-9:272) (p=9.4×10⁻¹⁰) and variants in IRF4 (p=2.8×10⁻⁵⁷), SLC45A2 (p=2.2×10⁻¹³⁰), HERC2 (p=2.8×10⁻¹⁷⁶), OCA2 (p=2.4×10⁻¹²¹) and MC1R (p=7.7×10⁻²²) associated with hair, eye and skin colour, freckling, tanning capacity and sun burning sensitivity and the Fitzpatrick phototype score, all highly correlated cross-phenotypes. Multitrait meta-analysis of anthropometric variation validated 27 loci in a two-stage meta-analysis with a large British ancestry cohort, six of which are newly reported here (p value threshold <5×10⁻⁹) at ZRANB2-AS2, PIK3R1, EPHA7, MAD1L1, CACUL1 and MAP3K9.

Conclusion

Considering multiple-related genetic phenotypes improve associated genome signal detection. These results indicate the potential value of data-driven multivariate phenotyping for genetic studies in large population-based cohorts to contribute to knowledge of complex traits.