Mechanisms by which the cystic fibrosis transmembrane conductance regulator may influence SARS-CoV-2 infection and COVID-19 disease severity.

Patients with cystic fibrosis (CF) exhibit pronounced respiratory damage and were initially considered among those at highest risk for serious harm from SARS-CoV-2 infection. Numerous clinical studies have subsequently reported that individuals with CF in North America and Europe-while susceptible to severe COVID-19-are often spared from the highest levels of virus-associated mortality. To understand features that might influence COVID-19 among patients with cystic fibrosis, we studied relationships between SARS-CoV-2 and the gene responsible for CF (i.e., the cystic fibrosis transmembrane conductance regulator, CFTR). In contrast to previous reports, we found no association between CFTR carrier status (mutation heterozygosity) and more severe COVID-19 clinical outcomes. We did observe an unexpected trend toward higher mortality among control individuals compared with silent carriers of the common F508del CFTR variant-a finding that will require further study. We next performed experiments to test the influence of homozygous CFTR deficiency on viral propagation and showed that SARS-CoV-2 production in primary airway cells was not altered by the absence of functional CFTR using two independent protocols. On the contrary, experiments performed in vitro strongly indicated that virus proliferation depended on features of the mucosal fluid layer known to be disrupted by absent CFTR in patients with CF, including both low pH and increased viscosity. These results point to the acidic, viscous, and mucus-obstructed airways in patients with cystic fibrosis as unfavorable for the establishment of coronaviral infection. Our findings provide new and important information concerning relationships between the CF clinical phenotype and severity of COVID-19.

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.

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.

Lockdowns implemented during the first surge of the COVID-19 pandemic created highly disruptive scenarios impacting many aspects of life, including health-related behaviors. While early studies on isolated health-related behaviors partly aid in the understanding of changes in some of these behaviors, there is robust evidence supporting the idea that health-related behaviors and their changes often co-occur and should be studied and analyzed as a whole. Hence, in this study, we used hypothesis-free methods to identify inter-dependent patterns of change in health-related behaviors including tobacco smoking, alcohol consumption, physical activity, sleep, and weight in a population-based sample of 10,032 adults from Catalonia, Spain. We found that 86% of participants modified their health-related behavior during the lockdown as we identified five patterns of behavioral change, ranging from general worsening to improvement, in diverse combinations. Additionally, we found that being female, older age, teleworking, highly educated, assuming caregiving responsibilities, and having a high exposure to pandemic news were main the determinants of patterns characterized by changing behaviors (both worsening and improving). Overall, our results highlight the heterogeneity, co-occurrence, and inter-play between health-related behaviors under a natural experiment, and identify common demographic, socio-environmental and behavioral factors that might predict changes in behavior.

Genome-wide epistasis study highlights genetic interactions influencing severity of COVID-19.

Coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) may lead to life-threatening respiratory symptoms. Understanding the genetic basis of the prognosis of COVID-19 is important for risk profiling of potentially severe symptoms. Here, we conducted a genome-wide epistasis study of COVID-19 severity in 2243 patients with severe symptoms and 12,612 patients with no or mild symptoms from the UK Biobank, followed by a replication study in an independent Spanish cohort (1416 cases, 4382 controls). Our study highlighted 3 interactions with genome-wide significance in the discovery phase, nominally significant in the replication phase, and enhanced significance in the meta-analysis. For example, the lead interaction was found between rs9792388 upstream of PDGFRL and rs3025892 downstream of SNAP25, where the composite genotype of rs3025892 CT and rs9792388 CA/AA showed higher risk of severe disease than any other genotypes (P = 2.77 × 10-12, proportion of severe cases = 0.24 ~ 0.29 vs. 0.09 ~ 0.18, genotypic OR = 1.96 ~ 2.70). This interaction was replicated in the Spanish cohort (P = 0.002, proportion of severe cases = 0.30 ~ 0.36 vs. 0.14 ~ 0.25, genotypic OR = 1.45 ~ 2.37) and showed enhanced significance in the meta-analysis (P = 4.97 × 10-14). Notably, these interactions indicated a possible molecular mechanism by which SARS-CoV-2 affects the nervous system. The first exhaustive genome-wide screening for epistasis improved our understanding of genetic basis underlying the severity of COVID-19.

Revisiting the UK Genetic Severity Score for NF2: a proposal for the addition of a functional genetic component.

BACKGROUND: Neurofibromatosis type 2 (NF2) is an autosomal dominant disorder characterised by the development of multiple schwannomas, especially on vestibular nerves, and meningiomas. The UK NF2 Genetic Severity Score (GSS) is useful to predict the progression of the disease from germline NF2 pathogenic variants, which allows the clinical follow-up and the genetic counselling offered to affected families to be optimised. METHODS: 52 Spanish patients were classified using the GSS, and patients' clinical severity was measured and compared between GSS groups. The GSS was reviewed with the addition of phenotype quantification, genetic variant classification and functional assays of Merlin and its downstream pathways. Principal component analysis and regression models were used to evaluate the differences between severity and the effect of NF2 germline variants. RESULTS: The GSS was validated in the Spanish NF2 cohort. However, for 25% of mosaic patients and patients harbouring variants associated with mild and moderate phenotypes, it did not perform as well for predicting clinical outcomes as it did for pathogenic variants associated with severe phenotypes. We studied the possibility of modifying the mutation classification in the GSS by adding the impact of pathogenic variants on the function of Merlin in 27 cases. This revision helped to reduce variability within NF2 mutation classes and moderately enhanced the correlation between patient phenotype and the different prognosis parameters analysed (R2=0.38 vs R2=0.32, p>0001). CONCLUSIONS: We validated the UK NF2 GSS in a Spanish NF2 cohort, despite the significant phenotypic variability identified within it. The revision of the GSS, named Functional Genetic Severity Score, could add value for the classification of mosaic patients and patients showing mild and moderate phenotypes once it has been validated in other cohorts.

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.

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.

Assessment of kinship detection using RNA-seq data.

Analysis of RNA sequencing (RNA-seq) data from related individuals is widely used in clinical and molecular genetics studies. Prediction of kinship from RNA-seq data would be useful for confirming the expected relationships in family based studies and for highlighting samples from related individuals in case-control or population based studies. Currently, reconstruction of pedigrees is largely based on SNPs or microsatellites, obtained from genotyping arrays, whole genome sequencing and whole exome sequencing. Potential problems with using RNA-seq data for kinship detection are the low proportion of the genome that it covers, the highly skewed coverage of exons of different genes depending on expression level and allele-specific expression. In this study we assess the use of RNA-seq data to detect kinship between individuals, through pairwise identity by descent (IBD) estimates. First, we obtained high quality SNPs after successive filters to minimize the effects due to allelic imbalance as well as errors in sequencing, mapping and genotyping. Then, we used these SNPs to calculate pairwise IBD estimates. By analysing both real and simulated RNA-seq data we show that it is possible to identify up to second degree relationships using RNA-seq data of even low to moderate sequencing depth.

Comprehensive analysis and ACMG-based classification of CHEK2 variants in hereditary cancer patients.

CHEK2 variants are associated with intermediate breast cancer risk, among other cancers. We aimed to comprehensively describe CHEK2 variants in a Spanish hereditary cancer (HC) cohort and adjust the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG-AMP) guidelines for their classification. First, three CHEK2 frequent variants were screened in a retrospective Hereditary Breast and Ovarian Cancer cohort of 516 patients. After, the whole CHEK2 coding region was analyzed by next-generation sequencing in 1848 prospective patients with HC suspicion. We refined ACMG-AMP criteria and applied different combined rules to classify CHEK2 variants and define risk alleles. We identified 10 CHEK2 null variants, 6 missense variants with discordant interpretation in ClinVar database, and 35 additional variants of unknown significance. Twelve variants were classified as (likely)-pathogenic; two can also be considered "established risk-alleles" and one as "likely risk-allele." The prevalence of (likely)-pathogenic variants in the HC cohort was 0.8% (1.3% in breast cancer patients and 1.0% in hereditary nonpolyposis colorectal cancer patients). Here, we provide ACMG adjustment guidelines to classify CHEK2 variants. We hope that this study would be useful for variant classification of other genes with low effect variants.

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.

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.

Stratified analyses refine association between TLR7 rare variants and severe COVID-19.

Despite extensive global research into genetic predisposition for severe COVID-19, knowledge on the role of rare host genetic variants and their relation to other risk factors remains limited. Here, 52 genes with prior etiological evidence were sequenced in 1,772 severe COVID-19 cases and 5,347 population-based controls from Spain/Italy. Rare deleterious TLR7 variants were present in 2.4% of young (<60 years) cases with no reported clinical risk factors (n=378), compared to 0.24% of controls (odds ratio (OR)=12.3, p=1.27x10-10). Incorporation of the results of either functional assays or protein modeling led to a pronounced increase in effect size (ORmax=46.5, p=1.74x10-15). Association signals for X-chromosomal TLR7 were also detected in the female-only subgroup, suggesting the existence of additional mechanisms beyond X-linked recessive inheritance in males. Additionally, supporting evidence was generated for a contribution to severe COVID-19 of the previously implicated genes IFNAR2, IFIH1 and TBK1. Our results refine the genetic contribution of rare TLR7 variants to severe COVID-19, and strengthen evidence for the etiological relevance of genes in the interferon signaling pathway.

DNA hypomethylation at ALOX12 is associated with persistent wheezing in childhood.

RATIONALE: Epigenetic changes may play a role in the occurrence of asthma-related phenotypes. OBJECTIVES: To identify epigenetic marks in terms of DNA methylation of asthma-related phenotypes in childhood, and to assess the effect of prenatal exposures and genetic variation on these epigenetic marks. METHODS: Data came from two cohorts embedded in the Infancia y Medio Ambiente (INMA) PROJECT: Menorca (n = 122) and Sabadell (n = 236). Wheezing phenotypes were defined at age 4-6 years. Cytosine-guanine (CpG) dinucleotide site DNA methylation differences associated with wheezing phenotypes were screened in children of the Menorca study using the Illumina GoldenGate Panel I. Findings were validated and replicated using pyrosequencing. Information on maternal smoking and folate supplement use was obtained through questionnaires. Dichlorodiphenyldichloroethylene was measured in cord blood or maternal serum. Genotypes were extracted from genome-wide data. MEASUREMENT AND MAIN RESULTS: Screening identified lower DNA methylation at a CpG site in the arachidonate 12-lipoxygenase (ALOX12) gene in children having persistent wheezing compared with those never wheezed (P = 0.003). DNA hypomethylation at ALOX12 loci was associated with higher risk of persistent wheezing in the Menorca study (odds ratio per 1% methylation decrease, 1.13; 95% confidence interval, 0.99-1.29; P = 0.077) and in the Sabadell study (odds ratio, 1.16; 95% confidence interval, 1.03-1.37; P = 0.017). Higher levels of prenatal dichlorodiphenyldichloroethylene were associated with DNA hypomethylation of ALOX12 in the Menorca study (P = 0.033), but not in the Sabadell study (P = 0.377). ALOX12 DNA methylation was strongly determined by underlying genetic polymorphisms. CONCLUSIONS: DNA methylation of ALOX12 may be an epigenetic biomarker for the risk of asthma-related phenotypes.

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.

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.

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.

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.

Deletion of LCE3C and LCE3B is a susceptibility factor for psoriatic arthritis: a study in Spanish and Italian populations and meta-analysis.

OBJECTIVE: The LCE3C_LCE3B-del variant is associated with psoriasis and rheumatoid arthritis. Its role in psoriatic arthritis (PsA) is unclear, however, as shown by 3 recent studies with contradictory results. In order to investigate whether LCE3C_LCE3B-del constitutes a risk factor for PsA susceptibility, we first tested this variant in patients with PsA from Spanish and Italian populations and then performed a meta-analysis including the previous case-control studies. METHODS: We genotyped LCE3C_LCE3B-del and its tag single-nucleotide polymorphism (SNP), rs4112788, in an original discovery cohort of 424 Italian patients with PsA and 450 unaffected control subjects. A Spanish replication cohort consisting of 225 patients with PsA and 469 control subjects was also genotyped. A meta-analysis considering 7,758 control subjects and 2,325 patients with PsA was also performed. RESULTS: We observed a significant association between PsA and the LCE3C_LCE3B-del tag SNP in the Italian and Spanish cohorts, with an overall corrected P value of 0.00019 and a corresponding odds ratio of 1.35 (95% confidence interval 1.14-1.59). Stratified analyses by subphenotype indicated a stronger association for patients with oligoarticular disease. Meta-analysis including data from all previous published studies confirmed an association of PsA with the LCE3C_LCE3B-del tag SNP. CONCLUSION: LCE3C_LCE3B-del is a susceptibility factor for PsA, confirming the existence of a shared risk factor involving the epidermal skin barrier in autoimmune disorders.

Sequence diversity of the uniparentally transmitted portions of the genome in the resident population of Catalonia.

Genomic reference databases of residing populations are available in different countries and regions. Since they represent the whole genetic diversity of a geographical region, they have wide applications, from biomedical studies to forensic identifications. Uniparentally transmitted portions of the genome specifically are highly suitable for kinship analyses, mixed DNA cases and geographical ancestry inferences. We have sampled 808 individuals currently residing in Catalonia within the GCAT cohort, from which we have generated 808 high-quality whole mitochondrial DNA (mtDNA) genomes and 399 sequences of the male-specific part of the Y chromosome (MSY). We observe higher genetic diversity than in classical population genetics datasets. We test the robustness of whole sequences for unequivocal identifications, and we found that they have higher resolution than mitochondrial control region and Y chromosome short tandem repeats (Y-STRs), and that most of the variants they present are at low frequencies, increasing the discrimination capacity between individuals. These results confirm the forensic applicability of whole uniparental sequences and provide one of the largest high-quality reference datasets ever published.