Genetic Architecture of Ischaemic Strokes after COVID-19 Shows Similarities with Large Vessel Strokes

We aimed to analyse whether patients with ischaemic stroke (IS) occurring within eight days after the onset of COVID-19 (IS-COV) are associated with a specific aetiology of IS. We used SUPERGNOVA to identify genome regions that correlate between the IS-COV cohort (73 IS-COV cases vs. 701 population controls) and different aetiological subtypes. Polygenic risk scores (PRSs) for each subtype were generated and tested in the IS-COV cohort using PRSice-2 and PLINK to find genetic associations. Both analyses used the IS-COV cohort and GWAS from MEGASTROKE (67,162 stroke patients vs. 454,450 population controls), GIGASTROKE (110,182 vs. 1,503,898), and the NINDS Stroke Genetics Network (16,851 vs. 32,473). Three genomic regions were associated (p-value < 0.05) with large artery atherosclerosis (LAA) and cardioembolic stroke (CES). We found four loci targeting the genes PITX2 (rs10033464, IS-COV beta = 0.04, p-value = 2.3 × 10-2, se = 0.02), previously associated with CES, HS6ST1 (rs4662630, IS-COV beta = -0.04, p-value = 1.3 × 10-3, se = 0.01), TMEM132E (rs12941838 IS-COV beta = 0.05, p-value = 3.6 × 10-4, se = 0.01), and RFFL (rs797989 IS-COV beta = 0.03, p-value = 1.0 × 10-2, se = 0.01). A statistically significant PRS was observed for LAA. Our results suggest that IS-COV cases are genetically similar to LAA and CES subtypes. Larger cohorts are needed to assess if the genetic factors in IS-COV cases are shared with the general population or specific to viral infection.

Patterns of item nonresponse behaviour to survey questionnaires are systematic and associated with genetic loci

Response to survey questionnaires is vital for social and behavioural research, and most analyses assume full and accurate response by participants. However, nonresponse is common and impedes proper interpretation and generalizability of results. We examined item nonresponse behaviour across 109 questionnaire items in the UK Biobank (N = 360,628). Phenotypic factor scores for two participant-selected nonresponse answers, 'Prefer not to answer' (PNA) and 'I don't know' (IDK), each predicted participant nonresponse in follow-up surveys (incremental pseudo-R2 = 0.056), even when controlling for education and self-reported health (incremental pseudo-R2 = 0.046). After performing genome-wide association studies of our factors, PNA and IDK were highly genetically correlated with one another (rg = 0.73 (s.e. = 0.03)) and with education (rg,PNA = -0.51 (s.e. = 0.03); rg,IDK = -0.38 (s.e. = 0.02)), health (rg,PNA = 0.51 (s.e. = 0.03); rg,IDK = 0.49 (s.e. = 0.02)) and income (rg,PNA = -0.57 (s.e. = 0.04); rg,IDK = -0.46 (s.e. = 0.02)), with additional unique genetic associations observed for both PNA and IDK (P < 5 × 10-8). We discuss how these associations may bias studies of traits correlated with item nonresponse and demonstrate how this bias may substantially affect genome-wide association studies. While the UK Biobank data are deidentified, we further protected participant privacy by avoiding exploring non-response behaviour to single questions, assuring that no information can be used to associate results with any particular respondents.

Genetic predictors of lifelong medication-use patterns in cardiometabolic diseases

Little is known about the genetic determinants of medication use in preventing cardiometabolic diseases. Using the Finnish nationwide drug purchase registry with follow-up since 1995, we performed genome-wide association analyses of longitudinal patterns of medication use in hyperlipidemia, hypertension and type 2 diabetes in up to 193,933 individuals (55% women) in the FinnGen study. In meta-analyses of up to 567,671 individuals combining FinnGen with the Estonian Biobank and the UK Biobank, we discovered 333 independent loci (P < 5 × 10-9) associated with medication use. Fine-mapping revealed 494 95% credible sets associated with the total number of medication purchases, changes in medication combinations or treatment discontinuation, including 46 credible sets in 40 loci not associated with the underlying treatment targets. The polygenic risk scores (PRS) for cardiometabolic risk factors were strongly associated with the medication-use behavior. A medication-use enhanced multitrait PRS for coronary artery disease matched the performance of a risk factor-based multitrait coronary artery disease PRS in an independent sample (UK Biobank, n = 343,676). In summary, we demonstrate medication-based strategies for identifying cardiometabolic risk loci and provide genome-wide tools for preventing cardiovascular diseases.

Cystatin C is associated with adverse COVID-19 outcomes in diverse populations

COVID-19 has highly variable clinical courses. The search for prognostic host factors for COVID-19 outcome is a priority. We performed logistic regression for ICU admission against a polygenic score (PGS) for Cystatin C (CyC) production in patients with COVID-19. We analyzed the predictive value of longitudinal plasma CyC levels in an independent cohort of patients hospitalized with COVID-19. In four cohorts spanning European and African ancestry populations, we identified a significant association between CyC-production PGS and odds of critical illness (n cases=2,319), with the strongest association captured in the UKB cohort (OR 2.13, 95% CI 1.58-2.87, p=7.12e-7). Plasma proteomics from an independent cohort of hospitalized COVID-19 patients (n cases = 131) demonstrated that CyC production was associated with COVID-specific mortality (p=0.0007). Our findings suggest that CyC may be useful for stratification of patients and it has functional role in the host response to COVID-19.

•

Cystatin C varies independently of renal function in COVID-19

•

A polygenic score for cystatin C production predicts critical COVID-19 illness

•

Elevated serum cystatin C is associated with COVID-19 mortality

Detailed stratified GWAS analysis for severe COVID-19 in four European populations

Given the highly variable clinical phenotype of Coronavirus disease 2019 (COVID-19), a deeper analysis of the host genetic contribution to severe COVID-19 is important to improve our understanding of underlying disease mechanisms. Here, we describe an extended genome-wide association meta-analysis of a well-characterized cohort of 3255 COVID-19 patients with respiratory failure and 12 488 population controls from Italy, Spain, Norway and Germany/Austria, including stratified analyses based on age, sex and disease severity, as well as targeted analyses of chromosome Y haplotypes, the human leukocyte antigen region and the SARS-CoV-2 peptidome. By inversion imputation, we traced a reported association at 17q21.31 to a ~0.9-Mb inversion polymorphism that creates two highly differentiated haplotypes and characterized the potential effects of the inversion in detail. Our data, together with the 5th release of summary statistics from the COVID-19 Host Genetics Initiative including non-Caucasian individuals, also identified a new locus at 19q13.33, including NAPSA, a gene which is expressed primarily in alveolar cells responsible for gas exchange in the lung.

Age-dependent impact of the major common genetic risk factor for COVID-19 on severity and mortality

BACKGROUND

There is considerable variability in COVID-19 outcomes amongst younger adults-and some of this variation may be due to genetic predisposition.

METHODS

We combined individual level data from 13,888 COVID-19 patients (N=7,185 hospitalized) from 17 cohorts in nine countries to assess the association of the major common COVID-19 genetic risk factor (chromosome 3 locus tagged by rs10490770) with mortality, COVID-19-related complications and laboratory values. We next performed meta-analyses using FinnGen and the Columbia University COVID-19 Biobank.

RESULTS

We found that rs10490770 risk allele carriers experienced an increased risk of all-cause mortality (HR 1.4, 95%CI 1.2-1.7). Risk allele carriers had increased odds of several COVID-19 complications: severe respiratory failure (OR 2.1, 95%CI 1.6-2.6), venous thromboembolism (OR 1.7, 95%CI 1.2-2.4), and hepatic injury (OR 1.5, 95%CI 1.2-2.0). Risk allele carriers ≤60 years had higher odds of death or severe respiratory failure (OR 2.7, 95%CI 1.8-3.9) compared to those >60 years (OR 1.5, 95%CI 1.2-1.8, interaction-p=0.038). Amongst individuals ≤60 years who died or experienced severe respiratory failure, 32.3% were risk variant carriers, compared to 13.9% of those not experiencing these outcomes. The genetic risk improved the prediction of death or severe respiratory failure similarly to, or better than, most established clinical risk factors.

CONCLUSIONS

The major common COVID-19 genetic risk factor is associated with increased risks of morbidity and mortality, which are more pronounced amongst individuals ≤60 years. The effect was similar in magnitude and more common than most established clinical risk factors, suggesting potential implications for future clinical risk management.

Genetic analyses identify widespread sex-differential participation bias

Genetic association results are often interpreted with the assumption that study participation does not affect downstream analyses. Understanding the genetic basis of participation bias is challenging since it requires the genotypes of unseen individuals. Here we demonstrate that it is possible to estimate comparative biases by performing a genome-wide association study contrasting one subgroup versus another. For example, we showed that sex exhibits artifactual autosomal heritability in the presence of sex-differential participation bias. By performing a genome-wide association study of sex in approximately 3.3 million males and females, we identified over 158 autosomal loci spuriously associated with sex and highlighted complex traits underpinning differences in study participation between the sexes. For example, the body mass index-increasing allele at FTO was observed at higher frequency in males compared to females (odds ratio = 1.02, P = 4.4 × 10^-36). Finally, we demonstrated how these biases can potentially lead to incorrect inferences in downstream analyses and propose a conceptual framework for addressing such biases. Our findings highlight a new challenge that genetic studies may face as sample sizes continue to grow.

Genetic analyses identify widespread sex-differential participation bias

Genetic association results are often interpreted with the assumption that study participation does not affect downstream analyses. Understanding the genetic basis of participation bias is challenging since it requires the genotypes of unseen individuals. Here we demonstrate that it is possible to estimate comparative biases by performing a genome-wide association study contrasting one subgroup versus another. For example, we showed that sex exhibits artifactual autosomal heritability in the presence of sex-differential participation bias. By performing a genome-wide association study of sex in approximately 3.3 million males and females, we identified over 158 autosomal loci spuriously associated with sex and highlighted complex traits underpinning differences in study participation between the sexes. For example, the body mass index-increasing allele at FTO was observed at higher frequency in males compared to females (odds ratio = 1.02, P = 4.4 × 10^-36). Finally, we demonstrated how these biases can potentially lead to incorrect inferences in downstream analyses and propose a conceptual framework for addressing such biases. Our findings highlight a new challenge that genetic studies may face as sample sizes continue to grow.

Age-dependent impact of the major common genetic risk factor for COVID-19 on severity and mortality

BACKGROUND

There is considerable variability in COVID-19 outcomes amongst younger adults-and some of this variation may be due to genetic predisposition. We characterized the clinical implications of the major genetic risk factor for COVID-19 severity, and its age-dependent effect, using individual-level data in a large international multi-centre consortium.

METHOD

The major common COVID-19 genetic risk factor is a chromosome 3 locus, tagged by the marker rs10490770. We combined individual level data for 13,424 COVID-19 positive patients (N=6,689 hospitalized) from 17 cohorts in nine countries to assess the association of this genetic marker with mortality, COVID-19-related complications and laboratory values. We next examined if the magnitude of these associations varied by age and were independent from known clinical COVID-19 risk factors.

FINDINGS

We found that rs10490770 risk allele carriers experienced an increased risk of all-cause mortality (hazard ratio [HR] 1·4, 95% confidence interval [CI] 1·2-1·6) and COVID-19 related mortality (HR 1·5, 95%CI 1·3-1·8). Risk allele carriers had increased odds of several COVID-19 complications: severe respiratory failure (odds ratio [OR] 2·0, 95%CI 1·6-2·6), venous thromboembolism (OR 1·7, 95%CI 1·2-2·4), and hepatic injury (OR 1·6, 95%CI 1·2-2·0). Risk allele carriers ≤ 60 years had higher odds of death or severe respiratory failure (OR 2·6, 95%CI 1·8-3·9) compared to those > 60 years OR 1·5 (95%CI 1·3-1·9, interaction p-value=0·04). Amongst individuals ≤ 60 years who died or experienced severe respiratory COVID-19 outcome, we found that 31·8% (95%CI 27·6-36·2) were risk variant carriers, compared to 13·9% (95%CI 12·6-15·2%) of those not experiencing these outcomes. Prediction of death or severe respiratory failure among those ≤ 60 years improved when including the risk allele (AUC 0·82 vs 0·84, p=0·016) and the prediction ability of rs10490770 risk allele was similar to, or better than, most established clinical risk factors.

INTERPRETATION

The major common COVID-19 risk locus on chromosome 3 is associated with increased risks of morbidity and mortality-and these are more pronounced amongst individuals ≤ 60 years. The effect on COVID-19 severity was similar to, or larger than most established risk factors, suggesting potential implications for clinical risk management.

FUNDING

Funding was obtained by each of the participating cohorts individually.

Combining land use regression models and fixed site monitoring to reconstruct spatiotemporal variability of NO2 concentrations over a wide geographical area

The epidemiological research benefits from an accurate characterization of both spatial and temporal variability of exposure to air pollution. This work aims at proposing a method to combine the high spatial resolution of Land Use Regression (LUR) models with the high temporal resolution of fixed site monitoring data, to model spatiotemporal variability of NO₂ over a wide geographical area in Northern Italy. We developed seasonal LUR models to reconstruct the spatial distribution of a scaling factor that relates local concentrations to those measured at two reference central sites, one for the northern flat area and one for the southern mountain area. We calculated the daily average concentrations at 19 locations spread over the study areas as the product of the local scaling factor and the reference central site concentrations. We evaluated model performance comparing modeled and measured NO₂ data. LUR model's R² ranges from 0.76 to 0.92. The main predictors refers substantially to traffic, industrial land use, buildings volume and altitude a.s.l. The model's performance in reproducing measured concentrations was satisfactory. The temporal variability of concentrations was well captured: Spearman correlation between model and measures was >0.7 for almost all sites. Model's average absolute errors were in the order of 10μgm^-3. The model for the southern area tends to overestimate measured concentrations. Our modeling framework was able to reproduce spatiotemporal differences in NO₂ concentrations. This kind of model is less data-intensive than usual regional atmospheric models and it may be very helpful to assess population exposure within studies in which individual relevant exposure occurs along periods of days or months.

Exposure to emissions from municipal solid waste incinerators and miscarriages: a multisite study of the MONITER Project

BACKGROUND

Miscarriages are an important indicator of reproductive health but only few studies have analyzed their association with exposure to emissions from municipal solid waste incinerators. This study analyzed the occurrence of miscarriages in women aged 15-49years residing near seven incinerators of the Emilia-Romagna Region (Northern Italy) in the period 2002-2006.

METHODS

We considered all pregnancies occurring in women residing during the first trimester of pregnancy within a 4km radius of each incinerator. Addresses were geocoded and exposures were characterized by a dispersion model (ADMS Urban model) producing pollution maps for incinerators based on PM10 stack measurements and for other pollution sources based on NOx ground measurements. Information on pregnancies and their outcomes was obtained from the Hospital Discharge Database. Simplified True Abortion Risks (STAR)×100 estimated pregnancies were calculated. We ran logistic regressions adjusting for maternal characteristics, exposure to other sources of pollution, and sites, considering the whole population and stratifying by miscarriage history.

RESULTS

The study analyzed 11,875 pregnancies with 1375 miscarriages. After adjusting for confounders, an increase of PM10 due to incinerator emissions was associated with an increased risk of miscarriage (test for trend, p=0.042). The odds ratio for the highest quartile of exposed versus not exposed women was 1.29, 95% CI 0.97-1.72. The effect was present only for women without previous miscarriages (highest quartile of exposed versus not exposed women 1.44, 95% CI 1.06-1.96; test for trend, p=0.009).

CONCLUSION

Exposure to incinerator emissions is associated with an increased risk of miscarriage. This result should be interpreted with those of a previous study on reproductive health conducted in the same area that observed an association between incinerator exposure and preterm births.

A comparison between self-reported and GIS-based proxies of residential exposure to environmental pollution in a case-control study on lung cancer

In epidemiological studies both questionnaire results and GIS modeling have been used to assess exposure to environmental risk factors. Nevertheless, few studies have used both these techniques to evaluate the degree of agreement between different exposure assessment methodologies. As part of a case-control study on lung cancer, we present a comparison between self-reported and GIS-derived proxies of residential exposure to environmental pollution. 649 subjects were asked to fill out a questionnaire and give information about residential history and perceived exposure. Using GIS, for each residence we evaluated land use patterns, proximity to major roads and exposure to industrial pollution. We then compared the GIS exposure-index values among groups created on the basis of questionnaire responses. Our results showed a relatively high agreement between the two methods. Although none of these methods is the "exposure gold standard", understanding similarities, weaknesses and strengths of each method is essential to strengthen epidemiological evidence.