SARS-CoV-2 susceptibility and COVID-19 disease severity are associated with genetic variants affecting gene expression in a variety of tissues

Variability in SARS-CoV-2 susceptibility and COVID-19 disease severity between individuals is partly due to genetic factors. Here, we identify 4 genomic loci with suggestive associations for SARS-CoV-2 susceptibility and 19 for COVID-19 disease severity. Four of these 23 loci likely have an ethnicity-specific component. Genome-wide association study (GWAS) signals in 11 loci colocalize with expression quantitative trait loci (eQTLs) associated with the expression of 20 genes in 62 tissues/cell types (range: 1:43 tissues/gene), including lung, brain, heart, muscle, and skin as well as the digestive system and immune system. We perform genetic fine mapping to compute 99% credible SNP sets, which identify 10 GWAS loci that have eight or fewer SNPs in the credible set, including three loci with one single likely causal SNP. Our study suggests that the diverse symptoms and disease severity of COVID-19 observed between individuals is associated with variants across the genome, affecting gene expression levels in a wide variety of tissue types

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2–4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

SARS-CoV-2 susceptibility and COVID-19 disease severity are associated with genetic variants affecting gene expression in a variety of tissues

Variability in SARS-CoV-2 susceptibility and COVID-19 disease severity between individuals is partly due to genetic factors. Here, we identify 4 genomic loci with suggestive associations for SARS-CoV-2 susceptibility and 19 for COVID-19 disease severity. Four of these 23 loci likely have an ethnicity-specific component. Genome-wide association study (GWAS) signals in 11 loci colocalize with expression quantitative trait loci (eQTLs) associated with the expression of 20 genes in 62 tissues/cell types (range: 1:43 tissues/gene), including lung, brain, heart, muscle, and skin as well as the digestive system and immune system. We perform genetic fine mapping to compute 99% credible SNP sets, which identify 10 GWAS loci that have eight or fewer SNPs in the credible set, including three loci with one single likely causal SNP. Our study suggests that the diverse symptoms and disease severity of COVID-19 observed between individuals is associated with variants across the genome, affecting gene expression levels in a wide variety of tissue types

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

A first update on mapping the human genetic architecture of COVID-19

peer reviewe

Novel Autosomal Recessive Splice-Altering Variant in PRKD1 Is Associated with Congenital Heart Disease

Congenital heart defects (CHDs) are the most common types of birth defects, and global incidence of CHDs is on the rise. Despite the prevalence of CHDs, the genetic determinants of the defects are still in the process of being identified. Herein, we report a consanguineous Saudi family with three CHD affected daughters. We used whole exome sequencing (WES) to investigate the genetic cause of CHDs in the affected daughters. We found that all affected individuals were homozygous for a novel splice-altering variant (NM_001330069.1: c.265-1G>T) of PRKD1, which encodes a calcium/calmodulin-dependent protein kinase in the heart. The homozygous variant was found in the affected patients with Pulmonary Stenosis (PS), Truncus Arteriosis (TA), and Atrial Septal Defect (ASD). Based on the family’s pedigree, the variant acts in an autosomal recessive manner, which makes it the second autosomal recessive variant of PRKD1 to be identified with a link to CHDs, while all other previously described variants act dominantly. Interestingly, the father of the affected daughters was also homozygous for the variant, though he was asymptomatic of CHDs himself. Since both of his sisters had CHDs as well, this raises the possibility that the novel PRKD1 variant may undergo autosomal recessive inheritance mode with gender limitation. This finding confirms that CHD can be associated with both dominant and recessive mutations of the PRKD1 gene, and it provides a new insight to genotype–phenotype association between PRKD1 and CHDs. To our knowledge, this is the first report of this specific PRKD1 mutation associated with CHDs

C2G Online Trust, Perceived Government Responsiveness and User Experience

Part 2: E-Government Services and Open GovernmentInternational audienceThe paper presents the results of the pilot study of C2G online trust, that covers citizens’ trust in communication with the government via e-government, e-participation and social media channels. Based on the survey carried out in St. Petersburg, we explore dimensions of C2G trust and test the impact of perceived government responsiveness, user experience and socio-demographic factors in shaping trust. Our findings suggest that both perceived responsiveness and user experience influence the level of trust, while age, gender and education are not significant when controlled to the frequency of Internet use. The research proposes to view C2G trust as a multidimensional phenomenon, as its dynamics may vary across the tools and sectors used. Implications for future research are given

Citizen Participation in Smart Government: A Conceptual Model and Two IoT Case Studies

In its simplest form, smart government can be understood as the combination of new technologies and organizational innovation strategies to further modernize the public sector. Within this development, the Internet of Things (IoT) often forms a key technological foundation, offering government authorities new possibilities for inter-action with citizens and local communities. On one hand, citizens can indirectly partic-ipate in governmental services’ value creation by using public infrastructure or (un)knowingly sharing their data with the community. On the other hand, smart gov-ernment initiatives may rely more intensively on citizens’ active participation to im-prove public service delivery, increase trust in government actions, and strengthen community sentiment. In this chapter, we discuss active and passive participation sce-narios of smart government initiatives and explain how sensor-based systems may en-hance citizens’ opportunities to participate in local governance. We present two prac-tical cases from Switzerland demonstrating these two citizen involvement modes. We argue that active and passive participation of citizens and other stakeholders play key role in generating necessary data for algorithmic decision-making to enable personal-ized interaction and real-time control of infrastructure in the future. We close with a discussion of the possibilities and boundaries of the IoT in the public sector and their possible influences on citizens’ private lives and policy-making