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

Quantification of Oil Spill Risk

The identification and evaluation of oil spill risk is important for contingency planning, as well as for the decision-making processes inherent in spill risk management. It needs to encompass both the probability of oil spills occurring under a number of types of circumstances, along with the potential consequences or impacts of the oil spillage. The quantification of oil spill risk provides policy makers and officials with more objective measures of probabilities, consequences, and overall risk to make informed decisions. Each potential source of oil spillage presents its own challenges for measuring the components of risk. This chapter presents state-of-the-art approaches to risk quantification for four varied spill sources-vessels, oil wells, sunken shipwrecks, and crude oil trains to demonstrate varied approaches.For vessel spills, risk analysis includes calculating the probabilities of vessel accidents that may result in spills through vessel traffic studies, coupled with outflow analyses that determine the probability of spillage and the volume of oil released. The consequences of vessel spills can be quantified through oil spill trajectory, fate, and effects modeling.Determining the probability and magnitude of well blowouts can be accomplished through the application of a fault-tree model. Again, the consequences of spillage can be determined with oil spill trajectory, fate, and effects modeling.Sunken wrecks containing oil present a unique form of spill risk. The wrecks may or may not leak or release oil in some future time until corrosion or disturbance breaks the vessels\u27 bunker or oil cargo tanks. The probability of spillage is dependent on evaluating the factors that may lead to a release. Spill trajectory, fate, and effects modeling can be used to predict potential spill consequences.The dramatic increase in the use of unit trains to transport crude oil in large quantities, coupled with the potential for accidental spills with devastating consequences of fire and explosion, have led to an urgent need to quantify risk. Again, modeling tools can be used to assist decision makers and planners in assessing this risk

Bad splits in bilateral sagittal split osteotomy: systematic review and meta-analysis of reported risk factors

An unfavourable and unanticipated pattern of the bilateral sagittal split osteotomy (BSSO) is generally referred to as a ‘bad split’. Patient factors predictive of a bad split reported in the literature are controversial. Suggested risk factors are reviewed in this article. A systematic review was undertaken, yielding a total of 30 studies published between 1971 and 2015 reporting the incidence of bad split and patient age, and/or surgical technique employed, and/or the presence of third molars. These included 22 retrospective cohort studies, six prospective cohort studies, one matched-pair analysis, and one case series. Spearman's rank correlation showed a statistically significant but weak correlation between increasing average age and increasing occurrence of bad splits in 18 studies (ρ = 0.229; P < 0.01). No comparative studies were found that assessed the incidence of bad split among the different splitting techniques. A meta-analysis pooling the effect sizes of seven cohort studies showed no significant difference in the incidence of bad split between cohorts of patients with third molars present and concomitantly removed during surgery, and patients in whom third molars were removed at least 6 months preoperatively (odds ratio 1.16, 95% confidence interval 0.73–1.85, Z = 0.64, P = 0.52). In summary, there is no robust evidence to date to show that any risk factor influences the incidence of bad split

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

Altres ajuts: Department of Health and Social Care (DHSC); Illumina; LifeArc; Medical Research Council (MRC); UKRI; Sepsis Research (the Fiona Elizabeth Agnew Trust); the Intensive Care Society, Wellcome Trust Senior Research Fellowship (223164/Z/21/Z); BBSRC Institute Program Support Grant to the Roslin Institute (BBS/E/D/20002172, BBS/E/D/10002070, BBS/E/D/30002275); UKRI grants (MC_PC_20004, MC_PC_19025, MC_PC_1905, MRNO2995X/1); UK Research and Innovation (MC_PC_20029); the Wellcome PhD training fellowship for clinicians (204979/Z/16/Z); the Edinburgh Clinical Academic Track (ECAT) programme; the National Institute for Health Research, the Wellcome Trust; the MRC; Cancer Research UK; the DHSC; NHS England; the Smilow family; the National Center for Advancing Translational Sciences of the National Institutes of Health (CTSA award number UL1TR001878); the Perelman School of Medicine at the University of Pennsylvania; National Institute on Aging (NIA U01AG009740); the National Institute on Aging (RC2 AG036495, RC4 AG039029); the Common Fund of the Office of the Director of the National Institutes of Health; NCI; NHGRI; NHLBI; NIDA; NIMH; NINDS.Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care or hospitalization 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