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

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 novel approach to estimate direct and indirect water withdrawals from satellite measurements: A case study from the Incomati basin

The Incomati basin encompasses parts of South Africa, Swaziland and Mozambique, and is a water stressed basin. Equitable allocation of water is crucial to sustain livelihoods and agro-ecosystems, and to sustain international agreements. As compliance monitoring of water distribution by flow meters is laborious, expensive and only partially feasible, a novel approach has been developed to estimate water withdrawals using satellite measurements. Direct withdrawals include pumping from rivers, impoundments and groundwater, for irrigation and other human uses. Indirect withdrawals include evaporation processes from groundwater storage, unconfined shallow aquifers, seepage zones, lakes and reservoirs, and inundations, in addition to evaporation from pristine land surface conditions. Indirect withdrawals intercept lateral flow of water and reduce downstream flow. An innovative approach has been developed that employs three main spatial data layers inferred from satellite measurements: land use, rainfall, and evaporation. The evaporation/rainfall ratio was computed for all natural land use classes and used to distinguish between evaporation from rainfall and incremental evaporation caused by water withdrawals. The remote sensing measurements were validated against measured evaporative flux, stream flow pumping volume, and stream flow reductions. Afforested areas in the whole basin was responsible for an indirect withdrawal of 1241 Mm3/yr during an average rainfall year while the tripartite agreement among the riparian countries specifies a permitted total withdrawal of 546 Mm3/yr. However, the irrigation sector is responsible for direct withdrawals of 555 Mm3/yr only while their allocated share is 1327 Mm3/yr – the long term total withdrawals are thus in line with the tripartite agreement. South Africa withdraws 1504 Mm3/yr while their share is 1261 Mm3/yr. The unmetered stream flow reduction from the afforested areas in South Africa represents the big uncertainty factor. The methodology described using remotely sensed measurements to estimate direct and indirect withdrawals has the potential to be applied more widely to water stressed basins having limited availability of field data