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

Energetic Path Finding Across Massive Terrain Data

Throughout history, the primary means of transportation for humans has been on foot. We present a software tool which can help visualize and predict where historical trails might lie through the use of a human-centered cost metric, with an emphasis on the ability to generate paths which traverse several thousand kilometers. To accomplish this, various graph simplification and path approximation algorithms are explored. We show that it is possible to restrict the search space for a path finding algorithm while not sacrificing accuracy. Combined with a multi-threaded variant of Dijkstra’s shortest path algorithm, we present a tool capable of computing a path of least caloric cost across the contiguous US, a dataset containing over 19 billion datapoints, in under three hours on a 2.5 Ghz dual core processor. The potential archaeological and historical applications are demonstrated on several examples

Energetic Path Finding Across Massive Terrain Data

Abstract. Throughout history, the primary means of transportation for humans has been on foot. We present a software tool which can help visualize and predict where historical trails might lie through the use of a human-centered cost metric, with an emphasis on the ability to generate paths which traverse several thousand kilometers. To accomplish this, various graph simplification and path approximation algorithms are explored. We show that it is possible to restrict the search space for a path finding algorithm while not sacrificing accuracy. Combined with a multi-threaded variant of Dijkstra’s shortest path algorithm, we present a tool capable of computing a path of least caloric cost across the contiguous US, a dataset containing over 19 billion datapoints, in under three hours on a 2.5 Ghz dual core processor. The potential archaeological and historical applications are demonstrated on several examples.