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

Ruminant health research – progress to date and future prospects, with an emphasis on Irish research

peer-reviewedThis review addresses the progress that has been made in ruminant health research over the last 60 yr, with an emphasis on Irish research. The review focuses on the economically important infectious diseases of dairy and beef cattle and of sheep, calf diseases, regulated and non-regulated infectious diseases, lameness, mastitis and parasitoses. The progress to date, current knowledge and future challenges are all addressed. Paradigm shifts have occurred in many of these diseases, the most profound of which is the change from increasing antimicrobial usage (AMU) to the realisation of the challenge of antimicrobial resistance (AMR) and the current reduction in AMU. Another major change in thinking is the move away from focus on the pathogen exclusively towards a more holistic view of the roles of host immunity and adequacy of management. In the last 60 yr, many new diseases have emerged but in parallel many new technologies have rapidly evolved to monitor and control these threats to animal health. Irish research has contributed substantially to improved current ruminant health. The major future challenge is how to manage ruminant health in a OneHealth world where animal, human and environmental health and sustainability are intimately intertwined and interdependent

Investigation of the performance of different mapping orders for GE on the max problem

Paper presented at the 14th European Conference, EuroGP 2011, Torino, Italy, April 27-29, 2011We present an analysis of how the genotype-phenotype map in Grammatical Evolution (GE) can effect performance on the Max Problem. Earlier studies have demonstrated a performance decrease for Position independent Grammatical Evolution (πGE ) in this problem domain. In πGE the genotype-phenotype map is changed so that the evolutionary algorithm controls not only what the next expansion will be but also the choice of what position in the derivation tree is expanded next. In this study we extend previous work and investigate whether the ability to change the order of expansion is responsible for the performance decrease or if the problem is simply that a certain order of expansion in the genotype-phenotype map is responsible. We conclude that the reduction of performance in the Max problem domain by πGE is rooted in the way the genotype-phenotype map and the genetic operators used with this mapping interact.Science Foundation Irelandti, ke, co, de, se, li -TS 02.12 12 month EMBARG

Dimensions of Early Identification

Several dimensions of early identification are discussed, including the relationship between early identification and prevention. A preventive component is described for the various forms of early identification—child find, screening, assessment, and program planning. Also discussed are recently published guidelines for screening and assessment and the assumptions on which these guidelines are based. Chief among these assumptions is the notion that risk and disability are multidetermined; hence, systems of early identification must similarly be founded on a multiple risk model. The implications of this model for selecting assessment instruments and for determining eligibility are described, as are future directions that should be explored in early identification.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/67883/2/10.1177_105381519101500105.pd