A genome-wide association study for survival from a multi-centre European study identified variants associated with COVID-19 risk of death

: The clinical manifestations of SARS-CoV-2 infection vary widely among patients, from asymptomatic to life-threatening. Host genetics is one of the factors that contributes to this variability as previously reported by the COVID-19 Host Genetics Initiative (HGI), which identified sixteen loci associated with COVID-19 severity. Herein, we investigated the genetic determinants of COVID-19 mortality, by performing a case-only genome-wide survival analysis, 60 days after infection, of 3904 COVID-19 patients from the GEN-COVID and other European series (EGAS00001005304 study of the COVID-19 HGI). Using imputed genotype data, we carried out a survival analysis using the Cox model adjusted for age, age2, sex, series, time of infection, and the first ten principal components. We observed a genome-wide significant (P-value < 5.0 × 10-8) association of the rs117011822 variant, on chromosome 11, of rs7208524 on chromosome 17, approaching the genome-wide threshold (P-value = 5.19 × 10-8). A total of 113 variants were associated with survival at P-value < 1.0 × 10-5 and most of them regulated the expression of genes involved in immune response (e.g., CD300 and KLR genes), or in lung repair and function (e.g., FGF19 and CDH13). Overall, our results suggest that germline variants may modulate COVID-19 risk of death, possibly through the regulation of gene expression in immune response and lung function pathways

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

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

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Optimizing Statistical Classifiers of Network Traffic

Supervised statistical approaches for the classification of network traffic are quickly moving from research laboratories to advanced prototypes, which in turn will become actual products in the next few years. While the research on the classification algorithms themselves has made quite significant progress in the recent past, few papers have examined the problem of determining the optimum working parameters for statistical classifiers in a straightforward and foolproof way. Without such optimization, it becomes very difficult to put into practice any classification algorithm for network traffic, no matter how advanced it may be. In this paper we present a simple but effiective procedure for the optimization of the working parameters of a statistical network traffic classifier. We put the optimization procedure into practice, and examine its effects when the classifier is run in very different scenarios, ranging from medium and large local area networks to Internet backbone links. Experimental results show not only that an automatic optimization procedure like the one presented in this paper is necessary for the classifier to work at its best, but they also shed some light on some of the properties of the classification algorithm that deserve further study

Detecting HTTP Tunnels with Statistical Mechanisms

Abstract — Application Level Gateways and firewalls are commonly used to enforce security policies at network boundaries, especially in large-sized business networks. However, several mechanisms can be used to circumvent these policies and bypass the whole security infrastructure: for example, tunneling an (otherwise blocked) application layer protocol into another one allowed by the policy, such as HTTP. In this paper we propose the application of a statistically-based traffic classification technique to solve this problem. By the analysis of inter–arrival time, size and order of the packets crossing a gateway, we show that it is possible to detect with high accuracy whether an observed flow is carrying a legitimate HTTP session, or the flow is being used to tunnel another protocol. This paper describes how this technique can be used effectively to enhance Application Level Gateways and firewalls, helping to better apply network security policies. I

Detection of encrypted tunnels across network boundaries

Abstract — The use of covert application-layer tunnels to bypass security gateways has become quite popular in recent years. By encapsulating blocked or controlled protocols such as peerto-peer, chat and e-mail into others allowed by the security policies, such as HTTP, SSH or even DNS, both legitimate and malicious users can effectively neutralize many security restrictions enforced at the network edge. Traditional firewalling techniques, based on Application Layer Gateways and even pattern-matching mechanisms are becoming practically useless as tunneling tools grow more sophisticated. In this paper we propose an effective solution to this problem based on a statistical traffic classification technique. Our mechanism relies on the creation of a statistical fingerprint of legitimate usage of a given protocol, such as regular remote interactive logins or secure copying activities. Such fingerprint can then be used to detect with high accuracy non-legitimate sessions, i.e., sessions that tunnel other protocols. Results from experiments conducted on a live network suggest that the technique can be very effective, even when the application layer protocol used as a tunnel is encrypted, such as in the case of SSH. I