Metabolites in plasma form biosignatures of a range of common complex human diseases. Discovering variants with pleiotropic effects across metabolites can reveal underlying biological mechanisms. We therefore performed uni- and multivariate genome-wide association studies (GWAS) on 249 circulating metabolic markers across 328,006 UK Biobank and Estonian Biobank participants. We investigated rare variation through whole exome sequencing gene burden tests, analysed the role of body mass index through Mendelian randomization, and performed genome-wide interaction analyses with sex. We discovered 15,585 loci summed over the univariate GWAS, with high pleiotropy across markers, linked to a wide range of disorders. Findings from common and rare variant gene tests converged on lipid homeostasis pathways. 31 loci interacted with sex, mapped to genes involved in cholesterol processing. The findings offer insights into the genetic architecture of circulating metabolites, revealing pleiotropic loci, highlighting the role of rare variation, and uncovering sex-specific molecular mechanisms of lipid metabolism.

This paper focuses on weather derivatives as efficient risk management instruments and proposes a more advanced approach for their pricing. An “hybrid” contract is introduced, combining insurance properties, specifically tailored for the region under study and introducing Value-at-Risk (VaR) and Expected Shortfall (ES) as appropriate measures for the strike price. The numerical results show that VaR and ES are both efficient ways for managing the so-called Tail Risk; further, being ES more conservative than VaR and due to its subadditivity property, it can be seen that seasonal contracts are generally better off than monthly contracts in reducing global risk.

We introduce a dynamical model for the time evolution of probability density functions incorporating uncertainty in the parameters. The uncertainty follows stochastic processes, thereby defining a new class of stochastic processes with values in the space of probability densities. The purpose is to quantify uncertainty that can be used for probabilistic forecasting. Starting from a set of traded prices of equity indices such as FTSEMIB, FTSE100 and S&P500 we do some empirical studies. We apply our dynamic probabilistic forecasting to option pricing, where our proposed notion of model uncertainty reduces to uncertainty on future volatility. A distribution of option prices follows, reflecting the uncertainty on the distribution of the underlying prices. We associate measures of model uncertainty of prices in the context of Cont (2006). As a further application we look at the Sharpe ratio and the VaR measure of market risk as well, proposing some decision rules for investors, regulators and risk managers.

Objective: Cognitive impairment is prevalent among individuals with epilepsy, and it is possible that genetic factors can underlie this relationship. Here, we investigated the potential shared genetic basis of common epilepsies and general cognitive ability (COG). Methods: We applied linkage disequilibrium score (LDSC) regression, MiXeR and conjunctional false discovery rate (conjFDR) to analyze different aspects of genetic overlap between COG and epilepsies. We used the largest available genome–wide association study data on COG ( n = 269,867) and common epilepsies ( n = 27,559 cases, 42,436 controls), including the broad phenotypes ′all epilepsy ′, focal epilepsies and genetic generalized epilepsies (GGE), and as well as specific subtypes. We functionally annotated the identified loci using a variety of biological resources and validated the results in independent samples. Results: Using MiXeR, COG (11.2k variants) was estimated to be almost four times more polygenic than ′all epilepsy′, GGE, juvenile myoclonic epilepsy (JME), and childhood absence epilepsy (CAE) (2.5k — 2.9k variants). The other epilepsy phenotypes were insufficiently powered for analysis. We show extensive genetic overlap between COG and epilepsies with significant negative genetic correlations (-0.23 to -0.04). COG was estimated to share 2.9k variants with both GGE and ′all epilepsy′, and 2.3k variants with both JME and CAE. Using conjFDR, we identified 66 distinct loci shared between COG and epilepsies, including novel associations for GGE (27), ′all epilepsy′ (5), JME (5) and CAE (5). The implicated genes were significantly expressed in multiple brain regions. The results were validated in independent samples (COG: p = 1.0 × 10 ^-14 ; ′all epilepsy′: p = 5.6 × 10 ^-3 ). Significance: Our study demonstrates a substantial genetic basis shared between epilepsies and COG and identifies novel overlapping genomic loci. Enhancing our understanding of the relationship between epilepsies and COG may lead to the development of novel comorbidity–targeted epilepsy treatments.

SARS-CoV-2 virus is currently one of the most virulent pathogens for humans. It started in China at the end of 2019 and now it is spread in all over the world. The origin and diffusion of the SARS-CoV-2 epidemic, is analysed based on the discussion of viral phylogeny presented in Cristianini & Hahn (2006). With the aim of understanding the spread of infection in the affected countries, it is crucial to modelize the spread of the virus and simulate its activity. In this paper, the prediction of coronavirus outbreak is done by using SIR model without vital dynamics, applying different numerical technique solving ODEs. We find out that ABM and MRT methods perform better than other techniques and that the activity of the virus will decrease in April but it never cease (for sometime the activity will remain low) and the next cycle will start around the middle July 2020 for Norway and Denmark, and October 2020 for Sweden, and September for Finland.