Common variants near MC4R are associated with fat mass, weight and risk of obesity.

To identify common variants influencing body mass index (BMI), we analyzed genome-wide association data from 16,876 individuals of European descent. After previously reported variants in FTO, the strongest association signal (rs17782313, P = 2.9 x 10(-6)) mapped 188 kb downstream of MC4R (melanocortin-4 receptor), mutations of which are the leading cause of monogenic severe childhood-onset obesity. We confirmed the BMI association in 60,352 adults (per-allele effect = 0.05 Z-score units; P = 2.8 x 10(-15)) and 5,988 children aged 7-11 (0.13 Z-score units; P = 1.5 x 10(-8)). In case-control analyses (n = 10,583), the odds for severe childhood obesity reached 1.30 (P = 8.0 x 10(-11)). Furthermore, we observed overtransmission of the risk allele to obese offspring in 660 families (P (pedigree disequilibrium test average; PDT-avg) = 2.4 x 10(-4)). The SNP location and patterns of phenotypic associations are consistent with effects mediated through altered MC4R function. Our findings establish that common variants near MC4R influence fat mass, weight and obesity risk at the population level and reinforce the need for large-scale data integration to identify variants influencing continuous biomedical traits

Novel Loci for Adiponectin Levels and Their Influence on Type 2 Diabetes and Metabolic Traits : A Multi-Ethnic Meta-Analysis of 45,891 Individuals

J. Kaprio, S. Ripatti ja M.-L. Lokki työryhmien jäseniä.Peer reviewe

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

Steady-state analysis of self-excited induction generators using genetic algorithm approach under different operating modes

The main focus of this paper is the steady-state analysis of self-excited induction generators (SEIGs). It employs the genetic algorithm approach (GAA) to estimate the steady-state performance of these machines. Further, theGAAis used for the solution of problems related to the operation of a number of SEIGs running in parallel. GA-based modelling is found to be effective to determine the generated voltage and frequency. Experimental results validate the proposed methodology

Put yourself in our shoes: considering children’s best interests in the asylum system

Data collection: Throughout 2014, 11 participating Law Centres uploaded anonymised data on 60 cases which met these selection criteria: 1. The child’s claimed age was under 18 years old at the point they claimed asylum 2. The Home Office treated the child as under 18 years old, even if local authority disputed this 3. The child was unaccompanied or separated  4. The child was seeking asylum alone, i.e. they were not a dependent on any adult’s asylum claim 5. The child’s substantive asylum interview took place between 1 December 2013 and 31 December 2014. For each case over 600 questions were asked. In addition to this, the Project ran two focus groups to obtain the views of young people who had recent experience of the asylum process in the UK.  Data analysis: This focused on ascertaining a clear picture of the related experiences of children and their legal representatives as they work together through the complex process of claiming international protection. This was set against existing national and international law and custom, highlighted throughout the report, which provides a frame of reference for lawyers seeking to promote their child clients’ best interests. Along with identifying areas of good practice by lawyers, immigration officials, statutory and voluntary care givers and other advocates, the analysis also suggested areas for improvement for those seeking to offer these children fair processes which will ensure their safety and long term security. Recommendations: The authors are aware of discussions of the limitations of the current system in the UK for deciding the future of children who arrive here on their own, and have deliberately restricted their recommendations to issues arising from information collected by lawyers working within the current systems and that are evidenced by the data collected