The Change Up Project : using social norming theory with young people to address domestic abuse and promote healthy relationships

This paper presents the findings of a secondary analysis of data collected during a pilot project, Change Up, which used a social norming approach (SNA) to address domestic violence and abuse (DVA) with young people aged 13–14. A SNA is based upon a well-articulated theory of behavior and evidence-based methodology for addressing social justice issues. This reflects a paradigm shift focusing upon strengths and positives, rather than pathologizing behaviours. Adopting a SNA, the Change Up project comprised a baseline survey followed by the intervention (workshop and peer-to-peer campaign), ending with a post-intervention survey. It was delivered in two high schools in a UK city between 2015 and 16. A secondary analysis of the survey data collected during the surveys and qualitative data collected at the end of each workshop was undertaken and this is reported here. Change Up data illustrates that most young people in the sample thought that DVA is unacceptable. There was, however, a gender difference in the norms held about the social acceptability of girls using physical violence against boys (and vice versa). The analysis of Change Up data indicates that a social norming approach to DVA programs aimed at young people can be successful in promoting attitude and behaviour change. It also highlights a continuing need for young people’s education about relationships and gender equality

Genetic predictors of fibrin D-dimer levels in healthy adults

BACKGROUND: Fibrin fragment D-dimer, one of several peptides produced when crosslinked fibrin is degraded by plasmin, is the most widely used clinical marker of activated blood coagulation. To identity genetic loci influencing D-dimer levels, we performed the first large-scale, genome-wide association search. METHODS AND RESULTS: A genome-wide investigation of the genomic correlates of plasma D-dimer levels was conducted among 21 052 European-ancestry adults. Plasma levels of D-dimer were measured independently in each of 13 cohorts. Each study analyzed the association between ≈2.6 million genotyped and imputed variants across the 22 autosomal chromosomes and natural-log-transformed D-dimer levels using linear regression in additive genetic models adjusted for age and sex. Among all variants, 74 exceeded the genome-wide significance threshold and marked 3 regions. At 1p22, rs12029080 (P=6.4×10) was 46.0 kb upstream from F3, coagulation factor III (tissue factor). At 1q24, rs6687813 (P=2.4×10) was 79.7 kb downstream of F5, coagulation factor V. At 4q32, rs13109457 (P=2.9×10) was located between 2 fibrinogen genes: 10.4 kb downstream from FGG and 3.0 kb upstream from FGA. Variants were associated with a 0.099-, 0.096-, and 0.061-unit difference, respectively, in natural-log-transformed D-dimer and together accounted for 1.8% of the total variance. When adjusted for nonsynonymous substitutions in F5 and FGA loci known to be associated with D-dimer levels, there was no evidence of an additional association at either locus. CONCLUSIONS: Three genes were associated with fibrin D-dimer levels. Of these 3, the F3 association was the strongest, and has not been previously reported

Comparison of HapMap and 1000 Genomes Reference Panels in a Large-Scale Genome-Wide Association Study

An increasing number of genome-wide association (GWA) studies are now using the higher resolution 1000 Genomes Project reference panel (1000G) for imputation, with the expectation that 1000G imputation will lead to the discovery of additional associated loci when compared to HapMap imputation. In order to assess the improvement of 1000G over HapMap imputation in identifying associated loci, we compared the results of GWA studies of circulating fibrinogen based on the two reference panels. Using both HapMap and 1000G imputation we performed a meta-analysis of 22 studies comprising the same 91,953 individuals. We identified six additional signals using 1000G imputation, while 29 loci were associated using both HapMap and 1000G imputation. One locus identified using HapMap imputation was not significant using 1000G imputation. The genome-wide significance threshold of 5×10-8 is based on the number of independent statistical tests using HapMap imputation, and 1000G imputation may lead to further independent tests that should be corrected for. When using a stricter Bonferroni correction for the 1000G GWA study (P-value < 2.5×10-8), the number of loci significant only using HapMap imputation increased to 4 while the number of loci significant only using 1000G decreased to 5. In conclusion, 1000G imputation enabled the identification of 20% more loci than HapMap imputation, although the advantage of 1000G imputation became less clear when a stricter Bonferroni correction was used. More generally, our results provide insights that are applicable to the implementation of other dense reference panels that are under development

EORTC phase II study of daily oral linomide in metastatic renal cell carcinoma patients with good prognostic factors

Following a previous EORTC GU-Group study, in which linomide showed some activity in poor prognosis patients, this study was initiated to determine the effect of linomide in more favourable patients. 35 patients with metastatic renal cell carcinoma with good prognostic factors, i.e. good performance status, prior nephrectomy, no prior systemic therapy, and no liver, bone or brain metastases, were treated with linomide, a quinoline derivative with immunomodulating properties, at a dose of 10 mg daily, after an initial dose escalation during the first 4 weeks of treatment. In 29 evaluable patients, no responses were observed (95% confidence interval 0-10%). Best overall response was no change in 9 patients, for a median duration of 4 months. Linomide in this schedule was poorly tolerated, with 17% (6 patients) of patients being withdrawn and 23% (8 patients) having dose reductions due to adverse events, mostly influenza-like symptoms of myalgia, arthralgia and fatigue. Several cases of pericarditis and neuropathy were observed. In spite of selection of favourable prognosis patients and an optimal daily dosing schedule, linomide was not an effective treatment in renal cell carcinoma. In view of toxicity and lack of efficacy, there is no rationale in further testing the drug in this disease. (C) 1997 Published by Elsevier Science Ltd

A meta-analysis of 120 246 individuals identifies 18 new loci for fibrinogen concentration.

Genome-wide association studies have previously identified 23 genetic loci associated with circulating fibrinogen concentration. These studies used HapMap imputation and did not examine the X-chromosome. 1000 Genomes imputation provides better coverage of uncommon variants, and includes indels. We conducted a genome-wide association analysis of 34 studies imputed to the 1000 Genomes Project reference panel and including ∼120 000 participants of European ancestry (95 806 participants with data on the X-chromosome). Approximately 10.7 million single-nucleotide polymorphisms and 1.2 million indels were examined. We identified 41 genome-wide significant fibrinogen loci; of which, 18 were newly identified. There were no genome-wide significant signals on the X-chromosome. The lead variants of five significant loci were indels. We further identified six additional independent signals, including three rare variants, at two previously characterized loci: FGB and IRF1. Together the 41 loci explain 3% of the variance in plasma fibrinogen concentration

Regional plot of 6p21.3, a non-overlapping locus that was more significantly associated with fibrinogen in the HapMap GWA study, including variants from both the HapMap (red) and 1000G (green) GWA studies.

<p>Regional plot of 6p21.3, a non-overlapping locus that was more significantly associated with fibrinogen in the HapMap GWA study, including variants from both the HapMap (red) and 1000G (green) GWA studies.</p

Summary of the differences between HapMap and 1000G imputation for the seven non-overlapping loci.

<p>Summary of the differences between HapMap and 1000G imputation for the seven non-overlapping loci.</p

Regional plots of non-overlapping loci that were more significantly associated with fibrinogen in the 1000G GWA study, including variants from both the HapMap (red) and 1000G (green) GWA studies.

<p>Regional plots of non-overlapping loci that were more significantly associated with fibrinogen in the 1000G GWA study, including variants from both the HapMap (red) and 1000G (green) GWA studies.</p

Summary of the differences between HapMap and 1000G imputation for the 29 overlapping loci.

<p>Summary of the differences between HapMap and 1000G imputation for the 29 overlapping loci.</p