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Background: During the set-up phase of an international study of genetic influences on outcomes from sepsis, we aimed to characterise potential differences in ethics approval processes and outcomes in participating European countries., Methods: Between 2005 and 2007 of the FP6-funded international Genetics Of Sepsis and Septic Shock (GenOSept) project, we asked national coordinators to complete a structured survey of research ethic committee (REC) approval structures and processes in their countries, and linked these data to outcomes. Survey findings were reconfirmed or modified in 2017., Results: Eighteen countries participated in the study, recruiting 2257 patients from 160 ICUs. National practices differed widely in terms of composition of RECs, procedures and duration of the ethics approval process. Eight (44.4%) countries used a single centralised process for approval, seven (38.9%) required approval by an ethics committee in each participating hospital, and three (16.7%) required both. Outcomes of the application process differed widely between countries because of differences in national legislation, and differed within countries because of interpretation of the ethics of conducting research in patients lacking capacity. The RECs in four countries had no lay representation. The median time from submission to final decision was 1.5 (interquartile range 1-7) months; in nine (50%) approval was received within 1 month; six took over 6 months, and in one 24 months; had all countries been able to match the most efficient approvals processes, an additional 74 months of country or institution-level recruitment would have been available. In three countries, rejection of the application by some local RECs resulted in loss of centres; and one country rejected the application outright., Conclusions: The potential benefits of the single application portal offered by the European Clinical Trials Regulation will not be realised without harmonisation of research ethics committee practices as well as national legislation.

Background: Genome-wide association studies (GWAS) of single nucleotide polymorphisms (SNPs) have been successful in identifying loci contributing genetic effects to a wide range of complex human diseases and quantitative traits. The traditional approach to GWAS analysis is to consider each phenotype separately, despite the fact that many diseases and quantitative traits are correlated with each other, and often measured in the same sample of individuals. Multivariate analyses of correlated phenotypes have been demonstrated, by simulation, to increase power to detect association with SNPs, and thus may enable improved detection of novel loci contributing to diseases and quantitative traits., Results: We have developed the SCOPA software to enable GWAS analysis of multiple correlated phenotypes. The software implements "reverse regression" methodology, which treats the genotype of an individual at a SNP as the outcome and the phenotypes as predictors in a general linear model. SCOPA can be applied to quantitative traits and categorical phenotypes, and can accommodate imputed genotypes under a dosage model. The accompanying META-SCOPA software enables meta-analysis of association summary statistics from SCOPA across GWAS. Application of SCOPA to two GWAS of high-and low-density lipoprotein cholesterol, triglycerides and body mass index, and subsequent meta-analysis with META-SCOPA, highlighted stronger association signals than univariate phenotype analysis at established lipid and obesity loci. The META-SCOPA meta-analysis also revealed a novel signal of association at genome-wide significance for triglycerides mapping to GPC5 (lead SNP rs71427535, p = 1.1x10 -8 ), which has not been reported in previous large-scale GWAS of lipid traits., Conclusions: The SCOPA and META-SCOPA software enable discovery and dissection of multiple phenotype association signals through implementation of a powerful reverse regression approach.

Background: Many studies report associations between human genetic factors and immunity to malaria but few have been reliably replicated. These studies are usually country-specific, use small sample sizes and are not directly comparable due to differences in methodologies. This study brings together samples and data collected from multiple sites across Africa and Asia to use standardized methods to look for consistent genetic effects on anti-malarial antibody levels., Methods: Sera, DNA samples and clinical data were collected from 13,299 individuals from ten sites in Senegal, Mali, Burkina Faso, Sudan, Kenya, Tanzania, and Sri Lanka using standardized methods. DNA was extracted and typed for 202 Single Nucleotide Polymorphisms with known associations to malaria or antibody production, and antibody levels to four clinical grade malarial antigens [AMA1, MSP1, MSP2, and (NANP)4] plus total IgE were measured by ELISA techniques. Regression models were used to investigate the associations of clinical and genetic factors with antibody levels., Results: Malaria infection increased levels of antibodies to malaria antigens and, as expected, stable predictors of anti-malarial antibody levels included age, seasonality, location, and ethnicity. Correlations between antibodies to blood-stage antigens AMA1, MSP1 and MSP2 were higher between themselves than with antibodies to the (NANP)4 epitope of the pre-erythrocytic circumsporozoite protein, while there was little or no correlation with total IgE levels. Individuals with sickle cell trait had significantly lower antibody levels to all blood-stage antigens, and recessive homozygotes for CD36 (rs321198) had significantly lower anti-malarial antibody levels to MSP2., Conclusion: Although the most significant finding with a consistent effect across sites was for sickle cell trait, its effect is likely to be via reducing a microscopically positive parasitaemia rather than directly on antibody levels. However, this study does demonstrate a framework for the feasibility of combining data from sites with heterogeneous malaria transmission levels across Africa and Asia with which to explore genetic effects on anti-malarial immunity.

We compare and contrast case-only designs for detecting gene x gene (G x G) interaction in rheumatoid arthritis (RA) using the genome-wide data provided by Genetic Analysis Workshop 16 Problem 1. Logistic as well as novel multinomial and proportional odds models that do not depend on the specification of additive or dominant models for susceptibility loci were applied to the case-only sample. We identified 519 significant interactions (p < 1 x 10-4 in at least one test). All methods detected unique significant interactions; 169 were common to more than one model and only 21 were common to all models. Results emphasize that categorization of the genetic variables and choice of regression model are critical and hugely influential in the identification of G x G. Porportional odds and multinomial methods provide new tools for identification of G x G interactions.