OBEDIS Core Variables Project : European Expert Guidelines on a Minimal Core Set of Variables to Include in Randomized, Controlled Clinical Trials of Obesity Interventions

Heterogeneity of interindividual and intraindividual responses to interventions is often observed in randomized, controlled trials for obesity. To address the global epidemic of obesity and move toward more personalized treatment regimens, the global research community must come together to identify factors that may drive these heterogeneous responses to interventions. This project, called OBEDIS (OBEsity Diverse Interventions Sharing - focusing on dietary and other interventions), provides a set of European guidelines for a minimal set of variables to include in future clinical trials on obesity, regardless of the specific endpoints. Broad adoption of these guidelines will enable researchers to harmonize and merge data from multiple intervention studies, allowing stratification of patients according to precise phenotyping criteria which are measured using standardized methods. In this way, studies across Europe may be pooled for better prediction of individuals' responses to an intervention for obesity - ultimately leading to better patient care and improved obesity outcomes.Peer reviewe

Mutation discovery in mice by whole exome sequencing

We report the development and optimization of reagents for in-solution, hybridization-based capture of the mouse exome. By validating this approach in a multiple inbred strains and in novel mutant strains, we show that whole exome sequencing is a robust approach for discovery of putative mutations, irrespective of strain background. We found strong candidate mutations for the majority of mutant exomes sequenced, including new models of orofacial clefting, urogenital dysmorphology, kyphosis and autoimmune hepatitis

Dietary factors impact on the association between CTSS variants and obesity related traits.

Cathepsin S, a protein coded by the CTSS gene, is implicated in adipose tissue biology--this protein enhances adipose tissue development. Our hypothesis is that common variants in CTSS play a role in body weight regulation and in the development of obesity and that these effects are influenced by dietary factors--increased by high protein, glycemic index and energy diets

A genome-wide association study in Europeans and South Asians identifies five new loci for coronary artery disease

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Fast and Accurate Approaches for Large-Scale, Automated Mapping of Food Diaries on Food Composition Tables

Aim of Study: The use of weighed food diaries in nutritional studies provides a powerful method to quantify food and nutrient intakes. Yet, mapping these records onto food composition tables (FCTs) is a challenging, time-consuming and error-prone process. Experts make this effort manually and no automation has been previously proposed. Our study aimed to assess automated approaches to map food items onto FCTs.Methods: We used food diaries (~170,000 records pertaining to 4,200 unique food items) from the DiOGenes randomized clinical trial. We attempted to map these items onto six FCTs available from the EuroFIR resource. Two approaches were tested: the first was based solely on food name similarity (fuzzy matching). The second used a machine learning approach (C5.0 classifier) combining both fuzzy matching and food energy. We tested mapping food items using their original names and also an English-translation. Top matching pairs were reviewed manually to derive performance metrics: precision (the percentage of correctly mapped items) and recall (percentage of mapped items).Results: The simpler approach: fuzzy matching, provided very good performance. Under a relaxed threshold (score > 50%), this approach enabled to remap 99.49% of the items with a precision of 88.75%. With a slightly more stringent threshold (score > 63%), the precision could be significantly improved to 96.81% while keeping a recall rate > 95% (i.e., only 5% of the queried items would not be mapped). The machine learning approach did not lead to any improvements compared to the fuzzy matching. However, it could increase substantially the recall rate for food items without any clear equivalent in the FCTs (+7 and +20% when mapping items using their original or English-translated names). Our approaches have been implemented as R packages and are freely available from GitHub.Conclusion: This study is the first to provide automated approaches for large-scale food item mapping onto FCTs. We demonstrate that both high precision and recall can be achieved. Our solutions can be used with any FCT and do not require any programming background. These methodologies and findings are useful to any small or large nutritional study (observational as well as interventional)

Efficient inference for genetic association studies with multiple outcomes

Combined inference for heterogeneous high-dimensional data is critical in modern biology, where clinical and various kinds of molecular data may be available from a single study. Classical genetic association studies regress a single clinical outcome on many genetic variants one by one, but there is an increasing demand for joint analysis of many molecular outcomes and genetic variants in order to unravel functional interactions. Unfortunately, most existing approaches to joint modeling are either too simplistic to be powerful or are impracticable for computational reasons. Inspired by Richardson and others (2010, Bayesian Statistics 9), we consider a sparse multivariate regression model that allows simultaneous selection of predictors and associated responses. As Markov chain Monte Carlo (MCMC) inference on such models can be prohibitively slow when the number of genetic variants exceeds a few thousand, we propose a variational inference approach which produces posterior information very close to that of MCMC inference, at a much reduced computational cost. Extensive numerical experiments show that our approach outperforms popular variable selection methods and tailored Bayesian procedures, dealing within hours with problems involving hundreds of thousands of genetic variants and tens to hundreds of clinical or molecular outcomes

Converging evidence for an association of ATP2B2 allelic variants with autism in male subjects

Background: Autism is a severe developmental disorder, with strong genetic underpinnings. Previous genome-wide scans unveiled a linkage region spanning 3.5 Mb, located on human chromosome 3p25. This region encompasses the ATP2B2 gene, encoding the plasma membrane calcium-transporting ATPase 2 (PMCA2), which extrudes calcium (Ca2) from the cytosol into the extracellular space. Multiple lines of evidence support excessive intracellular Ca2 signaling in autism spectrum disorder (ASD), making ATP2B2 an attractive candidate gene. Methods: We performed a family-based association study in an exploratory sample of 277 autism genetic resource exchange families and in a replication sample including 406 families primarily recruited in Italy. Results: Several markers were significantly associated with ASD in the exploratory sample, and the same risk alleles at single nucleotide polymorphisms rs3774180, rs2278556, and rs241509 were found associated with ASD in the replication sample after correction for multiple testing. In both samples, the association was present in male subjects only. Markers associated with autism are all comprised within a single block of strong linkage disequilibrium spanning several exons, and the “risk” allele seems to follow a recessive mode of transmission. Conclusions: These results provide converging evidence for an association between ATP2B2 gene variants and autism in male subjects, spurring interest into the identification of functional variants, most likely involved in the homeostasis of Ca2 signaling. Additional support comes from a recent genome-wide association study by the Autism Genome Project, which highlights the same linkage disequilibrium region of the gene

Imputation de données manquantes pour l'inférence de r éseau à partir de données RNA-seq

National audienceIn this article, the issue of gene network inference is addressed, in which inference is performed from expression data obtained by RNA-seq sequencing technique. Our proposal aims at integrating external information (another kind of 'omic for instance) measured on the same individuals and on additional individuals. The method is presented as a missing data imputation problem and is solved with multiple hot deck approaches in order to infer a more stable network. Our results will be illustrated on real data coming from a paneuropean project on obesity.Dans cette proposition de communication, nous nous intéressons au problème de l'inférence de réseaux de gènes à partir de données d'expression mesurées par RNA-seq. Nous présentons une approche qui permet d'intégrer de l'information externe (autre type d'omic par exemple) obtenue sur les mêmes individus ainsi que sur d'autres individus. Notre approche est présentée comme un problème d'imputation que nous résolvons avec des approches de type "hot deck" multiple pour obtenir un réseau plus stable. Nous illustrerons nos résultats sur des données réelles issues d'un programme d'études sur l'obésité