Uncertainty propagation in the Model Web: A case study with e-Habitat

e-Habitat is a Web Processing Service (WPS) designed to compute the likelihood of finding ecosystems with equal properties. Inputs to the WPS, typically thematic geospatial ¿layers¿, can be discovered using standardised catalogues, and the outputs tailored to specific end user needs. Because these layers can range from geophysical data captured through remote sensing to socio-economical indicators, e-Habitat is exposed to a broad range of different types and levels of uncertainties. Potentially chained to other services to perform ecological forecasting for example, e-Habitat would be an additional component further propagating uncertainties from a potentially long chain of model services. This integration of complex resources increases the challenges in dealing with uncertainty. For such a system, as envisaged by initiatives such as the Group on Earth Observation¿s ¿Model Web¿, to be used for policy or decision making, users must be provided with information on the quality of the outputs since all system components will be subject to uncertainty. UncertWeb will create the Uncertainty enabled Model Web by promoting interoperability between data and models with quantified uncertainty, building on existing open, international standards. UncertWeb will thus develop open source implementations of encoding standards, service interface profiles, discovery and chaining mechanisms, and generic tools to realize a "Model Web" taking uncertainty in data and models into account. It is the objective of this paper to discuss the main types of uncertainties e-Habitat has to deal with and to present the benefits of the use of the UncertWeb framework.JRC.H.3-Global environement monitorin

Partitioning the Heritability of Tourette Syndrome and Obsessive Compulsive Disorder Reveals Differences in Genetic Architecture

The direct estimation of heritability from genome-wide common variant data as implemented in the program Genome-wide Complex Trait Analysis (GCTA) has provided a means to quantify heritability attributable to all interrogated variants. We have quantified the variance in liability to disease explained

The Influence of Age and Sex on Genetic Associations with Adult Body Size and Shape : A Large-Scale Genome-Wide Interaction Study

Genome-wide association studies (GWAS) have identified more than 100 genetic variants contributing to BMI, a measure of body size, or waist-to-hip ratio (adjusted for BMI, WHRadjBMI), a measure of body shape. Body size and shape change as people grow older and these changes differ substantially between men and women. To systematically screen for age-and/or sex-specific effects of genetic variants on BMI and WHRadjBMI, we performed meta-analyses of 114 studies (up to 320,485 individuals of European descent) with genome-wide chip and/or Metabochip data by the Genetic Investigation of Anthropometric Traits (GIANT) Consortium. Each study tested the association of up to similar to 2.8M SNPs with BMI and WHRadjBMI in four strata (men 50y, women 50y) and summary statistics were combined in stratum-specific meta-analyses. We then screened for variants that showed age-specific effects (G x AGE), sex-specific effects (G x SEX) or age-specific effects that differed between men and women (G x AGE x SEX). For BMI, we identified 15 loci (11 previously established for main effects, four novel) that showed significant (FDR= 50y). No sex-dependent effects were identified for BMI. For WHRadjBMI, we identified 44 loci (27 previously established for main effects, 17 novel) with sex-specific effects, of which 28 showed larger effects in women than in men, five showed larger effects in men than in women, and 11 showed opposite effects between sexes. No age-dependent effects were identified for WHRadjBMI. This is the first genome-wide interaction meta-analysis to report convincing evidence of age-dependent genetic effects on BMI. In addition, we confirm the sex-specificity of genetic effects on WHRadjBMI. These results may providefurther insights into the biology that underlies weight change with age or the sexually dimorphism of body shape.Peer reviewe

Association of SLC32A1 missense variants with genetic epilepsy with febrile seizures plus

Objective To identify the causative gene in a large unsolved family with genetic epilepsy with febrile seizures plus (GEFS+), we sequenced the genomes of family members, and then determined the contribution of the identified gene to the pathogenicity of epilepsies by examining sequencing data from 2,772 additional patients. Methods We performed whole genome sequencing of 3 members of a GEFS+ family. Subsequently, whole exome sequencing data from 1,165 patients with epilepsy from the Epi4K dataset and 1,329 Australian patients with epilepsy from the Epi25 dataset were interrogated. Targeted resequencing was performed on 278 patients with febrile seizures or GEFS+ phenotypes. Variants were validated and familial segregation examined by Sanger sequencing. Results Eight previously unreported missense variants were identified in SLC32A1, coding for the vesicular inhibitory amino acid cotransporter VGAT. Two variants cosegregated with the phenotype in 2 large GEFS+ families containing 8 and 10 affected individuals, respectively. Six further variants were identified in smaller families with GEFS+ or idiopathic generalized epilepsy (IGE). Conclusion Missense variants in SLC32A1 cause GEFS+ and IGE. These variants are predicted to alter γ-aminobutyric acid (GABA) transport into synaptic vesicles, leading to altered neuronal inhibition. Examination of further epilepsy cohorts will determine the full genotype–phenotype spectrum associated with SLC32A1 variants.Sarah E. Heron, Brigid M. Regan, Rebekah V. Harris, Alison E. Gardner, Matthew J. Coleman, Mark F. Bennett ... et al