Boxplots for estimates of each component obtained from models ‘G’, ‘K’, ‘F’, ‘S’, ‘C’, ‘GK’, ‘GKC’, ‘GKSC’ and ‘GKFSC’.

<p>X-axis: the contributors to the simulated phenotype and the model used (matched model); Y-axis: proportion of total phenotypic variance captured by each design matrix. Yellow lines: simulated value for each component. Parameter settings: = 0.3, = 0.2, = 0.1, = 0.1 and = 0.05. For example, the 2^nd boxplot of the 3^rd graph means that, the simulated phenotypes are contributed by 30%, 20%, 10% and 40% of SNP-associated, pedigree-associated, couple environmental and residual effects respectively; we conducted variance component analyses for all replicates using the matched model ‘<b>GKC</b>’ and the estimates of range from about 8% to 12% with a mean of 10%, as expected.</p

Comparisons of sample sizes, number of non-zero off-diagonal entries and number of pairwise relationships of different degrees between GS10K and GS20K.

<p>Comparisons of sample sizes, number of non-zero off-diagonal entries and number of pairwise relationships of different degrees between GS10K and GS20K.</p

Heritability estimates using subpopulations of GS10K with different GRM cut-off points.

<p>X-axis: the number of individuals among whom the pairwise relationship is larger than a specified degree; Y-axis: Heritability estimates with ± 2 <i>s</i>.<i>e</i>.</p

Illustration of the model and matrices.

<p>The diagram shows the relationship between the tested genetic/environmental effect and the individuals in an example pedigree. Each colour represents a specific effect and individuals affected by that effect are circled with that colour. People in grey or black are the people not in or in the data. Examples of how the relationship matrices for those effects look are also given.</p

Results of variance component analyses for anthropometric and cardiometabolic traits using final models selected from the stepwise model selection and the full model in GS10K.

<p>Results of variance component analyses for anthropometric and cardiometabolic traits using final models selected from the stepwise model selection and the full model in GS10K.</p

Results of variance component analyses for anthropometric and cardiometabolic traits using final models selected from the stepwise model selection and the full model in GS20K.

<p>Results of variance component analyses for anthropometric and cardiometabolic traits using final models selected from the stepwise model selection and the full model in GS20K.</p

Results of variance component analysis using final selected models for anthropometric and cardiometabolic traits in GS20K.

<p>X-axis: names of phenotype; Y-axis: proportion of phenotypic/genetic variance explained by the different components. a) Proportion of phenotypic variance explained by genetics and environment for each trait. b) Proportion of phenotypic variance explained by different components kept in the selected model for each trait. c) Proportion of genetic variance explained by SNP-associated and pedigree-associated genetic effects.</p

Slamming the door on trade policy discretion? : the WTO Appellate Body’s ruling on market distortions and production costs in EU-Biodiesel (Argentina)

This paper presents a legal-economic analysis of the Appellate Body’s decision that the WTO’s Anti-Dumping Agreement (ADA) precludes countries from taking into account government-created price distortions of major inputs when calculating anti-dumping duties, made in EU-Biodiesel (Argentina). In this case, the EU made adjustments to the price of biodiesel’s principal input – soybeans – in determining the cost of production of biodiesel in Argentina. The adjustment was made based on the uncontested finding that the price of soybeans in Argentina was distorted by the existence of an export tax scheme that resulted in artificially low soybean prices. The Appellate Body found that the EU was not permitted to take tax policy-induced price distortions into account in calculating dumping margins. We analyze the economic rationale for Argentina’s export tax system, distortions in biodiesel markets in Argentina and the EU, and the remaining trade policy options for addressing distorted international prices. We also assess whether existing subsidies disciplines would be more effective in addressing this problem and conclude that they would not

Additional file 13: of Exploration of haplotype research consortium imputation for genome-wide association studies in 20,032 Generation Scotland participants

GS_HRC_imputed_Waist_Hip_Ratio_significant_SNPS. (XLSX 8 kb

Manželské mýty

Phenotype heritabilities. Figure S2. Miami plot for height. Figure S3. Miami plot for BMI. Figure S4. Miami plot for waist circumference. Figure S5. Miami plot for waist-to-hip ratio. Figure S6. Miami plot for body fat percentage. Figure S7. Miami plot for diastolic blood pressure. Figure S8. Miami plot for creatinine. Figure S9. Miami plot for urea. Figure S10. Miami plot for fasting glucose (all individuals included). Figure S11. Miami plot for fasting glucose (excluding diabetics and measurements of >7 mmol/L). Figure S12. Miami plot for HDL cholesterol. Figure S13. Miami plot for total cholesterol. Figure S14. Miami plot for total cholesterol adjusted for statin use. Figure S15. QQ plots for all traits analysed. Figure S16. Comparison of effect sizes for GUGC top hits in the GS:SFHS EHR analysis. (PDF 2160 kb