Power for 500–2000 trios and 500K markers, using mating type ratio equation R4, under an additive genetic model.

<p>Estimated power levels to detect the DSL using 500–2000 trios, assuming a 10% disease prevalence and additive mode of inheritance. The significance level is set to 5%. For the weighted Bonferroni method (Weighted), the partitioning parameters are <i>K</i> = 7 and <i>r</i> = 2. MAF denotes minor allele frequency. The power reflects the proportion of times the p-value of the DSL (Independence scenario and LD scenario (DSL only)) or a SNP in LD with the DSL (LD scenario (DSL+)) met the weighted Bonferroni (Weighted) or standard Bonferroni corrected (Standard) significance level. The standard Bonferroni correction adjusts for 500 K comparisons.</p

CAMP results: SNPs meeting genome-wide significance at α = 0.05.

<p>Results of the CAMP analysis with 402 families, 534,290 SNPs, assuming an additive mode of inheritance. Num. Info. Families indicates the number of families that were informative (i.e., at least one parent was heterozygous) for the marker of interest, and MAF denotes minor allele frequency. Markers with fewer than 20 families were removed from the analysis, as the asymptotic properties required for the test statistic may not hold. The power ranks are obtained from the conditional power of the test, calculated using our new technique with mating type ratio equation R4. The required significance level is obtained using the Ionita-Laza method <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1000197#pgen.1000197-IonitaLaza1" target="_blank">[9]</a> with <i>K</i> = 7, <i>r</i> = 2, and α = 0.05.</p

Power for 2000 trios and 500K markers, using mating type ratio equation R4, under an “unknown” genetic model.

<p>Estimated power levels to detect the DSL using 2000 trios, assuming a 10% disease prevalence. The significance level is set to 5%. For the weighted Bonferroni method (Weighted), the partitioning parameters are <i>K</i> = 7 and <i>r</i> = 2. “Under True Gen. Model”, Add. refers to the scenario where the true (but “unknown”) model is additive (as the results are analyzed using all three genetic models). Similar scenarios are provided for the dominant (Dom.) and recessive (Rec.) genetic models. MAF denotes minor allele frequency. The power reflects the proportion of times the p-value of the DSL (Independence scenario and LD scenario (DSL only)) or a SNP in LD with the DSL (LD scenario (DSL+)) met the weighted Bonferroni (Weighted) or standard Bonferroni corrected (Standard) significance level. The standard Bonferroni correction adjusts for 1.5 M comparisons (500 K markers ^* 3 genetic models).</p

Genotyping error models.

<p>The three genotype clusters represent the clouds generated from intensity plots. The AA cluster consists of all homozygous minor calls, the AB cluster heterozygous calls and the BB homozygous major calls. Each arrow represents one of the genotyping error models considered. For example, in Model #6 minor homozygotes (AA) can be miscalled as heterozygotes (AB).</p

Simulation results with LD—minor allele frequencies drawn from a Uniform (0.1,0.5) distribution.

<p>Average standardized transmission test over 1,000 replications for varying levels of genotype error and SNP chip sizes in the presence of LD. Each graph displays results for a single genotype error model from <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1000572#pgen-1000572-g001" target="_blank">Figure 1</a>. (A–F) correspond to Models 1–6, respectively. Legends are different in each graph.</p

Empirical significance — Percentage of genome-wide transmission test false positives in 10,000 datasets with LD.

<p>Proportion of 10,000 datasets simulated under the null hypothesis of no genotyping error and in the presence of LD such that . Columns 2 through 5 display results for various chip sizes when generating minor allele frequencies from a Uniform (0.1,0.5) distribution. Columns 6 through 9 display analogous results when generating minor allele frequencies from a Beta (2,8) distribution. Each row depicts results corresponding to a distinct genotyping error model from <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1000572#pgen-1000572-g001" target="_blank">Figure 1</a>.</p

Genome-wide transmission test statistic for 41 CEPH probands.

<p>Significance threshold at an overall <i>α</i>-level of 5% for <i>χ</i>²-statistics adjusted for 41 comparisons using Bonferroni-correction: 10.46.</p><p>The genome-wide transmission test statistic, , is reported for each CEPH proband with both parents genotyped, ordered by Pedigree ID. Each statistic is calculated using all available SNPs (Column 3), all concordant SNPs (Column 4) and the SNPs appearing on only one platform (Column 5). Test statistics using all concordant SNPs that are larger than the Bonferroni-adjusted value of 10.46 are presented in bold.</p

Simulation results—minor allele frequencies drawn from a Uniform (0.1,0.5) distribution.

<p>Average standardized transmission test over 1,000 replications for varying levels of genotype error and SNP chip sizes. Each graph displays results for a single genotype error model from <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1000572#pgen-1000572-g001" target="_blank">Figure 1</a>. (A–F) correspond to Models 1–6, respectively. Legends are different in each graph.</p

Empirical significance – Percentage of genome-wide transmission test false positives in 10,000 datasets with no LD.

<p>Proportion of 10,000 datasets simulated under the null hypothesis of no genotyping error and without LD such that . Columns 2 through 5 display results for various chip sizes when generating minor allele frequencies from a Uniform (0.1,0.5) distribution. Columns 6 through 9 display analogous results when generating minor allele frequencies from a Beta (2,8) distribution. Each row depicts results corresponding to a distinct genotyping error model from <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1000572#pgen-1000572-g001" target="_blank">Figure 1</a>.</p

ISWC 2017 full metadata

Metadata of the ISWC2017 conference (<a href="https://iswc2017.semanticweb.org/" rel="noopener noreferrer" target="_blank">https://iswc2017.semanticweb.org</a>), serialised into Turtle format. This data set covers information across all tracks (authors, papers, organisations, etc). For further information, please contact the authors via the following link: <a href="https://iswc2017.semanticweb.org/program/iswc-metadata/" rel="noopener noreferrer" target="_blank">https://iswc2017.semanticweb.org/program/iswc-metadata/</a><br><br>The metadata is in a single compressed .gz file that can be opened with standard compression utilities, metadata is provided in Terse RDF Triple Language; .ttl that can be openly accessed via text edit software.<br><br><div>ISWC 2017, the 16th International Semantic Web Conference, taking place on 21–25 October 2017 in Vienna, Austria, is the premier international forum for the Semantic Web / Linked Data Community. As such, it is committed to publishing data in a reusable way.<br></div><div><br></div><div>Data for the ISWC 2017 conference proceedings are available at <a href="https://springernature.figshare.com/semweb" rel="noopener noreferrer" target="_blank">https://springernature.figshare.com/semweb</a>. The conference proceedings papers themselves are available via <a href="https://dx.doi.org/10.1007/978-3-319-68288-4" rel="noopener noreferrer" target="_blank">https://dx.doi.org/10.1007/978-3-319-68288-4</a></div