Refinement of the credible sets.

<p>The y-axis shows the number of variants in the 95% (a) and 99% (b) likely credible sets. The boxplots show the median and interquartile range of the simulations, while each point denotes a single “replicate”. The color of the boxplots/points denotes the RAF of the simulated causal variant, while each panel is split by the effect size along the horizontal plane.</p

Quantifying fine-mapping success.

<p>Fraction of the simulations where the fine-mapped set is reduced to fewer than 10 variants for the comparison with <i>r2</i>-derived sets (a) and multi-ethnic study designs (b). Colors denote whether the set contains the causal variants and only one variant (dark purple), causal variant and fewer than 10 variants (medium purple), fewer than 10 variants but not the causal variant (light purple), or more than 10 variants (green). In both panels, the data is split by risk allele frequency (RAF) on the horizontal plane. Panel (a) is grouped by odds ratio (OR) on the x-axis, whereas the OR was set to 1.2 in all the multi-ethnic simulations.</p

Ranking of the causal variant across simulated loci.

<p>Cumulative percentage of simulations (y-axis) with decreasing ranking of the causal variant amongst all variants in the regions (x-axis). Panels are split by risk allele frequency of the causal variant along the vertical axis.</p

Fine-mapping the ankylosing spondylitis (AS) regions.

<p>a) Correlation between predicted power to detect genome-wide association signals and size of the 95% credible sets. Boxplots represent the distribution of the simulations at the respective power of each RAF/OR setting. The labelled dots show the distribution of the empirical AS data. Regression lines in the range of predicted power of the AS loci (15–99.9%) are derived from the simulations (dashed) and AS loci (dotted with confidence interval of regression line). b) Example <i>FCGR2A</i> region where the 99% credible set (purple dots) could be fine-mapped to a small region (pink) containing few variant.</p

CpG methylation in the <i>INS</i> promoter in T2D patients and controls.

<p><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0036278#s2" target="_blank">Results</a> are expressed as mean ± sd. <i>P</i>-values are calculated by Wilcoxon rank sum test.</p

Lack of correlation between age and <i>INS</i> promoter methylation in T1D patients (green) and Controls (red).

<p>Only CpG -180 methylation showed a slight downward trend with age (r = 0.09, p = 0.01). The figure also shows the distribution of individual methylation values, and the differences between T1D and control subjects for CpGs -19, -135, -180, and -234.</p

Heatmap analysis of the relationship between DNA methylation of the whole proximal <i>INS</i> promoter and disease status.

<p>Each row represents one individual.</p

A Protein Domain and Family Based Approach to Rare Variant Association Analysis

<div><p>Background</p><p>It has become common practice to analyse large scale sequencing data with statistical approaches based around the aggregation of rare variants within the same gene. We applied a novel approach to rare variant analysis by collapsing variants together using protein domain and family coordinates, regarded to be a more discrete definition of a biologically functional unit.</p><p>Methods</p><p>Using Pfam definitions, we collapsed rare variants (Minor Allele Frequency ≤ 1%) together in three different ways 1) variants within single genomic regions which map to individual protein domains 2) variants within two individual protein domain regions which are predicted to be responsible for a protein-protein interaction 3) all variants within combined regions from multiple genes responsible for coding the same protein domain (i.e. protein families). A conventional collapsing analysis using gene coordinates was also undertaken for comparison. We used UK10K sequence data and investigated associations between regions of variants and lipid traits using the sequence kernel association test (SKAT).</p><p>Results</p><p>We observed no strong evidence of association between regions of variants based on Pfam domain definitions and lipid traits. Quantile-Quantile plots illustrated that the overall distributions of p-values from the protein domain analyses were comparable to that of a conventional gene-based approach. Deviations from this distribution suggested that collapsing by either protein domain or gene definitions may be favourable depending on the trait analysed.</p><p>Conclusion</p><p>We have collapsed rare variants together using protein domain and family coordinates to present an alternative approach over collapsing across conventionally used gene-based regions. Although no strong evidence of association was detected in these analyses, future studies may still find value in adopting these approaches to detect previously unidentified association signals.</p></div

Boxplot of methylation values for the 7 CpGs in <i>INS</i> promoter with respect to SNPs other than rs689 in T1D patients.

<p>Methylation at CpGs -69, −102, −180 and −206 are associated with rs3842748. More distant SNPs showed a much weaker correlation with methylation at CpG -69 and -102 for rs4320932 and with methylation at CpGs -102, -135, -180 and −206 for rs6356. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0036278#pone.0036278.s001" target="_blank">Figure S1</a> for SNP locations. P-values are calculated by Krukal-Wallis test.</p

UCSC Genome Browser map of the <i>INS</i> locus : localization of genes, CpG islands (CGI), variants and CpG sites.

<p>Genes are represented by blue boxes (exons) and lines (intron). Under genes, CGI are in green. Green intensity and size of the box is function of the size and the number of CpG included in CGI. Polymorphisms are represented by black boxes, under CGI. Below, a zoom of the <i>INS</i> promoter allowing the localization and representation of the isolated CpG sites, according to the TSS, represented by the red arrow.</p