Forest plot of component effects of complete GWAS predictive model based on training set of SNPs.

<p>Odds ratios (black squares) from the complete multivariate model (“chromstate+eqtl [M3]”) for features predicting the membership of a SNP in the NHGRI GWAS Catalog are shown here with standard errors (gray lines). Smaller models are shown for comparison in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0140758#pone.0140758.s002" target="_blank">S2 Fig</a>. Four classes of SNP annotation are represented in the model, each with multiple levels: distance from gene, MAF, chromatin state in GM12878 LCLs (12), and evidence of eQTL association based on meta-analysis FDR. The base levels for each annotation are “0 kb (within gene)” [Distance from Gene], “>10%” [MAF], “Heterochromatin (13)” [ChromHMM], and “>50%” [FDR].</p

Multivariate logistic models predicting SNP membership in GWAS are well-calibrated.

<p>Top panel: Three models were developed for predicting the membership of a given SNP in the NHGRI GWAS Catalog, all incorporating at minimum the distance of the SNP from the transcript boundaries of its target gene and the minor allele frequency of the SNP. The "structure [M1]" model (white) also incorporates the NCBI gene structure classification of the gene (intron, coding, untranslated region, etc.) (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0140758#pone.0140758.s002" target="_blank">S2 Fig</a>); "chromstate [M2]" (gray) instead incorporates chromatin state (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0140758#pone.0140758.s002" target="_blank">S2 Fig</a>); "chromstate+eqtl [M3]" (black) incorporates both chromatin state and eQTL FDR class (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0140758#pone.0140758.g004" target="_blank">Fig 4</a>). The <i>x</i>-axis shows equal-sized bins of predicted probabilities of being a GWAS SNP. This particular choice of bins based on the widest range of probabilities (from M3) aids visual comparison of calibration among the three models by smoothing the proportions of observed GWAS SNPs. The <i>y</i>-axis shows the actual proportion of GWAS SNPs in that bin. The dashed green line at 3.5% represents the mean probability of a random SNP in the genome for being a GWAS hit or a close proxy (<i>r</i>² > 0.8) for one. Bottom panel: a table of absolute counts of SNPs in each predicted probability bin for each of the predictive models. For the M1 and M2 models, no SNPs had predicted probabilities > 6.3%.</p

Summary characteristics of cohort-specific and meta-analysis eQTL results.

<p>Summary characteristics of cohort-specific and meta-analysis eQTL results.</p

Genes associated with inflammatory and other categories of disease traits enriched for meta-analysis eQTL genes.

<p>In each histogram, the observed number of genes in the given category harboring at least one significant eQTL SNP (meta-analysis FDR < 5%) is marked with a dashed vertical line. The null distributions derived from 10,000 permutations are shown with gray bars.</p

Relationships of eQTL meta-analysis gene yields with representation in individual cohorts and a previous study.

<p>Counts of all significant eQTL genes (meta-analysis FDR < 5%, <b><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0140758#pone.0140758.t001" target="_blank">Table 1</a></b>) identified per source category are shown with white bars. The first four categories (“1C” through “4C”) represent the number of individual cohorts in which a gene was identified. The fifth category (“UNION”) is the union of the genes from the preceding four categories. The sixth category (“META”) is the set of genes identified in the meta-analysis. <i>Top panel</i>: For comparison, the counts of genes in each category also found by the meta-analysis are shown with overlapping gray bars. Among genes found in the meta-analysis, the count of genes not identified in any of the individual cohorts is shown with a black bar. <i>Bottom panel</i>: The counts of genes found in an eQTL study in WB by Westra <i>et al</i>. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0140758#pone.0140758.ref013" target="_blank">13</a>] are shown with black bars.</p

Evidence for an eQTL signal at the <i>IRF8</i> locus associated with systemic sclerosis.

<p>Top panel:–log₁₀ FDR for meta-analysis associations of nearby SNPs with expression of the longer isoform of <i>IRF8</i>. Middle panel: Chromatin states (CS) in LCLs (GM12878) [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0140758#pone.0140758.ref012" target="_blank">12</a>] and the target <i>IRF8</i> transcript. The two most strongly associated SNPs (including the systemic sclerosis GWAS [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0140758#pone.0140758.ref038" target="_blank">38</a>] index SNP rs11642873) overlap a predicted weak enhancer region (yellow). Nearby upstream is a predicted active promoter region (red) that is likely spurious given that it overlaps no gene, predicted or otherwise. Bottom panel: boxplots showing probe expression residuals by genotype of index SNP rs11642873 in the four individual cohorts, where the “A” allele is A and the “B” allele is C. None of the cohort-specific associations are individually significant at FDR < 5%, though the meta-analysis is significant at this level.</p

ROC curves for multivariate logistic models predicting SNP membership in GWAS.

<p>Components of the three predictive models are described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0140758#pone.0140758.g005" target="_blank">Fig 5</a>.</p

Functional annotations of SNP-pairs most strongly predicted to be in GWAS loci.

<p>Functional annotations of SNP-pairs most strongly predicted to be in GWAS loci.</p

Lipid loci with multiple signals in Europeans.

a<p>LD (<i>r</i>²/D′) with SNP showing the strongest evidence of association at each locus.</p>b<p>β: effect size from an additive model and corresponding to the effect allele, in the unit of mmol/L for HDL-C, LDL-C and natural log transformed TG.</p>c<p><i>P</i> values of sequential conditional analyses, in which we added the SNP with the strongest evidence of association into the regression model as a covariate and tested for the next strongest SNP until the strongest SNP showed a conditional <i>P</i> value>10⁻⁴ and had no annotation suggesting potential function.</p>d<p>Variance explained by SNPs at each locus was estimated based on European samples.</p>e<p><i>P</i> values of initial association in African Americans and East Asians.</p

Trans-ethnic fine-mapping narrowed the association signals.

a<p><i>P</i> meta: <i>P</i> values from meta-analysis combining samples of African American, East Asian and European ancestries.</p>b<p>Direction: effect direction of each individual studies in the order of ARIC, MEC, WHI batch1, WHI batch2, HyperGEN, CLHNS, TAICHI, Finnish T2D, Finnish unaffected, Norwegian T2D and Norwegian unaffected.</p>c<p><i>P</i> het: <i>P</i> values for heterogeneity, indicating whether observed effect sizes are homogeneous across ancestry samples.</p>d<p><i>I</i>²: index of the degree of heterogeneity.</p