Results of association testing at published loci. OR published is based on replication phase odds ratio previously published for these SNPs [6], [7].

<p>Note gene is the best proximal candidate or closest gene to the locus and may not be the true pathologically important species. Notably the power to detect association at these loci, based on previously published effect sizes, p<5E-8, and an additive model is effectively 0 (based on methodology of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0041859#pone.0041859-Skol1" target="_blank">[17]</a>). MAF, Minor Allele Frequency; OR, odds ratio; CI, confidence interval; IQ, Imputation quality from MACH (RSQR metric).</p

Gene set enrichment analysis for BP associated gene expression changes.

<p>*<b>NES</b>: normalized enrichment score;</p><p><b>GO-BP</b>: Gene ontology- biological process;</p><p><b>KEGG</b>: Kyoto encyclopedia of genes and genomes.</p><p>Gene set enrichment analysis for BP associated gene expression changes.</p

Global view of BP eQTLs effects on differentially expressed BP signature genes.

<p>A) 2-Dimensional plot of in whole blood eQTLs vs. transcript position genome wide. eQTL-transcript pairs at FDR<0.1 are shown in black dots; those that fall along the diagonal are cis eQTLs and all others are trans eQTLS. eQTL-transcript pair SNPs that are associated with BP in GWAS [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005035#pgen.1005035.ref003" target="_blank">3</a>] are highlighted with blue triangles. eQTL-transcript pair genes that are BP signature genes from analysis of differential gene expression in relation to BP are depicted by red circles. B) Regional association plots for rs3184504 proxy QTLs that showing association with BP in ICBP GWAS [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005035#pgen.1005035.ref003" target="_blank">3</a>]. −log10(p) indicated the −log10 transformed DBP association <i>p</i> values in ICBP GWAS [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005035#pgen.1005035.ref003" target="_blank">3</a>]. Color coding indicates the strength (measured by r²) of LD of each SNP with the top SNP (rs3184504). Five master <i>trans-</i>eQTLs (also BP GWAS SNPs) for BP signature genes are labeled in the figure. This figure was drawn by LocusZoom [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005035#pgen.1005035.ref032" target="_blank">32</a>].</p

Differentially expressed genes associated with BP and hypertension at Bonferroni correction <i>p</i><0.05 in meta-analysis of the six cohorts.

<p>*<b>Meta</b>: meta-analysis of all six cohorts.</p><p>Differentially expressed genes associated with BP and hypertension at Bonferroni correction <i>p</i><0.05 in meta-analysis of the six cohorts.</p

GWAS eQTLs for the top differentially expressed BP signature genes.

<p>* rs653178, intronic to <i>ATXN2</i> and in tight linkage disequilibrium with rs3184504 (r² = 1), was also associated with BP in ICBP GWAS and all the 6 genes;</p><p>⁺ A proxy SNP rs4698412 at LD r² = 1 associated with the same trait;</p><p>$ A proxy SNP rs4389526 at LD r² = 1 associated with the same trait;</p><p>^§ indicated eQTL were identified from[<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005035#pgen.1005035.ref012" target="_blank">12</a>].</p><p>^& highlighted p values indicated passing transcriptome-wide significance at Bonferroni corrected <i>p</i><0</p><p>GWAS eQTLs for the top differentially expressed BP signature genes.</p

Clinical characteristics of the study cohorts.

<p>Clinical characteristics of the study cohorts.</p

Effect size of differentially expressed BP genes in the Framingham Heart Study and the Illumina cohorts.

<p>A) SBP; B) DBP; C) HTN. The x-axis is the effect size of the differentially expressed genes in the FHS cohort and the y-axis is the effect size in the Illumina cohorts. The BP signature genes identified both in the FHS and the Illumina cohorts at <i>p</i><0.05 (Bonferroni corrected) are highlighted. <i>pi1</i> values indicate the proportion of significant signals among the tested associations [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005035#pgen.1005035.ref011" target="_blank">11</a>] (See details in the <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005035#sec009" target="_blank">Methods</a> section).</p

Validation of neutrophil and lymphoid specific <i>cis</i>-eQTLs in purified cell type eQTL datasets.

<p>A) We validated the neutrophil- and lymphoid-mediated <i>cis</i>-eQTL effects in four purified cell type datasets from the lymphoid lineage (B-cells, CD4+ T-cells, CD8+ T-cells and lymphoblastoid cell lines) and in two datasets from the myeloid lineage (monocytes and neutrophils). Compared to generic <i>cis</i>-eQTLs, large effect sizes were observed for neutrophil-mediated <i>cis</i>-eQTLs in myeloid lineage cell types, and small effect sizes in the lymphoid datasets. Conversely, lymphoid-mediated <i>cis</i>-eQTL effects had large effect sizes specifically in the lymphoid lineage datasets, while having smaller effect sizes in myeloid lineage datasets. These results indicate that our method is able to reliably predict whether a specific <i>cis</i>-eQTL is mediated by cell type. B) Comparison between average gene expression levels between different purified cell type eQTL datasets shows that neutrophil mediated <i>cis</i>-eQTLs have, on average a lower expression in cell types derived from the lymphoid lineage, and a high expression in myeloid cell types, while the opposite is true for lymphocyte mediated <i>cis</i>-eQTLs.</p

Effect of sample size on power to detect cell type specific <i>cis</i>-eQTLs.

<p>We systematically excluded datasets from our meta-analysis in order to determine the effect of sample size on our ability to detect significant interaction effects. The number of significant interaction effects was rapidly reduced when the sample size was decreased (the number of unique significant probes given a Bonferroni corrected P-value < 8.1 x 10^–6 is shown). In general, due to their low abundance in whole blood, lymphoid-mediated <i>cis</i>-eQTL effects are harder to detect than neutrophil-mediated cis-eQTL effects.</p

Method overview.

<p>I) Starting with a dataset that has cell count measurements, determine a set of probes that have a strong positive correlation to the cell count measurements. Calculate the correlation between these specific probes in the other datasets, and apply principal component analysis to combine them into a single proxy for the cell count measurement. II) Apply the prediction to other datasets lacking cell count measurements. III) Use the proxy as a covariate in a linear model with an interaction term in order to distinguish cell-type-mediated from non-cell-type-mediated eQTL effects.</p