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PubMed Central, ED Figure 8.: Nature. 2015 Oct 1; 526(7571): 75–81. doi:  10.1038/nature15394
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Nature. Author manuscript; available in PMC 2016 Apr 1.
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Nature. 2015 Oct 1; 526(7571): 75–81.
doi:  10.1038/nature15394

ED Figure 8.

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(A) SV-centric eQTL analysis of coding SVs. Shown is the proportion of coding SVs that are eQTLs as a function of the minimum VAF and the expression quartile. (B) Total number of coding SVs for corresponding filters. Common SVs (VAF>0.2) in highly expressed genes (>75% quantile) are very likely to correspond to SV-eQTLs (54%, see also Supplementary Table 8). (C) For all genes with significant eQTLs (FDR<10%), shown are raw p-values considering only SNPs (x-axes) or only SVs (y-axes). Genes with (strict lead) SV-eQTLs are shown in red. Genes with a SNP lead eQTL that is in linkage with an SV (r^2>0.5) are shown in orange. SNP lead eQTLs without an SV in LD are shown in blue. (D) Relative eQTL effect sizes for genetic and intergenic SV eQTLs (N=239) either with an SV-eQTL or an LD tagged SV (in log abundance scale). Shown are regression trends for both genic and intergenic SV eQTls. For genetic eQTLs, a clear relationship between SV effect size is found. For example, genic SVs >10kb have 3-fold larger effect sizes compared to genic SVs < 1kb; P=0.004; t-test.

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