Structural variants are a major source of gene expression differences in humans and often affect multiple nearby genes

Abstract

Structural variants (SVs) are an important source of human genome diversity, but their functional effects are poorly understood. We mapped 61,668 SVs in 613 individuals from the GTEx project and measured their effects on gene expression. We estimate that common SVs are causal at 2.66% of eQTLs, a 10.5-fold enrichment relative to their abundance in the genome. Duplications and deletions were the most impactful variant types, whereas the contribution of mobile element insertions was small (0.12% of eQTLs, 1.9-fold enriched). Multitissue analysis of eQTLs revealed that gene-altering SVs show more constitutive effects than other variant types, with 62.09% of coding SV-eQTLs active in all tissues with eQTL activity compared with 23.08% of coding SNV- and indel-eQTLs. Noncoding SVs, SNVs and indels show broadly similar patterns. We also identified 539 rare SVs associated with nearby gene expression outliers. Of these, 62.34% are noncoding SVs that affect gene expression but have modest enrichment at regulatory elements, showing that rare noncoding SVs are a major source of gene expression differences but remain difficult to predict from current annotations. Both common and rare SVs often affect the expression of multiple genes: SV-eQTLs affect an average of 1.82 nearby genes, whereas SNV- and indel-eQTLs affect an average of 1.09 genes, and 21.34% of rare expression-altering SVs show effects on two to nine different genes. We also observe significant effects on rare gene expression changes extending 1 Mb from the SV. This provides a mechanism by which individual SVs may have strong or pleiotropic effects on phenotypic variation.

Figure 1.

Features of SV-eQTLs. (A) Size distribution of eSVs compared with all common SVs. (B) Distribution of the number of eGenes per eVariant for SVs compared with SNVs and indels. “Coding” eGenes refer to eGenes whose exons are intersected by the associated eVariant, and “noncoding” eGenes are not intersected by the associated eVariant. Counts are shown for every eVariant; thus, eVariants with zero coding or zero noncoding eGenes are included in the distributions. (C,D) The number of eVariants, as shown by dot size and color, with the indicated combination of coding and noncoding eGenes, as defined above. Shown for SVs (C) and SNV/indels (D), with histograms showing the total number of eVariants, with the indicated number of associated coding or noncoding eGenes above the y- and x-axes, respectively. (E) Distribution of tissue specificity of eQTLs across tissues as evaluated by METASOFT, separated into the lowest quartile, middle two quartiles, and top quartile, for eQTLs in which the activity status is known in at least 43 of 48 evaluated tissues. The points indicate the fraction of SV-eQTLs or SNV- and indel-eQTLs that are active (m > 0.9) in the proportion of tissues indicated on the x-axis.

Figure 2.

Features of outlier-associated SVs. (A) Location of outlier-associated SVs relative to their associated outlier gene and the number of SV/outlier gene associations identified in each category. Percentages indicate the fraction of outlier/SV pairs found at each relative location compared with the total number of SV/outlier gene associations. Note that this definition allows one SV to be associated with multiple outlier genes, and thus, the SV is counted in multiple categories. Gene diagrams provide examples of possible SV location, shown in red, relative to the outlier gene. (B,C) Odds ratio (OR) of being outlier-associated by SV type (B) and SV size (C) for the SV category of interest compared with all other SVs. Note that BNDs were excluded from the size OR calculations owing to their ambiguous nature and thus size. (D) Distribution of SV sizes for singleton SVs <1 Mb identified in European individuals that were used in outlier analyses. Panels depict size distributions for all European-cohort singletons, control-associated singletons, multitissue outlier-associated singletons, and tissue-restricted outlier-associated singletons.

Figure 3.

Mechanistic insights into outlier-associated SVs. (A) Enrichment of outlier-associated SVs in functional genomic annotations compared with control-associated SVs. Asterisks indicate statistical significance based on a Fisher's exact test with Bonferroni correction for multiple testing. (B,C) The distribution of the number of noncoding primary (B) and secondary (C) outliers found within 1 Mb of the region surrounding tissue-restricted outlier-associated SVs, control-associated SVs, and a shuffled null.