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The impact of structural variation on human gene expression

June 2016

June 2016

DOI:10.1101/055962

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Alexandra Jane Scott

Sage Bionetworks

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Structural variants (SVs) are an important source of human genetic diversity but their contribution to traits, disease, and gene regulation remains unclear. The Genotype-Tissue Expression (GTEx) project presents an unprecedented opportunity to address this question due to the availability of deep whole genome sequencing (WGS) and multi-tissue RNA-seq data from 147 individuals. We used comprehensive methods to identify 24,157 high confidence SVs, and mapped cis expression quantitative trait loci (eQTLs) in 13 tissues via joint analysis of SVs, single nucleotide (SNV) and short insertion/deletion (indel) variants. We identified 24,801 eQTLs affecting the expression of 10,101 distinct genes. Based on haplotype structure and heritability partitioning, we estimate that SVs are the causal variant at 3.3-7.0% of eQTLs, which is nearly an order of magnitude higher than prior estimates from low coverage WGS and represents a 26- to 54-fold enrichment relative to their scarcity in the genome. Expression-altering SVs also have significantly larger effect sizes than SNVs and indels. We identified 787 putatively causal SVs predicted to directly alter gene expression, most of which (88.3%) are noncoding variants that show significant enrichment at enhancers and other regulatory elements. By evaluating linkage disequilibrium between SVs, SNVs and indels, we nominate 49 SVs as plausible causal variants at published genome-wide association study (GWAS) loci. Remarkably, 29.9% of the common SV-eQTLs are not well tagged by flanking SNVs, and we observe a notable abundance (relative to SNVs and indels) of rare, high impact SVs associated with aberrant expression of nearby genes. These results suggest that comprehensive WGS-based SV analyses will increase the power of both common and rare variant association studies.

Feature enrichment of SV-eQTLs. Fold enrichment and 95% confidence intervals (based on 100 random shuffled sets of the positions of SVs in each bin) for the overlap between the most significant SV and various annotated genomic features at the union of eQTLs discovered by SV-only or joint eQTL mapping. (a) Composition of each causality score bin by SV type. (b) Enrichment for an SV in each bin of causality to touch exons of the affected eGene (c-f) For the remaining plots in blue, SVs that overlapped with an exon of their affected eGene were excluded, yet the remaining SVs still showed significant enrichment in enhancers (c), in the 10 kb regions upstream (d) and downstream of transcriptions start sites (TSS) (e), and regions predicted to be highly occupied by transcription factors (FunSeq HOT regions) (f).

…

Gene expression outliers are associated with rare SVs. (a) Fold enrichment of rare variants within 5 kb of expression outliers (red) and fold enrichment of outliers within 5 kb of rare variants (blue) between the observed set of 5,047 outliers and 1,000 random permutations of their sample names. (b) Effect size distributions for each SV type within 5 kb of an outlier in the same individual, with "coding" SVs defined as those that overlap with exons of the outlier gene and "noncoding" defined by the remainder. (c) Size distribution histograms by minor allele frequency (MAF) for common (MAF>0.01), rare (MAF≤0.01), and rare SVs within 5 kb of an expression outlier in the same individual, excluding balanced rearrangements. A peak at ~300 bp in the topmost plot results from common Alu SINE insertions.

…

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The impact of structural variation on human gene expression

Colby Chiang1, Alexandra J. Scott1, Joe R. Davis2,3, Emily K. Tsang2, Xin Li2, Yungil Kim4, Farhan N.

Damani4, Liron Ganel1, GTEx Consortium, Stephen B. Montgomery2,3,5, Alexis Battle4, Donald F.

Conrad6,7,8*, Ira M. Hall1,6,8*

* Co-corresponding authors

1McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA

2Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA

3Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA

4Department of Computer Science, Johns Hopkins University, Baltimore, Maryland, USA

5Department of Computer Science, Stanford University, Stanford, CA, USA

6Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA

7Department of Pathology & Immunology, Washington University School of Medicine, St. Louis, MO,

USA

8Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA

.CC-BY-NC-ND 4.0 International licensecertified by peer review) is the author/funder. It is made available under a The copyright holder for this preprint (which was notthis version posted June 9, 2016. . https://doi.org/10.1101/055962doi: bioRxiv preprint

Abstract

Structural variants (SVs) are an important source of human genetic diversity but their contribution to

traits, disease, and gene regulation remains unclear. The Genotype-Tissue Expression (GTEx) project

presents an unprecedented opportunity to address this question due to the availability of deep whole

genome sequencing (WGS) and multi-tissue RNA-seq data from 147 individuals. We used

comprehensive methods to identify 24,157 high confidence SVs, and mapped cis expression

quantitative trait loci (eQTLs) in 13 tissues via joint analysis of SVs, single nucleotide (SNV) and short

insertion/deletion (indel) variants. We identified 24,801 eQTLs affecting the expression of 10,101

distinct genes. Based on haplotype structure and heritability partitioning, we estimate that SVs are the

causal variant at 3.3-7.0% of eQTLs, which is nearly an order of magnitude higher than prior estimates

from low coverage WGS and represents a 26- to 54-fold enrichment relative to their scarcity in the

genome. Expression-altering SVs also have significantly larger effect sizes than SNVs and indels. We

identified 787 putatively causal SVs predicted to directly alter gene expression, most of which (88.3%)

are noncoding variants that show significant enrichment at enhancers and other regulatory elements.

By evaluating linkage disequilibrium between SVs, SNVs and indels, we nominate 49 SVs as plausible

causal variants at published genome-wide association study (GWAS) loci. Remarkably, 29.9% of the

common SV-eQTLs are not well tagged by flanking SNVs, and we observe a notable abundance

(relative to SNVs and indels) of rare, high impact SVs associated with aberrant expression of nearby

genes. These results suggest that comprehensive WGS-based SV analyses will increase the power of

both common and rare variant association studies.

Main Text

Over the past decade, genome-wide association studies (GWAS) have linked thousands of common

genetic variants to human traits and diseases. Fine-mapping causal variants at GWAS loci has proven

difficult because the vast majority (~88%) reside in noncoding genomic regions, and in most cases the

causal variant(s) and relevant gene(s) or functional element(s) are not known1. This has confounded

the identification of therapeutic targets for precision medicine. To bridge the gap between molecular

and clinical phenotypes, genome-wide eQTL scans have sought to identify genetic determinants of

gene expression variation as markers of functional effect and a bridge connecting germline genetic

variation to somatic cell biology2-4. These studies have successfully identified tens of thousands of

eQTLs in a variety of human tissues.

A notable limitation of most extant eQTL studies is that, due to their reliance on SNP genotyping arrays,

it has been difficult to identify the causal variants underlying eQTL associations and to judge the relative

contribution of different variant classes to genetically regulated expression. Of particular interest is

structural variation (SV), a broad class of variation that includes copy number variants (CNVs),

balanced rearrangements and mobile element insertions (MEIs). Structural variation is recognized to be

an important source of genetic diversity – 5,000 to 10,000 SVs are detectable in a typical human

genome using short-read DNA sequencing technologies – but little is known about the mechanisms

through which SVs affect gene expression and phenotypic variation. Although SVs are less abundant

than SNVs, which represent ~4 million variant sites per genome5, SVs account for a greater number of

nucleotide sequence differences due to their size, and may therefore exhibit outsized phenotypic

effects6,7. Indeed, SVs have been identified as causal contributors to a number of rare and common

diseases, and are generally presumed to act through their effects on gene expression8.

Despite many noteworthy examples linking SVs to gene expression and phenotypic variation in

humans, more general and quantitative questions regarding the contribution of SVs relative to other

variant classes remain a matter of debate. Two early studies used low-resolution microarray

technologies to study the relationship between CNVs and gene expression, but their conclusions were

limited to large CNVs that are now known to comprise a small fraction of SV9,10. A recent study from the

1000 Genomes Project represents the most comprehensive analysis to date, using RNA-seq

expression profiles from lymphoblastoid cell lines (LCLs) of 462 individuals2 and SVs identified from

low-coverage (median 7.4X) WGS data11. This analysis identified 9,591 eQTLs, of which 54 had an SV

as the lead marker (denoted SV-eQTLs), implying that SVs are the causal variant at 0.56% of eQTLs.

However, the study’s shallow sequencing depth limited SV detection power and genotyping accuracy,

which are known to suffer in low-coverage data12. Furthermore, although gene expression is

differentially regulated across tissues, prior SV-eQTL studies have focused solely on LCLs, and it is not

known whether these observations extend to other cell types.

Here, we utilized multi-tissue RNA-seq expression data from the GTEx project to perform the first

comprehensive human eQTL mapping study from deep WGS (median 49.9X) data that directly

measures the contribution of SVs, SNVs, and indels.

We analyzed 147 human samples using SpeedSeq13 for alignment, data processing and per-sample

SV breakpoint detection via LUMPY12, followed by cohort-level breakpoint merging, refinement,

classification, and genotyping (Online Methods). We used complementary read-depth analysis with

Genome STRiP to detect additional CNVs14. Together, these methods yielded a total of 45,968

structural variants, including a “high confidence” set of 24,157 SVs that met strict quality filters and are

the basis for all subsequent analyses (Table 1). Several features indicate that these calls are high

quality: we detected consistent numbers and proportions of SVs per sample (Fig. 1a,b), African

American samples had an average of 27% more heterozygous LUMPY deletions than other samples

(in accordance with previous observations11), the SV minor allele frequency (MAF) distribution mirrored

that of SNVs and indels (Fig. 1c), and the SV size distribution is similar to prior WGS-based studies

(Fig. 1d, Supplementary Fig. 1). This comprehensive variant call set is a powerful resource for

functional analyses due to the high resolution (median breakpoint confidence interval: 39 bp) and

diverse variant types including deletions (50.4%), duplications (14.7%), multi-allelic CNVs (mCNVs;

6.8%), mobile element insertions (MEIs; 8.5%), inversions (0.2%), and novel adjacencies of

indeterminate type (hereafter denoted as “breakends”, or BNDs; 19.3%)15.

Detection

method

# of

variants

Median

resolution

(bp)

Median

size (bp)

# of

common

variants

SV-only

eQTLs

Joint eQTLs

SNV

GATK

21,764,904

6,982,921

17,024 (0.2%)

Indel

GATK

3,030,964

834,255

2,245 (0.3%)

Deletion (DEL)

BP, RD

11,692

966

3,373

546 (16.2%)

27 (0.8%)

493

kilobase*

3,699

275

63 (22.9%)

16 (5.8%)

Duplication (DUP)

BP, RD

2,514

577

798

103 (12.9%)

3 (0.4%)

1,054

kilobase*

4,993

833

148 (17.8%)

70 (8.4%)

Multi-allelic CNV

(mCNV)

1,635

kilobase*

3,676

992

241 (24.2%)

100 (10.1%)

Inversion (INV)

573

2 (10.5%)

0 (0.0%)

Mobile element

insertion (MEI)

2,053

1,610

269 (16.7%)

11 (1%)

Other SV (BND)

4,658

2,220

298 (13.4%)

6 (0.7%)

All SVs

24,157

178

10,120

1,670

(16.5%)

233 (2.3%)

All variants

24,820,025

7,827,296

19,269 (0.2%)

Table 1. Summary of variant types and discovery methods. SNVs and indels were detected using the Genome

Analysis Toolkit (GATK) and SVs were detected by breakpoint evidence (BP), read-depth evidence (RD), or both.

A subset of common variants were tested for cis-eQTLs, and were required to be on the autosomes or X

chromosome and present in a minimum of 10 of the individuals with RNA-seq in at least one tissue. The SV-only

eQTL mapping excluded SNVs and indels for greater sensitivity, while the joint eQTL mapping included all variant

types. *Resolution refers to the positional certainty at each breakpoint, with read-depth variants having

approximate breakpoint precision on the kilobase scale.

We mapped cis eQTLs using 10,120 common SVs and whole transcriptome RNA-seq data from 13

tissues. We defined a cis window to include SVs within 1 Mb of each gene, and applied a permutation-

based eQTL mapping approach using FastQTL, revealing 5,153 SV-eQTLs associated with expression

differences at 2,102 distinct genes (hereafter referred to as “eGenes”) and 1,670 distinct SVs

(Benjamini-Hochberg false discovery rate (FDR): 10%)16 (Supplementary Table 1).

SVs altered exons at 11.0% of eQTLs, providing a testable framework for their causal effects. Loss of

function variants such as deletions or exon-disrupting MEIs are expected to decrease gene expression,

exon duplications should increase gene expression, and neutral markers that tag a nearby causal

variant through linkage disequilibrium (LD) should show bidirectional effects. Indeed, 513/568 (90.3%)

of exon-altering eQTLs showed patterns of expression consistent with the SV class (Fig. 2a). This

finding establishes strong evidence of a causal role for SVs at a subset of eGenes. In contrast, the

remaining 4,585 eQTLs (89.0%) generally exhibited bidirectional expression effects (Fig. 2a). This may

reflect a complex regulatory landscape of both enhancing and repressing DNA elements, or loci at

which the SV is merely in LD with the true causal variant.

Figure 1. Structural variation call set. Number of SVs detected in sample by breakpoint evidence (with supporting

read-depth evidence for deletions and duplications) (a), and by read-depth evidence alone (b) in 147 deep (30-

50X) human whole genomes. Starred (*) samples exhibited abnormal read-depth profiles, and were excluded from

rare variant analyses. (c) The minor allele frequency distribution of SVs mirrored that of high quality SNVs and

indels detected by GATK. (d) Heat scatter plot showing the relationship between SV length and minor allele

frequency, with a peak at ~300 bp due to Alu SINE insertions.

To assess the relative contribution of SV, we expanded our eQTL analysis to include 6,982,921 SNVs

and 822,241 indels detected by the Genome Analysis Toolkit (GATK) 17. We performed joint eQTL

mapping with the complete set of genetic variants, nominating a most likely causal variant for each

eQTL identified. This produced 23,441 joint eQTLs affecting 9,547 distinct eGenes including 790 SV-

eQTLs (3.4%), 20,208 SNV-eQTLs (86.2%), and 2,443 indel-eQTLs (10.4%). The observation that SVs

are the lead marker at 3.4% of eQTLs provides an initial estimate of their contribution to gene

expression variation. This is ~6-fold larger than a similar estimate from the 1000 Genomes Project,

where merely 0.56% of eQTLs identified in LCLs had an SV as the lead marker11. These disparate

results are not due to differences among tissues – we find that SVs are the lead marker at 2.8% of

eQTLs identified in whole blood – and are unlikely to stem from trivial methodological differences given

the similarity of eQTL mapping methods used in the two studies, and the fact that we recapitulated 31

of 47 (66.0%) previously identified LCL SV-eQTLs at eGenes also expressed in available tissues. We

Samples

Asian

African American White

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

LUMPY high confidence SV call set

Samples

Number of SVs

1000

2000





5000







INV



Samples

Asian

African American White

American Indian or Alaskan



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



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

Genome STRiP high confidence SV call set

Samples

Number of SVs

500

1000

1500

2000







Minor allele frequency

Density

0.0 0.1 0.2 0.3 0.4 0.5

0.02

0.04

0.06

0.08 SV comprehensive

SV high confidence

SNV

Indel

Deletions

Duplications

Multi-allelic CNVs

Mobile element insertions

Inversions

Other

suspect that the key difference between these studies is the greater sensitivity and accuracy afforded

by deep WGS data, which underscores the power and novelty of our study.

Figure 2. eQTL effect size distributions and heritability partitioning with linear mixed models. (a) Effect size

distributions for coding and noncoding variants of each type, with the number of eQTLs of each type above each

distribution. The top panels (SV-only eQTLs) show the 5,153 eQTLs that were discovered by the SV-only

analysis, while the bottom two panels show the 23,441 eQTLs discovered by the joint analysis. Multi-allelic CNVs

are included in the “Dup” category for this plot. (b,c) Heat scatter plots showing the heritability of each eQTL

apportioned to the most significant SV in the cis window (x-axis) and the additive effect from the top 1,000 most

significant SNVs and indels in the cis window (y-axis) for (b) SV-only and (c) joint eQTL mapping analyses. Gray

lines mark the median of values for each axis.

We next applied fine-mapping approaches to infer the probability that each locus contained a causal SV

in the eGene’s cis window. At each of the 23,441 joint eQTLs, we identified the 100 SNVs and indels in

the 1 Mb cis window that were most significantly associated with the eGene’s expression by their

FastQTL nominal p-value, as well as the single most significant SV. We then used the CAVIAR

software package to apportion a causal likelihood and a relative ranking to each of these 101 markers

based on the magnitude and direction of association as well as the pairwise LD structure across the

region18. This approach aims to disentangle each variant’s causal contribution from its association due

Coding Noncoding

176 357 20 15

1867 249 53 197 2 1

1779 1288 899 3 616

18341 2194 122 397 11 7

−2

−1

−2

−1

SV−only eQTLs Joint eQTLs

SNV Indel Del Dup MEI Inv Other SNV Indel Del Dup MEI Inv Other

Variant type

Effect size (beta)

Variant type

SNV

Indel

Del

Dup

MEI

Inv

Other

chSV

hSV

to LD with nearby causal markers. At 3.3% of eQTLs overall (2.2-4.3% among tissues), the SV was

identified among the 101 candidates as the highest probability causal variant underlying the eQTL

association.

As an orthogonal estimate of contribution of SV, we applied a linear mixed model to partition the

heritability of each eGene’s expression into a fixed effect from the SV, and a random effect representing

the cumulative heritability of the 1,000 most significant SNPs and indels in the cis region (Fig 2b,c,

Supplementary Fig. 2). This method mirrors that of several prior studies that have examined relative

contributions of distinct variant classes on a quantitative trait19-21. Heritability partitioning revealed that

SVs account for 8.5% of total gene expression heritability when summing their effects across all eQTLs,

although we note that this includes numerous loci where the SV has a very small effect. More

importantly, at the 22,289 eQTLs that showed appreciable overall genetic heritability (>0.05), the SV

contributed more heritability than the additive effect of the other 1,000 variants in 7.0% of cases,

suggesting that the SV was the causal variant.

Taken together, the three independent analyses presented above – FastQTL-based eQTL mapping,

CAVIAR-based fine mapping and GCTA-based heritability estimation – indicate that SVs are the causal

variant at 3.4%, 3.3% and 7.0% of eQTLs, respectively. These are likely to be underestimates because

the genotyping error rate for SVs is higher than for SNVs and indels, giving the latter a relative

advantage to “win” causal variant prediction tests in regions of strong LD. Although the absolute

contribution of SVs to heritable expression variation is small compared to SNVs and indels, on a per-

variant basis, an SV is 26- to 54-times more likely to modulate expression than an SNV or an indel.

Moreover, SVs showed a 1.2-fold larger median effect size on gene expression than SNVs and indels

(p-value: < 1 x 10-15, Mann-Whitney U test), and deletions showed a 1.4-fold larger median effect size,

with direction of effect predominantly correlating with SV type (Fig. 2a). This result is unlikely to stem

from differences in statistical power given the observed allele frequency distributions of each variant

class, and the fact that SVs have consistently greater effect sizes across matched allele frequency bins

(Fig 1c, Supplementary Fig. 3). Together, these results demonstrate that SVs play an important and

outsized role in defining the landscape of genetically regulated gene expression.

We next sought to examine the genomic context of SV-eQTLs for clues into their molecular

mechanisms. We hypothesized that causal SVs would be enriched in functional elements such as gene

bodies, enhancers and repressors. To maximize the number of causal variants in this analysis, we first

created an aggregate eQTL set containing the union of all eQTLs identified by either the SV-only or

joint eQTL mapping (24,594 eQTLs affecting 10,094 distinct eGenes). We then derived a composite

“causality score” that incorporates the aforementioned CAVIAR and GCTA estimates of SV causality at

each eQTL by multiplying the CAVIAR posterior causal probability with the SV’s cis heritability fraction

(ℎ!"

!/ℎ!"#

!) (Supplementary Fig. 4). At each eGene we selected the SV within 1 Mb that had the

strongest association to the eGene’s expression, and allocated these 4,485 distinct SVs into seven bins

according to their composite score quantile, with the least causal bin comprising the bottom half of

composite scores, and the most causal bin comprising the 95th percentile. Different SV classes were

represented in roughly consistent proportions across the lower causality bins, but the most causal bin of

SVs had higher concentrations of multi-allelic CNVs and duplications (Fig. 3a).

Figure 3. Feature enrichment of SV-eQTLs. Fold enrichment and 95% confidence intervals (based on 100

random shuffled sets of the positions of SVs in each bin) for the overlap between the most significant SV and

various annotated genomic features at the union of eQTLs discovered by SV-only or joint eQTL mapping. (a)

Composition of each causality score bin by SV type. (b) Enrichment for an SV in each bin of causality to touch

exons of the affected eGene (c-f) For the remaining plots in blue, SVs that overlapped with an exon of their

affected eGene were excluded, yet the remaining SVs still showed significant enrichment in enhancers (c), in the

10 kb regions upstream (d) and downstream of transcriptions start sites (TSS) (e), and regions predicted to be

highly occupied by transcription factors (FunSeq HOT regions) (f).

We examined the overlap between SVs and annotated genomic features to assess enrichment in

various functional elements. SVs in the 95th percentile of causality showed a 35.5-fold enrichment for

altering eGene exons, amounting to 18.3% (71/389) of the most causal SVs, compared to 0.3% of SVs

in the least causal bin representing the lower half of causality scores (Fig. 3b). Recapitulating the trend

from SV-alone eQTL mapping (Fig. 2a), the expression effect direction was highly correlated with SV

type (74/84 showing the expected direction), strongly suggesting that this set of exon-altering SVs are

the causal variant at their respective eQTLs. Importantly, this analysis also demonstrates that our

●● ●

●

Fold enrichment of SVs overlapping eGene exons

Composite causality quantile

Fold enrichment

(0,0.5] (0.5,0.6] (0.6,0.7] (0.7,0.8] (0.8,0.9] (0.9,0.95] (0.95,1]

Fold enrichment, normalized to (0,0.5] causality bin

●

DENdb Enhancers

Composite causality quantile

Fold enrichment

0.8

0.9

1.0

1.1

1.2

1.3

1.4

1.5

(0,0.5] (0.5,0.6] (0.6,0.7] (0.7,0.8] (0.8,0.9] (0.9,0.95] (0.95,1]

1.5

Fold enrichment, normalized to (0,0.5] causality bin

●●

●

GENCODE v19 10 kb upstream TSS

Composite causality quantile

Fold enrichment

0.8

1.0

1.2

1.4

1.6

1.8

2.0

(0,0.5] (0.5,0.6] (0.6,0.7] (0.7,0.8] (0.8,0.9] (0.9,0.95] (0.95,1]

1.5

2.5

Fold enrichment, normalized to (0,0.5] causality bin

●

●●

●

FunSeq HOT regions

Composite causality quantile

Fold enrichment

0.6

0.8

1.0

1.2

1.4

(0,0.5] (0.5,0.6] (0.6,0.7] (0.7,0.8] (0.8,0.9] (0.9,0.95] (0.95,1]

1.5

Fold enrichment, normalized to (0,0.5] causality bin

500

1000

1500

(0,0.5] (0.5,0.6] (0.6,0.7] (0.7,0.8] (0.8,0.9] (0.9,0.95] (0.95,1]

Composite causality quantile

Count

SV type

Deletion

Duplication

MEI

Inversion

mCNV

Other (BND)

●

GENCODE v19 10 kb downstream 3 prime

Composite causality quantile

Fold enrichment

0.6

0.8

1.0

1.2

1.4

(0,0.5] (0.5,0.6] (0.6,0.7] (0.7,0.8] (0.8,0.9] (0.9,0.95] (0.95,1]

1.5

Fold enrichment, normalized to (0,0.5] causality bin

Enhancers

(plus 1 kb flanking sequence on both sides)

10 kb upstream of TSS 10 kb downstream of 3 prime gene end FunSeq HOT regions

(plus 1 kb flanking sequence on both sides)

Exons of affected gene

a b c

d e f

causality score effectively distinguishes neutral from causal SVs: no significant enrichment of exon-

altering SVs is observed in bins beneath the 80th percentile of scores, and enrichment rises

precipitously from the 80th to the 95th percentile.

However, the vast majority of SVs – including 81.7% of those in the 95th percentile of predicted

causality – do not alter eGene dosage or structure, and thus are likely to act through regulatory

mechanisms. We analyzed these 4,363 non-coding SVs for enrichment in other functional elements of

the genome with potential regulatory consequences. We found that several functional elements were

stratified by causality score and significantly enriched in the most causal bins, including the regions

within 1 kb of enhancers, the regions 10 kb upstream or downstream of gene transcripts, and regions

predicted by FunSeq to be highly occupied by transcription factors22-24 (Fig. 3c-f, Supplementary Fig.

5). In all cases, regulatory element enrichment was most pronounced in the top causality score bin –

providing further evidence of the effectiveness of our scoring method – yet more moderate enrichments

were also observed in lower bins. This demonstrates that SVs with strong causality predictions are

more likely to affect known regulatory elements, strongly suggesting that many are bona fide causal

variants that alter gene expression through regulatory effects. Given the extreme paucity of causal

regulatory variants discovered in the human genome to date, the set of 695 promising candidates from

the top 10% of causality scores will prove invaluable for future studies.

A major bottleneck for interpreting GWAS results is identifying causal variants and determining the

genes and molecular mechanisms contributing to disease. Bridging this gap is a driving motivation for

eQTL studies. Approximately 88% of GWAS loci are in noncoding regions of the genome1, suggesting

that they act through effects on gene regulation. Indeed, ~6% of GWAS loci were found to be in strong

LD (R2≥0.8) with eQTLs in a recent GTEx study using SNP arrays3 (7.7% with the WGS data described

here), and the true number of disease-associated eQTLs is believed to be higher25. Thus, the SV-

eQTLs identified here provide an unparalleled resource to assess the contribution of SV to common

disease.

To investigate the contribution of SVs to trait-associated loci, we first identified 4,874 SNPs from the

GWAS catalog that were non-redundant on a per-locus and per-disease basis, were genotyped in the

GTEx samples, and that had convincing evidence for disease association (p<5x10-8) 26. Of these, 842

were in LD (R2≥0.5) with a lead marker from our joint SV/SNP/indel eQTL analysis, suggesting that the

GWAS hit and the eQTL are produced by the same underlying causal variant. An SV was the candidate

causal variant at 3.2%, 3.3% and 14.3% of the 842 GWAS-associated eQTLs, depending on whether

causality is judged based on FastQTL, CAVIAR or GCTA (as in the prior causal SV analysis).

Combined with the eQTL fine mapping results presented above, this suggests that SVs underlie a

similar fraction GWAS-associated eQTLs as they do eQTL results on the whole, indicating that our

results are directly relevant to common disease biology.

We next screened for SVs that were likely to explain prior GWAS results. We identified 49 SVs in LD

(R2≥0.5) with GWAS loci that were predicted to be the causal variant for an eQTL (Supplementary

Table 2). Here, we define causal SVs as those in the top 10% of composite causality scores, a set that

shows significant enrichment with functional annotations (Fig. 3). Ultimately, experimental validation will

be required to definitively establish the causal relationship between any given variant and GWAS result.

However, there are a number of promising candidates among these 49 loci. In one case, a 294 bp

deletion is associated with decreased expression of the DAB2IP gene in thyroid tissue – apparently by

disrupting an intronic enhancer – and is linked to a risk allele for abdominal aortic aneurysm27 (R2=0.57;

Fig 4a). In another case, a 1,468 bp deletion in intron 10 of the PADI4 gene is linked (R2=0.70) to a risk

allele for rheumatoid arthritis28 (Fig. 4b). Multiple studies have reported significant association between

haplotypes of the PADI4 gene and rheumatoid arthritis29,30, and high levels of PADI4 mRNA have been

detected in pathological synovial tissues, yet none have implicated this deletion, which flanks an

annotated enhancer and is predicted to be the causal variant for increased PADI4 expression in lung. A

third example relates an SVA retroelement insertion to a GWAS risk allele for melanoma and

esophageal cancer (R2=0.85) 31,32 (Fig. 4c). The variant is a polymorphic SVA insertion that defines the

boundaries of alternative splice isoforms of CASP8. The insertion allele is associated with reduced

expression of CASP8, which encodes a protease with a critical role in the regulation of proliferation and

apoptotic cell death (Fig. 4d). Finally, we recapitulate several SVs previously recognized as clinically

associated markers, such as a ~32 kb deletion conferring risk for psoriasis33 (Fig. 4d) and a ~37 kb

deletion linked to circulating liver enzyme levels (gamma-glutamyl transferase) 34 (Supplementary Fig.

6).

Figure 4. Candidate SV-eQTLs at GWAS loci. Genomic position and haplotype block are shown on the x-axis,

and each variant’s association with the indicated eGene is shown on the y-axis. The rectangular points represent

the predicted causal SV, with the colors representing its linkage (R2) to each marker in the window. The labeled

diamond shows the reported risk allele for the specified GWAS phenotype (a) A 294 bp deletion that intersects an

enhancer in intron 1 of DAB2IP was linked to a risk allele for abdominal aortic aneurysm (rs7025486), and is also

predicted to be a causal eQTL for DAB2IP. (b) A 1,468 bp deletion associated with increased expression of

PADI4 is linked to a known risk allele for rheumatoid arthritis (rs2301888). (c) A polymorphic mobile element

insertion defining exon boundaries of CASP8 reduces the gene’s expression and is linked with a risk allele for

melanoma (rs13016963). (d) A large 32,197 bp deletion of the LCE3C and LCE3B genes that was previously

identified as a risk factor for psoriasis was recapitulated by our study.

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DAB2IP

Chromosome 9 position (Mb)



0 1 2 3 4 5

124.1 124.2 124.3 124.4 124.5 124.6 124.7 124.8





















DAB2IP

R2 with LUMPY_DEL_171677

Chromosome 9 position (Mb)

LUMPY_DEL_171677



rs7025486 (aortic aneurysm)



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PADI4

Chromosome 1 position (Mb)



0 2 4 6

17.4 17.5 17.6 17.7 17.8 17.9

















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

PADI4

R2 with LUMPY_DEL_94051

Chromosome 1 position (Mb)

LUMPY_DEL_94051



rs2301888

(rheumatoid arthritis)



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CASP8

Chromosome 2 position (Mb)



0 2 4 6 8

201.9 202 202.1 202.2 202.3 202.4





















CASP8

R2 with LUMPY_MEI_133419

Chromosome 2 position (Mb)

LUMPY_MEI_133419

(2,782 bp SVA insertion)

rs13016963





c d

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LCE3C

Chromosome 1 position (Mb)



0 2 4 6

152.4 152.45 152.5 152.55 152.6 152.65 152.7 152.75





















LCE3C

LCE3B

R2 with LUMPY_DEL_97064

Chromosome 1 position (Mb)

LUMPY_DEL_97064



rs4112788

(psoriasis)



However, GWAS based on SNP array genotypes can only inform eQTLs that are well tagged by a SNP

on the genotyping array. Notably 30.3% of common SVs, as well as 29.9% of those in the top 10% of

causality scores, lacked a SNP marker on the Illumina Omni 2.5 array in linkage disequilibrium

(R2≥0.5), indicating that a substantial portion of SV-eQTLs are untested in typical disease association

studies. In comparison, only 10.1% of SNP and 17.2% of indel joint eQTLs are not well tagged.

We next sought to assess the role of rare SVs on gene expression variation. In contrast to common

variant eQTLs, which are caused by ancient mutations that have been subjected to natural selection,

most rare variants arose recently and are more likely to have larger effect sizes and deleterious

consequences. Rare variants are difficult to study via traditional eQTL approaches because any given

variant is observed too infrequently within a set of samples to establish a statistical relationship with

gene expression35. However, the effect of rare variants on gene expression can be assessed indirectly

via bulk outlier enrichment analyses36. We thus identified 5,047 gene expression outliers (median: 30

per person; range: 10-298) in which an individual exhibited aberrant transcript dosage compared to the

data set as a whole (Online Methods). Next, we identified 5,660,254 rare variants (4,691 SVs,

4,830,727 SNVs, and 824,836 indels) that were positively genotyped in at most two individuals. To

reduce the effects of population stratification, we limited this analysis to the 117 Caucasian individuals

with RNA-seq data in at least 5 tissues.

Rare variants were significantly enriched by 1.2-fold (95% CI: 1.2-1.3) within the gene body and the 5

kb flanking sequence of expression outliers (Fig. 5a, Supplementary Table 3). This enrichment is

most pronounced for SVs (16.1-fold, 95% CI: 11.5-25.4), in which 355/5,047 (7.0%) of gene expression

outliers harbored a rare SV compared to the null expectation of 22/5,047 (0.4%) in 1,000 random

permutations of the sample expression values. Notably, expression-altering SVs were significantly

larger than rare SVs on the whole (p-value: < 1 x 10-15, Mann-Whitney U test), and duplications were

disproportionately represented (Fig. 5b,c). In several cases a single large SV caused multiple gene

expression outliers: a 21.3 Mb duplication event was associated with 161 outliers within the region, and

two large duplications (4.1 Mb and 2.5 Mb) were associated with 11 and 30 outliers, respectively.

However, the enrichment of rare SVs around outlier genes is not driven by a handful of large events,

since the majority of outlier-associated SVs (56/99) were only associated with a single gene

(Supplementary Fig. 7). Moreover, on a per-variant basis, 99/4,691 (2.1%) of the rare SVs had an

expression outlier within 5 kb compared to 10/4,691 (0.2%) in the permutation set, representing a 9.9-

fold enrichment (95% CI: 5.8-19.8) (Fig. 5b). These findings demonstrate that rare SVs are a common

cause of aberrantly expressed genes, contributing a median of approximately 1 gene expression outlier

per person. We expect this to be a large underestimate given the strict definition of expression outliers

used in this study – rare variants are likely to contribute to more modest changes in expression as well.

Figure 5. Gene expression outliers are associated with rare SVs. (a) Fold enrichment of rare variants within 5 kb

of expression outliers (red) and fold enrichment of outliers within 5 kb of rare variants (blue) between the observed

set of 5,047 outliers and 1,000 random permutations of their sample names. (b) Effect size distributions for each

SV type within 5 kb of an outlier in the same individual, with “coding” SVs defined as those that overlap with exons

of the outlier gene and “noncoding” defined by the remainder. (c) Size distribution histograms by minor allele

frequency (MAF) for common (MAF>0.01), rare (MAF≤0.01), and rare SVs within 5 kb of an expression outlier in

the same individual, excluding balanced rearrangements. A peak at ~300 bp in the topmost plot results from

common Alu SINE insertions.

500

1000

1500

100

200

300

400

500

Common (MAF > 0.01) Rare (MAF <= 0.01) Rare (MAF <= 0.01)

outlier associated

1e+02 1e+05 1e+08

SV length (bp)

Number of SVs

−5

Del

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(coding)

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(noncoding)

Dup

(noncoding)

Balanced

Rare structural variant type

Effect size (median z−score across tissues)

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SV SNV Indel Overall

Variant type

Enrichment (95% CI)

●

Per outlier

Per variant

a c

Our data show that rare SVs alter gene expression through diverse mechanisms. Of the 99 rare SVs

predicted to causally alter gene expression (permutation-based FDR: 0.2%), 79 (79.8%) are CNVs that

change dosage of the aberrantly expressed gene (Supplementary Table 4). Most gene expression

changes occur in the expected direction relative to the dosage alteration (Fig. 5c), but we observed 4

deletions and 2 duplications with expression effects in the opposite direction; all involve partial gene

alterations, which suggests complex regulatory effects rather than simple dosage compensation. The

next most common class (11, 11.1%) are non-coding CNVs that appear to act through regulatory

effects and – as in the case of the SV-eQTLs (Fig 2a) – show bidirectional effects on transcription.

Remarkably, we identified a number of atypical SVs with strong yet unpredicted effects on gene

expression. These include a 3.6 Mb inversion that alters the expression of 3 genes found at or near the

breakpoints (one with increased and two with decreased expression), a 391 bp intronic inversion that

causes increased expression, a complex 3-breakpoint balanced rearrangement that causes decreased

gene expression, and 9 complex CNVs involving a combination of multiple copy number variable

segments and/or adjacent balanced rearrangements, including one highly complex 6-breakpoint event

that resembles chromothripsis (Supplementary Table 5). These results are consistent with prior

studies describing the prevalence of complex SVs in “normal” human genomes, and reveal for the first

time the diversity of gene expression effects caused by rare complex SVs.

We compared the relative contribution of rare SVs, SNVs and indels to expression outliers. Although

the overall enrichment of SNVs and indels at gene expression outliers is mild due to the high

background prevalence of rare variants in these classes, enrichment increases dramatically when

analyses are restricted to high impact mutations (as judged by CADD37; Supplementary Fig. 8).

Overall, we observed a net excess of 443 outliers within 5 kb of a rare variant in the same individual

compared to the expected number from permutation tests, or 8.8% (443/5,047) of total outliers.

Moreover, by partitioning excess outliers among SVs, SNVs and indels, we estimate that 69.9% of gene

expression outliers with a genetic basis are likely explained by structural variation, whereas merely

15.9% and 14.4% are due to SNVs and indels, respectively (Online Methods, Supplementary Fig. 9).

We note that this approximation assumes similar proportions of causal variants for each variant type, so

may under-estimate the contribution of SNVs and indels. It also only captures the effects of rare

variants within 5 kb of the outlier gene and depends on our definition of expression outliers. While the

strength of the SV effect is due in part to 8 very large CNVs (> 1 Mb), GTEx individuals should be

representative of the general population in terms of the prevalence of large CNVs, and the relative

contribution of SVs remains noteworthy even when individuals with megabase-scale CNVs are

excluded from the analysis (SV: 37.9%, SNV: 38.0%, indel: 24.1%). These results indicate that rare

structural variants are a major cause of gene expression outliers in the human population, and suggest

that thorough ascertainment of SV will significantly increase the power of rare variant association

studies, and the efficacy of WGS-based disease diagnosis.

As human genetics moves deeper into the era of whole genome sequencing, it has become possible to

include all forms of genetic variation in cohort studies and clinical practices. Our results demonstrate

that comprehensive analysis of structural variation will be a critical aspect of these efforts.

Competing financial interests

AB owns stock in Google Inc. The authors declare no other competing financial interests.

Online methods

SV call set generation

We acquired 148 deep whole genome BAM files from the GTEx V6 data release (dbGaP accession

phs000424.v6.p1). We excluded one sample (GTEX-WHWD-0002) due to an abnormal insert size

distribution, which confounds SV detection. We realigned the remaining 147 whole genomes to GRCh

build 37 plus a contig for Epstein-Barr virus using SpeedSeq v0.0.3 (BWA-MEM v0.7.10-r789)

according to published practices13,38. We ran LUMPY v0.2.9 on each sample using the default

parameters in the LUMPY Express script, using the published list of excluded genomic regions from

SpeedSeq as well as the -P option to output probability curves for each breakpoint12. We merged the

147 VCF files using the l_sort.py and l_merge.py scripts included in LUMPY with the “--product” option

and 20 bp of slop, simultaneously combining variants with overlapping breakpoint intervals while

refining their spatial precision based on the probability curves to create a cohort-level VCF. We pruned

remaining variants with nearly overlapping breakpoint intervals by selecting the single variant with the

highest allele frequency among the overlapping set. Next, we genotyped each sample with SVTyper

(https://github.com/hall-lab/svtyper), which performs breakpoint sequencing of paired-end and split-read

discordants13. We define the term “allele balance” as the ratio of non-reference to total reads at each

breakpoint. Allele balance serves a proxy for genotype that is tolerant to inefficiencies in aligning the

alternate allele for SVs, and is used for most analyses in this paper. We then used CNVnator to

annotate the copy number of each spanning variant (putative deletions, duplications, and inversions).

We applied several filters to the LUMPY call set to flag low quality SVs. Since 68 samples were

sequenced on the Illumina HiSeq 2000 platform and 79 on the Illumina HiSeq X Ten platform, we

flagged variants whose linear correlation (R2) between genotype and sequencing platform exceeded

0.1. We further flagged deletions lacking split-read support that were smaller than 418 bp, which was

measured to be the empirical minimum deletion size at which all insert size libraries were able to

discriminate between concordant and discordant reads with 95% certainty. We determined that three

samples (GTEX-NPJ8-0004, GTEX-T2IS-0002, GTEX-OIZI-1026) had abnormal read-depth profiles

and we therefore flagged SVs private to any of those samples, effectively excluding them from rare

variant analyses. Finally, we flagged variants with a mean sample quality (MSQ, a measure of genotype

quality among positively genotyped samples that is independent of allele frequency) of less than 20 as

low quality.

Next, we reclassified variant types, requiring that deletions and duplications showed correlation

between read-depth and the allele balance at the breakpoint. For common SVs (at least 10 samples

with non-reference genotypes) we fit a linear regression and required a slope of at least 1.0 in the

appropriate direction (positive for duplications, negative for deletions) and R2 ≥ 0.2. For the remaining

low frequency SVs we required that > 50% of positively genotyped samples must be > 2 MAD (median

absolute deviation) (in the correct direction for deletion/duplication) and > 0.5 absolute copies from the

median of reference genotyped samples. For low frequency SVs on the sex chromosomes, we limited

the above criteria to the gender with more individuals to avoid gender confounders. We identified

mobile elements insertions (MEIs) in the reference genome as SVs with breakpoint orientations

indicative of deletions that had > 0.9 reciprocal overlap with an annotated SINE, LINE, or SVA element

with sequence divergence of less than 200 milliDiv, based on RepeatMasker annotations. Due to

limitations of our pipeline, we were only able to detect MEIs inserted into the reference genome based

on their absence in other genotyped samples.

We ran Genome STRiP 2.0 according to the best practices workflow for deeply sequenced genomes,

using a window size of 1000 bp, window overlap of 500 bp, reference gap length of 1000 bp, boundary

precision of 100 bp, and minimum refined length of 500 bp. We flagged CNVs for platform bias and the

three samples with abnormal coverage profiles as described above. For rare SVs detected by Genome

STRiP (private or doubletons in our call set) we merged fragmented variants with identical genotypes

within 10 Mb of each other whose combined footprint encompassed at least 10% of their span.

We then unified the LUMPY and Genome STRiP call sets while collapsing redundancies. Because

LUMPY variants are substantially more precise and have well-defined confidence intervals, we retained

LUMPY calls when an SV was detected by both algorithms with a reciprocal overlap of > 0.5 and a

matching variant type (mCNVs were allowed to merge with either LUMPY duplications or LUMPY

deletions). To ensure that SVs would be merged even when the Genome STRiP call was fragmented,

which occurs fairly often with GTEx WGS data, we also merged calls where > 0.9 of a Genome STRiP

CNV was contained in a LUMPY SV of the same type (or mCNV) and their correlation between LUMPY

allele balance and copy number had R2 > 0.25. This last step ensures that the merged variants have a

high degree of co-occurrence among samples, and are not simply independent variants that inhabit the

same genomic interval.

We measured allele frequency for LUMPY SVs as the ratio of non-reference to total alleles in the

population. For Genome STRiP variants we defined allele frequency as the fraction of samples that

deviate from the mode copy number value in the population.

We defined a high confidence call set from the variants that had not been flagged by the

aforementioned filters. In general, high confidence SVs had to be supported by multiple independent

evidence types. Since LUMPY deletions and duplications were identified by paired-end and/or split-read

evidence and had also met requisite read-depth support from reclassification they were automatically

considered high confidence. Similarly, MEIs were considered high confidence based on support from

reference genome repeat annotations. LUMPY inversions and BNDs (unclassified breakends) were

required to have a minimum variant quality score of 100. Inversions were further required to show

evidence from both sides of the event, and at least 10% of supporting reads derived from each of split-

read and paired-end evidence types. LUMPY BND variants were required to have at least 25% of

supporting reads derived from each of split-read and paired-end evidence types. Genome STRiP

variants that were merged as described above and those with GSCNQUAL score >= 10 were

considered high confidence. This set of 24,157 variants served as the basis for all analyses in this

paper.

Common eQTL mapping

We mapped cis-eQTLs to scan for significant associations between common variant genotypes and

gene expression in all tissues for which there were ≥ 70 individuals with both WGS data and RNA-seq

data. These include the following 12 tissues: whole blood, skeletal muscle, lung, tibial artery, aortic

artery, adipose (subcutaneous), thyroid, esophagus mucosa, esophagus muscularis, skin (sun-

exposed), tibial nerve, muscle (skeletal), as well as transformed fibroblasts. For convenience, we refer

to the transformed fibroblasts as a tissue type throughout this study. RNA-seq data from each tissue

was aligned with Tophat v1.4 using GENCODE v19 gene annotations by taking the union of exons for

gene level quantification, and RPKM values were calculated with RNA-SeQC39-41. Reads were required

to align exclusively within exons or span them (without aligning to intronic regions), align in proper

pairs, contain a maximum of six non-reference bases, and map uniquely to the gene. Samples were

quantile normalized within each tissue followed by inverse quantile normalization of each gene to

control outliers.

We selected common genetic markers for which there were at least 10 samples with minor allele

genotypes in our cohort. For Genome STRiP CNVs, we defined the reference genotype as the mode

copy number observed in our data set. We performed two independent cis-eQTL mapping runs. The

first, an “SV-only” eQTL analysis, used only common SV markers as genotype input for improved

sensitivity under a reduced multiple-testing burden. The second, a “joint” eQTL analysis, included the

10,120 common SVs as well as 6,982,921 SNVs and 834,255 indels detected by the Genome Analysis

Toolkit HaplotypeCaller v3.1-144-g00f68a317, allowing a fair comparison of the relative contribution of

different variant types.

We mapped cis-eQTLs with FastQTL v2.184 using a cis window of 1 Mb on either side of the

transcription start site (TSS) of autosomal and X chromosome genes with a permutation analysis to

identify the most significant marker for each gene42. We customized the FastQTL software to include an

SV for genotype-expression associations when the span of a deletion, duplication, mCNV, or MEI fell

within the cis window for a particular gene, or when the breakpoints of an inversion or uncharacterized

breakend (BND) fell within the cis window. For each tissue, we applied a set of covariates including

three genotyping principal components, gender, genotyping platform (HiSeq 2000 or HiSeq X Ten), and

a variable number of PEER (probabilistic estimation of expression residuals) factors determined by

number of samples per tissue, N (N < 150: 15 PEERs, 150 ≤ N ≤ 50: 30 PEERs, N ≥ 250: 35 PEERs)

43. Note that PEER factor sample sizes include RNA-seq data from individuals lacking WGS, providing

more samples for PEER correction than the 147 individuals in the remainder of this study. We

performed gene level multiple-testing correction for each of the SV-only and joint analyses using

Benjamini-Hochberg at a 10% false discovery rate (FDR).

Fine mapping of causal variants at eQTLs

We used CAVIAR to untangle linkage disequilibrium to predict a causal variant for each eQTL18.

CAVIAR assesses summary statistics in conjunction with LD across an associated locus to rank the

causal probability of each variant in a region. Thus, we ran FastQTL once again on the 24,780 eQTLs

that had previously met FDR thresholds in either the SV-only or joint cis-eQTL mapping analyses to

generate the nominal t-statistic for every common variant in the cis window. For each eQTL, we

selected the most significant SV as well as the 100 most significant SNVs or indels (based on nominal

p-value) in the cis window and estimated their pairwise LD using linear regression. For SVs, we used

allele balance rather than discrete genotype for computing LD. We ran CAVIAR at each of these eQTLs

using the t-statistics and signed R values of LD among the 101 variants with a causal set size of 1.

As an alternate estimate of the causal role of structural variation, at each eQTL discovered by either the

SV-only or joint analyses, we applied a linear mixed model (LMM) to partition the heritability of each

eGene’s expression into a fixed effect from the SV, and a random effect representing the cumulative

heritability of the 1,000 most significant SNPs and indels in the cis region. This method mirrors that of

several other studies that have examined relative contributions of distinct variant classes on a

quantitative trait in n individuals19-21. We first corrected for the same covariates as in the cis-eQTL

mapping analyses above by linear regression residualization, and then applied a linear model of the

form

!!=!!,!!

!+!!+!

!,!

where !! is a vector of the normalized expression values, !!,! is the effect of allele dosage of SV ! on

gene !, !

! is a n-length vector of genotypes at SV !, !! is a n-length vector of random effects drawn

from the genetic relatedness matrix (GRM) with !!~MVN 0,!!!

!, and ! is a random error term

drawn from !(0,!!!,!,!

!) representing unexplained variance. We defined the n x n dimensional GRM (!

with entries !!,!=(!!"!!!)(!!"!!!)

(!!"!!!)

!(!!"!!!)

! for the 1,000 SNV and indel variants (!!) that are most

significantly associated with the expression of eGene !.

Solving this equation with GCTA produces an estimate of variance where var !

!≈ℎ!"

var !!=!!,!

!var !

!+!!!

!+!!!,!

!!!!

!=ℎ!"

!+ℎ!

!+!!

ℎ!"#

!=ℎ!"

!+ℎ!

Heritability estimates for a small number of eQTLs could not be calculated due to non-positive definite

matrices likely arising from small sample sizes (808/23,441 of joint eQTLs and 930/24780 of eQTLs

detected by either SV-only or joint mapping). These loci were excluded from the heritability analysis and

composite causality scores described below. To estimate the fraction of cis heritability attributable to

SVs across all eQTLs in our data set, we counted the number of eQTLs where ℎ!"

!>ℎ!

! as a fraction of

joint eQTLs at which the overall heritability (ℎ!"#

!) was at least 0.05.

We combined the CAVIAR and heritability estimates of causality into a single composite score for each

eQTL by taking the product of the CAVIAR causal probability and the fraction of heritability attributed to

the SV (ℎ!!

!!/!ℎ!"

!+ℎ!

!). To bound the heritability fraction between 0 and 1, we set the minimum ℎ!"

! and

ℎ!

! to 10-6 and the maximum to 1 before taking the quotient. For SVs that were associated with multiple

eQTLs, including those that were independently ascertained in multiple tissues, we selected the eQTL

(tissue, gene pair) in which the SV had the highest causality score, resulting in a set of 4,485 distinct

SVs.

Though we calculated causality scores (CAVIAR, heritability, composite) for all eQTLs detected by the

SV-only or joint analyses, we restricted estimates comparing the relative contributions and effect sizes

of SVs, SNVs, and indels to only those 23,441 detected by the joint analysis to eliminate confounding

differences in statistical power between the eQTL mapping runs.

Feature enrichment

We performed intersections between SVs across the range of composite causality score quantiles and

various annotated genomic features (Fig. 3, Supplementary Fig. 1). We first allocated SVs into 7 bins

by the quantile of their composite causality score and then counted the number that intersected with

each feature, allowing 1 kb of flanking distance except for the following: exon-altering plot, no flanking

distance; proximity to TSS, 10 kb of directional flanking distance; GENCODE genes, no flanking

distance; GENCODE exons, no flanking distance; and topologically associated domain boundaries, 5

kb of flanking distance. SVs involved in multiple eQTLs were considered to touch an eGene if they

overlapped the exons of genes at any of those eQTLs. SVs from each causality bin were shuffled with

BEDTools into non-gapped regions of the genome within 1 Mb of a gene transcription start site44. We

calculated the fold enrichment of observed feature intersections compared to the median of 100 random

shuffled sets of the elements of each bin to control for each bin’s composition of variant types and size

distributions. The 95% confidence intervals were derived from the empirical distributions of feature

intersections from the of the shuffled set for each bin.

Enhancer positions were defined as those in the Dragon ENhancers database (DENdb) with a minimum

support of 224 (Fig. 3b). Positions 10 kb upstream and downstream of the TSS were defined from

GENCODE v19 gene positions (Fig. 3c,d), and FunSeq 2.1.0 regions were downloaded from the

author’s website (http://archive.gersteinlab.org/funseq2.1.0_data) 23. Topologically associated domain

boundaries from human embryonic stem cell lines were downloaded from

http://compbio.med.harvard.edu/modencode/webpage/hic/hESC_domains_hg19.bed45. All other

regions were defined by the ENCODE project and downloaded from the UCSC genome browser22,46.

eQTL linkage to GWAS hits

We defined a set of phenotype-associated SNPs from the GWAS catalog v1.0.1 (downloaded 2016-02-

04) 26. We selected for results with a p-value better than 5 x 10-8 and tested in Europeans. When

multiple markers within a 100 kb window met this criteria in a single study and a single phenotype, we

selected the most significant marker in the window to reduce redundancy, resulting in a set of 4,951

SNPs, of which 4,874 were genotyped in our cohort of 147 samples. We calculated LD between these

GWAS hits and variants in our cohort using a linear regression to approximate R2, using allele balance

rather than discrete genotypes for SVs detected with LUMPY.

Rare variant association with expression outliers

We began by defining gene expression outliers in each of 544 individuals with RNA-seq data across the

44 tissues available from the GTEx project. Since quantile normalization of expression values (as

applied in cis-eQTL mapping) can reduce the signal from true expression outliers, we derived PEER-

corrected expression values without quantile normalization to define expression outliers. For each

tissue, we filtered for genes on the autosomes or the X chromosome in which at least 10 individuals

had an RPKM (reads per kilobase of transcript per million mapped reads) > 0.1 and raw read counts >

6. Next we took the log2(RPKM + 2) transformation of the data, followed by Z-transformation across

each gene. We then removed PEER factors by linear regression residualization (using the same

number of factors per tissue as described above, see Common eQTL mapping) followed by a final Z-

transformation.

We then subsampled the 544 individuals above to select the 117 who were of Caucasian ancestry

(since this was the largest subpopulation in our cohort) and had available WGS sequence data. Among

these 117 individuals, we identified (sample, gene) pairs at which an individual’s absolute median Z-

score of a gene’s expression was at least 2, and there were at least 5 tissues with available expression

data for the gene. This amounted to 5,047 gene expression outliers (median: 30 per person, range: 10-

298). Next, we identified rare variants that were present in at most two individuals in our cohort of 147

individuals and positively genotyped in at least one of the 117 Caucasian individuals, amounting to

5,660,256 rare variants (4,691 SVs, 4,830,727 SNVs, and 824,836 indels).

We counted the number of rare SVs, SNVs, and indels that co-occurred in the outlier individual that

resided within the outlier transcript or 5 kb of flanking sequence. To define the frequency that this

occurs by chance, we performed 1,000 random permutations of the outlier individual names in our set

to determine the number of rare variants of each type that co-occur with an outlier in a random

individual. Notably, we retained the relative number of outliers per individual in each permutation so that

individuals with many outliers were still over-represented in the permuted sets.

We performed two reciprocal measures of enrichments. The “outlier-centric” approach tested for a

significant difference in the number of outliers that had a rare variant within 5 kb (Fig. 5a, red points).

However, for SVs in particular, a single large variant may be in proximity to many outliers, and we

controlled for this phenomenon with the “variant-centric” approach to test for a significant difference in

the number of rare variants that had an outlier gene within 5 kb (Fig. 5a, blue points).

To judge the enrichment across thresholds of variant functional impact, we computed a predicted

impact score with CADD v1.2 for all variants in our data set37. For SVs, we used the highest-scoring

base across the affected interval and the confidence intervals around the SV breakpoints. We then

computed to the percentile of these impact scores for SVs, SNVs, and indels separately across the full

set of allele frequencies. We show the fold-enrichment for the outlier-centric (Supplementary Fig. 7a-

c) and variant-centric (Supplementary Fig. 5d-f) approaches across the range of impact score

percentiles for each variant class.

To estimate the relative contribution of each variant type to expression outliers, we first defined the

fraction of outliers with a likely genetic basis. Across 1,000 shuffled permutations of the data, we

observed a median of 1,974 outliers (95% CI: 1,914-2,036) with a rare variant of any type in the outlier

individual. We identified 2,417 outliers with a rare variant, representing a net excess of 443 over the

expected value (95% CI: 381-503). Thus, of the 5,047 total outliers in our data set, an estimated 8.8%

(95% CI: 7.5-10.0%) of outliers have a genetic basis.

We then apportioned these 8.8% of genetically determined outliers amongst SVs, SNVs, and indels

according to the net excess of observed outliers within 5 kb of each variant types (Supplementary Fig.

8). For outliers that were within 5 kb of multiple variant types (overlaps on the Venn diagram), we

allocated the net excess percentage based on the relative strength of their overall fold-enrichment. To

achieve this, we first estimated the fraction of expression outliers with a genetic basis within each

variant type (!={!",!"#,!"#$% }) as !!=!!!

!, where ! is the number of observed outliers with 5 kb of

a rare variant of type ! and ! is the median from the permuted sets (!!" =0.94;!!"# =0.12;!!"#$% =

0.19 in our data set).. Then, for each overlapping region of the Venn diagram, we multiplied the net

excess by !!

!!!∈!

for each of the variant types in each Venn diagram area !.

To identify complex variants, we clustered high-confidence rare SVs with breakpoint evidence located

no more than 100 kb away from each other and present in the same individual(s). Rare SVs with only

read-depth support were not included in this clustering because of their imprecise boundaries. We

joined separate clusters if they contained two sides of the same uncharacterized BND. Clusters

containing SVs that were previously found to be associated with outlier gene expression were

reclassified as a complex deletion, complex duplication, or balanced complex rearrangement by manual

curation. During this manual curation, rare SVs with only read-depth support and present in the

appropriate sample(s) were added to the rare variant cluster if they overlapped other variants included

in the cluster. Upon manual inspection, one outlier-associated SV (LUMPY_BND_195398) with inverted

breakpoint orientation was visually determined to have amplified read-depth over the interval and thus

reclassified as a complex duplication.

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Genome-wide analysis of Structural Variants in Parkinson's Disease using Short-Read Sequencing data

Preprint

Full-text available

Aug 2022

Parkinsons disease is a complex neurodegenerative disorder, affecting approximately one million individuals in the USA alone. A significant proportion of risk for Parkinsons disease is driven by genetics. Despite this, the majority of the common genetic variation that contributes to disease risk is unknown, in-part because previous genetic studies have focussed solely on the contribution of single nucleotide variants. Structural variants represent a significant source of genetic variation in the human genome. However, because assay of this variability is challenging, structural variants have not been cataloged on a genome-wide scale, and their contribution to the risk of Parkinsons disease remains unknown. In this study, we 1) leveraged the GATK-SV pipeline to detect and genotype structural variants in 7,772 short-read sequencing data and 2) generated a subset of matched whole-genome Oxford Nanopore Technologies long-read sequencing data from the PPMI cohort to allow for comprehensive structural variant confirmation. We detected, genotyped, and tested 3,154 high-confidence common structural variant loci, representing over 412 million nucleotides of non-reference genetic variation. Using the long-read sequencing data, we validated three structural variants that may drive the association signals at known Parkinsons disease risk loci, including a 2kb intronic deletion within the gene LRRN4. Further, we confirm that the majority of structural variants in the human genome cannot be detected using short-read sequencing alone, encompassing on average around 4 million nucleotides of inaccessible sequence per genome. Therefore, although these data provide the most comprehensive survey of the contribution of structural variants to the genetic risk of Parkinsons disease to date, this study highlights the need for large-scale long-read datasets to fully elucidate the role of structural variants in Parkinsons disease.

Genome‐Wide Analysis of Structural Variants in Parkinson Disease

Article

Jan 2023

Objective: Identification of genetic risk factors for Parkinson's disease (PD) has to date been primarily limited to the study of single nucleotide variants, which only represent a small fraction of the genetic variation in the human genome. Consequently, causal variants for most PD risk are not known. Here we focused on structural variants (SVs), which represent a major source of genetic variation in the human genome. We aimed to discover SVs associated with PD risk by performing the first large-scale characterization of SVs in PD. Methods: We leveraged a recently developed computational pipeline to detect and genotype SVs from 7,772 Illumina short-read whole genome sequencing samples. Using this set of SV variants, we performed a genome-wide association study using 2,585 cases and 2,779 controls and identified SVs associated with PD risk. Furthermore, to validate the presence of these variants, we generated a subset of matched whole-genome long-read sequencing data. Results: We genotyped and tested 3,154 common SVs, representing over 412 million nucleotides of previously uncatalogued genetic variation. Using long-read sequencing data, we validated the presence of three novel deletion SVs that are associated with risk of PD from our initial association analysis, including a 2kb intronic deletion within the gene LRRN4. Interpretation: We identify three SVs associated with genetic risk of PD. This study represents the most comprehensive assessment of the contribution of SVs to the genetic risk of PD to date. This article is protected by copyright. All rights reserved.

The impact of rare variation on gene expression across tissues

Preprint

Full-text available

Sep 2016

Rare genetic variants are abundant in humans yet their functional effects are often unknown and challenging to predict. The Genotype-Tissue Expression (GTEx) project provides a unique opportunity to identify the functional impact of rare variants through combined analyses of whole genomes and multi-tissue RNA-sequencing data. Here, we identify gene expression outliers, or individuals with extreme expression levels, across 44 human tissues, and characterize the contribution of rare variation to these large changes in expression. We find 58% of underexpression and 28% of overexpression outliers have underlying rare variants compared with 9% of non-outliers. Large expression effects are enriched for proximal loss-of-function, splicing, and structural variants, particularly variants near the TSS and at evolutionarily conserved sites. Known disease genes have expression outliers, underscoring that rare variants can contribute to genetic disease risk. To prioritize functional rare regulatory variants, we develop RIVER, a Bayesian approach that integrates RNA and whole genome sequencing data from the same individual. RIVER predicts functional variants significantly better than models using genomic annotations alone, and is an extensible tool for personal genome interpretation. Overall, we demonstrate that rare variants contribute to large gene expression changes across tissues with potential health consequences, and provide an integrative method for interpreting rare variants in individual genomes.

Large-Scale Profiling Reveals the Influence of Genetic Variation on Gene Expression in Human Induced Pluripotent Stem Cells

Article

Apr 2017

In this study, we used whole-genome sequencing and gene expression profiling of 215 human induced pluripotent stem cell (iPSC) lines from different donors to identify genetic variants associated with RNA expression for 5,746 genes. We were able to predict causal variants for these expression quantitative trait loci (eQTLs) that disrupt transcription factor binding and validated a subset of them experimentally. We also identified copy-number variant (CNV) eQTLs, including some that appear to affect gene expression by altering the copy number of intergenic regulatory regions. In addition, we were able to identify effects on gene expression of rare genic CNVs and regulatory single-nucleotide variants and found that reactivation of gene expression on the X chromosome depends on gene chromosomal position. Our work highlights the value of iPSCs for genetic association analyses and provides a unique resource for investigating the genetic regulation of gene expression in pluripotent cells.

Fast and efficient QTL mapper for thousands of molecular phenotypes

Preprint

Full-text available

Aug 2015

Motivation: In order to discover quantitative trait loci (QTLs), multi-dimensional genomic data sets combining DNA-seq and ChiP-/RNA-seq require methods that rapidly correlate tens of thousands of molecular phenotypes with millions of genetic variants while appropriately controlling for multiple testing. Results: We have developed FastQTL, a method that implements a popular cis-QTL mapping strategy in a user- and cluster-friendly tool. FastQTL also proposes an efficient permutation procedure to control for multiple testing. The outcome of permutations is modeled using beta distributions trained from a few permutations and from which adjusted p-values can be estimated at any level of significance with little computational cost. The Geuvadis & GTEx pilot data sets can be now easily analyzed an order of magnitude faster than previous approaches. Availability: Source code, binaries and comprehensive documentation of FastQTL are freely available to download at http://fastqtl.sourceforge.net/

Fast and efficient QTL mapper for thousands of molecular phenotypes

Article

Full-text available

Dec 2015

An integrated map of structural variation in 2,504 human genomes

Article

Full-text available

Sep 2015

Structural variants are implicated in numerous diseases and make up the majority of varying nucleotides among human genomes. Here we describe an integrated set of eight structural variant classes comprising both balanced and unbalanced variants, which we constructed using short-read DNA sequencing data and statistically phased onto haplotype blocks in 26 human populations. Analysing this set, we identify numerous gene-intersecting structural variants exhibiting population stratification and describe naturally occurring homozygous gene knockouts that suggest the dispensability of a variety of human genes. We demonstrate that structural variants are enriched on haplotypes identified by genome-wide association studies and exhibit enrichment for expression quantitative trait loci. Additionally, we uncover appreciable levels of structural variant complexity at different scales, including genic loci subject to clusters of repeated rearrangement and complex structural variants with multiple breakpoints likely to have formed through individual mutational events. Our catalogue will enhance future studies into structural variant demography, functional impact and disease association.

DENdb: Database of integrated human enhancers

Article

Full-text available

Aug 2015

Enhancers are cis-acting DNA regulatory regions that play a key role in distal control of transcriptional activities. Identification of enhancers, coupled with a comprehensive functional analysis of their properties, could improve our understanding of complex gene transcription mechanisms and gene regulation processes in general. We developed DENdb, a centralized on-line repository of predicted enhancers derived from multiple human cell-lines. DENdb integrates enhancers predicted by five different methods generating an enriched catalogue of putative enhancers for each of the analysed cell-lines. DENdb provides information about the overlap of enhancers with DNase I hypersensitive regions, ChIP-seq regions of a number of transcription factors and transcription factor binding motifs, means to explore enhancer interactions with DNA using several chromatin interaction assays and enhancer neighbouring genes. DENdb is designed as a relational database that facilitates fast and efficient searching, browsing and visualization of information. Database URL: http://www.cbrc.kaust.edu.sa/dendb/

A global reference for human genetic variation

Article

May 2015

The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies.

Genome structural variation discovery and genotyping—sequencing versus arrays

Article

Dec 2012
Pathology

Evan E. Eichler

Structural variation of the genome is an important aspect in our understanding of human disease but has been difficult to systematically identify and genotype. Human genomes are particularly prone to structural variation when compared to other mammalian genomes due to idiosyncrasies in our genomic architectures. The recent application of massively parallel sequencing methods has led to an exponential increase in the discovery of smaller structural variation events. Significant global discovery biases remain, but the integration of experimental and computational approaches is proving fruitful for accurate characterisation of the copy, content, and structure of variable regions. I will present methods based on read-depth, split-read, and paired-end sequence approaches from both whole genome and exome sequence datasets and compare these to the utility of standard microarray approaches applied in the clinic. Next-generation sequencing platforms can assay copy and content of genes previously inaccessible by hybridisation-based methods but do not yet completely resolve the full complexity of structural variation.

Abundant contribution of short tandem repeats to gene expression variation in humans

Article

Dec 2015
Nat Genet

The contribution of repetitive elements to quantitative human traits is largely unknown. Here we report a genome-wide survey of the contribution of short tandem repeats (STRs), which constitute one of the most polymorphic and abundant repeat classes, to gene expression in humans. Our survey identified 2,060 significant expression STRs (eSTRs). These eSTRs were replicable in orthogonal populations and expression assays. We used variance partitioning to disentangle the contribution of eSTRs from that of linked SNPs and indels and found that eSTRs contribute 10-15% of the cis heritability mediated by all common variants. Further functional genomic analyses showed that eSTRs are enriched in conserved regions, colocalize with regulatory elements and may modulate certain histone modifications. By analyzing known genome-wide association study (GWAS) signals and searching for new associations in 1,685 whole genomes from deeply phenotyped individuals, we found that eSTRs are enriched in various clinically relevant conditions. These results highlight the contribution of STRs to the genetic architecture of quantitative human traits.

GENCODE: producing a reference annotation for ENCODE

Article

Jan 2006

J. Harrow

An integrated encyclopedia of DNA elements in the human genome

Article

Sep 2012

The human genome encodes the blueprint of life, but the function of the vast majority of its nearly three billion bases is unknown. The Encyclopedia of DNA Elements (ENCODE) project has systematically mapped regions of transcription, transcription factor association, chromatin structure and histone modification. These data enabled us to assign biochemical functions for 80% of the genome, in particular outside of the well-studied protein-coding regions. Many discovered candidate regulatory elements are physically associated with one another and with expressed genes, providing new insights into the mechanisms of gene regulation. The newly identified elements also show a statistical correspondence to sequence variants linked to human disease, and can thereby guide interpretation of this variation. Overall, the project provides new insights into the organization and regulation of our genes and genome, and is an expansive resource of functional annotations for biomedical research.

SpeedSeq: Ultra-fast personal genome analysis and interpretation

Article

Aug 2015
Br J Pharmacol

SpeedSeq is an open-source genome analysis platform that accomplishes alignment, variant detection and functional annotation of a 50× human genome in 13 h on a low-cost server and alleviates a bioinformatics bottleneck that typically demands weeks of computation with extensive hands-on expert involvement. SpeedSeq offers performance competitive with or superior to current methods for detecting germline and somatic single-nucleotide variants, structural variants, insertions and deletions, and it includes novel functionality for streamlined interpretation.

The impact of structural variation on human gene expression

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