Mapping copy number variation by
population-scale genome sequencing
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Citation Mills, Ryan E., et al., "Mapping copy number variation by population-
scale genome sequencing." Nature 470 (2011): p. 59-65 doi 10.1038/
nature09708 ©2011 Author(s)
As Published 10.1038/nature09708
Publisher Springer Science and Business Media LLC
Version Author's final manuscript
Citable link https://hdl.handle.net/1721.1/125843
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Mapping copy number variation by population scale genome
sequencing
A full list of authors and affiliations appears at the end of the article.
Summary
Genomic structural variants (SVs) are abundant in humans, differing from other variation classes
in extent, origin, and functional impact. Despite progress in SV characterization, the nucleotide
resolution architecture of most SVs remains unknown. We constructed a map of unbalanced SVs
(i.e., copy number variants) based on whole genome DNA sequencing data from 185 human
genomes, integrating evidence from complementary SV discovery approaches with extensive
experimental validations. Our map encompassed 22,025 deletions and 6,000 additional SVs,
including insertions and tandem duplications. Most SVs (53%) were mapped to nucleotide
resolution, which facilitated analyzing their origin and functional impact. We examined numerous
whole and partial gene deletions with a genotyping approach and observed a depletion of gene
disruptions amongst high frequency deletions. Furthermore, we observed differences in the size
spectra of SVs originating from distinct formation mechanisms, and constructed a map constructed
a map of SV hotspots formed by common mechanisms. Our analytical framework and SV map
serves as a resource for sequencing-based association studies.
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@
Correspondence should be addressed to (jan.korbel@embl.de). .
*
These authors contributed equally to this work.
#
Lists of paraticipants and affiliations appear in Supplementary Information.
Author Contributions: The authors contributed this study at different levels, as described in the following. SV discovery: K.W., C.S.,
R.H., K.C., C.A., A.A., S.C.Y., R.K.C., A.C., Y.F., I.H., F.H., Z.I., D.K., R.L., Y.L., C.L., R.L., X.J.M., H.E.P., L.D., G.T.M., J.S.,
J.W., K.Y., K.Y., E.E.E., M.B.G., M.E.H., S.A.M., and J.O.K. SV validation: R.E.M., K.W., K.C., A.A., S.C.Y., F.G., M.K.K., J.K.,
J.N., A.E.U., X.S., A.M.S., J.A.W., Y.Z., Z.Z., M.A.B., J.S., M.S., M.E.H., C.L, J.O.K. SV genotyping: K.W., R.H., M.E.H, and
S.A.M. Data analysis: R.E.M., C.S., C.A., A.A., R.H., K.C., S.C.Y., R.K.C., A.C., D.C., Y.F., F.H., L.M.I., Z.I., J.M.K., M.K.K.,
S.K., J.K., E.K., D.K., H.Y.K.L., J.L., R.L., Y.L., C.L., R.L., X.J.M., J.N., H.E.P., T.R., A.S., X.S., M.P.S., J.A.W., J.W., Y.Z., Z.Z.,
M.A.B., L.D., G.T.M., G.M. ,J.S., M.S., J.W., K.Y., K.Y., E.E.E., M.B.G., M.E.H., C.L, S.A.M., and J.O.K. Preparation of
manuscript display items: R.E.M., K.W., C.S., C.A., A.A., R.H., S.C.Y., L.M.I., S.K., E.K., M.K.K., X.J.M., X.S., J.A.W., M.B.G.,
S.A.M., and J.O.K. Co-chairs of the Structural Variation Analysis group: E.E.E., M.E.H., and C.L. The following were leading
contributors to the analysis described in this paper and therefore should be considered joint first authors: R.E.M., K.W., C.S., R.H.,
K.C., C.A., A.A., S.C.Y, and K.Y. The following equally contributed to directing the described analyses and participating in the
design of the study and should be considered joint senior authors: E.E.E, M.B.G., M.E.H., C.L, S.A.M., and J.O.K. The manuscript
was written by the following authors: R.E.M. and J.O.K.
Competing interests statement: The authors declare competing financial interests. H.E.P. and Y.F. are employees of Life
Technologies, the manufactures of the SOLiD sequencing platform. R.K.C. is an employee of Illumina Cambridge Ltd., the
manufacturer of the Illumina sequencing platform.
Data retrieval: The data sets described here can be obtained from the 1000 Genomes Project website at www.1000genomes.org (July
2010 Data Release). Individual SV discovery methods can be obtained from sources mentioned in Supplementary Table 1, or upon
request from the authors. Abbreviations used in this paper are summarized in the Supplementary Text.
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Nature. Author manuscript; available in PMC 2011 August 03.
Published in final edited form as:
Nature. 2011 February 3; 470(7332): 59–65. doi:10.1038/nature09708.
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Introduction
Unbalanced structural variants (SVs), or copy number variants (CNVs), involving large-
scale deletions, duplications, and insertions form one of the least well studied classes of
genetic variation. The fraction of the genome affected by SVs is comparatively larger than
that accounted for by single nucleotide polymorphisms1 (SNPs), implying significant
consequences of SVs on phenotypic variation. SVs have already been associated with
diverse diseases, including autism2,3, schizophrenia4,5 and Crohn’s disease6,7.
Furthermore, locus-specific studies suggest that diverse mechanisms may form SVs de novo,
with some mechanisms involving complex rearrangements resulting in multiple
chromosomal breakpoints8,9.
Initial microarray-based SV surveys focused on large gains and losses10,11,12, with recent
advances in array technology widening the accessible size spectrum towards smaller
SVs1,13. Microarray-based commonly mapped SVs to approximate genomic locations.
However, a detailed SV characterization, including analyses of SV origin and impact,
requires knowledge of precise SV sequences. Advances in sequencing technology have
enabled applying sequence-based approaches for mapping SVs at fine-
scale14,15,16,17,18,19,20,21. These approaches include: (i) paired-end mapping (or read
pair ‘RP’ analysis) based on sequencing and analysis of abnormally mapping pairs of clone
ends14,22,23,24 or high-throughput sequencing fragments15,17,18; (ii) read-depth (‘RD’)
analysis, which detects SVs by analyzing the read depth-of-coverage
16,21,25,26,27; (iii)
split-read (‘SR’) analysis, which evaluates gapped sequence alignments for SV
detection28,29; and (iv) sequence assembly (‘AS’), which enables the fine-scale discovery
of SVs, including novel (non-reference) sequence insertions30,31,32. Sequence-based SV
discovery approaches have thus far been applied to a limited (<20) number of genomes,
leaving the fine-scale architecture of most common SVs unknown.
Sequence data generated by the 1000 Genomes Project (1000GP) provide an unprecedented
opportunity to generate a comprehensive SV map. The 1000GP recently generated 4.1
Terabases of raw sequence in pilot projects targeting whole human genomes33
(Supplementary Table 1). These studies comprise a population-scale project, termed ‘low-
coverage project’, in which 179 unrelated individuals were sequenced with an average
coverage of 3.6X – including 59 Yoruba individuals from Nigeria (YRI), 60 individuals of
European ancestry from Utah (CEU), 30 of Han ancestry from Beijing (CHB), and 30 of
Japanese ancestry from Tokyo (JPT; the latter two were jointly analyzed as JPT+CHB). In
addition, a high-coverage project, termed the ‘trio project’, was carried out, with individuals
of a CEU and a YRI parent-offspring trio sequenced to 42X coverage on average.
We report here the results of analyses undertaken by the Structural Variation Analysis
Group of the 1000GP. The group’s objectives were to discover, assemble, genotype, and
validate SVs of 50 bp and larger in size, and to assess and compare different sequence-based
SV detection approaches. The focus of the group was initially on deletions, a variant class
often associated with disease9, for which rich control datasets and diverse ascertainment
approaches exist1,13,22,28. Less focus was placed on insertions and duplications34 and
none on balanced SV forms (such as inversions). Specifically, we applied nineteen methods
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to generate an SV discovery set. We further generated reference genotypes for most
deletions, assessed the SVs’ functional impact, and stratified SV formation mechanism with
respect to variant size and genomic context.
Prediction of SV candidate loci and assessment of discovery methods
We incorporated the SV discovery methods into a pipeline (Fig. 1AB), with the goal of
ascertaining different SV types and assessing each method for its ability to discover SVs.
The methods detected SVs by analyzing RD, RP, SR, and AS features, or by combining RP
and RD features (abbreviated as ‘PD’). Altogether we generated thirty-six SV callsets by
applying the methods on trio and low-coverage data, and by identifying SVs as genomic
differences relative to a human reference, corresponding to the reference genome, or to a set
of individuals (i.e. population reference; Supplementary Table 2). We initially identified
SVs as deletions, tandem duplications, novel sequence insertions, and mobile element
insertions (MEIs) relative to the human reference. Subsequent comparative analyses
involving primate genomes enabled us to classify SVs as deletions, duplications, or
insertions relative to inferred ancestral genomic loci, reflecting mechanisms of SV formation
(see below). DNA reads analyzed by SV discovery methods were initially mapped to the
human reference genome using a variety of alignment algorithms. Most of these algorithms
mapped each read to a single genomic position, although one algorithm (mrFAST16) also
considered alternative mapping positions for reads aligning onto repetitive regions (see
Supplementary Tables 2-4 for method-specific parameters and full SV callsets). We filtered
each callset by excluding SVs <50bp, which are reported elsewhere33. Many SVs exhibited
support from distinct SV discovery methods, as exemplified by a common deletion,
previously associated with body-mass index35 (BMI), that we identified with RP, RD, and
SR methods (Fig. 1C). Nonetheless, we observed notable differences between methods (Fig.
2ABC) in terms of genomic regions ascertained (Supplementary Fig. 1), accessible SV size-
range (Fig. 2A), and breakpoint precision (Fig.2C, Supplementary Fig. 2).
To estimate callset specificity, we carried out extensive validations (Methods), including
PCRs for over 3,000 candidate loci, and microarray data analyses for 50,000 candidate loci
(Supplementary Tables 3, 4; Supplementary Fig. 3). We combined PCR and array-based
analysis results to estimate false discovery rates (FDRs), and found that eight callsets (three
deletion, four insertion, and one tandem duplication callset) met the pre-specified specificity
threshold33 (FDR≤10%), whereas the other callsets yielded lower specificity (FDRs of
13%-89%).
We further assessed the sensitivity of deletion discovery methods by collating data from four
earlier surveys1,13,22,28 into a gold standard (Methods, Supplementary Tables 5, 6, and
Supplementary Fig. 4A), and specifically assessing the detection sensitivity for an individual
sequenced at high-coverage (NA12878) as well as for an individual sequenced at low-
coverage (NA12156). Unsurprisingly, given the typical trade-off between sensitivity and
specificity, in the trios the highest sensitivities were achieved by RD and RP methods with
FDR>10% (Fig. 2B). By comparison, in the low-coverage data, the individual method with
the greatest accuracy (FDR=3.7%) was the second most sensitive based on our gold standard
(Fig. 2B), and the most sensitive when expanding the gold standard to a larger set of
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individuals (Supplementary Fig. 4B). This method, Genome STRiP (to be described
elsewhere36), integrated both RP and RD features (PD), implying that considering different
evidence types can improve SV discovery.
Construction of a high-confidence SV discovery set
To construct our SV discovery set (“release set”), we joined calls from different discovery
methods corresponding to the same SV with a merging approach that was aware of each
callset’s precision in SV breakpoint detection (Supplementary Fig. 5 and Methods). Most
SVs in the release set (61%) were contributed by individual methods meeting the pre-
defined specificity threshold (FDR≤10%). The remaining 39% of calls were contributed by
lower specificity methods following experimental validation. Altogether, the release set
comprised 22,025 deletions, 501 tandem duplications, 5,371 MEIs, and 128 non-reference
insertions (Table 1, Supplementary Table 7). With our gold standard we estimated an overall
sensitivity of deletion discovery of 82% in the trios, and 69% in low-coverage sequence
(Fig. 2B) using a 1 bp overlap criterion. When instead applying a stringent 50% reciprocal
overlap criterion for sensitivity assessment (which required SV sizes inferred on different
experimental platforms to be in close agreement) our sensitivity estimates decreased by 12%
and 18%, respectively, in trio and low-coverage sequence (Supplementary Table 8). We
further examined an alternative approach that involved the pairwise integration of deletion
discovery methods, and tested its ability to discover SVs without relying on the inclusion of
lower specificity calls following experimental validation (“algorithm-centric set”; Fig. 1B).
While this alternative approach resulted in an increased number (by ~13%) of high-
specificity (FDR<10%) calls compared to the release set (Supplementary Text), it overall
resulted in fewer SV calls owing to its decreased sensitivity at the lower (<200bp) SV size
range. In the following analyses we thus focused on the release set.
Extent and impact of our SV discovery set
We next assessed the extent and impact of our SV discovery (release) set. The median SV
size was 729 bp (mean=8 kb), approximately four times smaller than in a recent tiling CGH
based study1, reflecting the high resolution of DNA sequence based SV discovery. We also
compared our set to a recent survey of SVs in an individual genome37 based on capillary
sequencing and array-based analyses24, and observed a similar size distribution for
deletions, but differences in the size distributions of other SV classes, reflecting underlying
differences in SV ascertainment (Supplementary Fig. 6). By comparing our SVs to databases
of structural variation and to additional personal genome datasets, we classified 15,556 SVs
in our set as novel, with an enrichment of low frequency SVs and small SVs amongst the
novel variants (Methods and Supplementary Text).
A major advantage of sequence-based SV discovery is the nucleotide resolution mapping of
SVs. We initially mapped the breakpoints of 7,066 deletions and 3,299 MEIs using SR and
AS features. Using the TIGRA-targeted assembly approach38 we further identified the
breakpoints of an additional 4,188 deletions and 160 tandem duplications, initially
discovered by RD, RP, and PD methods (Methods, Supplementary Table 2). Altogether, we
mapped ~15,000 SVs at nucleotide resolution, 48% of which were novel. Few deletion loci
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