Mapping copy number variation by population scale genome sequencing Ryan
 E.
 Mills1,*,
 Klaudia
 Walter2,*,
 Chip
 Stewart3,*,
 Robert
 E.
 Handsaker4,*,
 Ken
 Chen5,*,
 Can
 Alkan6,7,*,
 Alexej
 Abyzov8,*,
 Seungtai
 Chris
 Yoon9,*,
 Kai
 Ye10,*,
 R.
 Keira
 Cheetham11,
Asif
Chinwalla5,
Donald
F.
Conrad2,
Yutao
Fu12,
Fabian
Grubert13.,
Iman
 Hajirasouliha14,
 Fereydoun
 Hormozdiari14,
 Lilia
 M.
 Iakoucheva15,
 Zamin
 Iqbal16,
 Shuli
 Kang15,
 Jeffrey
 M.
 Kidd6,
 Miriam
 K.
 Konkel17,
 Joshua
 Korn4,
 Ekta
 Khurana8,18,
 Deniz
 Kural3,
 Hugo
 Y.
 K.
 Lam13,
 Jing
 Leng8,
 Ruiqiang
 Li19,
 Yingrui
 Li19,
 Chang‐Yun
 Lin20,
Ruibang
Luo19,
Xinmeng
Jasmine
Mu8,
James
Nemesh4,
Heather
E.
Peckham12,
 Tobias
 Rausch21,
 Aylwyn
 Scally2,
 Xinghua
 Shi1,
 Michael
 P.
 Stromberg3,
 Adrian
 M.
 Stütz21,
Alexander
Eckehart
Urban13,
Jerilyn
A.
Walker17,
Jiantao
Wu3,
Yujun
Zhang2,
 Zhengdong
 D.
 Zhang8,
 Mark
 A.
 Batzer17,
 Li
 Ding5,22,
 Gabor
 T.
 Marth3,
 Gil
 McVean23,
 Jonathan
 Sebat15,
 Michael
 Snyder13,
 Jun
 Wang19,24,
 Kenny
 Ye20,
 Evan
 E.
 Eichler6,7,*,
 Mark
B.
Gerstein8,18,25,*,
Matthew
E.
Hurles2,*,
Charles
Lee1,*,
Steven
A.
McCarroll4,26,*,
 and
Jan
O.
Korbel21,*,@
 for
the
1000
Genomes
Project#


1.
Department
of
Pathology,
Brigham
and
Women’s
Hospital
and
Harvard
Medical
School,
Boston,
MA
 2.
The
Wellcome
Trust
Sanger
Institute,
Wellcome
Trust
Genome
Campus,
Hinxton,
Cambridge,
CB10
1SA
UK.
 3.
Department
of
Biology,
Boston
College,
Boston,
MA
 4.
Broad
Institute
of
Harvard
and
Massachusetts
Institute
of
Technology,
Cambridge,
MA
 5.
The
Genome
Center
at
Washington
University,
St.
Louis,
MO
 6.
Department
of
Genome
Sciences,
University
of
Washington
School
of
Medicine,
Seattle,
WA

 7.
Howard
Hughes
Medical
Institute,
University
of
Washington,
Seattle,
Washington,
USA.
 8.
Program
in
Computational
Biology
and
Bioinformatics,
Yale
University,
New
Haven,
CT
 9.
Seaver
Autism
Center
and
Department
of
Psychiatry,
Mount
Sinai
School
of
Medicine,
New
York,
NY
 10.
Departments
of
Molecular
Epidemiology,
Medical
Statistics
and
Bioinformatics,
Leiden
University
Medical
Center,
Leiden,
 the
Netherlands

 11.
Illumina
Cambridge
Ltd,
Chesterford
Research
Park,
Little
Chesterford,
Essex
CB10
1XL,
UK

 12.
Life
Technologies,
Beverly,
MA
 13.
Department
of
Genetics,
Stanford
University,
Stanford,
CA
 14.
School
of
Computing
Science,
Simon
Fraser
University,
Burnaby,
British
Columbia,
 Canada.
 15.
Department
of
Psychiatry,
Department
of
Cellular
and
Molecular
Medicine,
Institute
for
Genomic
Medicine,
University
of
 California,
San
Diego,
La
Jolla,
CA
 16.
Wellcome
Trust
Centre
for
Human
Genetics,
University
of
Oxford,
OX3
7BN,
UK
 17.
Department
of
Biological
Sciences,
Louisiana
State
University,
Baton
Rouge,
Louisiana
 18.
Molecular
Biophysics
and
Biochemistry
Department,
Yale
University,
New
Haven,
CT
 19.
BGI‐Shenzhen,
Shenzhen
518083,
China
 20.
Albert
Einstein
College
of
Medicine,
Bronx,
NY
 21.
Genome
Biology
Research
Unit,
European
Molecular
Biology
Laboratory,
Heidelberg,
Germany
 22.
Department
of
Genetics,
Washington
University,
St.
Louis,
MO
 23.
Department
of
Statistics,
University
of
Oxford,
OX3
7BN,
UK
 24.
Department
of
Biology,
University
of
Copenhagen,
Copenhagen,
Denmark
 25.
Department
of
Computer
Science,
Yale
University,
New
Haven,
CT
 26.
Department
of
Genetics,
Harvard
Medical
School,
Boston,
MA
 
 *These
authors
contributed
equally
to
this
work.
 @ Correspondence
should
be
addressed
to
J.O.K.
(jan.korbel@embl.de).
 #Lists
of
participants
and
affiliations
appear
in
Supplementary
Information.





1


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 of SV hotspots formed by common mechanisms. Our analytical framework and SV map serves as a resource for sequencing-based association studies.




2



 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
 surveys
 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‐coverage16,21,25,26,27;
 (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
 
 3


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
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
 
 4


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
 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
high­confidence
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





5


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
 (4.4%)
 displayed
 different
 SV
 breakpoints
 in
 different
 samples,
 which
 is
 explainable
 by
 rare
 TIGRA
 mis‐ assemblies,
 or
 alternatively,
 by
 recurrently
 formed,
 multi‐allelic
 SVs
 (Supplementary
Text).
TIGRA
further
enabled
us
to
validate
an
additional
7,359
SVs
 discovered
 with
 RP
 or
 RD
 features
 by
 identifying
 the
 SVs’
 breakpoints
 (Methods),
 and
 to
 evaluate
 the
 mapping
 precision
 of
 SV
 discovery
 methods
 (Fig.
 2C,
 Supplementary
Figure
2).

 We
 further
 assessed
 the
 putative
 functional
 impact
 of
 SVs
 in
 our
 set
 by
 relating
 them
 to
 genomic
 annotation.
 Seventeen
 hundred
 SVs
 affected
 coding
 sequences,
 resulting
in
full
gene
overlaps
or
exon
disruptions
(Table
2),
many
of
which
led
to
 out‐of‐frame
 exons
 (Supplementary
 Table
 9).
 We
 related
 gene
 disruptions
 to
 gene
 functions,
 and
 observed
 significant
 enrichments
 for
 several
 functional
 categories
 including
 cell
 defense
 and
 sensory
 perception
 (Supplementary
 Table
10).
 High
 levels
 of
 structural
 variation,
 including
 copy‐number
 variation,
 were
 previously
 described
 for
 both
 processes15,22,39.
 These
 SVs
 might
 be
 maintained
 in
 the
 population
 by
 selection
 for
 the
 purpose
 of
 functional
 redundancy.
 While
 most
 SVs
 intersecting
 with
 genes
 were
 deletions,
 several
 validated
 tandem
 duplications
 and
 MEIs
also
intersected
with
coding
sequences
(Table
2).

 
 Population
genetic
properties
of
deletions

 We
next
sought
to
generate
genotypes
for
deletions
discovered
in
the
1000GP
data,
 both
to
facilitate
population
genetics
analyses
and
to
make
our
SV
set
amenable
to
 association
 studies
 in
 the
 form
 of
 a
 reference
 genotype
 set.
 In
 this
 regard,
 the
 Genome
 STRiP36
 genotyping
 method
 was
 developed,
 a
 method
 combining
 information
 from
 RD,
 RP,
 SR
 and
 haplotype
 features
 of
 population‐scale
 sequence
 data
 for
 genotyping
 (Methods,
 Supplementary
 Text).
 Using
 this
 approach
 we
 generated
 genotypes
 for
 13,826
 autosomal
 deletions
 in
 156
 individuals.
 The
 genotypes
 displayed
 99.1%
 concordance
 with
 CGH
 array1
 based
 genotypes
 (available
for
1,970
of
the
deletions),
suggesting
high
genotyping
accuracy.

 Fig.
 3
 presents
 allele
 frequency
 analyses
 based
 on
 these
 genotypes.
 As
 expected,
 common
 polymorphisms
 (minor
 allele
 frequency
 (MAF)
 >5%)
 were
 generally
 shared
across
populations,
while
rare
alleles
were
frequently
observed
in
only
one
 population
 (Figs.
 3ABC).
 We
 observed
 several
 candidates
 for
 monomorphic
 deletions
 (i.e.,
 genomic
 segments
 putatively
 deleted
 in
 all
 individuals),
 explainable
 
 6


by
 rare
 insertions
 present
 in
 the
 reference
 genome
 or
 by
 remaining
 genotyping
 inaccuracies
(Supplementary
Text).
 We
 next
 assessed
 the
 allele
 frequencies
 of
 gene
 deletions
 (Fig.
 3D).
 Similar
 to
 a
 recent
array‐based
study1,
we
observed
a
depletion
of
high
frequency
alleles
among
 deletions
 intersecting
 with
 protein‐coding
 sequence
 compared
 to
 other
 deletions
 (P=1.1x10‐11;
 KS
 test),
 consistent
 with
 purifying
 selection
 keeping
 most
 gene
 deletions
 at
 low
 frequency.
 Nonetheless,
 several
 coding
 sequence
 deletions
 were
 observed
 with
 high
 allele
 frequency
 (>80%).
 Most
 of
 these
 occurred
 in
 regions
 annotated
 as
 segmental
 duplications,
 consistent
 with
 lessened
 evolutionary
 constraint
in
functionally
redundant
gene
categories22.

Intriguingly,
common
gene
 deletions
 also
 affected
 many
 unique
 genes
 with
 no
 obvious
 paralogs.
 We
 further
 analyzed
 the
 abundance
 of
 gene
 deletions
 in
 different
 populations
 and
 observed
 highly
differentiated
loci,
albeit
with
no
statistically
significant
relationship
between
 differentiation
 and
 particular
 categories
 of
 gene
 overlap,
 i.e.,
 intronic
 vs.
 exonic
 (Supplementary
Text).
 By
 comparing
 deletion
 genotypes
 with
 genotypes
 of
 nearby
 SNPs,
 we
 found,
 consistent
with
earlier
studies1,13,40,
that
deletions
in
genomic
regions
accessible
to
 short
 read
 sequencing
 display
 extensive
 linkage
 disequilibrium
 (LD)
 with
 SNPs.
 81%
 of
 common
 deletions
 had
 one
 or
 more
 SNPs
 with
 which
 they
 are
 strongly
 correlated
 (r2>0.8;
 Supplementary
 Fig.
 7).
 This
 suggests
 that
 many
 deletions
 mapped
 in
 our
 study
 will
 be
 identifiable
 through
 tagging
 SNPs
 in
 future
 studies
 (Supplementary
 Text).
 On
 the
 other
 hand,
 a
 fifth
 of
 the
 genotyped
 deletions
 were
 not
 tagged
 by
 HapMap
 SNPs
 (a
 figure
 similar
 to
 the
 fraction
 of
 SNPs
 that
 are
 not
 tagged
by
HapMap
SNPs41),
implying
that
these
SVs
should
be
genotyped
directly
in
 association
studies.
Furthermore,
the
LD
properties
of
complex
SVs
(e.g.,
multiallelic
 SV)
 have
 not
 yet
 been
 fully
 ascertained
 as
 methods
 for
 genotyping
 such
 SVs
 with
 similar
accuracy
are
still
being
developed.
 
 SV
formation
mechanism
analysis
 Nucleotide
 resolution
 breakpoint
 information
 enables
 inference
 of
 SV
 formation
 mechanisms15,22.
 Recent
 studies
 broadly
 distinguished
 between
 several
 germline
 rearrangement
 classes,
 some
 of
 which
 may
 comprise
 more
 than
 one
 SV
 formation
 mechanism15,22,42,43:
non‐allelic
homologous
recombination
(NAHR),
associated
with
 long
 sequence
 similarity
 stretches
 around
 the
 breakpoints;
 rearrangements
 in
 the
 absence
of
extended
sequence
similarity
(abbreviated
as
“non‐homologous”
or
NH),
 associated
 with
 DNA
 repair
 by
 non‐homologous
 end‐joining
 (NHEJ)
 or
 with
 microhomology‐mediated
 break‐induced
 replication
 (MMBIR);
 the
 shrinking
 or
 expansion
 of
 variable
 number
 of
 tandem
 repeats
 (VNTRs),
 frequently
 involving
 simple
sequences,
by
slippage;
and
MEIs.
We
distinguished
among
the
classes
NAHR,
 NH,
VNTR,
and
MEI
by
examining
the
breakpoint
junction
sequence
of
SVs
initially
 discovered
as
deletions
or
tandem
duplications
relative
to
a
human
reference.






7


We
 first
 compared
 the
 SVs
 to
 orthologous
 primate
 genomic
 regions
 to
 distinguish
 deletions
from
insertions/duplications
with
respect
to
reconstructed
ancestral
loci
 using
 the
 BreakSeq
 classification
 approach43.
 This
 analysis
 showed
 that
 of
 the
 11,254
 nucleotide‐resolution
 SVs
 discovered
 as
 deletions
 relative
 to
 a
 human
 reference,
 21%
 actually
 represented
 insertions
 and
 2%
 represented
 tandem
 duplications
 relative
 to
 the
 putative
 ancestral
 genome.
 Of
 the
 remaining
 SVs,
 60%
 were
 classified
 as
 deletions
 relative
 to
 ancestral
 sequence,
 whereas
 the
 ancestral
 state
 of
 17%
 was
 undetermined.
 By
 comparison,
 out
 of
 160
 nucleotide‐resolution
 SVs
identified
as
tandem
duplications
relative
to
the
reference
genome,
91.6%
were
 classified
 as
 duplications
 relative
 to
 the
 ancestral
 genome,
 whereas
 the
 ancestral
 state
 of
 8.4%
 remained
 undetermined
 (Supplementary
 Text).
 Our
 breakpoint
 analysis
revealed
that
70.8%
of
the
deletions
and
89.6%
of
the
insertions
exhibited
 breakpoint
 microhomology/homology
 ranging
 from
 2‐376
 bp
 in
 size,
 with
 distribution
 modes
 of
 2
 bp
 (attributable
 to
 NH)
 and
 15
 bp
 (attributable
 to
 MEI),
 respectively
 (Fig.
 4A,
 Supplementary
 Text).
 As
 expected42,
 a
 small
 portion
 of
 the
 deletions
 (16.1%)
 displayed
 non‐template
 inserted
 sequences
 at
 their
 breakpoint
 junctions.
 By
 comparison,
 the
 tandem
 duplications
 showed
 extensive
 stretches
 displaying
≥95%
sequence
identity
at
the
breakpoints
linearly
correlating
in
length
 with
 SV
 size
 (Fig.
4A).
 In
 addition,
 most
 tandem
 duplications
 displayed
 2‐17
 bp
 of
 microhomology
at
the
breakpoint
junctions
(Supplementary
Text).
 We
 subsequently
 applied
 BreakSeq43
 to
 infer
 formation
 mechanisms
 for
 all
 SVs
 classified
 with
 regard
 to
 ancestral
 state.
 Using
 BreakSeq,
 we
 inferred
 NH
 as
 the
 dominating
 deletion
 mechanism,
 and
 MEI
 as
 the
 dominating
 insertion
 mechanism
 (Fig.
4BC,
Supplementary
Table
11).
Furthermore,
an
abundance
of
microhomology
 at
tandem
duplication
breakpoints
suggested
frequent
formation
of
this
SV
class
by
 a
rearrangement
process
acting
in
the
absence
of
homology
(NH).
When
relating
SV
 formation
 to
 the
 variant
 size
 spectrum,
 we
 observed
 marked
 insertion
 peaks
 for
 MEIs
 at
 300
 bp,
 corresponding
 to
 Alu
 elements,
 and
 at
 6
 kb,
 corresponding
 to
 L1/LINEs
 (Fig.
 4C).
 By
 comparison,
 NH
 and
 NAHR
 based
 mechanisms
 occurred
 across
 a
 wide
 size‐range,
 whereas
 VNTR
 expansion/shrinkage,
 consistent
 with
 earlier
findings1,
led
to
relatively
small
SV
sizes
(Figs.
4C,D).
 Furthermore,
 when
 displaying
 the
 genomic
 distribution
 of
 SVs
 (Fig.
 5A),
 we
 observed
a
notable
clustering
of
SVs
into
‘SV
hotspots’.
We
analyzed
this
clustering
 in
 detail
 by
 examining
 the
 distribution
 of
 non‐overlapping,
 adjacent
 SVs,
 and
 observed
a
marked
clustering
of
SVs
formed
by
NAHR,
VNTR,
and
NH,
respectively,
a
 signal
extending
to
hundreds
of
kilobases
(Fig.
5B).
The
clustering
was
influenced
by
 an
abundance
of
VNTR
near
the
centromeres43
and
NAHR
near
the
telomeres
(Fig.
 5A).
 A
 significant
 enrichment
 of
 NAHR
 near
 recombination
 hotspots
 (P=1.3e‐15)
 and
 segmental
 duplications
 (P=3.1e‐17)
 further
 contributed
 to
 the
 clustering
 (Supplementary
Table
13).

 To
 further
 explore
 this
 clustering
 we
 devised
 a
 segmentation
 approach
 for
 predicting
SV
hotspots
(Methods),
which
yielded
a
map
of
51
putative
SV
hotspots
 (Supplementary
 Table
 14).
 80%
 of
 the
 hotspots
 mainly
 comprised
 SVs
 originating





8


from
a
single
formation
mechanism
(Fig.
5C).
Most
of
these
corresponded
to
NAHR
 hotspots,
 although
 hotspots
 dominated
 by
 NH
 and
 VNTR
 also
 were
 evident.
 These
 observations
 suggest
 that
 SV
 formation
 is
 frequently
 associated
 with
 the
 locus‐ specific
propensity
for
genomic
rearrangement.
 Conclusions
and
discussion
 
 By
 generating
 an
 SV
 set
 of
 unprecedented
 size
 along
 with
 breakpoint
 assemblies
 and
 reference
 genotypes,
 we
 demonstrate
 the
 suitability
 of
 population‐scale
 sequencing
 for
 SV
 analysis.
 Nucleotide
 resolution
 data
 allow
 the
 construction
 of
 reference
 datasets
 and
 make
 SVs
 readily
 assessable
 across
 different
 experimental
 platforms
using
genotyping
approaches.
Our
fine‐scale
map
enabled
us
to
examine
 the
 functional
 impact
 of
 SVs,
 as
 exemplified
 by
 our
 analysis
 of
 gene
 disruption
 variants,
which
will
be
of
value
for
genome
and
exome
sequencing
studies.

 Our
 map
 further
 enabled
 us
 to
 examine
 size
 spectra
 of
 SV
 formation
 mechanisms
 and
led
us
to
identify
genomic
SV
hotspots
that
are
commonly
dominated
by
a
single
 formation
mechanism.
Recurrent
rearrangements,
implicated
in
genomic
disorders,
 are
 hypothesized
 to
 be
 associated
 with
 local
 genome
 architecture44,
 e.g.,
 with
 segmental
 duplications
 that
 facilitate
 NAHR.
 Also,
 DNA
 rearrangement
 in
 the
 absence
of
homology,
i.e.,
MMBIR,
has
been
implicated
in
recurrent
SV
formation8,45.
 In
 this
 regard,
 we
 noticed
 that
 out
 of
 the
 hotspots
 we
 report,
 six
 fall
 into
 critical
 regions
 of
 known
 genetic
 disorders
 associated
 with
 recurrent
 de
 novo
 deletions,
 including
 Miller‐Dieker
 syndrome
 and
 Leri‐Weill
 dyschondrosteosis
 (Supplementary
 Table
 14).
 Irrespective
 of
 potential
 disease
 relevance,
 or
 inferred
 mechanism
 of
 formation,
 our
 analysis
 revealed
 a
 map
 of
 SV
 hotspots
 that
 may
 constitute
 local
 centers
 of
 de
 novo
 SV
 formation,
 consistent
 with
 the
 concept
 that
 local
genome
architecture
contributes
to
genomic
instability44.
 Our
 study
 focused
 on
 characterizing
 deletions,
 which
 are
 often
 associated
 with
 disease9.
 Facilitated
 by
 ancestral
 analyses
 of
 SV
 loci,
 we
 also
 characterized
 insertions
and
tandem
duplications,
albeit
in
less
detail
than
deletions.

Companion
 papers
 with
 more
 detailed
 analyses
 of
 MEIs,
 and
 copy‐number
 variation
 within
 segmental
 duplications
 are
 published
 elsewhere34,46.
 Of
 note,
 most
 SV
 discovery
 methods
 depend
 on
 mapping
 reads
 onto
 their
 genomic
 locus
 of
 origin,
 i.e.,
 the
 ‘accessible’
 fraction
 of
 the
 genome,
 a
 fraction
 lessened
 in
 segmental
 duplications
 that
are
of
high
interest
to
SV
analysis.
Nonetheless,
owing
to
the
abilities
of
RP
and
 RD
 methods
 in
 detecting
 SVs
 in
 these
 regions
 and
 in
 interpreting
 reads
 with
 multiple
mapping
positions,
the
‘accessible’
fraction
of
the
genome
is
higher
for
SVs
 than
for
SNPs16.
In
the
future,
sequencing
technologies
generating
longer
DNA
reads
 will
 increase
 the
 accessible
 genome,
 and
 will
 enable
 the
 assessment
 of
 SVs
 embedded
in
long
repeat
structures,
such
as
balanced
inversions.
 Our
 SV
 resource
 will enable
 the
 discovery,
 genotyping,
 and
 imputation
 of
 SVs
 in
 larger
 cohorts.
 Numerous
 genomes
 will
 be
 sequenced
 in
 the
 coming
 months
 to
 facilitate
 disease
 association
 studies.
 Systematic
 characterization
 of
 SVs
 in
 these
 genomes
will
benefit
from
the
concepts
and
datasets
presented
here.
 
 9


Methods
Summary
 
 Samples
 
 Sequence
 data
 for
 179
 unrelated
 individuals
 and
 six
 individuals
 from
 parent‐ offspring
 trios
 were
 obtained
 as
 part
 of
 the
 1000GP.
 These
 data
 were
 generated
 with
 Illumina/Solexa,
 Roche/454,
 and
 Life
 Technologies/SOLiD
 sequencing
 technology
platforms.
 
 SV
discovery
and
breakpoint
assembly
 
 The
SV
discovery
methods
we
applied
comprised
six
RP,
four
RD,
three
SR,
four
AS,
 and
two
PD
based
methods.
TIGRA38
was
used
for
targeted
breakpoint
assembly.
 
 Experimental
validation
 We
 validated
 SV
 calls
 by
 PCR,
 array
 CGH
 and
 SNP
 microarrays,
 targeted
 assembly,
 and
 custom
 microarray‐based
 sequence
 capture.
 PCR
 was
 performed
 in
 various
 different
 laboratories33,
 CGH
 analysis
 was
 performed
 based
 on
 tiling
 array
 data
 provided
by
the
Genome
Structural
Variation
Consortium
(ArrayExpress:
E‐MTAB‐ 40),
and
SNP
array
analysis
based
on
data
obtained
from
the
International
HapMap
 Consortium
(http://hapmap.ncbi.nlm.nih.gov).

 
 Genotyping
 
 Genome
 STRiP36
 was
 used
 for
 deletion
 genotyping
 in
 low
 coverage
 sequence
 data.
 Initial
 genotype
 likelihoods
 were
 derived
 with
 a
 Bayesian
 model
 and
 imputation
 into
a
SNP
genotype
reference
panel
from
the
HapMap41
(Hapmap3r2)
was
achieved
 with
Beagle
(v3.1;
http://faculty.washington.edu/browning/beagle/beagle.html).
 
 SV
formation
mechanism
analysis
 
 SV
breakpoints
mapped
at
nucleotide
resolution
were
analyzed
with
BreakSeq43
to
 classify
SVs
relative
to
putative
ancestral
loci
and
to
infer
SV
formation
mechanisms.
 SV
hotspots
were
mapped
with
custom
Perl
and
R
scripts.





10


Display
Items
 

Table 1. Summary of discovered structural variation Deletions Individual Callsets <10% FDR Validated Experimentally* Release set
*

Tandem Duplications 501 501

11215 10810 22025

Mobile element insertions 5371 5371

Novel sequence insertions 128 128

Total 17087 10938 28025

Only tabulates validated calls which were not already present in the individual callsets with <10% FDR


 

Table 2. Functional impact of our fine resolution SV set. Figures in parentheses indicate numbers of validated SVs per category. We inferred gene overlap with Gencode gene annotation47. Gene Overlap Total Total Coding SV class Gene InterFull Intron exon overlap genic UTR overlap gene affected overlap overlap (partial) 654 1093 315 7319 9381 12644 Deletions (631) (1031) (290) (6481) (8433) (10386) Tandem duplications Mobile element insertions Novel sequence insertions Sum 2 (2) 656 (633) 7 (6) 3 (-) 1119 (1040) 9 (5) 36 (-) 2 (2) 351 (309) 197 (62) 1304 (97) 49 (49) 8869 (6689) 215 (75) 1348 (112) 51 (51) 10995 (8671) 286 (76) 4023 (758) 77 (77) 17030 (11280)




11



 Figure
Legends
 Figure
 1.
 SV
 discovery
 and
 genotyping
 in
 population
 scale
 sequence
 data.
 A.
Schematic
depicting
the
different
modes
(i.e.,
approaches)
of
sequence
based
SV
 detection
 we
 used.
 The
 RP
 approach
 assesses
 the
 orientation
 and
 spacing
 of
 the
 mapped
reads
of
paired‐end
sequences14,15
(reads
are
denoted
by
arrows);
the
RD
 approach
 evaluates
 the
 read
 depth‐of‐coverage25,26;
 the
 SR
 approach
 maps
 the
 boundaries
 (breakpoints)
 of
 SVs
 by
 sequence
 alignment28,29;
 the
 AS
 approach
 assembles
 SVs30,31,32.
 B.
Integrated
 pipeline
 for
 SV
 discovery,
 validation,
 and
 genotyping.
 Colored
 circles
 represent
 individual
 SV
 discovery
 methods
 (listed
 in
 Supplementary
 Table
 1),
 with
 modes
 indicated
 by
 a
 color
 scheme:
 green=RP;
 yellow=RD;
 purple=SR;
 red=AS;
 green
 and
 yellow=methods
 evaluating
 RP
 and
 RD
 (abbreviated
 as
 ‘PD’).
 C.
 Example
 of
 a
 deletion,
 previously
 associated
 with
 BMI35,

 identified
 independently
 with
 RP
 (green),
 RD
 (yellow),
 and
 SR
 (red)
 methods.
 Targeted
assembly
confirmed
the
breakpoints
detected
by
SR.
 Figure
 2.
 Comparative
 assessment
 of
 deletion
 discovery
 methods.
 A.
Deletion
 size‐range
ascertained
by
different
modes
of
SV
discovery.
Three
groups
are
visible,
 with
AS
and
SR,
PD
and
RP,
as
well
as
RD
and
‘RL’
(RP
analysis
involving
relatively
 long
 range
 (≥1
 kb)
 insert
 size
 libraries,
 resulting
 in
 a
 different
 deletion
 detection
 size
 range
 compared
 to
 the
 predominantly
 used
 <500kb
 insert
 size
 libraries),
 respectively,
ascertaining
similar
size‐ranges.
Pie
charts
display
the
contribution
of
 different
SV
discovery
modes
to
the
release
set.
Outer
pie
=
based
on
number
of
SV
 calls;
 inner
 pie
 =
 based
 on
 total
 number
 of
 variable
 nucleotides.
 Of
 note,
 not
 all
 approaches
 were
 applied
 across
 all
 individuals
 (see
 Supplementary
 Table
 2).
 B.
Sensitivity
and
FDR
estimates
for
individual
deletion
discovery
methods
based
on
 gold
 standard
sets
for
individuals
sequenced
at
high
(NA12878)
and
low‐coverage
 (NA12156),
respectively.
All
depicted
estimates
are
summarized
in
Supplementary
 Tables
 3,
 4,
 6.
 Vertical
 dotted
 lines
 correspond
 to
 the
 specificity
 threshold
 (FDR≤10%).
C.
Breakpoint
mapping
resolution
of
three
deletion
discovery
methods
 (the
 respective
 method
 names
 are
 in
 Supplementary
 Table
 2).
 The
 blue
 and
 red
 histograms
 are
 the
 breakpoint
 residuals
 for
 predicted
 deletion
 start
 and
 end
 coordinates,
respectively,
relative
to
assembled
coordinates
(here
assessed
in
low‐ coverage
data).
The
horizontal
lines
at
the
top
of
each
plot
mark
the
98%
confidence
 intervals
 (labeled
 for
 each
 panel),
 with
 vertical
 notches
 indicating
 the
 positions
 of
 the
most
probable
breakpoint
(the
distribution
mode).

 Figure
 3.
 Analysis
 of
 deletion
 presence
 and
 absence
 in
 two
 populations.

 A­C.

Deletion
allele
frequencies
and
observed
sharing
of
alleles
across
populations,
 displayed
for
deletions
discovered
in
the
CEU,
YRI,
and
JPT+CHB
population
samples
 in
terms
of
stacked
bars.
D.
Allele
frequency
spectra
for
deletions
intersecting
with
 intergenic
(blue),
intronic
(yellow),
and
protein‐coding
sequences
(red).

 Figure
 4.
 Contribution
 of
 SV
 formation
 mechanisms
 to
 the
 SV
 size
 spectrum.
 A.
Breakpoint
junction
homology/microhomology
length
plotted
as
a
function
of
SV
 size
for
SVs
originally
identified
as
deletions
compared
to
a
human
reference.
Dots
 
 12


are
colored
according
to
the
SVs’
classification
as
deletions,
insertions/duplications,
 or
 “undetermined”
 relative
 to
 inferred
 ancestral
 genomic
 loci.
 Gray
 lines
 mark
 groups
 of
 SVs
 likely
 formed
 by
 a
 common
 formation
 mechanism.
 The
 diagonal
 highlights
 tandem
 duplications
 (and
 few
 reciprocal
 deletion
 events),
 in
 which
 the
 length
of
the
duplicated
sequence
correlates
linearly
with
the
length
of
the
longest
 breakpoint
 junction
 sequence
 identity
 stretch.
 The
 ellipses
 indicate
 MEIs,
 i.e.,
 Alu
 (~300
bp)
and
L1
(~6
kb)
insertions,
associated
with
target
site
duplications
of
up
 to
28
bp
in
size
at
the
breakpoints.
The
horizontal
group
corresponds
mostly
to
NH‐ associated
deletions
with
<10
bp
microhomology
at
the
breakpoints.
The
remaining
 (ungrouped)
SVs
comprise
truncated
MEIs,
VNTR
expansion
and
shrinkage
events,
 as
well
as
NAHR‐associated
deletions
and
duplications.
B.
Relative
contributions
of
 SV
formation
mechanisms
in
the
genome.
Numbers
of
SVs
are
displayed
on
the
outer
 pie
chart
and
affected
base
pairs
on
the
inner.
Left
panel:
SVs
classified
as
deletions
 relative
 to
 ancestral
 loci.
 Right
 panel:
 SVs
 classified
 as
 insertions/duplications.
 C.
Size
 spectra
 of
 deletions
 classified
 relative
 to
 ancestral
 loci.
 D.
Size
 spectra
 of
 insertions/duplications.

 Figure
5.
Mapping
hotspots
of
SV
formation
in
the
genome.
A.
Distribution
of
SVs
 on
 chromosome
 10
 (“chr10”).
 Above
 the
 ideogram,
 colored
 bars
 indicate
 SV
 formation
mechanisms
(same
color
scheme
as
in
B
and
C);
bar
lengths
relate
to
the
 logarithm
 of
 SV
 size.
 Below
 the
 ideogram,
 bar
 lengths
 are
 directly
 proportional
 to
 allele
 frequencies.
 Arrows
 indicate
 an
 SV
 hotspot
 near
 the
 centromere
 underlying
 mainly
 VNTR,
 and
 several
 hotspots
 near
 the
 telomeres
 underlying
 mainly
 NAHR
 events.
 B.
Enrichment
 of
 SVs
 inferred
 to
 be
 formed
 by
 the
 same
 formation
 mechanism
 for
 different
 genomic
 window
 sizes.
 Displayed
 is
 an
 enrichment
 of
 nearby,
 non‐overlapping
 SVs
 formed
 by
 the
 same
 mechanism
 relative
 to
 an
 SV
 set
 where
 mechanism
 assignments
 are
 shuffled
 randomly.
 C.
SV
 hotspots
 are
 mostly
 dominated
 by
 a
 single
 formation
 mechanism.
 Colored
 bars
 depict
 numbers
 of
 SV
 hotspots
in
which
at
least
50%
of
the
variants
were
inferred
to
be
formed
by
a
single
 formation
 mechanism.
 The
 average
 abundance
 of
 NAHR‐classified
 SVs
 in
 NAHR
 hotspots
was
70%
(compared
with
77%
for
VNTR‐hotspots;
69%
for
NH).
The
gray
 bar
(“mixed”)
corresponds
to
SV
hotspots
with
no
single
mechanism
dominating.

 
 





13



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 Acknowledgements:
 We
 would
 like
 to
 acknowledge
 Claire
 Hardy,
 Richard
 Smith,
 Anniek
De
Witte,
and
Shane
Giles
for
their
assistance
with
validation.
M.A.B’s
group
 was
supported
by
grants
from
the
National
Institutes
of
Health
(RO1
GM59290)
and
 G.T.M’s
 group
 by
 grants
 R01
 HG004719
 and
 RC2
 HG005552,
 also
 from
 the
 NIH.
 J.O.K.’s
 group
 was
 supported
 by
 an
 Emmy
 Noether
 Fellowship
 of
 the
 German
 Research
 Foundation
 (Deutsche
 Forschungsgemeinschaft).
 J.W.’s
 group
 was
 supported
 by
 the
 National
 Basic
 Research
 Program
 of
 China
 (973
 program
 no.
 2011CB809200),
 the
 National
 Natural
 Science
 Foundation
 of
 China
 (30725008;
 30890032;
 30811130531;
 30221004),
 the
 Chinese
 863
 program
 (2006AA02Z177;
 2006AA02Z334;
 2006AA02A302;
 2009AA022707),
 the
 Shenzhen
 Municipal
 Government
 of
 China
 (grants
 JC200903190767A;
 JC200903190772A;
 ZYC200903240076A;
 CXB200903110066A;
 ZYC200903240077A;
 ZYC200903240076A
 and
 ZYC200903240080A),
 and
 the
 Ole
 Rømer
 grant
 from
 the
 Danish
Natural
Science
Research
Council.
C.L.’s
group
was
supported
by
grants
from
 the
 National
 Institutes
 of
 Health:
 P41
 HG004221,
 RO1
 GM081533,
 and
 UO1
 HG005209
 and
 X.S.
 was
 supported
 by
 a
 T32
 fellowship
 award
 from
 the
 NIH.
 
 We
 thank
 the
 Genome
 Structural
 Variation
 Consortium
 (http://www.sanger.ac.uk/humgen/cnv/42mio/)
 and
 the
 International
 HapMap
 Consortium
 for
 making
 available
 microarray
 data.
 The
 authors
 acknowledge
 the
 individuals
 participating
 in
 the
 1000
 Genomes
 Project
 by
 providing
 samples,
 including
 The
 Yoruba
 people
 of
 Ibadan,
 Nigeria,
 the
 community
 at
 Beijing
 Normal
 University,
the
people
of
Tokyo,
Japan,
and
the
people
of
the
Utah
CEPH
community.





16


Furthermore,
 we
 thank
 Richard
 Durbin
 and
 Lars
 Steinmetz
 for
 comments
 on
 the
 manuscript.
 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.
 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.
 





17


a
Reference Sample genome

MEI

b

Application of diverse SV discovery methods
Deletion (Del), Duplication (Dup), and Insertion (Ins) RP RD SR AS PD

Del

Dup

Ins

Reference-supporting SV-supporting read-pair (RP) SV-supporting read-depth (RD)

SV-supporting read for split-read analysis (SR) or assembly (AS) MEI Mobile element insertion support

c

Del NEGR1 Alu LINE
100 44

Validation of SVs (deletions, duplications and insertions)
NA19240 (YRI)

Targeted SV breakpoint assembly (focused on deletions)

90

DNA read mapping quality scores

33

80

22

Precision-aware merging of discovered SVs Release set (algorithms & extensive validations) inclusion of SVs inferred with individual methods (criterion: FDR<10%), followed by validation-aware SV inclusion Algorithm-centric set (algorithms & sparse validations) inclusion of SVs inferred with individual methods, and such with evidence from >2 methods (criterion: FDR<10%)

Depth of coverage

70

100 60

44 0 11 22 33

11

NA12878 (CEU)

70

80

90

SV discovery set Genotyping (focused on deletions)

60

72.52

72.54

72.56

72.58

72.60

Chromosome 1 position (in Mb)

0

a
0.0020
24.3%

1.3% 19.2%

b

0.0 0.2 0.4 0.6 0.8 1.0

0%

20%

40%

FDR
60%

80%

100%

0.5%

1 0.8 0.6
co ve ra g e

0.0015

Sensitivity

Density

2.0%

0.6%

21.2% 10.6%

17.4%

Sensitivity

49.7% 17.9%

0.0010

35.3%

0.0005

AS SR PD

RP RL RD

w

Lo

Release set

Tr

io

0.4 0.2 0

0.0000

0

1000

2000

c
Frequency
5000 SR (LN) n = 5375 4000 3000 2000 1000 0 −10 −5

Deletion size (bp)

3000

4000

5000

6000

7000
210 bp

100%

80%

60%

FDR

40%

20%

0%

19 bp 18 bp

300 250 200 150 100 50

RP (SI) n = 5229

220 bp

50 40 30 20 10

700 bp RD (YL) n = 501 900 bp

0

5

10

0

−100 −50

0

50 100

0

−500

−250

0

250

500

O set from breakpoint (bp)

a

2500 2000 1500 1000 500 0 0

Number of SVs

Number of SVs

CEU

b 2500
observed also in YRI observed also in JPT+CHB shared among all observed only in CEU

YRI

2000 1500 1000 500 0 0 0.2

observed also in CEU observed also in JPT+CHB shared among all observed only in YRI

Alternate allele frequency
JPT+CHB observed also in CEU observed also in YRI shared among all observed only in JPT+CHB

0.2

0.4

0.6

0.8

1

Alternate allele frequency
intergenic intersect with introns intersect with CDS

0.4

0.6

0.8

1

c

2000 1500 1000 500 0 0 0.2

Log10 (number of SVs)

2500

d

4 3 2 1 0 0

Number of SVs

Alternate allele frequency

0.4

0.6

0.8

1

Average alternate allele frequency

0.2

0.4

0.6

0.8

1

Length of longest sequence similarity stretch at the SV breakpoint junction (bp)

a

300 250 200 150 100 50

insertions/duplications deletions undetermined

c 1800
1600 Number of deletions 1400 1200 1000 800 600 400 200
Unclassified MEI VNTR NAHR NH

b

100

300

SV length (bp)

500

700

900

4000

6000

8000

0

d
Number of Insertions/duplications

100bp

1kb 10kb Size of deletion
Alu

100kb

1800 1600 1400 1200 1000 800 600 400 200 0

245

272

NAHR

164kb

1,496

NHR VNTR MEI

226 79 122

Unclassified MEI VNTR NAHR NH

12Mb

393kb 89kb

24Mb

1Mb

21kb

4,500

1,994

LINE

100bp 1kb 10kb 100kb Size of insertion/duplication

a
chr10

Enrichment Depletion (clustering of SV (no clustering) formation process)
0.1 10 1
200bp 500bp 1kb 2kb 5kb 10kb 20kb 50kb 100kb 200kb 500kb 1Mb

b

Numbers of genomic SV hotspots (color : dominated by single mechanism)
10 15 20 25 30 0 NAHR NH VNTR MEI mixed 5

NAHR NH MEI VNTR Control (NH vs. NAHR)

c