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Copy number variation occurs when sections of the genome get repeat...
We can detect CNVs through various mechanisms but we still don't kn...
Although whole genome sequencing costs have declined faster than Mo...
Exome sequencing is the sequencing of all the expressed genes in a ...
Mapped read depth is the number of bases sequenced and aligned at a...
Genetic dosage is the number of copies of a gene in a genome.
By evolution theory, negative selection is the selective removal of...
The Gencode Consortium aims to identify all gene features in the hu...
Essentially, if a gene is more intolerant to CNVs, it is more likel...
The fact that the highly expressed genes had significantly higher i...
Nature GeNetics VOLUME 48 | NUMBER 10 | OCTOBER 2016 110 7
A N A LY S I S
Copy number variation (CNV) affecting protein-coding genes 
contributes substantially to human diversity and disease. Here 
we characterized the rates and properties of rare genic CNVs 
(<0.5% frequency) in exome sequencing data from nearly 
60,000 individuals in the Exome Aggregation Consortium 
(ExAC) database. On average, individuals possessed 0.81 
deleted and 1.75 duplicated genes, and most (70%) carried 
at least one rare genic CNV. For every gene, we empirically 
estimated an index of relative intolerance to CNVs that 
demonstrated moderate correlation with measures of genic 
constraint based on single-nucleotide variation (SNV) and 
was independently correlated with measures of evolutionary 
conservation. For individuals with schizophrenia, genes 
affected by CNVs were more intolerant than in controls. 
The ExAC CNV data constitute a critical component of an 
integrated database spanning the spectrum of human genetic 
variation, aiding in the interpretation of personal genomes as 
well as population-based disease studies. These data are freely 
available for download and visualization online. 
CNV—in particular, gain or loss of coding sequence is known
to contribute substantially to phenotypic diversity and disease
1,2
.
Large CNVs (deletions or duplications) were initially discovered
from cytogenetic studies of individuals with Down syndrome
and intellectual disability
3–5
. Technological advances in survey-
ing changes in genetic dosage, along with the sequencing of the
human genome, have led to improved resolution for detection of
CNVs and other forms of structural variation
6,7
, a better under-
standing of the CNV mechanism
8
, and the further implication
of CNVs in various diseases
2,9–11
. Still, the ability to ascribe patho-
genicity to a particular CNV remains limited
12
.
Genotyping arrays have allowed for cost-effective strategies to detect
CNVs in large samples but will typically detect only relatively large
CNVs
13–15
. Conversely, whole-genome sequencing provides a compre-
hensive assessment of CNV (and other structural variation), but costs
9
currently limit its widespread application. It has recently been dem-
onstrated that CNVs can be detected from exome sequencing, using
information on relative read depth to infer chromosomal gains and
losses that affect targeted genes
16,17
. Unlike arrays, exome sequenc-
ing can potentially resolve genic CNVs to the level of a single exon.
Although still crude in comparison to whole-genome sequencing,
exome sequencing data can map smaller genic CNVs (<30 kb in
length) that may be undetected by arrays but still affect disease risk
18
.
Most crucially, exome sequencing data already exist across multiple
large studies and have been compiled under the auspices of ExAC (see
URLs)
19
. Here we leveraged this large resource (n ~60,000 participants)
to better characterize the rates and properties of rare CNVs, with
population frequencies on the order of 1 × 10
−2
and as low as 1 × 10
−5
.
We constructed the ExAC CNV data set using a previously developed
method (XHMM
17
). Specifically, for each autosomal gene, we used
sequencing read depth for an individual to calculate the posterior
probability of the individual being diploid across that gene (having
normal copy number state) versus deleted or duplicated. Notably,
this approach identifies genes for which we are unable to confidently
assess copy number for a given individual. It also flags genes that are
only partially affected by CNV (that is, where some exons are diploid)
rather than having full genic deletion or duplication.
Evolutionary theory predicts that negative selection will result in
deleterious mutations being rarer on average than neutral mutations,
which has been demonstrated for SNVs
20,21
and CNVs
22
. Although
large CNVs that affect many genes are likely to be deleterious
23
, certain
genes will be more sensitive to (intolerant of) dosage changes and
thus have fewer CNVs. In this work, we leverage the tens of thousands
of exome samples in the ExAC database to estimate genic frequen-
cies for rare CNV. We then calibrate these empirical frequencies by
expected rates of CNV to derive, for each gene, a measure of relative
intolerance to CNVs—that is, a trend of showing fewer CNVs than
expected. We show how the estimated CNV intolerance values are
related to measures derived from SNV and to evolutionary measures
of genic constraint. We conclude that considering CNV intolerance
Patterns of genic intolerance of rare copy number
variation in 59,898 human exomes
Douglas M Ruderfer
1–3
, Tymor Hamamsy
1
, Monkol Lek
3,4
, Konrad J Karczewski
3,4
, David Kavanagh
1,2
,
Kaitlin E Samocha
3,4
, Exome Aggregation Consortium
5
, Mark J Daly
3,4
, Daniel G MacArthur
3,4
,
Menachem Fromer
1–4,7
& Shaun M Purcell
1–4,6,7
1
Division of Psychiatric Genomics, Department of Psychiatry, Icahn School of
Medicine at Mount Sinai, New York, New York, USA.
2
Institute for Genomics
and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New
York, New York, USA.
3
Broad Institute of MIT and Harvard, Cambridge,
Massachusetts, USA.
4
Analytic and Translational Genetics Unit, Psychiatric
and Neurodevelopmental Genetics Unit, Massachusetts General Hospital,
Boston, Massachusetts, USA.
5
A list of members and affiliations appears in
the
Supplementary Note.
6
Department of Psychiatry, Brigham and Women’s
Hospital, Harvard Medical School, Boston, Massachusetts, USA.
7
These authors
contributed equally to this work. Correspondence should be addressed to D.M.R.
(douglas.ruderfer@mssm.edu) or S.M.P. (shaun.purcell@mssm.edu).
Received 17 March; accepted 12 July; published online 17 August 2016;
doi:10.1038/ng.3638
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can be used to predict the likelihood of a genic CNV being deleteri-
ous, and we demonstrate how genic intolerance can be employed in
the analysis of disease studies.
RESULTS
Characterizing CNV calls from exome sequencing data
Read depth information from targeted exome sequencing of 60,642
individuals was analyzed using XHMM
17
. Briefly, XHMM removes
systematic individual, batch, and target effects (artifact or common
copy number polymorphism) by use of principal-component analysis
on the entire read depth matrix (60,642 individuals × 219,437 targets).
A hidden Markov model applied per individual to the normalized
data is used to call CNVs at exon-level resolution and estimate genic
copy number probabilities (Online Methods). We performed quality
control and restricted analysis to genes where each CNV was rare
(observed in <600 individuals, corresponding to a maximum allele
frequency of ~0.5%). CNV quality was assessed using family trios
and demonstrated high specificity and sensitivity consistent with pre-
vious reports
17
(Online Methods). Additionally, a subset of 10,091
individuals had high-quality CNV calls from genotyping arrays
24
,
for whom we assessed the comparability of the CNVs called from
genotyping arrays and exome sequencing. The set of array-based
CNVs was filtered for high confidence on the basis of number of
markers (10), length (>100 kb), and frequency (<1%), as described
24
.
Of the most confidently called array-based CNVs—those that were
longer and intersected the most coding sequences (greater than
20 targets)—78% were also called in the high-confidence set of exome
sequencing CNVs (1,307/1,684). Array-based CNVs intersecting
fewer targets were less likely to be called in the exome sequencing set
(Supplementary Fig. 1), such that 62% of array-based CNVs affecting
more than three exons and 54% of all array-based CNVs affecting at
least one GENCODE protein-coding exon (3,200/5,927) were called
in the exome sequencing set. In comparison, of 12,947 CNVs in the
exome sequencing set, 3,268 (25%) were seen in the array-based call
set, with this overlap increasing as the number of targets encompassed
by the CNV increased (Supplementary Fig. 2). For the concordantly
called CNVs, the array-based calls encompassed more exons 70% of
the time; however, on average, 83% of the exons were included in
calls from both technologies (median = 93%). Individuals carried, on
average, 2.2 times more CNVs in the exome sequencing data set than
in the array-based call set (1.28 versus 0.59 CNVs).
The final ExAC CNV data set consisted of 59,898 individuals and
126,771 CNVs overlapping GENCODE autosomal protein-coding
genes. On average, individuals carried 2.1 high-confidence, rare
CNVs (0.82 deletions, 1.29 duplications) intersecting at least one of
the 19,430 GENCODE autosomal protein-coding genes (Fig. 1). The
largest group of 17,565 individuals (29%) carried exactly one rare cod-
ing CNV, with 12,812 (21%) carrying zero CNVs and 3,730 (6%) car-
rying more than five CNVs. The mean extent of CNV per individual
was 154 kb (median = 35 kb), representing more duplicated genomic
content (107 kb) than deleted content (46 kb). The average length
of CNV was 73 kb (median = 15 kb), with duplications being 83 kb
(median = 20 kb) and deletions being 56 kb (median = 9 kb). Eight-
four percent of CNVs were smaller than 100 kb, which has generally
been used as the size threshold for confidently calling CNVs from
genotyping arrays; 56% of CNVs were shorter than 20 kb.
Seventy percent of individuals had at least one gene affected by a
rare CNV (37% had at least one deleted gene, 54% had at least one
duplicated gene), with an average of 0.81 deleted genes and 1.75 dupli-
cated genes per individual across the data set (Fig. 2 and Table 1).
Sixteen percent of CNVs were greater than 100 kb in length, averaging
79 kb (59 kb for deletions, 91 kb for duplications) and 13 exons (9.7
exons for deletions, 15 exons for duplications). CNV rates varied
by population: individuals of African descent had the highest rate,
similar to that seen for SNV
25
. However, these rates were signifi-
cantly confounded by variables such as batch and overall read depth,
complicating the interpretation of this finding (Online Methods,
Supplementary Figs. 3 and 4, and Supplementary Table 1). As pre-
viously reported
26
, we identified a significantly higher CNV rate in
females, after adjusting for read depth, cohort, and ten principal com-
ponents of ancestry (mean female CNV rate = 1.74, mean male CNV
rate = 1.49, P = 1.14 × 10
−10
; Supplementary Table 1).
On average, each gene was deleted in 3.1 individuals and dupli-
cated in 6.6 individuals. Most of the protein-coding genome harbored
population-level rare variation in copy number, with only 1,872 genes
having no CNVs detected (6,578 genes without deletions, 3,038 genes
without duplications). Fifty-five percent of all CNVs overlapped only
a single gene (65% of deletions, 48% of duplications). Of these single-
gene CNVs, most (62%) were partial-gene CNVs (Fig. 2 and Table 1),
with some exons deleted or duplicated but also with some exons
confidently assigned as diploid (Online Methods).
A measure of genic intolerance to CNVs
To quantify the effect of genic CNV, we defined genes that harbored
fewer CNVs than expected as being more intolerant’. We expect
that CNVs in intolerant genes, when they do occur, will be more
likely to have deleterious effects, analogous to predictions from
genic constraint scores based on SNVs
19,27,28
. However, it is not
straightforward to model genic CNV rates expected under neutral-
ity in a direct manner, as can be done for SNVs using trinucleotide
mutation rates and a gene’s known sequence. To derive expected
values, we therefore fit a linear regression model for the observed
CNV rate per gene based on gene length, coding-sequence length,
number of targets, GC content, sequence complexity, genomic locali-
zation within pairs of segmental duplications, and sequencing read
depth (Online Methods, Supplementary Fig. 5, and Supplementary
Table 2). Intolerance scores were calculated as normalized and
winsorized model residuals, negated such that higher positive
values indicate greater intolerance (a lower than expected rate
of CNVs for that gene). As defined, CNV intolerance scores are
therefore independent of the predictor variables used in the linear
regression (Supplementary Fig. 6).
0
0 1 2 3 4 5 6 7 8 9 10 11 12 13
Number of CNVs
14 15 16 17 18 19 20 21 22 23 24
5,000
10,000
15,000
Number of
individuals
Amount of CNV (kb)
100,000
1,000
10
0.1
1050 15 20 25
100,000
Amount of CNV (kb)
1,000
10
0.1
Proportion
of individuals
0.4
0.2
0
Number of CNVs
Figure 1 Distribution of the number and amount of CNV across 59,898
exome-sequenced individuals. Plots include a histogram of the number of
CNVs per individual (top), a 2D density plot of CNV number and amount
(bottom), and a density plot of the amount of CNV per individual (right).
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Intolerance scores based only on deletions were highly correlated
with those based only on duplications (r = 0.37, P << 1 × 10
−20
),
and both scores were highly correlated with the combined score
(r = 0.7 for deletions, r = 0.89 for duplications; the difference in cor-
relation reflects the greater number of duplications). The results
from a complementary approach to predict haploinsufficiency
29
that
compared genes sensitive to gene loss to those where having a single
copy resulted in no discernable aberrant phenotype demonstrated
significant correlation with CNV intolerance scores (r = 0.12, P = 2
× 10
−36
). CNV intolerance scores were also significantly correlated
with a measure of genic constraint based on missense SNVs
27
(r = 0.2,
P = 2 × 10
−137
) derived from the ExAC sample
19
, with this effect being
stronger for deletions (r = 0.23, P = 2 × 10
−176
) than for duplications
(r = 0.14, P = 1 × 10
−63
). This correlation was consistent across the
distribution of scores, with increased CNV intolerance score corre-
sponding to increases in both SNV scores (based on either missense
or loss-of-function variants) (Supplementary Fig. 7). Similarly, CNV
intolerance scores also correlated with an index of haploinsufficiency
(pLI)
19
based on loss-of-function variants (nonsense and canonical
splice-site SNVs) derived from this sample (all CNVs: r = 0.18, P = 6
× 10
−110
; deletions: r = 0.23, P = 1 × 10
−176
; duplications: r = 0.11,
P = 1 × 10
−39
). Unlike for SNV-based scores, CNV intolerance scores
will be correlated across multiple genes affected by larger CNVs. We
therefore calculated CNV intolerance scores from CNVs that only
affected a single gene and identified similar correlations with pLI
(r = 0.22 for deletions, r = 0.06 for duplications). Although single-
gene CNVs are likely more individually informative for quantifying
intolerance, the sole use of these CNVs in creating the scores would
reduce the number of events by half. We therefore use the scores based
on all CNVs going forward but provide both scores for download
online (see URLs).
CNV intolerance scores were also associated with an independ-
ent measure of evolutionary constraint, GERP
30
. Genes with higher
mean per-base GERP scores (calculated including introns) tended
to have higher CNV intolerance scores (r = 0.13, P = 5 × 10
−46
).
In a joint linear regression of genic GERP score on CNV intoler-
ance and SNV constraint scores, all terms were independently and
positively associated with genic GERP score (CNV intolerance score,
P = 3 × 10
−33
; SNV constraint score from missense variants,
P = 6 × 10
−27
; SNV constraint score from loss-of-function variants,
P = 3 × 10
−5
), suggesting that both CNV- and SNV-based scores
contribute non-redundant information regarding the potential
deleteriousness of genic CNVs.
Characterizing CNV-tolerant and CNV-intolerant genes
For a particular gene, intolerance of genetic variation such as CNV
implies higher functional importance of that gene
19
. We thus con-
sidered the relationship between the intolerance of a gene to CNV
and its expression across 27 tissues
31
, focusing on the 7,754 genes
that were highly expressed in at least one of those tissues (but not
all of them). We found that, for the majority of tissues (n = 17), the
highly expressed genes indeed had significantly higher intolerance
scores than all other genes within this subset (Fig. 3a). Notably,
genes highly expressed in the brain showed the most intolerance to
CNV. Tissues expressing genes that were more intolerant of CNV
also tended to show relatively fewer genes with homozygous loss-of-
function SNVs and short indels (‘complete knockouts’) in a recent
survey of the Icelandic population
32
(Spearmans
ρ
= 0.45, P = 0.019;
Supplementary Table 3). Genes highly expressed in three tissues
duodenum, liver, and pancreas—demonstrated significantly lower
intolerance scores (greater tolerance) than average genes, raising the
hypothesis of greater robustness to dosage changes in these tissues.
Genes previously defined as haploinsufficient
29
or essential
33
showed higher CNV intolerance scores in comparison to all genes
(P = 2 × 10
−25
and 2 × 10
−12
, respectively; Supplementary Table 4).
In contrast, genes implicated in recessive disorders (see URLs) and
those corresponding to genes with no identifiable aberrant pheno-
type when disrupted in mice
15
tended to show greater tolerance to
CNV (P = 0.007 and 0.009, respectively; Supplementary Table 4).
With the exception of the genes implicated in recessive disorders,
similar overall results were recently obtained in an analysis of a large
data set of CNVs from genotyping arrays
15
(Supplementary Table 4).
Applying generic gene set enrichment analysis to the most and least
CNV-intolerant genes (top and bottom 5%, 787 genes each; Fig. 3b),
intolerant genes were significantly enriched in Gene Ontology (GO)
sets related to neuronal and axon development and synapse organi-
zation and assembly, consistent with the aforementioned higher
intolerance to CNV of genes that are highly expressed in brain tissue
(neuron development (GO:0048666), P = 2 × 10
−6
; synapse organiza-
tion (GO:0050808), P = 6 × 10
−6
; Supplementary Tables 58).
Application of CNV intolerance to schizophrenia
ExAC-derived genic CNV intolerance scores can be used alongside
other genic annotations in disease association studies. As a proof of
principle, we set aside a single case–control study present in ExAC
(4,793 schizophrenia cases and 6,102 controls
34
) and calculated
intolerance scores in the remaining 47,787 individuals as described
above. As previously reported
24
, this sample of schizophrenia
cases showed a higher number of genes affected by CNV than the
0
0.25
0.50
0.75
1.00
Proportion of CNVs
Partial gene
Full gene
Multigene
0
a b
0.2
0.4
0.6
Number of genes deleted or
duplicated per individual
Proportion of individuals
Deletion
Duplication
All
Deletions
Duplications
0 1 2 3 4 5 6 7 8 9 10+
Figure 2 Genic summary of rare deletions and duplications in the ExAC
sample. (a) Proportion of individuals having from zero to ten or more
genes deleted (red) or duplicated (blue). (b) Proportion of CNVs that affect
multiple genes (multigene), affect the entirety of a single gene (full gene),
or partially disrupt a single gene (partial gene). The two rightmost bars
show these proportions separately for deletions and duplications.
Table 1 Number of total genes affected and mean number of
gene-level CNVs per individual
All Deletions Duplications
Genes (n = 15,734) n Rate n Rate n Rate
All 13,862 2.565 9,156 0.817 12,696 1.747
Single gene 7,159 0.881 4,723 0.399 5,268 0.481
Partial gene 4,886 0.543 3,358 0.251 3,435 0.292
The bottom two rows consider only CNVs affecting a single entire gene (single gene) or
only part of a gene (partial gene); deletions and duplications are shown separately to
the right. Rate is the mean number of CNVs per individual.
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A N A LY S I S
controls (2.12 versus 1.78 genes affected by CNV per individual,
P = 1 × 10
−10
). Over and above the number of genes affected, cases
carried higher mean intolerance across all genes targeted by CNVs
than controls (−1.35 versus −1.42, P = 0.007). (Note that, as expected,
genes for which we observed any CNV in a given sample in fact tended
to be more tolerant; thus, both groups had negative means.) Further,
cases carried greater normalized intolerance (Online Methods) for
CNVs than controls (0.44 versus 0.33, P = 1 × 10
−11
). To assess the
independent information contained in the CNV intolerance score, we
calculated the normalized mean SNV-based constraint score for each
individual and tested whether these scores correlated with disease
status. We identified significantly higher constraint in schizophrenia
cases than in controls from the constraint score based on missense
variants (P = 4 × 10
−4
), the constraint score based on loss-of-function
variants (P = 2 × 10
−4
), and pLI (P = 8 × 10
−8
). In a joint test of all
scores from independent annotations, the CNV intolerance scores
remained the most significant predictor (CNV, P = 6 × 10
−7
; SNV
(missense variants), P = 0.17; pLI, P = 0.004). This finding suggests
that it will be beneficial to develop disease risk association testing
frameworks that jointly consider the type of CNVs with respect to
their genic intolerance scores, as well as the number of deleted or
duplicated genes.
DISCUSSION
Here we have presented gene-level frequencies and intolerance scores
for CNVs from nearly 60,000 individuals, providing a data-driven
means for estimating the likely deleteriousness of genic CNVs.
Consistent with the relevance of CNV to gene function, the current
estimates of CNV intolerance show non-random profiles with respect
to tissue-specific gene expression patterns, to independent measures
of genic constraint, and to risk of disease. We provide summaries
of these data at the gene and exon level and detailed quality control
metrics online.
Limitations of this work include the relative difficulty in ascer-
taining accurate copy number calls from targeted (exome) short-
read sequencing and the inability to accurately call common or
more complex variants, along with the rarity of these events,
which increases the noise around point estimates of frequency
and corresponding intolerance scores. In generating intolerance
scores, we attempted to control for gene-to-gene variability in
observed CNV rates resulting from factors other than evolutionary
selection on the phenotypic consequences of bearing a CNV in that
gene, for example, gene size and sequencing coverage. Yet, although
we attempted to model the increased rates of CNV proximal to seg-
mental duplications, our incomplete knowledge of CNV mutational
–0.1 0.0 0.1 0.2
a b
CNV intolerance score
5
10
15
–log
10
(P)
Not sigificant
Pancreas (542)
Liver (1,313)
Duodenum (1,655)
Small intestine (1,718)
Kidney (1,773)
Colon (1,833)
Stomach (1,617)
Salivary gland (1,183)
Prostate (1,684)
Testis (2,550)
Skin (1,497)
Esophagus (1,920)
Gall bladder (2,046)
Heart (1,545)
Thyroid gland (1,954)
Urinary bladder (2,171)
Adipose tissue (1,483)
Adrenal gland (1,657)
Ovary (1,696)
Lung (1,827)
Appendix (1,838)
Lymph node (1,981)
Bone marrow (1,471)
Placenta (1,771)
Spleen (1,834)
Endometrium (1,778)
Brain (1,774)
Genes highly expressed in tissue (number of genes)
Synapse organization
and assembly
PCDHB16, PCDHB14,
PCDHB13, PCDHB8,
PCDHB6, PCDHB2
Nucleoplasm
CLK3, CLK2
Neuron/axon
development
MAPT, SPTBN2,
TENM2, ANK3,
MAP2K1, SEMA7A,
SPTAN1, CDH2,
CLASP2, RELN, PTEN,
CLASP1, DST, KIF13B,
SLIT2, CNTN1, WNT3,
PSEN1, ARHGEF11,
KIF5B, STMN1
Protein localization
>25 genes
RNA binding
>25 genes
Regulation of
transcription
WDR61, DAB2,
MAP2K1,
SUPT6H, BRD4
Protein binding
TLN1, MAPT,
SPTAN1
Armadillo/ β
-catenin-
like repeats
IPO9, TNPO3, KPNB1,
TNPO1, IPO5
Systolic blood pressure
CSK, ATXN2, HEXIM1,
HEXIM2, NPPA, ARID3B,
PLCD3, NT5C2, LMAN1L
Aldo-keto reductases
AKR1B1, AKR1B15,
AKR1C1, AKR1C2, AKR1B10
Zinc Fingers
> 25 genes
Myosin filament
MYH1, MYH4, MYH8,
MYH11, MYH9, MYH13
Protocadherin genes
PCDHGB7, PCDHGB6, PCDHGB3,
PCDHGA11, PCDHGA10, PCDHGA9,
PCDHGA7, PCDHGA6, PCDHGA5,
PCDHGA2, PCDHGA1
Metallothioneins
MT1A, MT1B, MT1E,
MT1F, MT1G, MT1H,
MT1M, MT1X
Size of pathway
Signicance among the 5% most intolerant genes
Signicance among the 5% most tolerant genes
Figure 3 Brain-relevant genes demonstrate the greatest intolerance to dosage changes from CNVs. (a) After removing genes highly expressed in all
tissues (FPKM >20), 27 tissues
31
were rank-ordered by the mean ExAC CNV intolerance scores for the highly expressed genes in each tissue; mean
intolerance scores are indicated by the middle bar in each box, and box width represent s.e.m. Box color denotes the significance of a two-sided t test
of the difference in intolerance scores between tissue-expressed genes and all others; white bars indicate no significant difference (P > 0.05).
The vertical dashed blue line marks the mean CNV intolerance score for all genes. (b) Network diagrams of pathways significantly enriched for the
5% most CNV-intolerant (red) and CNV-tolerant (blue) genes (created using the Enrichment Map Cytoscape plugin
35
). Results are based on tests of
nine categories of pathways (GO molecular, GO biological, GO cellular, Human Phenotype, Mouse Phenotype, Domain, Pathway, Gene Family, and
Disease); only those surpassing Bonferroni-corrected (P < 0.05) and false discovery rate (FDR) significance thresholds are shown. Node size represents
the number of genes in a pathway, node color represents the significance of enrichment, and the thickness of a pairwise edge corresponds to the
proportion of genes overlapping between the corresponding pair of gene sets. Groupings were manually assigned a label, and the genes listed were
those present in all significant pathways within a group.
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mechanisms can add noise and bias to these estimates of intolerance,
in particular in regions of known recurrence.
It is also important to note that many ExAC sample participants
were ascertained on the basis of disease status. Insofar as a minority
of genes had significantly higher rates of CNV because of this, these
genes will have slightly deflated intolerance estimates in comparison
to those derived from a phenotypically screened control sample.
Despite these limitations, the analyses presented here point to
the value of more comprehensive assessments of genetic variation.
Whether a gene tolerates deletion or duplication is most directly esti-
mated by considering the empirical patterns of genic CNV rates in
large samples, as performed here. Combination of CNV intolerance
scores with other measures of genic constraint, including those based
on SNVs and evolutionary analyses, is likely to yield better and more
general metrics for assessing the likely impact of any type of genic
variant, leading to improved interpretation of personal genomes and
disease association studies.
URLs. Exome Aggregation Consortium (ExAC) web browser, http://
exac.broadinstitute.org/; genes implicated in recessive disorders,
http://research.nhgri.nih.gov/CGD.
METHODS
Methods and any associated references are available in the online
version of the paper.
Note: Any Supplementary Information and Source Data files are available in the
online version of the paper.
ACKNOWLEDGMENTS
We would like to acknowledge E. Fluder and K. Shakir for their help in running
XHMM at the large scale required for over 60,000 samples. Work at the Icahn
School of Medicine at Mount Sinai was supported by the Institute for Genomics
and Multiscale Biology (including computational resources and staff expertise
provided by the Department of Scientific Computing) and NIH grants R01-
HG005827 and R01-MH099126 (to S.M.P.).
AUTHOR CONTRIBUTIONS
D.M.R., M.F., and S.M.P. designed the study. M.L., K.J.K., and D.G.M. handled
sample and data management. D.M.R., T.H., K.E.S., M.F., and S.M.P. contributed
to statistical analyses. D.K., D.M.R., and K.J.K. designed and implemented website
visualizations. D.M.R., M.J.D., D.G.M., M.F., and S.M.P. contributed to primary
interpretations. D.M.R., M.F., and S.M.P. performed the primary drafting of the
manuscript. All authors contributed to, read, and approved the final manuscript.
COMPETING FINANCIAL INTERESTS
The authors declare competing financial interests: details are available in the online
version of the paper.
Reprints and permissions information is available online at http://www.nature.com/
reprints/index.html.
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ONLINE METHODS
CNV calling in exome sequencing data of 60,642 individuals. XHMM was
run as previously described
17
. Briefly, GATK DepthOfCoverage was employed
to calculate mean per-base coverage (counting unique fragments based on reads
mapping with quality >20), across 219,437 targets (including 7,439 and 708 on
chromosomes X and Y, respectively, and 9 on the mitochondrial genome). To
accommodate the variety of exome capture methods used across the various
component projects, these targets were liberally defined as the Illumina ICE
v1 targets plus GENCODE v19 coding regions, both padded by 2 bp, from
which the unique set of relevant ‘exome targets’ was finalized. A total of 31,769
of these targets were subsequently filtered out before CNV calling, 21,072 for
having mean sequencing depth (across all samples) <10×, 8,875 for having
low-complexity sequence (as defined by RepeatMasker) over >25% of its span,
225 for having GC content <10% or >90%, 1,582 for covering <10 bp, and 15 for
spanning >10 kb. The resulting sample × target read depth matrix was scaled
by mean-centering the targets, after which principal-component analysis of
the full matrix was performed; note that, with the LAPACK implementation
in XHMM, this still required 800 GB of RAM and ~1 month of computation
time. For data normalization, the top 388 principal components (those with
variance >70% of the mean variance across all components) were removed
from the data to account for systematic biases at the target or sample level,
such as GC content or sequencing batch effects. Subsequently, three targets
were removed for still having high variance after normalization (s.d. >50),
and sample-level z scores were calculated (with absolute values capped at 40).
CNVs were called using the Viterbi HMM algorithm with default XHMM
parameters, and XHMM CNV quality scores were calculated as previously
described using the forward–backward HMM algorithm and modifications as
previously described. In addition, all called CNVs were statistically genotyped
across all samples using the same XHMM quality scores and output as a single
uniformly called VCF file.
Quality control of CNV data. In total, we attempted CNV calling for 60,642
of the 60,706 (99.9%) ExAC samples, with the remainder either having failed
calling for low overall read depth or not included because of upstream data
access issues. The CNVs output by XHMM were first frequency filtered to
remove common CNVs, that is, those seen more than 600 times (>1%), defined
as overlapping more than 50% of their respective targets. On the basis of previ-
ous work
17
, we retained only CNVs with quality scores greater than or equal to
60. We removed any individual having a CNV count greater than 3 s.d. above
the mean, that is, 24 CNVs (n = 775 samples removed). Thus, our final data
set consisted of 59,898 individuals and 126,771 CNVs overlapping GENCODE
autosomal protein-coding genes.
Filtering of genes. Of the 20,345 GENCODE v19 genes labeled as protein cod-
ing, we limited our analyses to the set of 19,430 genes occurring on autosomes,
with CNVs on sex chromosomes removed because of technical issues. Next,
we removed any gene where half or more of its targets were filtered out dur-
ing CNV calling (1,068 genes). We further removed genes having unusually
low (<30×) or high (>200×) mean coverage (944 genes). Using data from a
recent report on CNV from whole-genome sequencing data for 849 genomes
sequenced from the 1000 Genomes Project
36
, we removed any gene known to
be multiallelic (735 genes). Finally, we removed any gene in which there existed
any CNV with a frequency greater than 0.5% (1,193 genes). This filtering
yielded a final set of 15,734 genes for all subsequent genic analyses.
Assessment of CNV quality in parent–child trios. To assess overall CNV
quality, we used 241 previously described
37,38
parent–offspring trios from
Bulgaria to confirm that apparent de novo rates and parent-to-child transmis-
sion broadly conformed to expectations of random Mendelian segregation
(note that the offspring had a diagnosis of schizophrenia and were not part of
the primary ExAC data set, which included only unrelated individuals). Poor
sensitivity would result in severely reduced transmission statistics, whereas
poor specificity would induce many false positive CNV calls and increased rates
of de novo CNV. Through reasonable estimates of transmission and de novo
events, we could infer high specificity and sensitivity for the CNV calls
overall. Defining CNV transmission as implemented in the PLINK/Seq cnv-
denovo command
17
, we assessed whether the rate of transmission for CNVs
converged to the expected Mendelian rate of 50% across a range of quality
score thresholds. Using the recommended quality score cutoff (SQ 60),
median per-trio CNV transmission rates were at the expected 50%, with
the aggregate transmission rate across CNVs in all trios falling to 43% (44%
for deletions, 42% for duplications). These rates excluded situations where
an offspring’s CNV was neither confidently called as deleted or duplicated
(SQ 60) nor confidently called as diploid (DQ 60). Including these more
uncertain events and conservatively counting them as non-transmissions
resulted in aggregate transmission rates of 32%. Nevertheless, these results
remain consistent with high specificity, as confirmed by a low mean of 0.058
de novo CNVs per trio (half of which were over 1 kb in length and spanned
five or more exons), which only increased to 0.13 de novo CNVs per trio
when treating uncertain events in the parents as diploid. Indeed, a comparable
de novo CNV rate of 0.051 was found in a larger version of this cohort (622
trios) using genotyping arrays
38
.
Gene- and exon-specific copy number calls. We defined gene-specific copy
number state for each individual, assessing the probability of a CNV occurring
anywhere between the transcriptional start and end sites of a gene. Specifically,
this analysis was performed by defining the genomic interval spanned by each
gene and then using the sample × target matrix of z scores to statistically geno-
type these gene regions across all samples. This genotyping procedure yielded a
VCF file containing key copy number metrics, including those corresponding to
the probability that an individual is confidently called diploid for the extent of
the gene or, alternatively, has some deletion or duplication therein. All of these
probability-derived metrics were calculated using the forward–backward
HMM algorithm modified to efficiently calculate posterior probabilities
across all targets in a gene, analogous to genotyping across all targets in a
particular called CNV region. Although XHMM performs exome-wide cor-
rection for both regional and individual read depth variability, we found that
increased sample read depth was still correlated with increased numbers of
CNVs (Supplementary Fig. 1). In the absence of large-scale validation efforts
and given the focus on CNVs that were rare at any particular locus, it was not
feasible to easily normalize out this effect. However, we did account for poten-
tial confounders, such as gene size and read depth, in calculating gene-specific
diploid quality (by defining a threshold for diploid quality of 3 s.d. below
the mean for all individuals). Using this approach, we obtained confidence
measures for deletion, duplication, and diploid status for every individual at
every gene. We further employed the same strategy to call exon-specific copy
number states, again starting with genic exons and overlapping these with
all targets at which read depth was calculated and normalized; note that this
analysis typically included a single target per exon, but for a small proportion
of exons two or more targets were included because of the slight difference
in the definition of target regions for CNV calling and GENCODE exonic
regions. Genic CNV counts derived from this procedure correlated with the
number of loss-of-function variants in a gene (Supplementary Fig. 7).
Creating genic CNV intolerance scores. For the 15,734 genes that survived
quality control, we constructed genic measures of intolerance for all CNVs
and separately for deletions and duplications. In the absence of a high-quality
mutation model for CNVs, we employed an empirical approach incorporating
genomic information. From a set of 9,396 unique pairs of segmental duplica-
tions on the same chromosome downloaded from the UCSC Genome Browser,
we created a subset of 2,790 non-redundant pairs, requiring that the genomic
intervals between them be less than 80% overlapping and less than 4 Mb in
length. We identified a significant increase in the number of CNVs in genes
within these regions (Supplementary Fig. 1), so we included this factor in
predicting CNV frequency. Ultimately, we calculated genic intolerance from
the residuals of a logistic regression of CNV frequency on gene length, read
depth, GC content, sequence complexity, and the number of pairs of segmen-
tal duplications the gene is located between, along with higher-order terms.
We next calculated z scores such that positive values represent a lower fre-
quency of CNV (more intolerance), winsorizing the negative tail at 5%.
Stratifying CNVs by genic content affected. We stratified CNVs by the
number of genes and exons for which they were called (confidently) to affect
dosage. Specifically, we defined single-gene CNVs as those with a gene-specific
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Nature GeNetics
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confidence score greater than 60 in one of the 15,734 genes that remained
after gene quality control, but we also strictly required overlap with only one
of the 19,430 GENCODE autosomal protein-coding genes. CNVs overlapping
more than one gene were labeled as multigene CNVs. Using the exon-level
CNV calls, we further refined our single-gene CNVs into three classes: (i) full-
gene CNVs corresponded to genes where all exons were confidently called as
deleted or duplicated; (ii) ambiguous CNVs corresponded to genes with at least
one exon confidently called as deleted or duplicated but no exons confidently
called as diploid; and (iii) partial-gene CNVs corresponded to genes in which
there was at least one exon confidently called as deleted or duplicated and at
least one exon confidently called as diploid.
Predefined gene sets. We collated three groupings of gene sets to test for
enrichment. The first was a set of highly expressed genes from expression
data of 27 tissue types (pancreas, liver, duodenum, small intestine, kidney,
colon, stomach, salivary glands, testis, prostate, skin, esophagus, gall bladder,
thyroid gland, heart, adipose tissue, urinary bladder, ovary, adrenal glands,
lymph nodes, appendix, lung, bone marrow, placenta, spleen, endometrium,
and brain) previously published
31
. We defined highly expressed genes for each
tissue as those having FPKM values greater than 20 but excluded genes that
were highly expressed in all tissues. The second was a set of disease-impli-
cated genes collated in a previous study analyzing a large set of CNVs
15
; these
included sets of genes involved in dominant and recessive diseases, genes
implicated in cancer, haploinsufficient genes, genes whose counterparts are
essential in mice, genes intolerant to loss-of-function variants, and genes
not related to a specific phenotype in any such database (Supplementary
Tables 3 and 4).
Gene set enrichment analysis. We selected the genes with the top and bottom
5% of CNV intolerance scores (n = 787 each set) and ran gene set enrichment
analysis with ToppFun
39
, which uses a hypergeometric test of gene sets across
18 possible categories, of which we selected 9 categories of pathways (GO
molecular, GO biological, GO cellular, Human Phenotype, Mouse Phenotype,
Domain, Pathway, Gene Family, and Disease). The most CNV-intolerant genes
were enriched in GO sets related to neuronal and axon development and syn-
apse organization and assembly. The most CNV-tolerant genes were enriched
for metallothioneins and myosin filament genes (Fig. 2b and Supplementary
Tables 58).
36. Handsaker, R.E. et al. Large multiallelic copy number variations in humans. Nat.
Genet. 47, 296–303 (2015).
37. Kirov, G. et al. De novo CNV analysis implicates specific abnormalities of postsynaptic
signalling complexes in the pathogenesis of schizophrenia. Mol. Psychiatry 17,
142–153 (2012).
38. Fromer, M. et al. De novo mutations in schizophrenia implicate synaptic networks.
Nature 506, 179–184 (2014).
39. Chen, J., Bardes, E.E., Aronow, B.J. & Jegga, A.G. ToppGene Suite for gene list
enrichment analysis and candidate gene prioritization. Nucleic Acids Res. 37,
W305–W311 (2009).
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Discussion

Mapped read depth is the number of bases sequenced and aligned at a given reference base position and the depth can determine the confidence level with which variant discovery can be made, those genes with low read depth we will be unable to confidently assess. The Gencode Consortium aims to identify all gene features in the human genome and is a reference annotation genome for research (including 1000 genomes project). Essentially, if a gene is more intolerant to CNVs, it is more likely to be have higher functional importance (b/c of negative selection and previous arguments). The fact that the highly expressed genes had significantly higher intolerance scores than other genes in the subset confirms that genes of higher functional importance are more intolerant to CNVs. Also, the fact that genes highly expressed in the brain had the highest intolerance scores is significant for the application of this methodology to studying brain diseases like schizophrenia. Genetic dosage is the number of copies of a gene in a genome. We can detect CNVs through various mechanisms but we still don't know how they arose in the first place. Also, there is no conclusive evidence that correlates a specific copy number variation to a specific mechanism yet. Exome sequencing is the sequencing of all the expressed genes in a genome, so the coding portion of the genome. This constitutes 1.5 percent of the genome. Although whole genome sequencing costs have declined faster than Moore's law, declining below $1000 per genome, for large genetic studies, genotyping or exome sequencing is still the precedent because whole genome sequencing costs are very high for research budgets. By evolution theory, negative selection is the selective removal of rare alleles that are deleterious. Copy number variation occurs when sections of the genome get repeated and the number of repeats or copies varies between individuals in the human population. It is a duplication or deletion event. Researchers believe that two thirds of the genome is composed of repeats and 4.8-9.5% of the human genome can be classified as copy number variations. In mammals, copy number variants play a role in disease phenotype.