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A general framework for estimating the relative pathogenicity of
human genetic variants
Martin Kircher1,*, Daniela M. Witten2,*, Preti Jain3,4, Brian J. O’Roak1,4, Gregory M.
Cooper3,#, and Jay Shendure1,#
Martin Kircher: mkircher@uw.edu; Daniela M. Witten: dwitten@u.washington.edu; Preti Jain: pjain@hudsonalpha.org;
Brian J. O’Roak: oroak@uw.edu; Gregory M. Cooper: gcooper@hudsonalpha.org; Jay Shendure: shendure@uw.edu
1Department of Genome Sciences, University of Washington, Seattle, WA, USA
2Department of Biostatistics, University of Washington, Seattle, WA, USA
3HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA
Abstract
Our capacity to sequence human genomes has exceeded our ability to interpret genetic variation.
Current genomic annotations tend to exploit a single information type (e.g. conservation) and/or
are restricted in scope (e.g. to missense changes). Here, we describe Combined Annotation
Dependent Depletion (CADD), a framework that objectively integrates many diverse annotations
into a single, quantitative score. We implement CADD as a support vector machine trained to
differentiate 14.7 million high-frequency human derived alleles from 14.7 million simulated
variants. We pre-compute “C-scores” for all 8.6 billion possible human single nucleotide variants
and enable scoring of short insertions/deletions. C-scores correlate with allelic diversity,
annotations of functionality, pathogenicity, disease severity, experimentally measured regulatory
effects, and complex trait associations, and highly rank known pathogenic variants within
individual genomes. The ability of CADD to prioritize functional, deleterious, and pathogenic
variants across many functional categories, effect sizes and genetic architectures is unmatched by
any current annotation.
Technical Report
A strength of genomic approaches to study disease is the replacement of informed but biased
hypotheses with unbiased but generic ones, like the “equal treatment” of all genetic variants
in genome-wide association studies (GWAS). However, for both rare variants of large effect
and common variants of weak effect, the use of prior knowledge can be critical for disease
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#To whom correspondence should be addressed: shendure@uw.edu, gcooper@hudsonalpha.org.
*These authors contributed equally to this work
4Present address: Department of Molecular & Medical Genetics, Oregon Health & Science University, Portland, OR, USA
G.C. and J.S. designed the study; M.K. processed the annotation data and scores, developed and implemented the simulator and scripts
required for scoring; P.J. and B.O. prepared and provided data sets and annotations; D.W. and M.K. developed the model and
performed model training; D.W. performed the analysis of individual features and interactions; M.K., D.W., G.C., and J.S. analyzed
the model’s performance on different data sets; G.C. analyzed the GWAS data; J.S., G.C., M.K. and D.W. wrote the manuscript with
input from all authors.
NIH Public Access
Author Manuscript
Nat Genet. Author manuscript; available in PMC 2014 September 01.
Published in final edited form as:
Nat Genet. 2014 March ; 46(3): 310–315. doi:10.1038/ng.2892.
NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript
gene discovery1–4. For example, exome sequencing is an effective discovery strategy
because it focuses on protein-altering variation, which is enriched for causal effects5.
While many existing annotations are useful for prioritizing causal variants to boost
discovery power (e.g. PolyPhen6, SIFT7, and GERP8), current approaches tend to suffer
from one or more of four major limitations. First, annotations vary widely with respect to
both inputs and outputs. For example, conservation metrics8–10 are defined genome-wide
but do not use functional information and are not allele-specific, while protein-based
metrics6,7 apply only to coding, and often only to missense, variants, thereby excluding
>99% of human genetic variation. Second, each annotation has its own metric and these
metrics are rarely comparable, making it difficult to evaluate the relative importance of
distinct variant categories or annotations. Third, annotations trained on known pathogenic
mutations are subject to major ascertainment biases and may not generalize. Fourth, it is a
major practical challenge to obtain, let alone to objectively evaluate or combine, the existing
panoply of partially correlated and partially overlapping annotations; this challenge will only
magnify as large-scale projects like ENCODE11 continually increase the amount of relevant
data available. The net result of these limitations is that many potentially relevant
annotations are ignored, while the subset that are used are applied and combined in ad hoc
and subjective ways that undermine their utility.
Here, we describe a general framework, Combined Annotation Dependent Depletion
(CADD), for integrating diverse genome annotations and scoring any possible human single
nucleotide variant (SNV) or small insertion/deletion (indel) event. The basis of CADD is to
contrast the annotations of fixed or nearly fixed derived alleles in humans relative to
simulated variants. Deleterious variants – that is, variants that reduce organismal fitness –
are depleted by natural selection in fixed but not simulated variation. CADD therefore
measures deleteriousness, a property that strongly correlates with both molecular
functionality and pathogenicity12. Importantly, metrics of deleteriousness, in contrast with
pathogenicity or molecular functionality, have major advantages. Whereas the latter are
limited in scope to a small set of genetically or experimentally well-characterized mutations
and subject to major ascertainment biases, deleteriousness can be measured systematically
across the genome assembly (see refs 8, 9, 10 and below). Further, selective constraint on
genetic variants is related to the totality of their phenotype-relevant effects rather than any
individual molecular or phenotypic consequence. Measures of deleteriousness can therefore
provide, in principle, a genome-wide, data-rich, functionally generic, and organismally
relevant estimate of variant impact.
We identified differences between human genomes and the inferred human-chimpanzee
ancestral genome13 where humans carry a derived allele with a frequency of at least 95%
(14.9 million SNVs and 1.7 million indels). Nearly all of these events are fully fixed in the
human lineage, with fewer than 5% appearing as nearly fixed polymorphisms in the 1000
Genomes Project14 variant catalog (derived allele frequency (DAF) ≥ 95%). To simulate an
equivalent number of de novo mutations, we used an empirical model of sequence evolution
with CpG dinucleotide-specific rates and mutation rates locally estimated at a 1 megabase
(Mb) scale (Supplementary Note). Mutation rate parameters as well as the size distribution
of indels were estimated from six-way primate genome alignments15.
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To generate annotations, we used the Ensembl Variant Effect Predictor16 (VEP), data from
the ENCODE project11 and information from UCSC genome browser tracks17
(Supplementary Table 1). The annotations span a range of data types including conservation
metrics like GERP8, phastCons9, and phyloP10; regulatory information11 like genomic
regions of DNase hypersensitivity18 and transcription factor binding19; transcript
information like distance to exon-intron boundaries or expression levels in commonly
studied cell lines11; and protein-level scores like Grantham20, SIFT7, and PolyPhen6. The
resulting variant-by-annotation matrix contained 29.4 million variants (half fixed or nearly
fixed human derived alleles (“observed”), half simulated de novo mutations (“simulated”))
and 63 distinct annotations, some of which are composites that summarize many underlying
annotations (Supplementary Note, Supplementary Tables 1–2).
We first assessed the validity of our general approach by constructing a series of univariate
models that contrast observed and simulated variants using each of the 63 annotations as
individual predictors (Supplementary Note). Nearly all models were highly significant
(Supplementary Tables 3–5) and consistent with expectation. For example, we find a nearly
20-fold depletion of nonsense variants, a 2-fold depletion of missense variants, and no
depletion of intergenic or upstream/downstream variants (Supplementary Table 6).
Nonsense and missense mutations that occur near the starts of cDNAs were more depleted
than those occurring near the ends (Supplementary Table 7), and variants within 20, and
especially within 2, nucleotides of splice junctions were also depleted (Supplementary Fig.
1). The best performing individual annotations were protein-level metrics such as PolyPhen6
and SIFT7, but these evaluated only missense variants (0.63% of all variants in the training
data are missense; of these, 88% had defined PolyPhen values and 90% had defined SIFT
values). Conservation metrics were the strongest individual genome-wide annotations
(Supplementary Table 3).
We also examined correlations between annotations (Supplementary Fig. 2) and the value of
adding interaction terms between annotations (Supplementary Fig. 3). Many annotations
were correlated and many interactions were statistically significant, but only a handful of
interacting pairs meaningfully improved a simple additive model. Overall, these analyses
demonstrate that substantial biological differences are present between the observed and
simulated variants with respect to the 63 annotations, and that linear models capture much of
this information.
We next trained a support vector machine21 (SVM) with a linear kernel on features derived
from the 63 annotations, supplemented by a limited number of interaction terms
(Supplementary Note, Supplementary Tables 1–2, Supplementary Fig. 4). Ten models,
independently trained on observed variants and different samples of simulated variants, were
highly correlated (all pairwise Spearman rank correlations >0.99; Supplementary Fig. 5). An
average of these models was applied to score all 8.6 billion possible SNVs of the human
reference genome (GRCh37). To simplify interpretation in some contexts, we also defined
phred-like22 scores (“scaled C-scores”) based on the rank of the C-score of each variant
relative to all 8.6 billion possible SNVs, ranging from 1 to 99 (Supplementary Note). For
example, substitutions with the highest 10% (10−1) of all scores - that is, least likely to be
observed human alleles under our model - were assigned values of 10 or greater (“≥C10”),
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while variants in the highest 1% (10−2), 0.1% (10−3), etc. were assigned scores ≥C20, ≥C30,
etc.
We first calculated the proportion of all possible substitutions with a given scaled C-score
having specific functional consequences (Fig. 1; Supplementary Table 8). Although trained
solely on the difference between observed and simulated variants, rather than on sets of
known disease causing variants that might introduce ascertainment bias, the C-scores of
potential nonsense variants are highest (median 37), followed by missense and canonical
splice site variants (median 15) and with intergenic variants comprising the bottom of the
list (median 2). At the same time, 76% of potential SNVs with ≥C20 are non-coding (i.e.
categories other than missense, nonsense, canonical splice or stop loss), while 74% of
potential missense and 18% of potential nonsense SNVs are below C20. Further, within each
functional class there are distinctions that are biologically relevant and likely predictively
useful. For example, potential nonsense variants – often treated as a homogeneous group in
disease studies – in olfactory receptors score lower than in other genes, while potential
nonsense variants in genes found previously to be “essential”23 score higher (Fig. 1 lower
panel, Supplementary Fig. 6). C-scores thus capture considerable information both between
and within functional categories. Of note, these same distinctions are absent or muted with
other measures, either due to missingness (e.g., for missense-only measures) or lack of
functional awareness (e.g., conservation measures cannot distinguish between a nonsense
and missense allele at a given position).
We next compared scaled C-scores with levels of genetic diversity, finding that C-scores are
negatively correlated with the DAF of variants identified in the 1000 Genomes Project14 or
the Exome Sequencing Project24 (ESP) (Fig. 2a; Supplementary Figs. 7–9), depletion of
human genetic variation from the 1000 Genomes Project catalog (Fig. 2b), and depletion of
chimp-derived variants (Fig. 2c). Importantly, these validation datasets have minimal
overlap with the “observed” subset of the training data, which consists only of fixed or
nearly fixed (>95% DAF) human derived alleles. Furthermore, although we cannot fully
eliminate confounding by these factors, the negative correlation between C-scores and the
DAF of standing variation is robust to controlling for variation in background selection,
local GC content, local CpG density, and site-based conservation (Supplementary Fig. 9).
We next sought to assess the utility of CADD to prioritize functional and disease-relevant
variation within five distinct contexts.
First, for MLL2, the gene mutated in Kabuki syndrome, C-scores enable discrimination of a
diverse set of disease-associated alleles25 versus rare, likely benign variants from ESP24
(Wilcoxon rank sum test p = 9.9 × 10−94; n = 210/679). Other metrics were markedly
inferior in terms of accuracy or comprehensiveness (Supplementary Fig. 10).
Second, for HBB, the gene mutated in beta-thalassemia, C-scores of disease-associated
alleles26 – a set of indels (n=93) and SNVs (n=119) with regulatory/upstream (n=54),
splicing (n=37), missense (n=22), nonsense (n=18) and other effects – are significantly, and
more strongly than other measures, correlated with three levels of phenotypic severity
(Kruskal-Wallis rank sum test p = 2.4 × 10−7; n = 48/65/99, Supplementary Fig. 11).
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Third, pathogenic variants curated by the NIH ClinVar database27 are well separated from
likely benign alleles (ESP24 DAF ≥ 5%) matched to the same categorical consequences
(Wilcoxon rank sum test p < 10−300, n = 8174/8174, Fig. 3; Supplementary Figs. 12–16).
We note that there is substantial overlap between ClinVar and the training data underlying
PolyPhen. When these sites are excluded from the test dataset, or when PolyPhen is
excluded as a training feature from CADD, C-scores continue to outperform all or nearly all
missense-only metrics and conservation measures (Supplementary Fig. 12).
Fourth, C-scores strongly correlate with the number of observations for somatic cancer
mutations in p53 reported to the International Agency for Research on Cancer (Spearman
rank correlation 0.38, p = 6 × 10−73, n = 2068, Supplementary Note).
Fifth, we examined two enhancers28 and one promoter29 in which we previously performed
saturation mutagenesis. C-scores are significantly correlated, and overall more so than
measures of sequence conservation, with the experimentally measured absolute expression
fold change of individual variants (Spearman rank correlation of combined data = 0.31, p =
1.9 × 10−65, n = 2847; Supplementary Fig. 17).
Collectively, these analyses demonstrate that CADD is quantitatively predictive of
deleteriousness, pathogenicity, and molecular functionality, both protein-altering and
regulatory, in a variety of experimental and disease contexts. Within each of these contexts,
CADD’s predictive utility is much better than measures of sequence conservation, the only
comprehensive type of variant score, and also tends to be better, in most cases substantially
so, than function-specific metrics when restricted to the appropriate variant subsets.
We next considered how CADD may be useful in evaluating candidate variation within
exome or genome-wide studies.
First, we analyzed de novo exome variants (SNVs and indels) identified in children with
autism spectrum disorders30–34 (ASD) and intellectual disability35,36 (ID) along with
unaffected siblings or controls, including 88 nonsense, 1,015 missense, 359 synonymous, 32
canonical splice site, and 150 other variants, including indels. Variants in affected children
are significantly more deleterious than those in unaffected siblings/controls, considering
each disease separately (Supplementary Table 9) or combined (ASD+ID Wilcoxon rank sum
test p = 2.0 × 10−4, n = 1130/514). Additionally, de novo variants in ID probands are
significantly more deleterious than those of ASD probands (p = 4.7 × 10−5, n=170/960),
suggesting a more deleterious global mutation burden in ID, consistent with the observation
of increased sizes and numbers of copy number variants in ID relative to ASD37.
Second, it is well established that annotations like PolyPhen and conservation are valuable
in the sequencing-based identification of disease-causal genes by virtue of their ability to
highly rank pathogenic variants1,2,38. We therefore examined the distribution of C-scores in
the genomes of 11 individuals representing diverse populations39,40, and find that CADD
highly ranks known disease-causal variants (ClinVar pathogenic) within the complete
spectrum of variation in personal genomes (Fig. 4; Supplementary Fig. 16 and
Supplementary Table 10–11). Furthermore, CADD is both more quantitative and
comprehensive in this task (e.g., ~27% of pathogenic ClinVar SNVs are not scored by
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PolyPhen because of missing values or its restriction to missense variation). Given its
considerable superiority over the best available protein-based and conservation metrics in
terms of ranking known pathogenic variants in the complete spectrum of variation within
personal genomes, it is likely that CADD will improve the power of sequence-based disease
studies beyond current standard approaches.
Finally, we analyzed CADD scores for single nucleotide polymorphisms (SNPs) identified
by GWAS of complex traits, contrasting them with nearby control SNPs matched for allele
frequency and genotyping array availability (Fig. 5, Supplementary Note). We find that lead
GWAS SNPs have significantly higher C-scores than control SNPs (one-sided Wilcoxon
rank sum test, p-value = 1.3 × 10−12, n = 5498/5498); nearby SNPs in linkage disequilibrium
with lead SNPs (“tags”) score lower on average than leads but are also significantly higher
than their matched controls (p-value = 5.1 × 10−107). C-score differences remain significant
after controlling for properties like gene-body effect, gene expression level, conservation,
and regulatory element overlap; each of these are significantly different between associated
and control SNPs but none can fully explain the C-score discrepancy (Supplementary Note).
C-scores of trait-associated SNPs furthermore correlate with the size of the underlying
association study and with statistical significance of the association itself (Fig. 5;
Supplementary Figure 16; Supplementary Note), likely due to the increased ability of larger
studies and stronger association statistics to enrich for causal variants. While for the most
part not causal, our analysis suggests that GWAS-identified SNPs, especially strongly
associated lead SNPs from large studies, are enriched for causal variants, consistent with
previously observed GWAS enrichments for individual annotations11,41–44.
With CADD, we describe a generic, expandable framework for integrating information
contained in diverse annotations of genetic variation to a single score. We demonstrate that
in a variety of contexts this approach is better, in some cases modestly but in many cases
dramatically, than other widely used annotations at prioritizing functional and pathogenic
variants. Further, beyond utility in any one setting, there are practical and conceptual
advantages to CADD that should prove of major value to genetic studies of human disease.
First, the information content of many individual annotations is objectively merged into a
single value, which is far preferable to ad hoc approaches for combining annotations and
likely to improve performance, consistent with benefits seen for “consensus” methods in
missense-specific annotation45. Second, CADD can readily incorporate expansions to
existing annotations and entirely new annotations. The ability to indefinitely and readily
integrate new information is crucial in light of projects like ENCODE, which are
continuously and rapidly expanding available annotations11. Third, CADD combines the
generality of conservation-based metrics with the specificity of subset-relevant functional
metrics (e.g. PolyPhen), exploiting the advantages of both while attenuating their respective
disadvantages.
CADD also has a number of limitations which may restrict its utility for certain analyses or
represent areas for improvement. First, C-scores measure reductions in variation, which
correlate with deleteriousness but are also affected by local mutation rate, background
selection, biased gene conversion, and other phenomena, potentially limiting accuracy.
Second, C-scores reflect the proportion of variants with a given annotation pattern that are
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visible to selection but may not capture differences in selective intensity; other approaches,
such as polymorphism-to-divergence comparisons, may be more accurate for estimating
selective coefficients46. Third, there is a strong need for more “gold standard” data,
particularly for non-coding regions of the genome, the current paucity of which limits the
development of better annotations as well as our ability to validate predictions. Fourth, it is
at present not possible to precisely calibrate the relationship between CADD-estimated
deleteriousness and the likelihood that a variant is pathogenic. As such, C-scores are best
interpreted in terms of “likelihood of deleteriousness” rather than “likelihood of
pathogenicity”, e.g. the quantifiable extent of depletion of a given C-score from chimp-
derived alleles (Fig. 2c, Supplementary Table 11). Especially for discovering causal
variants, CADD should be treated as one piece of information contributing to the totality of
evidence for pathogenicity, and evaluated as a supplement, not a replacement, for genetic
information.
The “one-stop” nature of CADD is likely to be of great practical and conceptual value to
future sequencing studies. It will minimize the scope and diversity of annotations that have
to be generated, tracked, and evaluated by a lab or project, and reduce the need for ad hoc
combinations of filters, scores, and parameters as is now routinely done. For example, an
oft-used approach in exome studies is to merge missense (with or without an annotation of
“damage” or given level of conservation), nonsense, and splice-disrupting variants into a
single, internally unranked list of “protein-altering” variants prior to genetic analysis5. With
CADD, one might avoid arbitrary filters/thresholds altogether, including both coding and
non-coding variants on a single, meaningfully ranked list. For example, a recent study of
recessive, non-syndromic pancreatic agenesis identified 5 causal non-coding variants that
disrupt function of a distal enhancer of PTF1A47. C-scores for these non-coding, disease-
causal variants (scaled scores between 23.2 and 24.5) rank them above 99.5% of all possible
human SNVs, above 97% of missense SNVs in a typical exome, and higher than 56% of
Mendelian pathogenic SNVs in ClinVar27.
Both in research and in the clinic, our capacity to define catalogs of genetic variants exceeds
our ability to systematically evaluate their potential impacts. This challenge will deepen as
sequencing accelerates, as genomes displace exomes, and as the array of functional
categories and annotations expand. A unified, quantitative, and scalable framework capable
of exploiting many genomic annotations will be essential to meet this challenge. We
anticipate that the model described here and the accompanying freely available pre-
computed scores for all possible GRCh37/hg19 SNVs (http://cadd.gs.washington.edu/) will
be broadly useful immediately, and improve over time, enabling better interpretation of
variants of uncertain significance in a clinical setting and improving discovery power for
genetic studies of both Mendelian and complex diseases.
Online Methods
Simulated and observed variants
The basis of the CADD framework is to capture correlates of selective constraint as
manifested in differences between simulated variants and observed human derived changes.
For the simulated variants, we developed a genome-wide simulator of de novo germline
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variation. The simulator was motivated by the parameters of the General Time Reversible
(GTR) model50, but because the standard GTR does not naturally accommodate asymmetric
CpG-specific mutation rates, we use a fully empirical model of sequence evolution with a
separate rate for CpG dinucleotides and local adjustment of mutation rates (see
Supplementary Note). Simulation parameters were obtained from Ensembl Enredo-Pecan-
Ortheus (EPO)13,15 whole genome alignments of six primate species (Ensembl Compara
release 66). A custom script and the associated rate matrices underlying the genome-wide
simulator are available as Supplementary File 1. We applied these parameters to simulate
single nucleotide (SNV) and insertion/deletion (indel) variants based on the human reference
sequence (GRCh37).
For observed human derived changes, we extracted sites where the human reference genome
differs from the inferred human-chimp ancestral genome from the Ensembl EPO 6 primate
alignments defined above, excluding variants in the most recent 1000 Genomes Project14
data (1000G, variant release 3, 20101123) with a frequency of greater than 5%, and
including variants where the human reference carries an ancestral allele (i.e. matching the
inferred human-chimp ancestor sequence) but where the derived allele is observed with
frequency above 95% in the 1000G data. We identified a total of 14,893,290 SNVs, and
627,071 insertions and 1,107,414 deletions (less than 50bp in length).
Variant annotation matrix
We used the Ensembl Variant Effect Predictor (VEP, Ensembl Gene annotation v68)16 to
obtain gene model annotation for single nucleotide and indel variants. For single nucleotide
variants within coding sequence, we also obtained SIFT7 and PolyPhen-26 scores from VEP.
We combined output lines describing MotifFeatures with the other annotation lines,
reformatted it to a pure tabular format and reduced the different Consequence output values
to 17 levels and implemented a four-level hierarchy in case of overlapping annotations (see
Supplementary Note). To the 6 VEP input derived columns (chromosome, start, reference
allele, alternative allele, variant type: SNV/INS/DEL, length) and 26 actual VEP output
derived columns, we added 56 columns providing diverse annotations (e.g. mapability
scores and segmental duplication annotation as distributed by UCSC51,52; PhastCons and
phyloP conservation scores53 for three multi-species alignments9 excluding the human
reference sequence in score calculation; GERP++ single-nucleotides scores, element scores
and p-values54, also defined from alignments with the human reference excluded;
background selection score40,55; expression value, H3K27 acetylation, H3K4 methylation,
H3K4 trimethylation, nucleosome occupancy and open chromatin tracks provided for
ENCODE cell lines in the UCSC super tracks52; genomic segment type assignment from
Segway56; predicted transcription factor binding sites and motifs11; overlapping ENCODE
ChIP-seq transcription factors11, 1000 Genome variant14 and Exome Sequencing Project57
variant status and frequencies, Grantham scores20 associated with a reported amino acid
substitution). The Supplementary Note provides a full description and Supplementary Table
1 lists all columns of the obtained annotation matrix.
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Imputation and final training data set
From the annotations described above, some columns are not useful for model training or
needed to be excluded from training as they differ between the simulated variants and the
human-chimpanzee ancestor differences for technical reasons (see Supplementary Note for a
complete list; note that no allele frequency information was used in model training). In order
to fit models, we imputed missing values in genome-wide measures by the genome average
obtained from the simulated data, or set missing values to 0 where appropriate
(Supplementary Table 2). Further, we created an “undefined” category for the categorical
annotations in order to accommodate missing values. In order to deal with missing values in
annotations that are not defined on a subset of variants (e.g. information only available for
protein-coding genes), we set the missing values to zero and also created indicator variables
that contain a 1 if the corresponding variant is undefined, and a 0 otherwise. Since insertions
and deletions may produce arbitrary length Ref/Alt and nAA/oAA columns (and thus not a
fixed number of categorical levels), these values were set to N for Ref/Alt and set to
“undefined” for nAA/oAA.
Sites from the simulation were labeled +1 and human derived variants as −1. Only insertions
and deletions shorter than 50bp were considered for model training and the Length column
was capped at 49 for the prediction of longer events. The ratio of indel events to SNV events
obtained for the simulation (1:8.46).
Model training
We generated ten training data sets by sampling an equal number of 13,141,299 SNVs,
627,071 insertions and 926,968 deletions from both the simulated variant and observed
variant datasets. In order to train each support vector machine (SVM) model, the processed
data was converted to a sparse matrix representation after converting all n-level categorical
values to n individual Boolean flags. 1% of sites (~132,000 SNVs, 6,000 insertions and
9,000 deletions each) were randomly selected and used as a test data set. All other sites were
used to train linear SVMs using the LIBOCAS v0.96 library21. The SVM model fits a
hyperplane as defined below. X1,…,Xn are the 63 annotations described above (which
expand to 166 features due to the treatment of categorical annotations), W1,…,W11 are the
Boolean features that indicate whether a given feature (out of cDNApos, relcDNApos,
CDSpos, relCDSpos, protPos, relProtPos, Grantham, PolyPhenVal, SIFTval, as well as
Dst2Splice ACCEPTOR and DONOR) is undefined, 1{A} is an indicator variable for
whether the event A holds, and D is the set of bStatistic, cDNApos, CDSpos, Dst2Splice,
GerpN, GerpS, mamPhCons, mamPhyloP, minDistTSE, minDistTSS, priPhCons, priPhyloP,
protPos, relcDNApos, relCDSpos, relProtPos, verPhCons, and verPhyloP. Due to the coding
of categorical values using Boolean variables, the total number of features in this model is
949.
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SVM models were trained, using various values for the generalization parameter (C), which
assigns the cost of misclassifications. Supplementary Fig. 4 shows the model training
convergence in 2000 iterations (~70h) for different settings of C. These results indicate that
model training only converges within a reasonable amount of time for C values around
0.0025 and below. We therefore trained models for all ten training data sets with C=0.0025.
We determined the average of the model parameters and used the average model.
Model testing and validation
We annotated all 8.6 billion possible substitutions in the human reference genome
(GRCh37), and applied the model to score all possible substitutions. When scoring sites with
multiple VEP annotation lines, we score all possible annotations first and then report the one
with the highest deleteriousness after applying the four hierarchy levels. We mapped the C-
scores to a phred-like scale (“scaled C-scores”) ranging from 1 to 99 based on their rank
relative to all possible substitutions in the human reference genome, i.e. −10log10(rank/total
number of substitutions).
We used several datasets extracted from the literature and public databases to look at the
performance of the model scores (see Supplementary Note for details): (1) C-scores in
specific gene classes motivated by the analysis performed by Khurana et al.58 (i.e.
HGMD48, non-immune essential genes described by Liao et al.23, GWAS genes as available
from the Genome.gov catalog, LoF genes from MacArthur et al.49 and olfactory genes from
the Ensembl 68 gene build). (2) 210 mutations in MLL2 associated with Kabuki syndrome
from Makrythanasis et al.25. We complemented those with 679 putatively benign variants
observed in the Exome Sequencing Project (ESP)57. (3) We downloaded a total of 119
SNVs, 30 insertions and 63 deletions (all required to be at most 50nt) within or near HBB
that give rise to thalassemia from HbVar26. Disease categories were used as defined by
HbVar, except that all types that are not “beta0” or “beta+” were pooled into one category,
“other”. (4) We obtained the NCBI ClinVar27 data set (release date June 16 2012) and
extracted variants that were marked “pathogenic” or “non-pathogenic (benign)”. We also
selected a set of apparently benign (≥5% allele frequency) variants from ESP that were
matched to the pathogenic ClinVar sites in terms of their Consequence annotations. In
addition, we generated a data set where we matched ESP and ClinVar frequencies to three
decimal precisions of the alternative allele frequency. Due to the overlap of ClinVar and
ESP variants with the PolyPhen training data set, we trained a separate classifier without the
PolyPhen features and we also checked the performance on the subset of ClinVar and ESP
variants not used for PolyPhen training. To compare the performance of CADD with other
publically available missense annotations not used in model training, we downloaded scores
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from dbNSFP 2.059. (5) We combined high confidence de novo mutations from five family
based autism exome sequencing studies30–34, a total of 948 ASD probands and 590
unaffected siblings. Further, we obtained the coding variants as described above for two
family-based intellectual disability (ID) studies35,36, 151 ID and 20 unrelated control
families. (6) We obtained the expression fold change for each base substitution in ALDOB
and ECR11 from Patwardhan et al.28. This data set contains a total of 777 variants for
ALDOB and 1,860 variants for ECR11. Further, we obtained the HBB promoter data of
Patwardhan et al.29. The promoter data set contains a total of 210 variants associated with an
expression fold change. (7) We obtained a list of 23,788 single nucleotide somatic cancer
mutations in p53 which were reported to the International Agency for Research on Cancer
(IARC). These mutations correspond to 2,068 distinct variants; we recorded the number of
times that each variant was reported. (8) We obtained GATK VCF variant call files for all
autosomes and the X chromosome from shotgun sequencing of eleven men originating from
diverse human populations40. (9) We obtained the NHGRI genome-wide association study
(GWAS) catalog on December 18, 2012, and obtained 9,977 distinct SNP-trait associations
spanning 7,531 unique SNPs in 1000 Genomes; these variants are referred to as “lead
SNPs”. We used the Genome Variation Server (GVS, http://gvs.gs.washington.edu/
GVS137/) to find all SNPs within 100 kb of a lead SNP that have a pairwise correlation of
R2 >= 0.8 within Utah residents with ancestry from northern and western Europe (CEU).
This resulted in an additional 56,538 unique SNPs, referred to as “tag SNPs”. We also
developed “control” SNP sets, selected to match trait-associated SNPs for a variety of
features that may bias SNPs found by GWAS in the absence of any causal effects.
Supplementary Material
Refer to Web version on PubMed Central for supplementary material.
Acknowledgments
We thank P. Green and members of the Shendure Lab for helpful discussions and suggestions. Our work was
supported by National Institutes of Health (N.I.H.) grants U54HG006493 (to J.S. and G.C), DP5OD009145 (to
D.W.) and DP1HG007811 (to J.S.).
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Figure 1.
Relationship of scaled C-scores and categorical variant consequences. The upper plot shows the proportion of substitutions with
a specific consequence for each scaled C-score bin, while the middle panel shows the proportion of substitutions with a specific
consequence after first normalizing by the total number of variants observed in that category. The legend indicates the median
and range of scaled C-score values for each category. Consequences are obtained from the Ensembl Variant Effect Predictor16
(Supplementary Note), e.g. “noncoding change” refers to changes in annotated non-coding transcripts. Detailed counts of
functional assignments in each C-score bin are in Supplementary Table 8. The lower panel shows violin plots of the median C-
scores of potential nonsense (stop-gained) variants for genes that: harbor at least 5 known pathogenic mutations48 (“disease”);
are predicted to be “essential”23; harbor variants associated with complex traits41 (“GWAS”); harbor at least 2 loss-of-function
mutations in 1000 Genomes49 (“LoF”); encode olfactory receptor proteins; or are in a random selection of 500 genes (“Other”;
see Supplementary Note).
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Figure 2.
Relationship between scaled C-scores and: the average derived allele frequency (DAF) of variants identified in the 1000
Genomes Project14 or ESP24 (upper panel); the under-representation of polymorphic sites in 1000 Genomes (middle panel); and
chimpanzee lineage derived variants (lower panel). The dashed lines in the upper plot indicate the mean DAF and confidence
intervals indicate 1.96x standard errors of the mean (SEM) DAF in each bin. Under-representation is defined as the proportion
of 1000 Genomes (middle panel) or chimpanzee-derived (lower panel) variants in a specific scaled C-score bin divided by the
frequency with which that scaled C-score is observed for all possible mutations of the human reference assembly (10C-score/−10).
The stronger under-representation of chimpanzee-derived variants relative to 1000 Genomes variants is expected given that the
former are mostly fixed or high-frequency variants (and have survived many generations of purifying selection) while the latter
are mostly low-frequency variants. Depletion values in both panels for C-score bins other than 0 are significantly different from
expectation (binomial proportion test, all p-values <10−11).
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Figure 3.
Receiver operating characteristics (ROC) for discriminating curated, pathogenic mutations defined by the NIH ClinVar
database27 matched to apparently benign ESP alleles (DAF ≥ 5%)24 with the same categorical consequence. The left panel
shows genome-wide variants for which GerpS, PhCons, and PhyloP scores are defined (n=16,334), while the middle panel limits
the analysis to missense changes (n=15,154), with missing values imputed to an upper value limit of each score, and right panel
to missense changes for which PolyPhen, SIFT and Grantham scores are all defined (n=13,358). Versions of the right panel that
exclude the overlap between PolyPhen training data and the ClinVar database or use a CADD model trained without PolyPhen
as a feature are shown in Supplementary Fig. 12. Area under the curve (AUC) values are provided in the figure legend for each
of the scores used.
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Figure 4.
Ranking of pathogenic ClinVar variants among the variants identified by whole genome sequencing of eleven human
individuals from diverse populations. Left panel: Cumulative distributions of the ranks of 9,831 pathogenic ClinVar variants
when “spiked in” to each of 11 personal genomes. For example, C-scores of ~30% of ClinVar variants rank in the top 0.1% of
all variants within a personal genome, and most rank in the top 1%. About 25% of pathogenic ClinVar SNVs are not scored by
PolyPhen/SIFT because of missing values or its restriction to missense variation; note also that ranks for PolyPhen/SIFT are
computed among missense variants only and are therefore derived from far fewer total variants (see a plot restricted to missense
variation in Supplementary Fig. 16). Right panel: A QQ-plot of the C-scores of the SNVs identified from the eleven individuals
and pathogenic ClinVar SNVs. For a given scaled C-score observed in an individual, the fraction of that individual’s variants
with a C-score at least that large was computed (y-axis). The C-score corresponding to this quantile of the distribution of all
possible variants is displayed on the x-axis. High C-scores are underrepresented compared to the set of all possible variants. In
contrast, known disease-causal variants from ClinVar have large C-scores relative to the set of all possible variants. This fact
can be exploited to prioritize causal variants identified from whole genome sequencing of individual genomes (left panel and
Supplementary Tables 10–11).
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Figure 5.
C-scores for GWAS SNPs are higher than nearby control SNPs and dependent on study sample size. The average scaled C-score
(y-axis) is plotted for each category of SNP, as indicated by color, relative to the sample sizes of the association studies in which
the SNPs were identified (x-axis). Sample size bins are log2-scaled and mutually exclusive; for example, the bin labeled “1024”
represents all SNPs from studies with between 512 and 1024 samples. Error bars are ±1 standard errors of the mean (SEM).
Shaded rectangles represent the overall, i.e. across all sample sizes, scaled C-score means ±1 SEM for each category as
indicated by the color.
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