A general framework for estimating the relative pathogenicity of human genetic variants

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 gene discovery^1–4. 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 metrics^8–10 are defined genome-wide but do not use functional information and are not allele-specific, while protein-based metrics^6,7 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.

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”), 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).

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).

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).

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 (10^{C-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).

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).

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).

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.

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 disorders^30–34 (ASD) and intellectual disability^35,36 (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 variants^1,2,38. We therefore examined the distribution of C-scores in the genomes of 11 individuals representing diverse populations^39,40, 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 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.

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).

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 annotations^11,41–44.

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.

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 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 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 UCSC^51,52; 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 score^40,55; 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.

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,…,X_n 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.

$$\begin{array}{l}0={\beta }_0+\sum _{i=1}^{166}{\beta }_i{X}_i+\sum _{i=1}^5\sum _{j=1}^5{\mathrm{\gamma }}_{ij}{1}_{\{i\text{th}\text{Ref}\text{category}\text{and}j\text{th}\text{Alt}\text{category}\}}\\ +\sum _{i=1}^{21}\sum _{j=1}^{21}{\mathrm{\delta }}_{ij}{1}_{\{i\text{th}\text{oAA}\text{category}\text{and}j\text{th}\text{nAA}\text{category}\}}\\ +\sum _{i=1}^{11}{\mathrm{\tau }}_i{W}_i+\sum _{i=1}^{17}\sum _{j\in D}{\mathrm{\alpha }}_{ij}{1}_{\{i\text{th}\text{Consequence}\text{category}\}}{X}_j\end{array}$$

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 from dbNSFP 2.0⁵⁹. (5) We combined high confidence de novo mutations from five family based autism exome sequencing studies^30–34, 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) studies^35,36, 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.