Decision letter: Mutation saturation for fitness effects at human CpG sites

Field: Biochemistry, Genetics and Molecular Biology (Life Sciences)

Article Figures and data Abstract Introduction Results Discussion Materials and methods Appendix 1 Data availability References Decision letter Author response Article and author information Metrics Abstract Whole exome sequences have now been collected for millions of humans, with the related goals of identifying pathogenic mutations in patients and establishing reference repositories of data from unaffected individuals. As a result, we are approaching an important limit, in which datasets are large enough that, in the absence of natural selection, every highly mutable site will have experienced at least one mutation in the genealogical history of the sample. Here, we focus on CpG sites that are methylated in the germline and experience mutations to T at an elevated rate of ~10-7 per site per generation; considering synonymous mutations in a sample of 390,000 individuals, ~ 99 % of such CpG sites harbor a C/T polymorphism. Methylated CpG sites provide a natural mutation saturation experiment for fitness effects: as we show, at current sample sizes, not seeing a non-synonymous polymorphism is indicative of strong selection against that mutation. We rely on this idea in order to directly identify a subset of CpG transitions that are likely to be highly deleterious, including ~27 % of possible loss-of-function mutations, and up to 20 % of possible missense mutations, depending on the type of functional site in which they occur. Unlike methylated CpGs, most mutation types, with rates on the order of 10-8 or 10-9, remain very far from saturation. We discuss what these findings imply for interpreting the potential clinical relevance of mutations from their presence or absence in reference databases and for inferences about the fitness effects of new mutations. Introduction A central goal of human genetics is to identify pathogenic mutations and predict how likely they are to cause disease. To this end, exome sequencing in cases and controls is often used to help identify variants with potentially large effects on disease risk (Rauch et al., 2012; Sanders et al., 2012; Need et al., 2012; Akbari et al., 2021). Even where this approach yields an enrichment of variants in cases, however, the specific subset of mutations that contributes to disease often remains unknown; similarly, in individual patients, sequencing habitually yields candidate mutations of which the significance is unclear (Richards et al., 2015; Harrison et al., 2021). Numerous scores have therefore been developed to help prioritize among candidate mutations, based on protein structure, functional annotations, evolutionary patterns, or other features (Cooper et al., 2005; Adzhubei et al., 2010; Pollard et al., 2010; McLaren et al., 2016; Ioannidis et al., 2016; Rentzsch et al., 2019). In particular, a common approach to pinpoint sites at which mutations are likely to be pathogenic is to examine whether they appear to be under purifying selection. For instance, comparisons of sequences across species have been widely used to identify highly conserved genomic regions maintained by selection over millions of years, presumably because of their functional importance (Cooper et al., 2005; Pollard et al., 2010; Boffelli et al., 2003; Siepel et al., 2005). The same general approach is also useful when applied within humans, where information about purifying selection is contained in whether or not a site is segregating a mutation and at what frequency (Sawyer and Hartl, 1992; Eyre-Walker and Keightley, 2007; Boyko et al., 2008; Williamson et al., 2005; Lek et al., 2016; Karczewski et al., 2020; Yi et al., 2010). For this application, however, the low diversity levels in the genome pose a major difficulty, as a site may be monomorphic simply by chance, that is when mutations at that site have no fitness consequences at all or, at the other extreme, because the mutations are embryonically lethal. In particular, because most sites are monomorphic in samples of hundreds or even thousands of humans, there is little information to distinguish sites under strong selection from those at which mutations are only weakly deleterious. With a view to capturing natural variation at a larger number of sites in the genome and identifying more mutations with large effects on disease risk, there have been extensive efforts to collate available exome sequences from hundreds of thousands of individuals (Lek et al., 2016; Karczewski et al., 2020; Dewey et al., 2016; Szustakowski, 2020; Van Hout et al., 2020; Taliun et al., 2021). These efforts were also motivated by the idea that public repositories composed of relatively healthy adults not ascertained for a specific severe disease can serve as reference datasets, such that seeing a variant of unknown function in these datasets is indicative of it being benign (Lek et al., 2016; Claussnitzer et al., 2020; Ghouse et al., 2018). The validity of that assumption remains to be evaluated, however, especially as the repositories grow in size. Beyond their utility in human genetics, these datasets provide an unprecedented opportunity to learn about the fitness effects of new mutations. Modeling the distribution of fitness effects (DFE) has a long history in population genetics (Sawyer and Hartl, 1992; Eyre-Walker and Keightley, 2007; Otto, 2000), but until recently, inferences were based on genetic variation in samples of at most a couple of thousand chromosomes (Eyre-Walker and Keightley, 2007; Boyko et al., 2008; Williamson et al., 2005; Eyre-Walker et al., 2006; Kim et al., 2017). As is well appreciated, the fitness effects at the few sites segregating at such sample sizes are a small and biased draw from the DFE and thus the inferred distribution of fitness effects is unlikely to recapitulate the true DFE in the genome. Moreover, for lack of sufficient information with which to distinguish weakly from strongly selected mutations, a number of approaches have relied on a specific and arbitrary parametric form for the distribution of fitness effects across sites. In that regard, not only do inferences based on small samples result in relatively noisy parameter estimates, the results can be misleading, especially about the fraction of sites under strong selection (Kim et al., 2017). Current samples in humans may allow for these limitations to start to be overcome. Motivated by these considerations, we focus on a class of mutations known to experience mutations an order of magnitude more frequently than other types of sites in the human genome: CpG sites that are methylated in the germline (Duncan and Miller, 1980; Nachman and Crowell, 2000; Kong et al., 2012; Figure 1—figure supplement 1). We use these sites as a test case for what can be learned about selection when neutral sites are saturated, i.e., have all experienced at least one mutation in the history of the sample, and draw out implications for the interpretation of mutations as pathogenic and for inferences about fitness effects. Results Mutation saturation at CpGs An attractive feature of methylated CpG (mCpG) sites is that a single mechanism, the spontaneous deamination of methyl-cytosine, is believed to underlie the uniquely high rate of C > T mutations at these sites (Duncan and Miller, 1980); thus, germline methylation at CpG sites is strongly predictive of their mutability (Kong et al., 2012; Jónsson et al., 2017; Gao et al., 2019; Figure 1—figure supplement 2). Here, we define ‘methylated’ CpG sites in exons as those that are methylated ≥65 % of the time in both testes and ovaries. For these ~1.1 million sites (of 1.8 million total CpG sites in sequenced exons), we calculate a mean haploid, autosomal C > T mutation rate of 1.17 × 10–7 per generation using de novo mutations (DNMs) in a sample of ~2900 sequenced parent-offspring trios (Materials and methods, Figure 1—figure supplements 1–2, Halldorsson et al., 2019). Although methylation levels are the dominant predictor of mutation rates at CpG sites, they are not the only influence. Notably, CpG transitions differ somewhat in their mutation rates based on their trinucleotide context (Figure 1—figure supplement 3a; Aggarwala and Voight, 2016); even so, they are consistently an order of magnitude higher than the genome average (Kong et al., 2012). Broader scale features, such as replication timing, have also been reported to shape mutation rates (Stamatoyannopoulos et al., 2009; Smith et al., 2018). Nonetheless, considering methylated CpGs inside and outside exons, which differ in a number of these features, there is no appreciable difference in average DNM rates (Fisher Exact Test (FET) p-value = 0.1, Figure 1—figure supplement 4a). Similarly, the rate at which two DNMs occur at the same site, a summary statistic that reflects the variance in mutation rates, is not significantly different for methylated CpGs inside versus outside exons (FET p-value = 0.35; Figure 1—figure supplement 4b). Thus, while there is some variation in mutability per site among methylated CpGs, it appears to be small relative to the mean mutation rate across all methylated CpGs. Considering all such CpG sites therefore, we ask what fraction are segregating at existing sample sizes. To this end, we collate polymorphism data made public by gnomAD (Karczewski et al., 2020), the UK Biobank (Szustakowski, 2020), and the DiscovEHR collaboration between the Regeneron Genetics Center and Geisinger Health System (Dewey et al., 2016) in order to ascertain whether both C and T alleles are present in a sample of ~390K individuals (Materials and methods). To focus on the subset of genic changes most likely to be neutrally-evolving, we consider the ~350,000 methylated CpG sites at which C > T mutations do not change the amino acid. At these sites, 94.7 % of all possible synonymous CpG transitions are observed in the gnomAD data alone, and 98.8 % in the combined sample including all three datasets (Figure 1). In other words, nearly every methylated CpG site where a mutation to T is putatively neutral has experienced at least one such mutation in the history of the sample of 390K individuals. Even in the least mutable CpG trinucleotide context, 98 % of putatively neutral sites are segregating in current samples (Figure 1—figure supplement 3b). These observations imply that in the absence of selection, almost every methylated CpG site would be segregating a T at current sample sizes--and further that not seeing a T provides strong evidence it was removed by selection. Figure 1 with 4 supplements see all Download asset Open asset Fraction of methylated CpG sites that are polymorphic for a transition, by sample size. The combined dataset encompasses three non-overlapping data sources: gnomAD (v2.1), the UK Biobank (UKB), and the DiscovEHR cohort. ‘European’ samples include the populations designated as ‘EUR’ in 1000 Genomes, ‘Non-Finnish European’ subsets of exome and whole genome datasets in gnomAD, as well as the UK Biobank and DiscovEHR, which have >90% samples labeled as of European ancestry. Testing a neutral model for individual sites The mutation saturation at methylated CpG sites provides a robust approach to identify individual sites that are not neutrally-evolving. One way to view it is in terms of a p-value: under a null model with no selection, from which we assume that synonymous sites are drawn, all but 1.2 % of neutral sites are segregating in a sample of 390K individuals. Therefore, if a given non-synonymous site, say, is invariant in a sample of ≥390K individuals, we can reject the neutral null model for this site at a significance level of 0.012. Similarly, we can ask about the probability that an invariant non-synonymous site is neutral, using a false discovery rate (FDR) approach: given that 1.2 % of neutral sites are invariant, whereas 7.4 % of non-synonymous sites are, the FDR is 1.2/7.4 = 16%. Thus, at current sample sizes, there is a substantial amount of information about whether individual CpG transitions are deleterious. By contrast, in a smaller sample with only 10 % of putatively neutral sites segregating, there is almost no information about selection in observing individual sites to be invariant (p ≤ 0.9). This approach implicitly assumes that synonymous and non-synonymous sites do not differ in their distributions of mutation rates and that their distributions of genealogical histories are also the same, i.e.,, that the two types of sites are subject to comparable effects of linked selection. While we cannot examine whether the distributions of mutation rates are identical for lack of data, we verify that the mean de novo mutation rates do not differ for synonymous sites and for various non-synonymous annotations (Figure 2a); we also check that the distributions of methylation levels (conditional on ≥65%), an important determinant of mutation rates, are similar for synonymous and non-synonymous sites (with a significant but small shift towards higher methylation and thus presumably higher mutation rates for non-synonymous sites; Figure 2—figure supplement 1). In turn, the standard assumption of similar distributions of genealogical histories seems sensible, given that the sites are interdigitated within genic regions (McDonald and Kreitman, 1991). Under these few and at least somewhat testable assumptions, the approach based on mutation saturation at methylated CpG sites then enables us to directly pinpoint individual sites that are not neutrally evolving. We note further that if synonymous sites are not all neutral and instead some fraction are under selection, the same idea would apply, but the null model would have to be modified accordingly. Figure 2 with 5 supplements see all Download asset Open asset Comparing de novo mutation rates and the fraction of segregating sites across annotations. (a) DNM rates for CpG transitions at highly methylated sites by annotation class, rescaled by the total DNM rate in exons. Fisher exact tests (FETs) of the proportion of sites with DNMs in each annotation compared to all other annotations yield p-values > 0.1 in all cases. (b) Fraction of highly methylated CpG sites that are segregating as a C/T polymorphism in an annotation class, relative to the fraction of synonymous sites segregating. Error bars are 95 % confidence intervals assuming the number of segregating sites is binomially distributed (FET p-values << 10–5 for comparisons of all annotations with synonymous sites). LOF variants are defined as stop-gained and splice donor/acceptor variants that do not fall near the end of the transcript, and meet the other criteria to be classified as ‘high-confidence’ loss-of-function in the gnomAD data (see Materials and Methods). (c) The amount of data for synonymous and missense changes involving highly methylated CpG transitions by the type of functional protein site. (d) The proportion of synonymous and missense segregating C/T polymorphisms in different classes of functional sites. Error bars are 95 % confidence intervals assuming the number of segregating sites is binomially distributed (FET p-values << 10–5 for comparisons of all missense annotations with synonymous sites; Materials and methods). All annotations are obtained using the canonical transcripts of protein coding genes (see Materials and methods). Comparing the fraction of segregating sites across annotations Under these same weak assumptions, it is also possible to compare the proportion of methylated CpG sites polymorphic for a transition across annotations. Here, we consider the fraction of sites segregating a transition in each annotation class in a sample of 780K chromosomes, rescaled by the fraction segregating at synonymous sites. All categories of missense, loss-of-function, and regulatory variants show a significant depletion in the fraction of segregating sites compared to synonymous variants (Figure 2b). The deficit for a given annotation is an indicator of the deleteriousness of de novo mutations in that annotation. Specifically, in our sample of 780K, the deficit for each annotation reflects sites for which we can reject neutrality at a significance level of 0.012. These data therefore suggest that there are ~27 % fewer loss-of-function variants than would be expected under neutrality; at invariant sites within this annotation, neutrality can be rejected at an FDR of only 4.4 % ( = 1.2/27). A 27 % deficit of loss-of-function variants is again seen if we match the sites to synonymous mutational opportunities with the same predicted level of linked selection, i.e., with similar genealogical histories (McVicker et al., 2009; Figure 2—figure supplement 2a). Supporting the widely used assumption that LOF mutations within a gene are equivalent (after filtering for those at the end of transcripts; Karczewski et al., 2020; Cassa et al., 2017), when we compare the set of CpG sites at which mutations are annotated as leading to protein-truncation in the first versus the second half of transcripts, approximately the same number are missing mutations relative to synonymous sites in both subsets (Figure 2—figure supplement 3; FET p-value = 0.9). By comparison, the fraction of missense mutations and splice region variants not observed in current samples is only about 5.3%, and the FDR 22.6 % ( = 1.2/5.3) (whether or not we match for the effects of linked selection; see Figure 2—figure supplement 2a). While LOF and missense annotation classes are most commonly used in determinations of variant pathogenicity, any two sets of methylated CpGs with similarly-distributed mutation rates can be ranked in this manner. As one example, we stratify missense mutations by the type of functional site in which they occur. For the subset of sites at which missense mutations may disrupt or alter binding, particularly DNA-binding, there is a ~ 12–20% deficit in segregating sites relative to what is seen at synonymous sites, in contrast, say, to the much smaller deficit at missense changes within trans-membrane regions (Figure 2c–d, Figure 2—figure supplement 4; Figure 2—figure supplement 2b). In other words, observing a DNA-binding missense site that is invariant provides stronger statistical evidence that it is deleterious than observing an invariant missense site with no additional functional information (e.g. the FDR is ~1/20 vs. ~1/5). We can also check that the fraction of sites segregating is inversely proportional to the predicted functional importance of the sites using CADD scores (Rentzsch et al., 2019), widely used measures of constraint that incorporate functional annotations and measures of conservation. Across deciles, mean de novo transition rates at methylated CpGs are similar (Figure 2—figure supplement 5a) and, as expected, the fraction of segregating sites decreases with increasing CADD scores (Figure 2—figure supplement 5b). We note, however, that mutation rates may not always be similar across comparison groups: considering all CpG sites in exons (i.e. not only highly methylated ones), for example, de novo mutation rates are much more variable across CADD deciles (Figure 2—figure supplement 5c). Consequently, the depletion of segregating sites no longer has a simple interpretation (Figure 2—figure supplement 5d), instead reflecting a combination of differences in mutation rates and fitness effects. By implication, while CADD scores are meant to isolate the effects of selection, they will in some cases classify sites that have high mutation rates as less constrained, and vice versa. What can be learned about other mutation types? Given that current exome samples are informative about selection on transitions at methylated CpGs, a natural question is to ask to what extent there is also information for less mutable types, with mutation rates on the order of 10–8 or 10–9 per site per generation. For sites with mutation rate on the order of 10–9, which is the case for the vast majority of non-CpGs, the fraction of possible synonymous sites that in a sample of 780K chromosomes is very for instance, it is 5 % for T > A mutations, which occur at an average rate of 1.2 × 10–9 (Figure 1—figure supplement and 27 % even for other C > T mutations, which occur at a rate of × 10–8 per site (Figure 1—figure supplement compared to % for C > T mutations at methylated CpGs (Figure For invariant sites of these less mutable types, there is little information with which to the to the neutral null in current this lack of in the p-value monomorphic sites would be ≤ for T > A sites and ≤ for C > T sites. Figure with 2 supplements see all Download asset Open asset Comparing the fraction of sites observed and expected to be segregating under by mutation type and sample size. (a) Fraction of possible synonymous C > T mutations at CpG sites methylated in the germline and at all other C sites, and the fraction of possible synonymous T > A mutations that are observed in a sample of given size. (b) Fraction of sites segregating in assuming a specific model and a given mutation rate (see Materials and methods). large samples have to be for other mutation types to saturation on the of the that individuals, i.e., the of the which to the number of over which mutations have at the site. For a mutation that at rate 1.17 × 10–7 per the average of the would have to be than million × for at least one such mutation to be expected at a site. synonymous CpG sites are to saturation when they experience mutations to T at this rate that this is in the given that more than one mutation has at a substantial fraction of sites (Karczewski et al., 2020; et al., the average of the the 390K individuals is expected to be about million from the probability of at least one mutation under a see Materials and methods). The that mutation types with rates on the order of 10–8 are far from saturation further that the average of the for these 390K individuals is than million These thus provide a of the of the genealogical history by these 390K individuals. To examine the between sample mutation rate and the amount of variation at a we neutral at a single site with the three different mutation rates under a variant of the model for population in and in which we set the population to 10 million for the While this model is an it observed diversity levels for synonymous mutations well (Figure with the under our of a sample of 780K chromosomes has a an average of million (Figure supplement first the of the is expected to much more than with the number of samples et al., 2012). increasing the number of samples by a of only the average (Figure Figure supplement thus, a site that at rate 10–9 per generation is expected to have experienced mutations in the genealogical history of a sample of and 0.1 mutations in a sample of 10 The is that saturation for mutation rates of 10–8 or 10–9 per site per generation may not be any time of our model are subject to the about the history and in about the population in humans (Figure supplement Moreover, for we model one or at most two when samples that individuals from more genetic have longer genealogical histories (Figure supplement see Figure and thus more most for the very large sample sizes the is a more model et al., 2012; et al., Nonetheless, the that less mutable types will remain very far from saturation in the In the absence of information about single sites for most mutational types in the it is possible to learn to a about selection using of sites. we a of synonymous sites with the same average mutation rate per as a single methylated then at least one site per is polymorphic in % of (see Figure supplement 2 for an with T > A mutations and as % of individual methylated CpG sites are segregating. Thus, if a of non-synonymous sites with the same average mutation rate is invariant, the p-value with the is that one or more sites in the is likely to be under selection. strong is the selection that to invariant methylated CpG saturation to identify a subset of sites that are not few assumptions, but provides no information about how strong selection is at those sites. To learn about the of selection with methylated CpG sites being a of strong are we a a distribution on and a mutation rate distribution across sites. Here, we assume a relatively on the selection from 10–7 to 1 and the = for autosomal mutations with fitness effects in we only to the parameter in et al., 2019), as well as a mutation rate of 1.2 × 10–7 per site per generation. We rely on the model for population in and as is standard (Sawyer and Hartl, 1992; Boyko et al., 2008; Williamson et al., 2005; Eyre-Walker et al., 2006; Kim et al., 2017; Cassa et al., 2017; et al., we also assume that is over even as the population changes Under these assumptions, we the distribution of at a site, given that the site is segregating with 10 or fewer of the T or segregating with more than 10 (Figure and Methods). These distributions are of the DFE at an individual site on seeing or of the T Figure 4 with 5 supplements see all Download asset Open asset the of selection with invariant and segregating sites. (a) and of for a C > T mutation at a methylated CpG site observed at or at various sample sizes. (b) (i.e. by of > for a C > T mutation at a methylated CpG site observed at or at various sample sizes. (c) of a methylated CpG site segregating a T in if the mutation has no fitness effects = and if it is deleterious (with a selection = or highly deleterious (with a selection = provide a natural way to the of statistical evidence that from the at a single site, we consider the that a mutation is subject to > i.e., is under strong selection (see Materials and methods). In small in which most sites are being monomorphic is with both neutrality and very strong selection (Figure and the are to reflecting the that the our (Figure 4b). In contrast, with larger sample sizes, in which putatively neutral CpG sites the distribution for invariant sites is highly is not segregating is likely strongly the than Notably, at current sample sizes of 390K individuals, there is some on the (Figure supplement but the of > at an invariant methylated CpG are Given our of the are (Figure which that most of the ~27 % of LOF mutations and % of missense mutations not seen in current samples are subject to this of selection. While the of selection to clinical is not selection on that order are likely to be of relevance to determinations of in clinical et al., 2017; et al., mutations with > × may be highly deleterious to some individuals that enough to but in their mutations classified as pathogenic in et al., or