Abstract
The rate of single-nucleotide polymorphism varies substantially across the human genome and fundamentally influences evolution and incidence of genetic disease. Previous studies have only considered the immediately flanking nucleotides around a polymorphic site—the site's trinucleotide sequence context—to study polymorphism levels across the genome. Moreover, the impact of larger sequence contexts has not been fully clarified, even though context substantially influences rates of polymorphism. Using a new statistical framework and data from the 1000 Genomes Project, we demonstrate that a heptanucleotide context explains >81% of variability in substitution probabilities, highlighting new mutation-promoting motifs at ApT dinucleotide, CAAT and TACG sequences. Our approach also identifies previously undocumented variability in C-to-T substitutions at CpG sites, which is not immediately explained by differential methylation intensity. Using our model, we present informative substitution intolerance scores for genes and a new intolerance score for amino acids, and we demonstrate clinical use of the model in neuropsychiatric diseases.
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Acknowledgements
We thank C. Brown, M. Bucan, P. Babb, K. Siewert, K. Johnson, S. Bumgarner and two anonymous reviewers for helpful comments on the manuscript. B.F.V. is grateful for support of the work from the Alfred P. Sloan Foundation (BR2012-087), the American Heart Association (13SDG14330006), the W.W. Smith Charitable Trust (H1201) and the US National Institutes of Health/National Institute of Diabetes and Digestive and Kidney Disorders (R01DK101478).
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V.A. and B.F.V. conceived and designed the experiments, developed the model, performed the statistical analysis, developed and contributed analysis tools, and wrote the manuscript. B.F.V. supervised the research.
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Integrated supplementary information
Supplementary Figure 1 Illustration of the intuition supporting our substitution probability model.
(a) Defining the non-Bayesian probability and Bayesian posterior probability of nucleotide substitution for a 7-mer context. Here we use the example CTACGAT, where position 4 is the polymorphic site and the three nucleotides located 5′ and 3′ constitute the remainder of that site’s local 7-mer sequence context. We count (i) the number of occurrences of that 7-mer context found in the reference genome and (ii) the number of times we observe a polymorphic substitution at position 4. The example shown here is a C-to-T substitution. To generate the posterior probabilities, we sum the observed counts of occurrences and substitutions with a count obtained from the modeled prior. We apply this mathematics to all 7-mer sequence contexts for all substitution classes and then merge the reverse-complementary pairs (the A-to-C class was merged with the T-to-G class, etc.). This results in a total of 24,576 parameters, each representing a unique 7-mer sequence context. (b) Illustration showing how the same 7-mer sequence context on different codon frames leads to different types of amino acid change. Depicted are three cases where a C-to-T substitution that occurs in the sequence context CTA[C/T]GAT at either position 1, 2 or 3 of a codon results in a synonymous, nonsynonymous or nonsense change in amino acid identity.
Supplementary Figure 2 Scatter plot of nucleotide substitution probabilities for each 7-mer sequence context, inferred from 1000 Genomes and HapMap variants.
The substitution probabilities in both cases are strongly correlated with each other (R2 = 0.91, P << 10−100).
Supplementary Figure 3 Genome-wide nucleotide substitution probabilities are correlated across different human populations.
(a) The nucleotide substitution probabilities estimated from the 1-mer model for three human population groups (African, European and Asian) obtained from the 1000 Genomes Project. (b) The nucleotide substitution probabilities estimated from the 7-mer context in the same three populations. Because the x axis for this plot represents 24,576 sequence contexts, it was not practical to list them individually as was done in a. The contexts are represented graphically, sorted from lowest to highest nucleotide substitution probability, as observed in the African group. Data for the European and Asian groups were then represented according to the order obtained for the African group, to make comparison possible across the populations for any given sequence context.
Supplementary Figure 5 C-to-T substitution probabilities and methylation patterns.
Probabilities of C-to-T substitutions are shown for the following sequence contexts: CpG Me−, CpG 7-mer contexts that were unmethylated in all sperm samples; CpG Me+, CpG 7-mer contexts that were methylated in all sperm samples. ***P << 10−100.
Supplementary Figure 6 Correlation between average methylation intensity and probability of C-to-T substitution in the CpG 7-mer context.
(a) Scatterplot of average methylation intensity in brain samples against substitution probability at each 7-mer CpG context. (b) Scatterplot of average methylation intensity in oocyte samples against substitution probability at each 7-mer CpG context. (c) Scatterplot of average methylation intensity in blood samples against substitution probability at each 7-mer CpG context. (d) Scatterplot of average methylation intensity in blastocyst samples against substitution probability at each 7-mer CpG context. In all cases, the substitution probability is moderately correlated (R2 ~0.3) with methylation intensity at each 7-mer CpG sequence context.
Supplementary Figure 7 Substitution probabilities at 7-mer CpG sequence contexts and the distance of the contexts from genes.
Box-and-whisker plot of the distances between sequence contexts that contains a CpG site (C at polymorphic position 4, fixed G at position 5) and the gene nearest to that context found in the human reference genome. LOW plots the distances from sequence contexts identified in the bottom 1% smallest substitution probabilities in the C-to-T substitution class (n = 10). ALL represents the distances from all sequence contexts containing a CpG (n = 1,024). HIGH represents the distances from sequence contexts in the top 1% smallest substitution probabilities from the C-to-T substitution class (n = 10). Each distribution is significantly different from the others (pairwise P << 10−100 by Wilcoxon rank-sum test).
Supplementary Figure 8 Methylation intensity values in various sequence contexts containing a CpG site.
Box-and-whisker plot of methylation intensity values in various sequence contexts containing a CpG site. Methylation intensity represents the average intensity values across all sperm samples. Poly-CpG represents sequence contexts that segregate additional CpG dinucleotides beyond the CpG site at positions 4 and 5 (note that a 7-mer sequence context with a CpG site can segregate up to two additional CpG dinucleotides). Each distribution is significantly different from the others (pairwise P < 10−5 by Wilcoxon rank-sum test).
Supplementary Figure 9 Nucleotide substitution probabilities and recombination rate.
Scatterplot of nucleotide substitution probabilities inferred from only 1000 Genomes regions with a high recombination rate (>3 cM/Mb in the YRI population) and separately from regions with a low recombination rate (<0.05 cM/Mb in the YRI population) for each change in a 7-mer sequence context. The substitution probabilities in both cases are strongly correlated with each other (R2 = 0.97, P << 10−100).
Supplementary Figure 10 Human substitution probabilities are strongly correlated with human-chimpanzee and human-macaque divergence rates.
(a) Scatterplot of nucleotide substitution probabilities against nucleotide divergence rates between human and chimpanzee at each 7-mer sequence context. (b) Scatterplot of nucleotide substitution probabilities against nucleotide divergence rates between human and macaque at each 7-mer sequence context. In both cases, the substitution probabilities and divergence rates are strongly correlated with each other (R2 = 0.96, P << 10−100).
Supplementary Figure 11 Substitution probabilities across the variant frequency spectrum.
Scatterplot of nucleotide substitution probabilities inferred from only 1000 Genomes low to high frequency variants (MAF ≥1%) and separately from rare variants (singletons and doubletons only) for each change in a 7-mer sequence context. The substitution probabilities in both cases are strongly correlated with each other (R2 = 0.98, P << 10−100).
Supplementary Figure 12 Nucleotide substitution probabilities in the coding genome.
Posterior probabilities of nucleotide substitution for each type of amino acid substitution in the coding genome, estimated using the 7-mer coding context model. Sequences contexts are further stratified by color to indicate presence of a CpG (C at the polymorphic position 4 and G at position 5, for C-to-A, C-to-G and C-to-T substitution classes = CpG+; otherwise, CpG−) and where evidence of substitution was only observed in the intergenic region. The inset shows a magnified view specifically of the distribution for nonsense substitutions.
Supplementary Figure 13 Violin plot for trends in amino acid replacement types across different amino acids.
(a) Note that the mean probability is different for glycine and tyrosine substitutions, although the expected trend holds (synonymous > missense > nonsense). (b) Some amino acid substitutions deviate from this expected trend owing to the CpG context in the coding genome.
Supplementary Figure 14 The 7-mer context model improves power to detect pathogenic variants.
Log10 ratios of substitution probabilities for the 3-mer model with codon context for coding sequences matched to noncoding sequences for each type of amino acid replacement. We consider all variants from the 1000 Genomes Project (African, yellow) or the Human Gene Mutation Database (HGMD; orange). Larger values indicate fewer substitutions in the coding genome than expected from matched noncoding sequences (intolerance), consistent with selective constraint acting on these replacements. **P < 10−53; NS, not significant by Wilcoxon rank-sum test.
Supplementary Figure 16 Comparison and correlation of various gene score measures.
(a,b) Comparison of our presented gene score (Aggarwala) built from the 1000 Genomes African group using the coding 7-mer model with the scores presented by Petrovski et al. (a) and Samocha et al. (b). Note that in a, all HGNC gene IDs could not be mapped to Ensembl 75 genes, and in b only a subset of gene scores were publicly available.
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Aggarwala, V., Voight, B. An expanded sequence context model broadly explains variability in polymorphism levels across the human genome. Nat Genet 48, 349–355 (2016). https://doi.org/10.1038/ng.3511
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DOI: https://doi.org/10.1038/ng.3511
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