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. 2022 Jul 30;39(8):msac169. doi: 10.1093/molbev/msac169

Is the Mutation Rate Lower in Genomic Regions of Stronger Selective Constraints?

Haoxuan Liu 1,2, Jianzhi Zhang 3,✉
Editor: Weiwei Zhai
PMCID: PMC9372563  PMID: 35907247

Abstract

A study of the plant Arabidopsis thaliana detected lower mutation rates in genomic regions where mutations are more likely to be deleterious, challenging the principle that mutagenesis is blind to its consequence. To examine the generality of this finding, we analyze large mutational data from baker's yeast and humans. The yeast data do not exhibit this trend, whereas the human data show an opposite trend that disappears upon the control of potential confounders. We find that the Arabidopsis study identified substantially more mutations than reported in the original data-generating studies and expected from Arabidopsis' mutation rate. These extra mutations are enriched in polynucleotide tracts and have relatively low sequencing qualities so are likely sequencing errors. Furthermore, the polynucleotide “mutations” can produce the purported mutational trend in Arabidopsis. Together, our results do not support lower mutagenesis of genomic regions of stronger selective constraints in the plant, fungal, and animal models examined.

Keywords: Arabidopsis, yeast, human, natural selection, mutation

A central tenet of evolutionary biology is that mutations occur randomly with respect to their consequences (Luria and Delbruck 1943; Lederberg and Lederberg 1952). This tenet, however, has been repeatedly challenged in the last decade, in a large part due to the availability of large genomic sequence data that allow testing its validity across the genome. For example, by analyzing synonymous polymorphisms in Escherichia coli, Martincorena et al. reported that genes subject to stronger purifying selection or with higher expressions mutate less often, and proposed that this mutational trend reflects adaptive risk management (Martincorena et al. 2012). However, synonymous polymorphisms may be nonneutral (Lind et al. 2010; Sharon et al. 2018; Shen et al. 2022), distorting the estimation of mutation rates. Indeed, a reanalysis based on mutations observed in a mutation accumulation (MA) experiment in the near absence of selection invalidated the polymorphism-based result (Chen and Zhang 2013). More importantly, it was pointed out that selection for modifiers that lower the mutation rate of a gene because of the deleterious effects of mutations in the gene is extremely weak; consequently, selective optimization of gene-specific mutation rates is theoretically untenable (Chen and Zhang 2013). Nevertheless, Xie et al. reported that human genes expressed relatively strongly in the testis mutate less often than those expressed relatively weakly, proposing that testis gene expression is regulated for the purpose of optimizing gene-specific germline mutation rates (Xia et al. 2020). A subsequent scrutiny, however, identified several flaws in the analysis and found the original observation unsupported (Liu and Zhang 2020).

More recently, based on exceptionally large data of de novo mutations in the model plant Arabidopsis thaliana, Monroe et al. reported that the mutation rate is 58% lower inside than immediately outside genes and is 37% lower in essential than nonessential genes (Monroe et al. 2022). They also observed a positive correlation between the mutation rate of a gene and the nonsynonymous to synonymous substitution rate ratio (dN/dS) of the gene across the genome. Because dN/dS is commonly regarded as a measure of the protein function-related selective constraint of a gene (lower the dN/dS, higher the constraint), the above finding suggests lower mutation rates for more strongly constrained genes. Monroe et al. detected several genomic and epigenomic features that are correlated with the local mutation rate, so proposed that the mutation rate is modulated via these features. Because a genomic or epigenomic feature could be associated with a sizable fraction of the genome, the proposed mechanism allows a modifier to affect the mutation rate of a large number of nucleotide sites so could in principle arise through natural selection (Monroe et al. 2022). For example, the authors estimated that a modifier that lowers the mutation rate of all coding sequences of a third of essential genes by 30% could be selectively fixed. Alternatively, the proposed mechanism could have arisen as a byproduct of some other biological processes (Zhang 2022). Regardless, if the reported mutational trend in Arabidopsis holds true in diverse evolutionary lineages, many evolutionary phenomena would require reinterpretation (Zhang 2022). In this study, we investigate the generality of the Arabidopsis-based finding. We show that the Arabidopsis result is found in neither baker's yeast nor humans. To understand the source of the Arabidopsis finding, we examine the mutational data analyzed by Monroe and colleagues. We show that the authors identified substantially more mutations than expected and that many of the mutations called are dubious, contributing to the unusual mutational trend observed.

 

The Arabidopsis Mutational Trend Is not Found in Yeast or Humans

To examine the generality of the Arabidopsis finding, we turned to other species with the largest data of de novo mutations—baker's yeast (Saccharomyces cerevisiae) and humans (Homo sapiens). We focused on the correlation between the mutation rate of a gene and its dN/dS because of its direct relevance to the mutagenesis and evolution of genes. In yeast, we acquired mutational data from three MA studies (Zhu et al. 2014; Sharp et al. 2018; Liu and Zhang 2019), including 427 MA lines and 3,296 single nucleotide variations (SNVs). The dN/dS ratios were computed by comparing orthologous gene sequences between S. cerevisiae and its sister species S. paradoxus (Goncalves et al. 2011). The human mutational data came from two studies with a total of 217,247 SNVs identified from the genome sequences of 3,450 parents-offspring trios (Jonsson et al. 2017; An et al. 2018), while the dN/dS ratios were estimated from human and chimpanzee orthologous gene sequences.

Following Monroe et al., we correlated dN/dS with the mutation rate across all genes in yeast and humans, respectively. No significant linear or rank correlation was found in yeast (table 1). Because this result could be due to a lack of statistical power owing to the relatively small number of mutations in the data, we grouped all genes into 50 equal-size bins according to their dN/dS and then computed the mutation rate for each bin. We found the mutation rate to be negatively correlated with dN/dS across the 50 bins, with marginal statistical significance (Spearman's rank correlation = –0.26, P = 0.065). Hence, if anything, the yeast data signal a trend that is opposite to that in Arabidopsis.

Table 1.

Relationships among Mutation Rate, dN/dS, and Six Other Factors in Yeast and Humans.

Species Partial correlation between dN/dS and mutation rate across genes Multiple linear regressiona
Controlled variables Rank correlation (P-value) Linear correlation (P-value) Independent variables Coefficient P-value
Yeast None –0.0063 (0.68) –0.0149 (0.33) d N/dS –5.63 × 10−5 0.43
Expression level –0.0145 (0.34) –0.0114 (0.46) Expression level 3.85 × 10−10 0.03
Gene length –0.0202 (0.19) –0.0149 (0.33) Gene length 5.26 × 10−9 0.51
Nucleosome occupancy –0.0137 (0.37) –0.0150 (0.33) Nucleosome occupancy –2.55 × 10−7 0.67
Replication timing –0.0082 (0.59) –0.0171 (0.26) Replication timing –8.59 × 10−5 2.6 × 10−3
GC content –0.0230 (0.13) –0.0138 (0.37) GC content 4.24 × 10−4 0.44
DNA curvature –0.0134 (0.38) –0.0169 (0.27) DNA curvature 5.64 × 10−5 0.41
All of the above –0.0167 (0.28) –0.0122 (0.43)
Humans None –0.0633 (4.6 × 10−13) –0.0126 (0.15) d N/dS 9.51 × 10−7 0.62
Expression level –0.0596 (9.2 × 10−12) –0.0125 (0.15) Expression level –3.53 × 10−9 0.38
Gene length –0.0432 (7.8 × 10−7) –0.0135 (0.12) Gene length 9.41 × 10−12 0.14
Nucleosome occupancy –0.0625 (8.7 × 10−13) 0.0021 (0.81) Nucleosome occupancy 8.27 × 10−7 3.9 × 10−6
Replication timing –0.0635 (3.9 × 10−13) –0.0113 (0.20) Replication timing –6.57 × 10−5 3.8 × 10−3
GC content –0.0675 (1.1 × 10−14) 0.00042 (0.96) GC content 3.14 × 10−5 0.18
DNA curvature –0.0620 (1.3 × 10−12) –0.0036 (0.68) DNA curvature –1.12 × 10−5 0.24
All of the above –0.0095 (0.28) 0.0044 (0.62)
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a

Mutation rate is the dependent variable in the multiple linear regression.

In humans, we observed a significant negative (rank) correlation between mutation rate and dN/dS (table 1), contrasting the Arabidopsis finding. Selection against mutagenesis should be stronger at genes with relatively high fractions of deleterious mutations than at those with relatively low fractions of deleterious mutations (Kimura 1967), so the observation in humans is not due to selection against mutagenesis. Because mutations have a higher average probability of fixation in less constrained than more constrained genes and because mutations of a highly mutagenic sequence tend to lower the local mutation rate, it has been predicted that the mutation rate should become lower in less constrained than more constrained genes (Oman et al. 2022). Our observation is consistent with this prediction. Notwithstanding, there are several factors known to influence or otherwise be correlated with the mutation rate (or dN/dS). If these factors are also correlated with dN/dS (or mutation rate), a spurious relationship may result between dN/dS and mutation rate. We thus computed partial correlations between dN/dS and mutation rate by individually or jointly controlling the following six factors—gene length (Lipman et al. 2002), DNA curvature (Duan et al. 2018), nucleosome occupancy (Li and Luscombe 2020), expression level (Park et al. 2012), GC content (Kiktev et al. 2018), and replication timing (Stamatoyannopoulos et al. 2009). In yeast, all partial correlations remain non-significant (table 1). In humans, the negative correlation between dN/dS and mutation rate becomes non-significant when the six factors are jointly controlled (table 1). We also performed the same analysis in A. thaliana using the mutational data from Monroe et al., finding that the positive correlation between dN/dS and mutation rate exists with or without controlling the potential confounders (supplementary table S1, Supplementary Material online).

We ran a multiple linear regression to simultaneously evaluate the potential influences of dN/dS and the above six factors on the mutation rate of a gene (table 1). The results show that the mutation rate is significantly dependent on replication timing and gene expression level in yeast and nucleosome occupancy and replication timing in humans, respectively. However, in neither species is the mutation rate significantly dependent on dN/dS. Therefore, the reported trend in Arabidopsis of lower mutation rates of genes with lower dN/dS ratios is absent in yeast and humans.

Monroe et al. Reported Much Higher Mutation Rates Than Did Previous Arabidopsis Studies

To investigate why the mutational trend reported by Monroe et al. is not replicated in yeast and humans, we looked into the mutational data analyzed by Monroe et al., which comprised three separate datasets: Dataset 1 was derived from a published MA experiment (Weng et al. 2019), Dataset 2 was based on MA specifically performed for the study (Monroe et al. 2022), and Dataset 3 was a published somatic mutation dataset (Wang et al. 2019) (table 2).

Table 2.

Differences in the Number of Mutations Identified Between Monroe et al.'s Study and Former Studies.

Dataset Sample size Mutation no. in Monroe et al. Mutation no. in original studies Sequencing depth References
1. Training dataset 107 MA lines × 25 generations 8,574 2,209 36× Weng et al. (2019)
2. New dataset 400 MA lines × 8.3 generations 359,133 NA ∼30× Monroe et al. (2022)
3. Somatic dataset 64 somatic samples from two plants 773,141 17 52.3× Wang et al. (2019)
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The original authors of Dataset 1 reported an SNV mutation rate of 6.95 × 10–9 per site per generation (Weng et al. 2019), similar to various previous estimates (Ossowski et al. 2010; Yang et al. 2015). They identified 2,209 mutations that included SNVs and insertions/deletions (indels) (Weng et al. 2019), but Monroe et al. reported 3.9 times that number by reanalyzing the published sequencing reads (table 2). The original authors screened for germline (homozygous) mutations, while Monroe et al. screened for both germline and somatic (heterozygous) mutations. However, the detectability of somatic mutations, each of which should occur in only one seedling, is expected to be minute, because 40 seedlings were pooled from each sample and sequenced to a depth of 36× but Monore et al. required at least three reads to call a mutation. Consequently, the 3.9-fold difference in data size is unlikely to be mainly caused by the inclusion of somatic mutations.

The expected sample size of Dataset 2 is 1.24 times that of Dataset 1 (Dataset 1: 107 lines × 25 generations/line = 2,675 generations; Dataset 2: 400 lines × 8.3 generations/line = 3,320 generations). Despite similar sequencing depths for the two datasets, the number of mutations reported by Monroe et al. for Dataset 2 is 41.9 times that reported by the same authors for Dataset 1 (table 2).

Dataset 3 came from 64 somatic samples taken from two A. thaliana plants (Wang et al. 2019). The original authors identified 17 mutations, but Monroe et al. reported 773,141 mutations by reanalyzing the published sequencing reads, a 45,479-fold difference (table 2). The mutation rate estimated by the original authors was approximately 4.35 × 10−9 per nucleotide per generation (Wang et al. 2019). By contrast, the corresponding rate from Monroe et al.'s reanalysis becomes 2.0 × 10−4 per nucleotide per generation, orders of magnitude higher than the mutation rate of any species ever known (Lynch et al. 2016).

Monroe et al.'s Mutational Data Include Many Potential Sequencing Errors at Polynucleotide Tracts

To find out the causes of the massive differences in the number of mutations called between Monroe et al.'s study and the previous studies, we analyzed the mutations in Dataset 1. Monroe et al. reported 8,574 mutations in this dataset, while the original authors reported 2,209 mutations (Weng et al. 2019). We found that 6,326 of the 8,574 mutations were not called in the original study. By reviewing the variant calling file (Weng et al. 2019), we found that, 57% of the 8,574 mutations appeared in more than one of the 107 MA lines, which is highly improbable for de novo mutations. One of the common errors in Illumina sequencing is caused by polynucleotides (Heydari et al. 2019). Indeed, we found that 51% of the 6,326 extra mutations reported by Monroe et al. are located within 20 nucleotides from a poly(A) or poly(T) tract (referred to as polynucleotides-associated mutations), while this fraction is 15% for the 2,209 originally reported mutations (P < 10−5, chi-squared test). Of the 8,574 mutations reported by Monroe et al., those that appeared in more than one line are more likely to be polynucleotides-associated than those that appeared in only one line (49% versus 33%, chi-square test, P < 10−5). In addition, most of the extra mutations are based on reads with multiple mismatches and low sequencing qualities (e.g., see supplementary fig. S1A, Supplementary Material online for some mutations found in five samples and supplementary fig. S1B, Supplementary Material online for some mutations found in 12 samples), while the mutations reported in the original study do not suffer from these problems (supplementary fig. S1C, Supplementary Material online). For instance, the median quality score is 67% lower for the extra mutations than the originally identified mutations. This comparison suggests that, for Dataset 1, many extra mutations identified by Monroe et al. are unreliable and are likely sequencing errors at polynucleotide tracts. Because Monroe et al.'s mutation rate estimates from Datasets 2 and 3 would be even greater than that from Dataset 1, it is expected that the mutations they identified from Datasets 2 and 3 suffer from the same problem if not additional problems.

False-Positive Mutations at Polynucleotides Can Create the Mutational Trend Observed by Monroe et al.

It has been reported that the dN/dS ratio of a gene is correlated with the AT content of the gene, because of the correlation of the gene expression level with both the AT content and dN/dS (Park et al. 2013; Zhang and Yang 2015). Indeed, we found that dN/dS is significantly positively correlated with the AT content in A. thaliana (fig. 1A) and that genes with higher densities of poly(A) + poly(T) have higher dN/dS ratios (fig. 1B). Therefore, if poly(A) and poly(T) cause over-detection of mutations, a spurious positive correlation could arise between dN/dS and mutation rate.

Fig. 1.

Fig. 1.

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Relationships among AT content, density of polynucleotides, and dN/dS in Arabidopsis thaliana. (A) Pearson's correlation (r) and Spearman's correlation (ρ) between the coding region AT content and dN/dS across genes. Each dot represents a gene. The blue line is the linear regression. (B) Correlations between the number of poly(A) + poly(T) tracts per 1000 nucleotides (i.e., density) in the coding sequence (CDS) of a gene and its dN/dS. Each dot represents a gene. The blue line is the linear regression. (C) AT content in coding, intron, and intergenic regions, respectively. Errors are too small to present. (D) Mean density of poly(A) + poly(T) tracts in coding, intron, and intergenic regions, respectively. (E) Number of mutations per site in coding, intron, and intergenic regions, respectively, calculated using the sum of the three datasets in Monroe et al. (2022). (F) Correlations between the no. of mutations per site generated by simulation and dN/dS across genes. Each dot represents a gene. In the simulation, 70% of mutations are randomly distributed at non-polynucleotide sites across the genome while the remaining mutations are randomly distributed among poly(A) and poly(T) tracts. A similar Pearson's correlation is observed upon log transformations of the data. (G) Numbers of simulated mutations per site in coding, intron, and intergenic regions, respectively. In (D), (E), and (G), error bars represent 95% confidence intervals predicted by Poisson distributions.

Monroe et al. (2022) also reported that the mutation rate of introns and that of intergenic regions exceed the mutation rate of coding regions in Arabidopsis. Interestingly, in A. thaliana, the AT content and the density of poly(A) + poly(T) are both the lowest in coding sequences, higher in introns, and highest in intergenic sequences (fig. 1C and D), which closely resembles the observation from Monroe et al.'s mutational data (fig. 1E), suggesting that Monroe et al.'s observation of mutation rate differences among the three groups of genomic regions could also be artifacts of false-positive detections of mutations around polynucleotides.

To test if over-detection of mutations around polynucleotides is sufficient to generate the mutational patterns reported by Monroe et al., we simulated the same number of mutations as the total number of mutations reported by Monroe et al. in the three datasets they analyzed (1,140,848). Because 30% of these mutations are associated with polynucleotides, we randomly distributed 70% of the mutations at non-polynucleotide sites across the genome and the remaining mutations at polynucleotides. From the simulated data, we found a positive correlation between the frequency of mutations and dN/dS across genes (fig. 1F), as well as a variation in mutation frequency among coding regions, introns, and intergenic regions (fig. 1G) that is qualitatively similar to Monroe et al.'s results (fig. 1E). These trends disappear when all mutations are randomly distributed across the genome (supplementary fig. S2, Supplementary Material online). These findings suggest that false detections of mutations around polynucleotides are sufficient to produce the purported mutational trend of Arabidopsis.

Conclusion

In summary, we showed that the trend of lower mutation rates in selectively more constrained genes that was recently reported in Arabidopsis is present in neither yeast nor humans. Additionally, no mutation rate difference was found between genic and intergenic regions in prior yeast and fruit fly MA studies (Sharp and Agrawal 2016; Melde et al. 2022). We discovered that Monroe et al. (2022) identified orders of magnitude more mutations than reported previously from the same data and expected from A. thaliana's known mutation rate. Many of the extra mutations reported by Monroe et al. appear to be sequencing errors associated with polynucleotides and these errors have the potential to create the purported unusual mutational trend. Together, our findings suggest that mutation rate is not lower in evolutionarily more constrained genomic regions in any of the plant, fungal, or animal models examined so far. While there is ample theoretical and empirical evidence that the genome-wide mutation rate is subject to natural selection (Kimura 1967; Sniegowski et al. 2000; Baer et al. 2007; Lynch 2011; Lynch et al. 2016; Liu and Zhang 2021), selection optimizing mutation rates of local genomic regions may simply be too weak (Chen and Zhang 2013) to have any effect in any species. While our analysis focused on SNV mutations, it is worth noting that yeast indel mutations occur less frequently in genic than intergenic regions, probably because, relative to intergenic regions, genic regions contain a lower density of repetitive sequences that are prone to indel mutations (Melde et al. 2022).

For various reasons, a genomic or epigenomic feature may be correlated with the selective constraint of a genomic region. For instance, because highly expressed genes tend to be under strong selective constraints (Zhang and Yang 2015), a genomic/epigenomic feature of high expression may be found more frequently in genes under stronger selective constraints. If this feature affects the mutation rate directly or indirectly, we might observe a correlation between the local mutation rate and selective constraint. However, given the theoretical understanding of the weakness of selection acting on local mutation rates, one should seriously consider the possibility that such potential correlations are not results of selection on local mutation rates but byproducts of some other processes (Zhang 2022). Our observation of a negative correlation between dN/dS and mutation rate across human genes is a testament to this possibility.

Materials and Methods

De novo mutations were retrieved from three yeast studies (Zhu et al. 2014; Sharp et al. 2018; Liu and Zhang 2019), two human studies (Jonsson et al. 2017; An et al. 2018), and three Arabidopsis studies (Wang et al. 2019; Weng et al. 2019; Monroe et al. 2022). The dN/dS ratio for each gene in yeast (between S. cerevisiae and S. paradoxus) and humans (between Homo sapiens and Pan troglodytes) were obtained from a previous study (Goncalves et al. 2011) and Ensembl (https://www.ensembl.org/Help/View?id=135), respectively. Gene expression levels in yeast were retrieved from a previous study (Chou et al. 2017), and those in human testis were retrieved from the GTEx project (The GTEx Consortium 2013). The dN/dS ratios (between A. thaliana and A. lyrata) and expression levels of A. thaliana genes were previously published (Monroe et al. 2022). Nucleosome occupancy information was acquired from the database NucMap (Zhao et al. 2019). Data on DNA replication timing was obtained from previous studies (Muller and Nieduszynski 2012; Concia et al. 2018; Pratto et al. 2021). DNA curvature was calculated following a previous study (Duan et al. 2018).

The aligned read files for the 107 Arabidopsis MA lines (Weng et al. 2019) were downloaded from NCBI Short Read Archive under the accession number SRP133100 and were visualized by IGV (Robinson et al. 2017). A polynucleotide tract is a run of the same seven or more nucleotides. A. thaliana has an AT-rich genome that comprises 165,937 instances of poly(A) or poly(T), but only 928 instances of poly(G) or poly(C). The number and location of polynucleotides were determined using a custom Perl script.

In table 1 and supplementary table S1, Supplementary Material online the mutation rate of a gene refers to the mutation rate in the coding region except for humans where the mutation rate of the entire genic region is computed because there were too few mutations in coding regions. Correspondingly, gene length refers to the coding sequence length except in the case of humans, where the total length of exons and introns is considered. Rank correlation, linear correlation, and multiple linear regression were performed in R.

Supplementary Material

msac169_Supplementary_Data
Click here for additional data file. (569.1KB, pdf)

Acknowledgments

We thank three anonymous reviewers for valuable comments. This work was supported by National Institutes of Health research grant R35GM139484 to J.Z.

Contributor Information

Haoxuan Liu, Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI 48109, USA; Evolutionary and Organismal Biology Research Center, School of Medicine, Zhejiang University, Hangzhou 310000, China.

Jianzhi Zhang, Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI 48109, USA.

Supplementary Material

Supplementary data are available at Molecular Biology and Evolution online.

Data Availability

All data used in this study are publicly available as described in Materials and Methods.

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Associated Data

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Supplementary Materials

msac169_Supplementary_Data
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Data Availability Statement

All data used in this study are publicly available as described in Materials and Methods.

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