Very few create a new phenotype. Mutations can be inherited and therefore passed on from one individual to another. If a mutation causes a new phenotype that makes an organisms better suited to a particular environment, it can lead to rapid change in the characteristics of the individuals in that species. For example, if a mutation led to brighter feather colouring in birds, the brighter feathers may allow those individuals with the mutation to reproduce more frequently, because they are seen as being more attractive and a desirable mate.
What causes a mutation? Mutations can be caused by high-energy sources such as radiation or by chemicals in the environment.
They can also appear spontaneously during the replication of DNA. Mutations generally fall into two types: point mutations and chromosomal aberrations. In point mutations, one base pair is changed. The human genome, for example, contains over 3. Mistakes, although surprisingly rare, do happen.
About one in every 10 10 10,,, base pair is changed. The most common type of mistake is a point substitution. More uncommon is the failure to copy one of the bases deletion , the making of two copies for a single base point duplication or the addition of a new base or even several bases insertion.
Chromosomal aberrations are larger-scale mutations that can occur during meiosis in unequal crossing over events, slippage during DNA recombination or due to the activities of transposable events. Genes and even whole chromosomes can be substituted, duplicated, or deleted due to these errors Figure 1.
Point substitutions are in red, and the yellow box with dashes indicates a deletion of 12 bases. Mutations can have a range of effects. They can often be harmful. Others have little or no detrimental effect. And sometimes, although very rarely, the change in DNA sequence may even turn out to be beneficial to the organism.
A mutation that occurs in body cells that are not passed along to subsequent generations is a somatic mutation. A mutation that occurs in a gamete or in a cell that gives rise to gametes are special because they impact the next generation and may not affect the adult at all. Such changes are called germ-line mutations because they occur in a cell used in reproduction germ cell , giving the change a chance to become more numerous over time. If the mutation has a deleterious affect on the phenotype of the offspring, the mutation is referred to as a genetic disorder.
Alternately, if the mutation has a positive affect on the fitness of the offspring, it is called an adaptation. Thus, all mutations that affect the fitness of future generations are agents of evolution.
Mutations are essential to evolution. Every genetic feature in every organism was, initially, the result of a mutation. The new genetic variant allele spreads via reproduction, and differential reproduction is a defining aspect of evolution.
It is easy to understand how a mutation that allows an organism to feed, grow or reproduce more effectively could cause the mutant allele to become more abundant over time. Even deleterious mutations can cause evolutionary change, especially in small populations, by removing individuals that might be carrying adaptive alleles at other genes. Hyla versicolor , is an example of mutation and its potential effects.
When an ancestral Hyla chrysocelis gray treefrog failed to sort its 24 chromosomes during meiosis, the result was H. We will call u the forward mutation rate.
Let A 2 mutate to A 1 at a frequency of v per generation. We will call v the backward mutation rate. Let the frequency of allele A 1 be p t at time t in the population, and let the frequency of allele A 2 be q t at time t. In every generation, a proportion of the A 1 alleles will mutate to A 2 alleles.
This proportion will be the forward mutation rate u times the frequency of allele A 1 p , up. In every generation, a proportion of the A 2 alleles will mutate to A 1 alleles.
This proportion will be the backward mutation rate v times the frequency of allele A 2 q. What happens to the frequency of the A 2 allele under these conditions? In every generation, the frequency of the A 2 allele q will increase by up due to forward mutation. At the same time, the frequency of A 2 will decrease by vq due to the backward mutation. The net change in A 2 will depend on the difference between the gain in A 2 and the loss in A 2.
These are typical forward and backward mutation rates. What is the new frequency of A 2 after one generation of mutation? We find that there is not much change in the frequency of A 2 after one generation of mutation.
In general, after t generations, the frequency of the A 1 wild-type allele will be. To calculate the number of generations required to change allele frequencies by a given amount, solve for t, which gives:.
We can use this formula to calculate the number of generations needed to change allele frequencies under the assumption that mutation is the only evolutionary force acting on a population.
To move the frequency of A 1 from 1. To move it from 0. In general, as the frequency of the wild-type allele decreases, it takes longer to accomplish the same amount of change. This simple model should convince you that mutation is a very weak force when it comes to changing allele frequencies. But mutation is very important for introducing new alleles new DNA sequences into populations. The limits of selection on mutation rate modifiers are especially acute in recombining organisms such as humans because a variant that increases the mutation rate can recombine away from deleterious mutations it generates elsewhere in the genome.
Given these theoretical predictions, we hypothesized that there may be substantial scope for modifiers of mutation rates to segregate within human populations, or between closely related species. Most triplet sequence contexts have mutation rates that vary across the evolutionary tree of mammals Hwang and Green, , but evolution of the mutation spectrum over short time scales has been less well described.
Weak natural mutators have recently been observed in yeast Bui et al. To investigate the mutational processes in different human populations, we classified each single nucleotide variants SNV in the Genomes Phase 3 data Auton et al.
We collapsed strand complements together to obtain 96 SNV categories. Since the detection of singletons may vary across samples, and because some singletons may result from cell-line or somatic mutations, we only considered variants seen in more than one copy.
We further excluded variants in annotated repeats since read mapping error rates may be higher in such regions and in PhyloP conserved regions to avoid selectively constrained regions Pollard et al. From the remaining sites, we calculated the distribution of derived SNVs carried by each Phase 3 individual. We used this as a proxy for the mutational input spectrum in the ancestors of each individual.
To explore global patterns of the mutation spectrum, we performed principal component analysis PCA in which each individual was characterized simply by the fraction of their derived alleles in each of the 96 SNV categories Figure 1A. PCA is commonly applied to individual-level genotypes, in which case the PCs are usually highly correlated with geography Novembre et al.
Although the triplet mutation spectrum is an extremely compressed summary statistic compared to typical genotype arrays, we found that it contains sufficient information to reliably classify individuals by continent of origin. The first principal component separated Africans from non-Africans, and the second separated Europeans from East Asians, with South Asians and admixed native Americans Figure 1—figure supplement 2 appearing intermediate between the two.
A Principal component analysis of individuals according to the fraction of derived alleles that each individual carries in each of 96 mutational types. B Heatmaps showing, for pairs of continental groups, the ratio of the proportions of SNVs in each of the 96 mutational types. Red colors indicate a greater fraction of a given mutation type in the first-listed group relative to the second.
See Figure 1—figure supplements 1 , 2 and 3 for heatmap comparisons between additional population pairs as well as a description of PCA loadings and the p - valuesof all mutation class enrichments. Figure 1—figure supplement 4 demonstrates that these patterns are unlikely to be driven by biased gene conversion.
In Figure 1—figure supplement 5 , we see that this mutation spectrum structure replicates on both strands of the transcribed genome as well as the non-transcribed portion of the genome. Figure 1—figure supplements 6 , 7 and 8 show that most of this structure replicates across multiple chromatin states and varies little with replication timing. This text file shows the number of SNPs in each of the 96 mutational categories that passed all filters in each Genomes continental group.
Remarkably, we found that the mutation spectrum differences among continental groups are composed of small shifts in the abundance of many different mutation types Figure 1B. One possible concern is that batch effects or other sequencing artifacts might contribute to differences in mutational spectra.
Therefore we replicated our analysis using genomes from the Simons Genome Diversity Project Mallick et al. The SGDP genomes were sequenced at high coverage, independently from Genomes, using an almost non-overlapping panel of samples. We found extremely strong agreement between the mutational shifts in the two data sets Figure 2. Each panel shows natural-log mutation spectrum ratios between a pair of continental groups, based on Genomes x-axis and SGDP y-axis data.
These heatmaps use the same labeling and color scale as in Figure 1. All Genomes ratios in this figure were estimated after projecting the Genomes site frequency spectrum down to the sample size of SGDP. The greatest discrepancies between Genomes and SGDP involve transversions at CpG sites, which are among the rarest mutational classes.
These discrepancies might result from data processing differences or random sampling variation, but might also reflect differences in the fine-scale ethnic composition of the two panels. Africa and to a lesser extent in South Asians. We calculated allele frequencies both in Genomes and in the larger UK10K genome panel Walter et al. As expected for a signal that is primarily European, we found particular enrichment of these mutations at low frequencies.
But surprisingly, the enrichment peaks around 0. This suggests that these four mutation types comprise the signature of a single mutational pulse that is no longer active. No other mutation types show such a pulse-like distribution in UK10K, although several types show evidence of monotonic rate change over time Figure 3—figure supplements 3 , 4 and 5. In the UK10K data, which has the largest sample size, the peak occurs at 0.
See Figure 3—figure supplements 3 , 4 and 5 for labeled allele frequency distributions of all 96 mutation types most represented here as unlabeled grey lines.
See Figure 3—figure supplement 6 for heatmap comparisons of the Genomes populations partitioned by allele frequency, which provide a different view of these patterns. Inset shows the observed and predicted frequency distributions of this mutation under the inferred model. This time-range is consistent with results showing this signal in a pair of prehistoric European samples from and years ago, respectively Mathieson and Reich, We hypothesize that this mutation pulse may have been caused by a mutator allele that drifted up in frequency starting 15, years ago, but that is now rare or absent from present day populations.
Although low frequency allele calls often contain a higher proportion of base calling errors than higher frequency allele calls do, it is not plausible that base-calling errors could be responsible for the pulse we have described. In the UK10K data, a minor allele present at 0. Encouraged by these results, we sought to find other signatures of recent mutation pulses. We generated heatmaps and PCA plots of mutation spectrum variation within each continental group, looking for fine-scale differences between closely related populations Figure 4 and Figure 4—figure supplement 1 through 6.
In some cases, mutational spectra differ even between very closely related populations. This signature appears to be present in only a handful of Chinese individuals, and no Kinh or Dai individuals. A PCA of east Asian samples from Genomes, based on the relative proportions of each of the 96 mutational types. See Figure 4—figure supplement 2 through 6 for other finescale population PCAs. B Heatmaps showing, for pairs of east Asian samples, the ratio of the proportions of SNVs in each of the 96 mutational types.
See Figure 4—figure supplement 1 for additional finescale heatmaps. C and D Relative enrichment of each mutational type in Japanese and Dai, respectively as a function of allele frequency.
Six mutation types that are enriched in JPT are indicated. This text file shows the number of SNPs in each of the 96 mutational categories that passed all filters in each finescale Genomes population. PCA reveals relatively little fine-scale structure within the mutational spectra of Europeans or South Asians Figure 4—figure supplements 5 and 6. However, Africans exhibit some substructure Figure 4—figure supplement 3 , with the Luhya exhibiting the most distinctive mutational spectrum.
Unexpectedly, a closer examination of PC loadings reveals that the Luhya outliers are enriched for the same mutational signature identified in the Japanese. The sharing of this signature may suggest either parallel increases of a shared mutation modifier, or a shared aspect of environment or life history that affects the mutation spectrum. Finally, given our finding of extensive fine-scale variation in mutational spectra between human populations, we hypothesized that mutational variation between species is likely to be even greater.
To compare the mutation spectra of the great apes in more detail, we obtained SNV data from the Great Ape Diversity Panel, which includes 78 whole genome sequences from six great ape species including human Prado-Martinez et al.
Overall, we find dramatic variation in mutational spectra among the great ape species Figure 5 and Figure 5—figure supplement 1. Boxes indicate labels in B. For additional comparisons see Figure 5—figure supplement 1. B Schematic phylogeny of the great apes highlighting notable changes in SNV abundance. As noted previously Moorjani et al.
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