Cryptic Variation in the Human Mutation Rate

Hodgkinson, Alan; Ladoukakis, Emmanuel D.; Eyre‐Walker, Adam

doi:10.1371/journal.pbio.1000027

Cited by 109 publications

(147 citation statements)

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“…However, a recent study using air chromatography found ∼0.364% methylation at cytosines in S. cerevisiae (42). If methylated cytosines in yeast have similar 10×-50× elevations in mutation rate as in humans (43)(44)(45), 1/5-1/25 of all CCG and TCG sites would have to be methylated for the observed ∼2× overall increase in mutation rate at these sites. Taking 0.364% to be the methylation rate in S. cerevisiae and assuming all methylation takes place within CCG and TCG contexts where we found elevated mutation rates, which correspond to ∼3% of the analyzed sequences, this value leads to an estimate of ∼1/22 of CCG and TCG sites being methylated.…”

Section: Discussionmentioning

confidence: 95%

Precise estimates of mutation rate and spectrum in yeast

Zhu

Siegal

Hall

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

397

444

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Mutation is the ultimate source of genetic variation. The most direct and unbiased method of studying spontaneous mutations is via mutation accumulation (MA) lines. Until recently, MA experiments were limited by the cost of sequencing and thus provided us with small numbers of mutational events and therefore imprecise estimates of rates and patterns of mutation. We used whole-genome sequencing to identify nearly 1,000 spontaneous mutation events accumulated over ∼311,000 generations in 145 diploid MA lines of the budding yeast Saccharomyces cerevisiae. MA experiments are usually assumed to have negligible levels of selection, but even mild selection will remove strongly deleterious events. We take advantage of such patterns of selection and show that mutation classes such as indels and aneuploidies (especially monosomies) are proportionately much more likely to contribute mutations of large effect. We also provide conservative estimates of indel, aneuploidy, environment-dependent dominant lethal, and recessive lethal mutation rates. To our knowledge, for the first time in yeast MA data, we identified a sufficiently large number of single-nucleotide mutations to measure context-dependent mutation rates and were able to (i) confirm strong AT bias of mutation in yeast driven by high rate of mutations from C/G to T/A and (ii) detect a higher rate of mutation at C/G nucleotides in two specific contexts consistent with cytosine methylation in S. cerevisiae.neighbor-dependent mutation rate | strongly deleterious mutation S pontaneous mutations are the source of all genetic variation in nature. The rate of emergence of new mutations and the relative proportions of advantageous, neutral, and deleterious mutations are key determinants in how species evolve and adapt to new selective challenges. Unfortunately, our knowledge of the properties of spontaneous mutations remains incomplete primarily due to the difficulty of observing large enough numbers of mutational events in an unbiased way.Analyzing patterns of divergence in nonfunctional sequences is a statistically powerful method used to study relative rates of different mutation classes. This method is applicable to most organisms and now can generally be carried out on a genomewide scale. However, this approach relies crucially on the assumption that mutations in certain regions, such as pseudogenes or fourfold degenerate codon positions, are not affected by selection and are thus reliable approximations of true mutation rate. It is now becoming apparent that selection or selectionlike processes, such as biased gene conversion, are acting even at these sequences and can substantially bias the observed patterns (1-5).Studies focusing on mutations in reporter genes use a more restrictive method that can be applied only in model organisms. In some cases, such reporter genes can be placed genome-wide and thus provide estimates of genomic variation in mutation rates. However, this approach is limited by the inability to detect mutations without a visible phenotype and thus ...

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Section: Discussionmentioning

confidence: 95%

Precise estimates of mutation rate and spectrum in yeast

Zhu

Siegal

Hall

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

397

444

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“…Therefore, CpGs that have been lost are not causing the excess of triallelic sites. However, we have previously shown that the mutation rate varies across the human genome in a cryptic nature that is not associated with any specific context effect (Hodgkinson et al 2009). This was demonstrated by showing that there is an excess of sites with a SNP at the orthologous position in humans and chimpanzees (coincident SNPs).…”

Section: Excess Of Triallelic Sitesmentioning

confidence: 99%

“…To investigate the effects of cryptic variation in the mutation rate on the number of triallelic sites we used the method described in Hodgkinson et al (2009), but we considered only non-CpG human triallelic SNPs against chimpanzee nonCpG biallelic SNPs. We did not correct for the effects of adjacent nucleotides on the mutation rate since there are not enough data to estimate these for triallelic sites and the effects are small for biallelic sites (Hodgkinson et al 2009).…”

Section: à5mentioning

confidence: 99%

“…We did not correct for the effects of adjacent nucleotides on the mutation rate since there are not enough data to estimate these for triallelic sites and the effects are small for biallelic sites (Hodgkinson et al 2009). As such, the expected number of coincident SNPs is simply the total number of alignments divided by the number of positions in the alignment that were not part of a CpG dinucleotide.…”

Section: à5mentioning

confidence: 99%

“…However, other adjacent nucleotides also influence the mutation rate (Blake et al 1992;Zhao et al 2003;Hwang and Green 2004). Furthermore, we have recently shown that there is variation in the mutation rate that does not depend upon the identity of the adjacent nucleotides or any specific context (Hodgkinson et al 2009). …”

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Human Triallelic Sites: Evidence for a New Mutational Mechanism?

Hodgkinson

Eyre‐Walker

2010

Genetics

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Most SNPs in the human genome are biallelic; however, there are some sites that are triallelic. We show here that there are approximately twice as many triallelic sites as we would expect by chance. This excess does not appear to be caused by natural selection or mutational hotspots. Instead we propose that a new mutation can induce another mutation either within the same individual or subsequently during recombination. We provide evidence for this model by showing that the rarer two alleles at triallelic sites tend to cluster on phylogenetic trees of human haplotypes. However, we find no association between the density of triallelic sites and the rate of recombination, which leads us to suggest that triallelic sites might be generated by the simultaneous production of two new mutations within the same individual on the same genetic background. Under this model we estimate that simultaneous mutation contributes $3% of all distinct SNPs. We also show that there is a twofold excess of adjacent SNPs. Approximately half of these seem to be generated simultaneously since they have identical minor allele frequencies. We estimate that the mutation of adjacent nucleotides accounts for a little less than 1% of all SNPs.

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Mutation Rate of Non‐ Cp G DNA

Furano

Walser

2009

Encyclopedia of Life Sciences

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Base substitutions (mutations) change the informational content of deoxyribonucleic acid (DNA). Therefore, understanding the determinants of the base mutation rate is a fundamental problem in biology. Biochemically, mutations are a function of the fidelity of DNA replication, damage and repair. Each can be affected by nucleotide composition and context. However, various downstream factors determine the ultimate survival of these mutations in a population, including natural selection and chance (genetic drift). The result in mammals is two dramatically different mutation rates. The C of most CpG dinucleotides mutates approximately 10‐ to 50‐fold faster than Cs in other contexts or any other nucleotide (i.e. in non‐CpG sites). But, the non‐CpG mutation rate also varies between different genomic regions. Here we address the difference between CpG and non‐CpG mutation rates and the factors that are correlated with the variance in non‐CpG rates. A predominant one is CpG content. Key concepts: Evolutionary processes determine the extent to which the base substitutions (mutations), which result from biochemical errors inherent in the processes that replicate and maintain the integrity of the genome, contribute to the mutation rate experienced by a population. These biochemical mutations are a function of the fidelity of DNA replication, the efficiency with which replication errors and DNA damage are repaired and whether DNA repair processes themselves produce errors in undamaged DNA.

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Cryptic Variation in the Human Mutation Rate

Cited by 109 publications

References 35 publications

Precise estimates of mutation rate and spectrum in yeast

Precise estimates of mutation rate and spectrum in yeast

Human Triallelic Sites: Evidence for a New Mutational Mechanism?

Mutation Rate of Non‐ Cp G DNA

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