Editors' ChoiceGenetics

Making Sense of Missense Mutations

See allHide authors and affiliations

Science Translational Medicine  03 Apr 2013:
Vol. 5, Issue 179, pp. 179ec57
DOI: 10.1126/scitranslmed.3006205

In 1974, Takeo Maruyama predicted that deleterious variants in the genome are on average younger than neutral variants. Such a theoretic prediction makes sense because deleterious variants will be removed by selection, and so, those that are still found in the population are more likely to have arisen recently. Thus, if a neutral variant and a deleterious variant both have minor allele frequencies of 0.1%, on average the deleterious variant will have arisen more recently than the neutral variant.

To test this theory, Kiezun and colleagues used both simulated and real DNA sequence data drawn from the Genome of the Netherlands project. The simulations demonstrated that the theoretical basis is sound; with perfect knowledge of the true level of selection against a variant, the model showed that deleterious variants, even mildly deleterious ones, are in fact evolutionarily more recent. The real data also showed this pattern. The authors compared the estimated age of synonymous mutations (those that preserve the amino acid sequence of proteins) with those of missense mutations (those that change the amino acid sequence of proteins). On average, missense mutations were estimated to be more recent than synonymous mutations, which is consistent with the higher chance of deleterious mutations being younger.

One of the current key challenges for human genetics is the interpretation of variants identified via sequencing. This work demonstrates that evaluation of the age of a variant may help to identify that subset of genetic variation that has an impact on human disease. In particular, such methods may be powerful for finding variants that are negatively selected against in the population.

A. Kiezun et al., Deleterious alleles in the human genome are on average younger than neutral alleles of the same frequency. PLoS Genet. 9, e1003301 (2013). [Abstract]

Stay Connected to Science Translational Medicine

Navigate This Article