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Genetics
Article . 2003 . Peer-reviewed
License: OUP Standard Publication Reuse
Data sources: Crossref
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Genetics
Article
Data sources: UnpayWall
Genetics
Article . 2004
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Estimating Mutation Rate: How to Count Mutations?

Authors: Yun-Xin, Fu; Haying, Huai;

Estimating Mutation Rate: How to Count Mutations?

Abstract

Abstract Mutation rate is an essential parameter in genetic research. Counting the number of mutant individuals provides information for a direct estimate of mutation rate. However, mutant individuals in the same family can share the same mutations due to premeiotic mutation events, so that the number of mutant individuals can be significantly larger than the number of mutation events observed. Since mutation rate is more closely related to the number of mutation events, whether one should count only independent mutation events or the number of mutants remains controversial. We show in this article that counting mutant individuals is a correct approach for estimating mutation rate, while counting only mutation events will result in underestimation. We also derived the variance of the mutation-rate estimate, which allows us to examine a number of important issues about the design of such experiments. The general strategy of such an experiment should be to sample as many families as possible and not to sample much more offspring per family than the reciprocal of the pairwise correlation coefficient within each family. To obtain a reasonably accurate estimate of mutation rate, the number of sampled families needs to be in the same or higher order of magnitude as the reciprocal of the mutation rate.

Keywords

Models, Statistical, Models, Genetic, Multigene Family, Sample Size, Mutation, Humans

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    influence
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
18
Top 10%
Top 10%
Average
hybrid