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The American Journal of Human Genetics
Article
License: Elsevier Non-Commercial
Data sources: UnpayWall
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The American Journal of Human Genetics
Article . 2000
License: Elsevier Non-Commercial
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The American Journal of Human Genetics
Article . 2000 . Peer-reviewed
License: Elsevier Non-Commercial
Data sources: Crossref
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DNA Pooling in Mutation Detection with Reference to Sequence Analysis

Authors: Amos, Christopher I.; Frazier, Marsha L.; Wang, Wenfu;

DNA Pooling in Mutation Detection with Reference to Sequence Analysis

Abstract

We discuss pooling methods of mutation detection for identifying rare mutations. We provide mathematical formulae for obtaining the optimal pool size as a function of the mutation frequency in the study population and the specificity of the test. The optimal pool size depends strongly on the specificity of the test. With a test that has 99% specificity, pooling can reduce the number of tests that need to be performed by 80%, whereas, with a test with 95% specificity, pooling reduces the number of samples that must be tested by only 50%. We used the software PHRED to call mutations after sequencing of pooled samples with known STK11 mutations. We found that, when the area under the curve for the less prominent peak was used to call mutations, we were able to pool pairs of samples and correctly identify mutations. Pooling of three samples did not lead to an adequately specific test for the basic automated allele-calling procedures that we used. We discuss methods by which the specificity may be improved to permit pooling of three or more samples when testing for mutations by sequencing.

Related Organizations
Keywords

DNA Mutational Analysis, Study design, DNA, Protein Serine-Threonine Kinases, Sensitivity and Specificity, Specimen Handling, Automation, AMP-Activated Protein Kinase Kinases, Gene Frequency, Sample Size, Mutation, Genetics, Humans, Genetics(clinical), False Positive Reactions, DNA pooling, False Negative Reactions, Alleles, Software

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    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).
    46
    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.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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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!
46
Top 10%
Top 10%
Top 10%
hybrid