Fixed-bin analysis for statistical evaluation of continuous distributions of allelic data from VNTR loci, for use in forensic comparisons
The detection of DNA polymorphisms by RFLP analysis is having a major impact on identity testing in forensic science. At present, this approach is the best effort a forensic scientist can make to exclude an individual who has been falsely associated with an evidentiary sample found at a crime scene....
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Published in | American journal of human genetics Vol. 48; no. 5; pp. 841 - 855 |
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Main Authors | , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
Chicago, IL
University of Chicago Press
01.05.1991
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Subjects | |
Online Access | Get full text |
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Summary: | The detection of DNA polymorphisms by RFLP analysis is having a major impact on identity testing in forensic science. At present, this approach is the best effort a forensic scientist can make to exclude an individual who has been falsely associated with an evidentiary sample found at a crime scene. When an analysis fails to exclude a suspect as a potential contributor of an evidentiary sample, a means should be provided to assess suitable weight to the putative match. Most important, the statistical analysis should not place undue weight on a genetic profile derived from an unknown sample that is attributed to an accused individual. The method must allow for limitations in conventional agarose-submarine-gel electrophoresis and Southern blotting procedure, limited sample population data, possible subpopulation differences, and potential sampling error. A conservative statistical method was developed based on arbitrarily defined fixed bins. This approach permits classification of continuous allelic data, provides for a simple and portable data-base system, and is unlikely to underestimate the frequency of occurrence of a set of alleles. This will help ensure that undue weight is not placed on a sample attributed to an accused individual. |
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Bibliography: | ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23 ObjectType-Article-1 ObjectType-Feature-2 |
ISSN: | 0002-9297 1537-6605 |