A Test Statistic to Detect Errors in Sib-Pair Relationships

Several authors have proposed algorithms to detect Mendelian errors in human genetic linkage data. Most currently available methods use likelihood-based methods on multiplex family data to identify typing or pedigree errors. These algorithms cannot be applied in many sib-pair collections, because of...

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Bibliographic Details
Published in:American journal of human genetics 1998-01, Vol.62 (1), p.181-188
Main Authors: Ehm, Margaret Gelder, Wagner, Michael
Format: Article
Language:English
Subjects:
Algorithms
Biological and medical sciences
Female
General aspects. Genetic counseling
Genetic Diseases, Inborn - genetics
Genetic Linkage
Genetic linkage analysis
Genetic Markers
Genotype
Humans
Identity/identical by state
Male
Medical genetics
Medical sciences
Models, Statistical
Nuclear Family
Pedigree errors
Probability
Sibling pairs
Statistical genetics
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Summary:Several authors have proposed algorithms to detect Mendelian errors in human genetic linkage data. Most currently available methods use likelihood-based methods on multiplex family data to identify typing or pedigree errors. These algorithms cannot be applied in many sib-pair collections, because of lack of parental-genotype information. Nonetheless, misspecifying the relationships between individuals has serious consequences for sib-pair linkage studies: false relationships bias the statistics designed to identify linkage with disease phenotypes. To test the hypothesis that two individuals are sibs, we propose a test statistic based on the summation, over a large number of genetic markers, of the number of alleles shared identical by state by a pair of individuals, for each marker. The test statistic has an approximately normal distribution under the null hypothesis, and extreme negative values correspond to nonsib pairs. Power and significance studies show that the test statistic calculated by use of 50 unlinked markers has 96% power to detect half-sibs and has 100% power to detect unrelated individuals as not full-sib pairs, with a 5% false-positive rate. Furthermore, extreme positive values of the test statistic identify sibs as MZ twins.
ISSN:0002-9297
1537-6605
DOI:10.1086/301668