title: A test statistic to detect errors in sib-pair relationships.
abstract: 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.
authors.affiliation: Bioinformatics Group, Glaxo Wellcome, Inc., Research Triangle Park, NC 27709, USA. mge37216@glaxowellcome.com
authors.forename: M
authors.initials: M
authors.lastname: Ehm
authors.forename: M
authors.initials: M
authors.lastname: Wagner
mesh_terms: D000465:Algorithms; D005260:Female; D030342:Genetic Diseases, Inborn / Q000235:genetics*; D008040:Genetic Linkage; D005819:Genetic Markers; D005838:Genotype; D006801:Humans; D008297:Male; D015233:Models, Statistical; D009679:Nuclear Family; D011336:Probability
chemical_list: D005819:Genetic Markers