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To analyze incomplete families, the following statistical tests can be used: LRAT-a simple likelihood-based association test, TRANSMIT, SIBASSOC/STDT, and RCTDT. We compared these four tests, for the diallelic case, on simulated data sets. The comparisons focused on the power to detect linkage and association when different familial structures, resistance to population stratification, resistance to misclassification of the disease status of the healthy sib, and the effect of nonpaternity were considered. The simulations lead to the following conclusions. The type I errors of TRANSMIT, SIBASSOC/STDT, and RCTDT were not affected by population stratification. LRAT showed bias under strong population stratification. High nonpaternity rates can lead to inflated type I errors, highlighting the importance of identification of half sibs. Under different homogeneous models, the power of TRANSMIT was very similar to that of LRAT, and, similarly, no difference in power was observed between SIBASSOC/STDT and RCTDT. Under various recessive and additive models, TRANSMIT was slightly more powerful than SIBASSOC/STDT when monoparental families with one affected and one unaffected sib were analyzed. Under various dominant models, SIBASSOC/STDT was slightly more powerful than TRANSMIT. Misclassification of the disease status of healthy sibs, as well as the discarding of incomplete families, resulted in a consistent loss of power.

Original publication

DOI

10.1086/302992

Type

Journal article

Journal

Am J Hum Genet

Publication Date

07/2000

Volume

67

Pages

120 - 132

Keywords

Adult, Alleles, Bias, Child, Preschool, Chromosome Mapping, Computer Simulation, Female, Gene Frequency, Genes, Dominant, Genes, Recessive, Genetic Diseases, Inborn, Genetic Linkage, Haplotypes, Humans, Likelihood Functions, Linkage Disequilibrium, Male, Models, Genetic, Nuclear Family, Parents, Research Design, Software