title: Statistical properties of Teng and Risch's sibship type tests for detecting an association between disease and a candidate allele.
abstract: Risch and Teng [Genome Res 1998;8:1273-1288] and Teng and Risch [Genome Res 1999;9:234-241] proposed a class of transmission/disequilibrium test-like statistical tests based on the difference between the estimated allele frequencies in the affected and control populations. They evaluated the power of a variety of family-based and nonfamily-based designs for detecting an association between a candidate allele and disease. Because they were concerned with diseases with low penetrances, their power calculations assumed that unaffected individuals can be treated as a random sample from the population. They predicted that this assumption rendered their sample size calculations slightly conservative. We generalize their partial ascertainment conditioning by including the status of the unaffected sibs in the calculations of the distribution and power of the statistic used to compare the allele frequency in affected offspring to the estimated frequency in the parents, based on sibships with genotyped affected and unaffected sibs. Sample size formulas for our full ascertainment methods are presented. The sample sizes for our procedure are compared to those of Teng and Risch. The numerical results and simulations indicate that the simplifying assumption used in Teng and Risch can produce both conservative and anticonservative results. The magnitude of the difference between the sample sizes needed by their partial ascertainment approximation and the full ascertainment is small in the circumstances they focused on but can be appreciable in others, especially when the baseline penetrances are moderate. Two other statistics, using different estimators for the variance of the basic statistic comparing the allele frequencies in the affected and unaffected sibs are introduced. One of them incorporates an estimate of the null variance obtained from an auxiliary sample and appears to noticeably decrease the sample sizes required to achieve a prespecified power.
authors.affiliation: Department of Statistics, The Biostatistics Center, George Washington University, Rockville, MD 20852, USA. zli@biostat.bse.gwu.edu
authors.forename: Z
authors.initials: Z
authors.lastname: Li
authors.forename: M H
authors.initials: MH
authors.lastname: Gail
authors.forename: D
authors.initials: D
authors.lastname: Pee
authors.forename: J L
authors.initials: JL
authors.lastname: Gastwirth
mesh_terms: D016022:Case-Control Studies; D002874:Chromosome Mapping; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D030342:Genetic Diseases, Inborn; D006801:Humans; D008957:Models, Genetic; D012107:Research Design