title: Removing the sampling restrictions from family-based tests of association for a quantitative-trait locus.
abstract: One strategy for localization of a quantitative-trait locus (QTL) is to test whether the distribution of a quantitative trait depends on the number of copies of a specific genetic-marker allele that an individual possesses. This approach tests for association between alleles at the marker and the QTL, and it assumes that association is a consequence of the marker being physically close to the QTL. However, problems can occur when data are not from a homogeneous population, since associations can arise irrespective of a genetic marker being in physical proximity to the QTL-that is, no information is gained regarding localization. Methods to address this problem have recently been proposed. These proposed methods use family data for indirect stratification of a population, thereby removing the effect of associations that are due to unknown population substructure. They are, however, restricted in terms of the number of children per family that can be used in the analysis. Here we introduce tests that can be used on family data with parent and child genotypes, with child genotypes only, or with a combination of these types of families, without size restrictions. Furthermore, equations that allow one to determine the sample size needed to achieve desired power are derived. By means of simulation, we demonstrate that the existing tests have an elevated false-positive rate when the size restrictions are not followed and that a good deal of information is lost as a result of adherence to the size restrictions. Finally, we introduce permutation procedures that are recommended for small samples but that can also be used for extensions of the tests to multiallelic markers and to the simultaneous use of more than one marker.
authors.affiliation: Department of Biostatistics, University of Washington, Seattle, WA 98195-7232, USA. steph@biostat.washington.edu
authors.forename: S A
authors.initials: SA
authors.lastname: Monks
authors.forename: N L
authors.initials: NL
authors.lastname: Kaplan
mesh_terms: D000483:Alleles; D002874:Chromosome Mapping / Q000379:methods* / Q000706:statistics & numerical data*; D003198:Computer Simulation; D005189:False Positive Reactions; D005260:Female; D018628:Gene Dosage; D008040:Genetic Linkage / Q000235:genetics*; D005819:Genetic Markers / Q000235:genetics; D005838:Genotype; D006801:Humans; D008297:Male; D008433:Mathematics; D008957:Models, Genetic; D009679:Nuclear Family; D019655:Quantitative Trait, Heritable; D015203:Reproducibility of Results; D018401:Sample Size; D018709:Statistics, Nonparametric
chemical_list: D005819:Genetic Markers