title: A survey of affected-sibship statistics for nonparametric linkage analysis.
abstract: We have compared the power of a large number of allele-sharing statistics for "nonparametric" linkage analysis with affected sibships. Our rationale was that there is an extensive literature comparing statistics for sibling pairs but that there has not been much guidance on how to choose statistics for studies that include sibships of various sizes. We concentrated on statistics that can be described as assigning scores to each identity-by-descent-sharing configuration that a pedigree might take on (Whittemore and Halpern 1994). We considered sibships of sizes two through five, 27 different genetic models, and varying recombination fractions between the marker and the trait locus. We tried to identify statistics whose power was robust over a wide variety of models. We found that the statistic that is probably used most often in such studies-S(all)-performs quite well, although it is not necessarily the best. We also found several other statistics (such as the R criterion, S(robdom), and the Sobel-and-Lange statistic C) that perform well in most situations, a few (such as S(-#geno) and the Feingold-and-Siegmund version of S(pairs)) that have high power only in very special situations, and a few (such as S(-#geno), the N criterion, and the Sobel-and-Lange statistic B) that seem to have low power for the majority of the trait models. For the most part, the same statistics performed well for all sibship sizes. We also used our results to give some suggestions regarding how to weight sibships of different sizes, in forming an overall statistic.
authors.affiliation: Department of Human Genetics, University of Pittsburgh, 130 DeSoto Street, Pittsburgh, PA 15261, USA. hsengul@watson.hgen.pitt.edu
authors.forename: H
authors.initials: H
authors.lastname: Sengul
authors.forename: D E
authors.initials: DE
authors.lastname: Weeks
authors.forename: E
authors.initials: E
authors.lastname: Feingold
mesh_terms: D000483:Alleles; D002874:Chromosome Mapping / Q000379:methods* / Q000706:statistics & numerical data*; D005799:Genes, Dominant; D005808:Genes, Recessive; D006801:Humans; D016555:Matched-Pair Analysis; D008957:Models, Genetic; D009679:Nuclear Family; D010375:Pedigree; D019683:Penetrance; D011995:Recombination, Genetic / Q000235:genetics; D018401:Sample Size; D018709:Statistics, Nonparametric