title: Powerful allele sharing statistics for nonparametric linkage analysis.
abstract: Nonparametric linkage analysis is widely used to map susceptibility genes for complex diseases. This paper introduces six nonparametric statistics for measuring marker allele sharing among the affected members of a pedigree. We compare the power of these new statistics and three previous statistics to detect linkage with Mendelian diseases having recessive, additive, and dominant modes of inheritance. The nine statistics represent all possible combinations of three different IBD scoring functions and three different schemes for sampling genes among affecteds. Our results strongly suggest that the statistic T(rec)(blocks) is best for recessive traits, while the two statistics T(kin)(pairs) and T(all)(kin) vie for best for an additive trait. The best statistic for a dominant trait is less clear. The statistics T(kin)(pairs) and T(all)(kin) are equally promising for small sibships, but in extended pedigrees the statistics T(dom)(blocks) and T(dom)(pairs) appear best. For a complex trait, we advocate computing several of these statistics.
authors.affiliation: Section on Biostatistics, Department of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, NC 27157-1063, USA. elange@wfubmc.edu
authors.forename: Ethan M
authors.initials: EM
authors.lastname: Lange
authors.forename: Kenneth
authors.initials: K
authors.lastname: Lange
mesh_terms: D000483:Alleles; D001943:Breast Neoplasms / Q000235:genetics; D003198:Computer Simulation; D005260:Female; D019398:Genes, BRCA1; D005799:Genes, Dominant; D005808:Genes, Recessive; D008040:Genetic Linkage; D005823:Genetics; D005838:Genotype; D006720:Homozygote; D006801:Humans; D008297:Male; D008540:Meiosis; D008957:Models, Genetic; D015233:Models, Statistical; D010375:Pedigree