title: Two-locus developments of the weighted pairwise correlation method for linkage analysis.
abstract: Different studies of complex traits assumed to be influenced by two unliked loci found that two-locus linkage analysis is more powerful than the classical one-locus strategy. The Weighted Pairwise Correlation (WPC) approach is a nonparametric method for linkage analysis that has the advantage to analyze any kind of phenotypes and to consider extended pairs of relatives. In this report, we propose different two-locus extensions of the WPC method based on an additive or a multiplicative effect of two unlinked marker loci on the phenotype. Both methods and their corresponding statistics are easily derived from the classical WPC approach. Compared to the additive model, the multiplicative approach, which can be understood as a statistical interaction effect of the two markers, does not need to specify any additional parameter and allows one to test both the global effect of the two markers (T(AB)test) and the effect of one marker, e.g., B, taking into account the effect of the other, A (T(AB/A) test). When compared to classical one-locus tests by means of simulations, two-locus tests have comparable 0.05 type I error and are more powerful. In particular, tests based on the multiplicative approach appear to be quite interesting in addition to single locus tests to detect the combined role of two markers (T(AB)), or to investigate the role of a marker taking into account a known linked marker (T(AB/A)), especially when these markers have complex effects on the phenotype (e.g., statistical interaction).
authors.affiliation: INSERM U. 436, Mathematical and Statistical Modeling in Biology and Medicine, Hôpital Pitié-Salpêtrière, Paris, France. azj@biomath.jussieu.fr
authors.forename: A
authors.initials: A
authors.lastname: Zinn-Justin
authors.forename: L
authors.initials: L
authors.lastname: Abel
mesh_terms: D000483:Alleles; D008040:Genetic Linkage; D005819:Genetic Markers / Q000235:genetics; D005838:Genotype; D006801:Humans; D008957:Models, Genetic
chemical_list: D005819:Genetic Markers