title: Covariates in linkage analysis.
abstract: We apply a novel technique to detect significant covariates in linkage analysis using a logistic regression approach. An overall test of linkage is first performed to determine whether there is significant perturbation from the expected 50% sharing under the hypothesis of no linkage; if the overall test is significant, the importance of the individual covariate is assessed. In addition, association analyses were performed. These methods were applied to simulated data from multiple populations, and detected correct marker linkages and associations. No population heterogeneity was detected. These methods have the advantages of using all sib pairs and of providing a formal test for heterogeneity across populations.
authors.affiliation: Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, USA.
authors.forename: J P
authors.initials: JP
authors.lastname: Rice
authors.forename: N
authors.initials: N
authors.lastname: Rochberg
authors.forename: R J
authors.initials: RJ
authors.lastname: Neuman
authors.forename: N L
authors.initials: NL
authors.lastname: Saccone
authors.forename: K Y
authors.initials: KY
authors.lastname: Liu
authors.forename: X
authors.initials: X
authors.lastname: Zhang
authors.forename: R
authors.initials: R
authors.lastname: Culverhouse
mesh_terms: D008040:Genetic Linkage; D005820:Genetic Testing; D005838:Genotype; D006801:Humans; D015810:Linkage Disequilibrium; D016015:Logistic Models; D008957:Models, Genetic; D015233:Models, Statistical; D020412:Multifactorial Inheritance; D010551:Personality / Q000235:genetics*; D012307:Risk Factors