Covariates in linkage analysis

John P. Rice, Nan Rochberg, Rosalind J. Neuman, Nancy L. Saccone, Kuang Yu Liu, Xu Zhang, Robert Culverhouse

Research output: Contribution to journalArticle

29 Scopus citations

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.

Original languageEnglish
Pages (from-to)S691-S695
JournalGenetic Epidemiology
Volume17
Issue numberSUPPL. 1
StatePublished - Dec 10 1999

Keywords

  • Affected family based controls
  • Harm avoidance

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  • Cite this

    Rice, J. P., Rochberg, N., Neuman, R. J., Saccone, N. L., Liu, K. Y., Zhang, X., & Culverhouse, R. (1999). Covariates in linkage analysis. Genetic Epidemiology, 17(SUPPL. 1), S691-S695.