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Covariates in linkage analysis
Author(s) -
Rice John P.,
Rochberg Nan,
Neuman Rosalind J.,
Saccone Nancy L.,
Liu KuangYu,
Zhang Xu,
Culverhouse Robert
Publication year - 1999
Publication title -
genetic epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.301
H-Index - 98
eISSN - 1098-2272
pISSN - 0741-0395
DOI - 10.1002/gepi.13701707113
Subject(s) - linkage (software) , covariate , biology , genetics , statistics , mathematics , gene
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