Testing for association between disease and linked marker loci: a log-linear-model analysis.
One approach frequently used for identifying genetic factors involved in the process of a complex disease is the comparison of patients and controls for a number of genetic markers near a candidate gene. The analysis of such association studies raises some specific problems because of the fact that genotypic and not gametic data are generally available. We present a log-linear-model analysis providing a valid method for analyzing such studies. When studying the association of disease with one marker locus, the log-linear model allows one to test for the difference between allelic frequencies among affected and unaffected individuals, Hardy-Weinberg (H-W) equilibrium in both groups, and interaction between the association of alleles at the marker locus and disease. This interaction provides information about the dominance of the disease susceptibility locus, with dominance defined using the epidemiological notion of odds ratio. The degree of dominance measured at the marker locus depends on the strength of linkage disequilibrium between the marker locus and the disease locus. When studying the association of disease with several linked markers, the model becomes rapidly complex and uninterpretable unless it is assumed that affected and unaffected populations are in H-W equilibrium at each locus. This hypothesis must be tested before going ahead in the analysis. If it is not rejected, the log-linear model offers a stepwise method of identification of the parameters causing the difference between populations. This model can be extended to any number of loci, alleles, or populations.
Tiret
L
L
Institut National de la Santé et de la Recherche Médicale (INSERM), Unité 258, Hôpital Broussais, Paris, France.
Amouyel
P
P
Rakotovao
R
R
Cambien
F
F
Ducimetière
P
P
D000483:Alleles; D004194:Disease / Q000209:etiology*; D008040:Genetic Linkage; D005819:Genetic Markers / Q000235:genetics*; D005838:Genotype; D006801:Humans; D016014:Linear Models
D005819:Genetic Markers