A Bayesian approach to disease gene location using allelic association.
A Bayesian approach to analysing data from family-based association studies is developed. This permits direct assessment of the range of possible values of model parameters, such as the recombination frequency and allelic associations, in the light of the data. In addition, sophisticated comparisons of different models may be handled easily, even when such models are not nested. The methodology is developed in such a way as to allow separate inferences to be made about linkage and association by including theta, the recombination fraction between the marker and disease susceptibility locus under study, explicitly in the model. The method is illustrated by application to a previously published data set. The data analysis raises some interesting issues, notably with regard to the weight of evidence necessary to convince us of linkage between a candidate locus and disease.
Denham
Michael C
MC
School of Applied Statistics, The University of Reading, PO Box 240, Earley Gate, Reading RG6 6FN, UK. M.C.Denham@rdg.ac.uk
Whittaker
John C
JC
D000483:Alleles; D000704:Analysis of Variance; D001499:Bayes Theorem; D002648:Child; D003198:Computer Simulation; D016001:Confidence Intervals; D003922:Diabetes Mellitus, Type 1 / Q000235:genetics; D008040:Genetic Linkage; D020022:Genetic Predisposition to Disease; D006801:Humans; D016013:Likelihood Functions; D008957:Models, Genetic; D016008:Statistical Distributions