Article
A Bayesian approach to disease gene location using allelic association

Authors: Denham M.C.¹; Whittaker J.C.²

Source: Biostatistics, Volume 4, Number 3, 1 July 2003 , pp. 399-409(11)

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Abstract:

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 thgr , 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.

Keywords: Allelic association; Markov chain Monte Carlo; Bayesian model choice

Document Type: Original article

Affiliations: 1: School of Applied Statistics, The University of Reading, PO Box 240, Earley Gate, Reading RG6 6FN, UK M.C.Denham@rdg.ac.uk 2: Department of Epidemiology and Public Health, Imperial College School of Medicine, St Mary's Campus, Norfolk Place, London W2 1PG, UK

Publication date: 2003-07-01

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Among the important scientific developments of the 20th century is the explosive growth in statistical reasoning and methods for application to studies of human health. Examples include developments in likelihood methods for inference, epidemiologic statistics, clinical trials, survival analysis, and statistical genetics. Substantive problems in public health and biomedical research have fueled the development of statistical methods, which in turn have improved our ability to draw valid inferences from data. The objective of Biostatistics is to advance statistical science and its application to problems of human health and disease, with the ultimate goal of advancing the public's health.