Bayesian latent trait modeling of migraine symptom data

Chen, Carla Chia Ming, Keith, Jonathan M., Nyholt, Dale R., Martin, Nicholas , & Mengersen, Kerrie (2009) Bayesian latent trait modeling of migraine symptom data. Human Genetics, 126(2), pp. 277-288.

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Definition of disease phenotype is a necessary preliminary to research into genetic causes of a complex disease. Clinical diagnosis of migraine is currently based on diagnostic criteria developed by the International Headache Society. Previously, we examined the natural clustering of these diagnostic symptoms using latent class analysis (LCA) and found that a four-class model was preferred. However, the classes can be ordered such that all symptoms progressively intensify, suggesting that a single continuous variable representing disease severity may provide a better model. Here, we compare two models: item response theory and LCA, each constructed within a Bayesian context. A deviance information criterion is used to assess model fit. We phenotyped our population sample using these models, estimated heritability and conducted genome-wide linkage analysis using Merlin-qtl. LCA with four classes was again preferred. After transformation, phenotypic trait values derived from both models are highly correlated (correlation = 0.99) and consequently results from subsequent genetic analyses were similar. Heritability was estimated at 0.37, while multipoint linkage analysis produced genome-wide significant linkage to chromosome 7q31-q33 and suggestive linkage to chromosomes 1 and 2. We argue that such continuous measures are a powerful tool for identifying genes contributing to migraine susceptibility.

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6 citations in Scopus
5 citations in Web of Science®
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ID Code: 30091
Item Type: Journal Article
Refereed: Yes
Keywords: Bayesian, Modeling, Migraine
DOI: 10.1007/s00439-009-0671-4
ISSN: 0340-6717
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Current > Institutes > Institute of Health and Biomedical Innovation
Past > Schools > Mathematical Sciences
Copyright Owner: Copyright 2009 Springer Verlag
Deposited On: 31 Jan 2010 22:52
Last Modified: 20 May 2015 00:10

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