Bayesian latent trait modeling of migraine symptom data

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.

Linkage and heritability analysis of migraine symptom groupings: A comparison of three different clustering methods on twin data

Migraine is a painful disorder for which the etiology remains obscure. Diagnosis is largely based on International Headache Society criteria. However, no feature occurs in all patients who meet these criteria, and no single symptom is required for diagnosis. Consequently, this definition may not accurately reflect the phenotypic heterogeneity or genetic basis of the disorder. Such phenotypic uncertainty is typical for complex genetic disorders and has encouraged interest in multivariate statistical methods for classifying disease phenotypes. We applied three popular statistical phenotyping methods—latent class analysis, grade of membership and grade of membership “fuzzy” clustering (Fanny)—to migraine symptom data, and compared heritability and genome-wide linkage results obtained using each approach. Our results demonstrate that different methodologies produce different clustering structures and non-negligible differences in subsequent analyses. We therefore urge caution in the use of any single approach and suggest that multiple phenotyping methods be used.

Industrial Relations: Proceedings from the 1999 Australasian Drama Studies Association Conference

These papers were presented at “Industrial Relations”, the Australasian Drama Studies Association conference hosted by Theatre & Teaching Studies in the Academy of the Arts, Queensland University of Technology, from the 5th to the 9th of July, 1999. Conference delegates included scholars and artists from across the tertiary education and professional theatre sectors, including, of course, many individuals who work across and between both those worlds. More than a hundred delegates from Australia, New Zealand, England, Belgium and Canada attended the week’s events, which included: • Over sixty conference papers covering a variety of topics from project reports to academy/industry partnerships, theatre history, audience reception studies, health & safety, cultural policy, performance theory, theatre technology and more; • Performances ranging from drama to dance, music and cabaret; • Workshops, panel discussions, forums and interviews; • Keynote addresses from Wesley Enoch, Josette Feral and Keith Johnstone; and • A special “Links with Industry” day, which included the launch of ADSA’s “Links with Industry” brochure, an interview between Mark Radvan and David Williamson, and a panel session featuring Jules Holledge, Zane Trow, Katharine Brisbane, John Kotzas, Gay McAuley and David Watt.