Related Experiment Video
Updated: Jun 24, 2026

Basics of Multivariate Analysis in Neuroimaging Data
Published on: July 24, 2010
Linkage and heritability analysis of migraine symptom groupings: a comparison of three different clustering methods
Carla C M Chen1, Kerrie L Mengersen, Jonathan M Keith
1School of Mathematical Sciences, Queensland University of Technology, QLD, Australia. carla.chen@qut.edu.au
Abstract:
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.
Related Concept Videos
Genome-wide Association Studies-GWAS
GWAS does not require the identification of the target gene involved in...
Multiple Comparison Tests
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
Behavioral Genetics and Its Designs
The primary methodologies used in behavior genetics include family studies, twin studies, and adoption studies, each providing unique...