Abstract
Complex human diseases commonly differ in their phenotypic characteristics, e.g., Crohn’s disease (CD) patients are heterogeneous with regard to disease location and disease extent. The genetic susceptibility to Crohn’s disease is widely acknowledged and has been demonstrated by identification of over 100 CD associated genetic loci. However, relating CD subphenotypes to disease susceptible loci has proven to be a difficult task. In this paper we discuss the use of cluster analysis on genetic markers to identify genetic-based subgroups while taking into account possible confounding by population stratification. We show that it is highly relevant to consider the confounding nature of population stratification in order to avoid that detected clusters are strongly related to population groups instead of disease-specific groups. Therefore, we explain the use of principal components to correct for population stratification while clustering affected individuals into genetic-based subgroups. The principal components are obtained using 30 ancestry informative markers (AIM), and the first two PCs are determined to discriminate between continental origins of the affected individuals. Genotypes on 51 CD associated single nucleotide polymorphisms (SNPs) are used to perform latent class analysis, hierarchical and Partitioning Around Medoids (PAM) cluster analysis within a sample of affected individuals with and without the use of principal components to adjust for population stratification. It is seen that without correction for population stratification clusters seem to be influenced by population stratification while with correction clusters are unrelated to continental origin of individuals.
Highlights
Many human diseases have a complex genetic architecture and differ in their expression of symptoms
In the following description we describe the application of latent class analysis and the use of more traditional cluster analysis methods on our available data set in separate sections
Cleynen et al [9] applied latent class analysis to molecularly reclassify Crohn’s disease (CD) patients based on a selection of 46 markers identified from GWA studies on CD and/or meta-analysis of these [4]
Summary
Many human diseases have a complex genetic architecture and differ in their expression of symptoms. Such phenotypic variability, i.e., variation in the phenotypic expression of one underlying disease, has important consequences for the treatment of affected individuals. Classification of inflammatory bowel disease (IBD) cases into similar groups of patients is highly relevant to determine the appropriate mode of therapy delivery and care intensity per patient group [1,2]. A meta-analysis of 15 genome-wide association studies (GWAS) and Immunochip data has recently identified 163 loci associated to IBD, of which 30 loci were uniquely associated to Crohn’s disease while 23 loci were ulcerative-colitis-specific [5]
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