|Title||Investigating non-linear associations in high dimensional ‘omics data|
|Presenter||Nicola Armstrong (Murdoch University)|
|Session||Biostatistics and Bioinformatics|
In ‘omics studies, the trait or variable of interest is often either binary (e.g. cancer vs normal), or quantitative (e.g. height). Associations between the trait and, for instance, gene expression measurements, are then uncovered through use of either logistic or linear regression. The predominance of these two methods is partly due to computational issues, including the time required to run an analysis. However, they also place restrictions on the type of trait that can be considered: often a categorical classification is collapsed to two categories only, or a nominal scale is considered to be continuous in order to enable a fast analysis. Additionally, for quantitative traits, it may be limiting to assume that any association is in fact linear. Here, we investigate the use of mutual information (MI) to simultaneously uncover associations both between and within SNP and gene expression data and report the results. We use data on the Sydney Memory and Ageing Study (MAS) (n=521). For these participants, SNP genotyping was undertaken using the Affymetrix SNP 6.0 array and gene expression measured using the Illumina HT-12 array. We focus particularly on several traits that can be coded in different ways, such as alcohol consumption and smoking status (e.g. never/ever; current/past; or frequency) and investigate the impact on the associations identified.
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