Research

Association of repeatedly measured intermediate risk factors for complex diseases with high dimensional SNP data

Sandra Waaijenborg and Aeilko H Zwinderman

Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Academic Medical Center, University of Amsterdam, Meibergdreef 9, 1100 DD Amsterdam, the Netherlands

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Algorithms for Molecular Biology 2010, 5:17doi:10.1186/1748-7188-5-17

Published:	11 February 2010

Abstract

Background

The causes of complex diseases are difficult to grasp since many different factors play a role in their onset. To find a common genetic background, many of the existing studies divide their population into controls and cases; a classification that is likely to cause heterogeneity within the two groups. Rather than dividing the study population into cases and controls, it is better to identify the phenotype of a complex disease by a set of intermediate risk factors. But these risk factors often vary over time and are therefore repeatedly measured.

Results

We introduce a method to associate multiple repeatedly measured intermediate risk factors with a high dimensional set of single nucleotide polymorphisms (SNPs). Via a two-step approach, we summarized the time courses of each individual and, secondly apply these to penalized nonlinear canonical correlation analysis to obtain sparse results.

Conclusions

Application of this method to two datasets which study the genetic background of cardiovascular diseases, show that compared to progression over time, mainly the constant levels in time are associated with sets of SNPs.