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

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.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.Among the examples of complex diseases, several of the major (lethal) diseases in the western world can be found, including cancer, cardiovascular diseases and diabetes. Increasing our understanding of the underlying genetic background is an important step that can contribute in the development of early detection and treatment of such diseases. While many of the existing studies have divided their study population into controls and cases, this classification is likely to cause heterogeneity within the two groups. This heterogeneity is caused by the complexity of gene regulation, as well as many extra- and intracellular factors; the same disease can be caused by (a combination of) different pathogenetic pathways, this is referred to as phenogenetic equivalence. Due to this heterogeneity, the genetic markers responsible for, or involved in the onset and progression of the disease are difficult to identify [1]. Moreover, the risk of misclassification is increased if the time of onset of the disease varies.In order to overcome these problems, rather than dividing the study population into cases and controls, it is preferable to identify the phenotype of a complex disease by a set of intermediate risk factors. Because of the high diversity of pathogenetic causes that can lead to a complex disease, such intermediate risk factors are likely to have a much stronger relationship with the measured genetic markers. Intermediate