Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí y học Molecular Biology cung cấp cho các bạn kiến thức về ngành sinh học đề tài: Association of repeatedly measured intermediate risk factors for complex diseases with high dimensional SNP data. | Waaijenborg and Zwinderman Algorithms for Molecular Biology 2010 5 17 http content 5 1 17 AMD ALGORITHMS FOR ID MOLECULAR BIOLOGY RESEARCH Open Access Association of repeatedly measured intermediate risk factors for complex diseases with high dimensional SNP data Sandra Waaijenborg and Aeilko H Zwinderman 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. Background 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 .
