Search

Traditional genetic studies of single traits may be unable to detect the pleiotropic effects involved in complex diseases. To detect the correlation that exists between several phenotypes involved in the same biological process, we introduce an original methodology to analyze sets of correlated phenotypes involved in the coagulation cascade in genome-wide association studies. The methodology consists of a two-stage process. First, we define new phenotypic meta-variables (linear combinations of the original phenotypes), named metaphenotypes, by applying Independent Component Analysis for the multivariate analysis of correlated phenotypes (i.e. the levels of coagulation pathway–related proteins). The resulting metaphenotypes integrate the information regarding the underlying biological process (i.e. thrombus/clot formation). Secondly, we take advantage of a family based Genome Wide Association Study to identify genetic elements influencing these metaphenotypes and consequently thrombosis risk. Our study utilized data from the GAIT Project (Genetic Analysis of Idiopathic Thrombophilia). We obtained 15 metaphenotypes, which showed significant heritabilities, ranging from 0.2 to 0.7. These results indicate the importance of genetic factors in the variability of these traits. We found 4 metaphenotypes that showed significant associations with SNPs. The most relevant were those mapped in a region near the HRG, FETUB and KNG1 genes. Our results are provocative since they show that the KNG1 locus plays a central role as a genetic determinant of the entire coagulation pathway and thrombus/clot formation. Integrating data from multiple correlated measurements through metaphenotypes is a promising approach to elucidate the hidden genetic mechanisms underlying complex diseases., Postprint (published version)

The open source environment R is one of the most widely used software for statistical computing. It provides a variety of applications including statistical genetics. Most of the powerful tools for quantitative genetic analyses are stand-alone free programs developed by researchers in academia. SOLAR is one of the standard software programs to perform linkage and association mappings of the quantitative trait loci (QTLs) in pedigrees of arbitrary size and complexity. solarius allows the user to exploit the variance component methods implemented in SOLAR. It automates such routine operations as formatting pedigree and phenotype data. It parses also the model output and contains summary and plotting functions for exploration of the results. In addition, solarius enables parallel computing of the linkage and association analyses that makes the calculation of genome-wide scans more efficient., Postprint (author's final draft)

Chronic heart failure (CHF) is recognized as major and escalating public health problem. Approximately 69% of CHF patients suffer from cardiac death within 5 years after the initial diagnosis. Until now, no generally accepted ECG risk predictors in CHF patients are available. The objective of this study was to investigate the suitability of the new developed non-linear method segmented symbolic dynamics (SSD) for risk stratification in patients with ischemic cardiomyopathy (ICM) in comparison to other indices from time and frequency domain, non-linear dynamics, and clinical markers. Twenty-four hour Holter ECGs were recorded from 256 ICM patients. Heart rate variability (HRV) was analyzed from the filtered beat-to-beat interval time series. For calculating SSD, NN interval time series were segmented in 1 min overlapping windows with a window length of 30 min. For each window a symbol- and word-transformation was performed and probabilities of word type occurrences were calculated. Several indices from frequency domain and non-linear dynamics revealed high univariate significant differences (p<0.01) discriminating low (n = 221) and high risk ICM patients (n = 35). For multivariate risk stratification in ICM patients the two optimal mixed parameter sets consisting of either two clinical and three non-clinical indices (two from SSD) or three clinical and two non-clinical indices (one from SSD) achieved 74 and 75% sensitivity and 79 and 76% specificity, respectively. These results suggest that the new SSD enhances considerably risk stratification in ICM patients. The multivariate analysis including SSD leads to an optimum accuracy of 81%., Peer Reviewed, Postprint (published version)