Andrey Ziyatdinov

Intensity drift removal in LC/MS metabolomics by common variance compensation

UNLABELLED Liquid chromatography coupled to mass spectrometry (LC/MS) has become widely used in Metabolomics. Several artefacts have been identified during the acquisition step in large LC/MS metabolomics experiments, including ion suppression, carryover or changes in the sensitivity and intensity. Several sources have been pointed out as responsible for these effects. In this context, the drift effects of the peak intensity is one of the most frequent and may even constitute the main source of variance in the data, resulting in misleading statistical results when the samples are analysed. In this article, we propose the introduction of a methodology based on a common variance analysis before the data normalization to address this issue. This methodology was tested and compared with four other methods by calculating the Dunn and Silhouette indices of the quality control classes. The results showed that our proposed methodology performed better than any of the other four methods. As far as we know, this is the first time that this kind of approach has been applied in the metabolomics context. AVAILABILITY AND IMPLEMENTATION The source code of the methods is available as the R package intCor at http://b2slab.upc.edu/software-and-downloads/intensity-drift-correction/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Data simulation in machine olfaction with the R package chemosensors.

In machine olfaction, the design of applications based on gas sensor arrays is highly dependent on the robustness of the signal and data processing algorithms. While the practice of testing the algorithms on public benchmarks is not common in the field, we propose software for performing data simulations in the machine olfaction field by generating parameterized sensor array data. The software is implemented as an R language package chemosensors which is open-access, platform-independent and self-contained. We introduce the concept of a virtual sensor array which can be used as a data generation tool. In this work, we describe the data simulation workflow which basically consists of scenario definition, virtual array parameterization and the generation of sensor array data. We also give examples of the processing of the simulated data as proof of concept for the parameterized sensor array data: the benchmarking of classification algorithms, the evaluation of linear- and non-linear regression algorithms, and the biologically inspired processing of sensor array data. All the results presented were obtained under version 0.7.6 of the chemosensors package whose home page is chemosensors.r-forge.r-project.org.

Age and gender effects on 15 platelet phenotypes in a Spanish population

INTRODUCTION Several studies have analysed the platelet parameters in human blood, nevertheless there are no extensive analyses on the less common platelet phenotypes. The main objective of our study is to evaluate the age and gender effects on 15 platelet phenotypes. METHODS We studied 804 individuals, ranging in age from 2 to 93 years, included in the Genetic Analysis of Idiopathic Thrombophilia 2 (GAIT 2) Project. The 15 platelet phenotypes analysed were the platelets counts, platelet volumes, plateletcrits, immature platelet fraction (IPF) and platelet function assay (PFA). A regression-based method was used to evaluate the age and gender effects on these phenotypes. RESULTS Our results were consistent with the previously reported results regarding platelet counts and plateletcrit (PCT). They showed a decrease with increasing age. The mean platelet volume (MPV), platelet distribution width (PDW) and platelet-large cell ratio (P-LCR) increased with age, but did not present any gender effect. All the IPF phenotypes increased with age, whereas the PFA phenotypes did not show any relation to age or gender. DISCUSSION To sum up, our study provides a comprehensive analysis of the age and gender effects on the platelet phenotypes in a family-base sample. Our results suggest more reasonable age stratification into two distinct groups: childhood, ranging from 2 to 12 years, and the mature group, from 13 to 93 years. Moreover, the PFA phenotypes were maintained constant while the platelet counts, the MPV and IPF levels vary with age.

Genetic Determinants of Thrombin Generation and Their Relation to Venous Thrombosis: Results from the GAIT-2 Project

Background Venous thromboembolism (VTE) is a common disease where known genetic risk factors explain only a small portion of the genetic variance. Then, the analysis of intermediate phenotypes, such as thrombin generation assay, can be used to identify novel genetic risk factors that contribute to VTE. Objectives To investigate the genetic basis of distinct quantitative phenotypes of thrombin generation and its relationship to the risk of VTE. Patients/Methods Lag time, thrombin peak and endogenous thrombin potential (ETP) were measured in the families of the Genetic Analysis of Idiopathic Thrombophilia 2 (GAIT-2) Project. This sample consisted of 935 individuals in 35 extended families selected through a proband with idiopathic thrombophilia. We performed also genome wide association studies (GWAS) with thrombin generation phenotypes. Results The results showed that 67% of the variation in the risk of VTE is attributable to genetic factors. The heritabilities of lag time, thrombin peak and ETP were 49%, 54% and 52%, respectively. More importantly, we demonstrated also the existence of positive genetic correlations between thrombin peak or ETP and the risk of VTE. Moreover, the major genetic determinant of thrombin generation was the F2 gene. However, other suggestive signals were observed. Conclusions The thrombin generation phenotypes are strongly genetically determined. The thrombin peak and ETP are significantly genetically correlated with the risk of VTE. In addition, F2 was identified as a major determinant of thrombin generation. We reported suggestive signals that might increase our knowledge to explain the variability of this important phenotype. Validation and functional studies are required to confirm GWAS results.

The Central Role of KNG1 Gene as a Genetic Determinant of Coagulation Pathway-Related Traits: Exploring Metaphenotypes

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.

solarius: an R interface to SOLAR for variance component analysis in pedigrees

UNLABELLED : 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. AVAILABILITY AND IMPLEMENTATION solarius is available on CRAN and on GitHub https://github.com/ugcd/solarius CONTACT : aziyatdinov@santpau.cat.

Genetic determinants of Platelet Large-Cell Ratio, Immature Platelet Fraction, and other platelet-related phenotypes

INTRODUCTION Platelets play a significant role in arterial thrombosis and are involved also in venous thrombosis. The genetic determinants of several platelet-related phenotypes have been studied previously. However, to the best of our knowledge, the genetic determinants of other platelet phenotypes have not been reported such as platelet-large-cell ratio (P-LCR) index, immature platelet fraction (IPF) parameters and overall platelet function measured through the PFA-100 system. MATERIALS AND METHODS As part of the GAIT-2 (Genetic Analysis of Idiopathic Thrombophilia 2) Project, 935 individuals from 35 large Spanish families, ascertained through a proband with thrombophilia, were studied. Using variance component methods, implemented in the SOLAR package, the heritability of the following sets of platelet-related phenotypes was determined: platelet count and indices, IPF, and platelet function. RESULTS AND CONCLUSIONS High heritabilities of the platelet count and index phenotypes (from 0.41 to 0.64) were found, especially for those related to platelet volume. The heritabilities of the IPF phenotypes, as a measure of platelet turnover, were the highest (from 0.65 to 0.69). The heritabilities of the platelet function phenotypes were high also (0.45 and 0.62). The covariate age influenced all of the platelet phenotypes. Smoking influenced the platelet indices related to platelet volume and all the IPF phenotypes. Venous thrombosis showed a heritability of 0.67. We did not find a genetic correlation between any of the platelet-related phenotypes and venous thrombosis. The high heritabilities found for all of the platelet phenotypes provid promising data for the identification of new genes that underly these phenotypes.

Common Principal Component Analysis For Drift Compensation Of Gas Sensor Array Data

Two Component Correction methods for drift compensation in chemical sensor arrays have been evaluated in terms of classification accuracy. A well established linear method based on computing Principal Components of the reference gas is applied to a dataset, where three different reference gases are examined. The proposed method in this contribution is based on computing a Common Component Principal Analysis, which discovers a variance direction followed by all gasses in feature space. This new method—employing no reference gas—has shown the same performance as the traditional approach with the best fitted reference gas.

Platelet count and plateletcrit are associated with an increased risk of venous thrombosis in females. Results from the RETROVE study

• In females, elevated platelet counts (over 312·10 9 /L) were associated with a double risk of VTE.

Exploring correlation between bone metabolism markers and densitometric traits in extended families from Spain

Osteoporosis is a common multifactorial disorder characterized by low bone mass and reduced bone strength that may cause fragility fractures. In recent years, there have been substantial advancements in the biochemical monitoring of bone metabolism through the measurement of bone turnover markers. Currently, good knowledge of the genetics of such markers has become an indispensable part of osteoporosis research. In this study, we used the Genetic Analysis of Osteoporosis Project to study the genetics of the plasma levels of 12 markers related to bone metabolism and osteoporosis. Plasma phenotypes were determined through biochemical assays and log-transformed values were used together with a set of covariates to model genetic and environmental contributions to phenotypic variation, thus estimating the heritability of each trait. In addition, we studied correlations between the 12 markers and a wide variety of previously described densitometric traits. All of the 12 bone metabolism markers showed significant heritability, ranging from 0.194 for osteocalcin to 0.516 for sclerostin after correcting for covariate effects. Strong genetic correlations were observed between osteocalcin and several bone mineral densitometric traits, a finding with potentially useful diagnostic applications. In addition, suggestive genetic correlations with densitometric traits were observed for leptin and sclerostin. Overall, the few strong and several suggestive genetic correlations point out the existence of a complex underlying genetic architecture for bone metabolism plasma phenotypes and provide a strong motivation for pursuing novel whole-genome gene-mapping strategies.