Helena Brunel

MISS: a non-linear methodology based on mutual information for genetic association studies in both population and sib-pairs analysis.

MOTIVATION Finding association between genetic variants and phenotypes related to disease has become an important vehicle for the study of complex disorders. In this context, multi-loci genetic association might unravel additional information when compared with single loci search. The main goal of this work is to propose a non-linear methodology based on information theory for finding combinatorial association between multi-SNPs and a given phenotype. RESULTS The proposed methodology, called MISS (mutual information statistical significance), has been integrated jointly with a feature selection algorithm and has been tested on a synthetic dataset with a controlled phenotype and in the particular case of the F7 gene. The MISS methodology has been contrasted with a multiple linear regression (MLR) method used for genetic association in both, a population-based study and a sib-pairs analysis and with the maximum entropy conditional probability modelling (MECPM) method, which searches for predictive multi-locus interactions. Several sets of SNPs within the F7 gene region have been found to show a significant correlation with the FVII levels in blood. The proposed multi-site approach unveils combinations of SNPs that explain more significant information of the phenotype than their individual polymorphisms. MISS is able to find more correlations between SNPs and the phenotype than MLR and MECPM. Most of the marked SNPs appear in the literature as functional variants with real effect on the protein FVII levels in blood. AVAILABILITY The code is available at http://sisbio.recerca.upc.edu/R/MISS_0.2.tar.gz

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.

SNP sets selection under mutual information criterion, application to F7/FVII dataset

One of the main goals of human genetics is to find genetic markers related to complex diseases. In blood coagulation process, it is known that genetic variability in F7 gene is the most responsible for observed variations in FVII levels in blood. In this work, we propose a method for selecting sets of Single Nucleotide Polymorphisms (SNPs) significantly correlated with a phenotype (FVII levels). This method employs a feature selection algorithm (variant of Sequential Forward Selection, SFS) based on a criterion of statistical significance of a mutual information functional. This algorithm is applied to a sample of independent individuals from the GAIT project. Main SNPs found by the algorithm are in correspondence with previous results published using family-based techniques.

lme4qtl: linear mixed models with flexible covariance structure for genetic studies of related individuals

BackgroundQuantitative trait locus (QTL) mapping in genetic data often involves analysis of correlated observations, which need to be accounted for to avoid false association signals. This is commonly performed by modeling such correlations as random effects in linear mixed models (LMMs). The R package lme4 is a well-established tool that implements major LMM features using sparse matrix methods; however, it is not fully adapted for QTL mapping association and linkage studies. In particular, two LMM features are lacking in the base version of lme4: the definition of random effects by custom covariance matrices; and parameter constraints, which are essential in advanced QTL models. Apart from applications in linkage studies of related individuals, such functionalities are of high interest for association studies in situations where multiple covariance matrices need to be modeled, a scenario not covered by many genome-wide association study (GWAS) software.ResultsTo address the aforementioned limitations, we developed a new R package lme4qtl as an extension of lme4. First, lme4qtl contributes new models for genetic studies within a single tool integrated with lme4 and its companion packages. Second, lme4qtl offers a flexible framework for scenarios with multiple levels of relatedness and becomes efficient when covariance matrices are sparse. We showed the value of our package using real family-based data in the Genetic Analysis of Idiopathic Thrombophilia 2 (GAIT2) project.ConclusionsOur software lme4qtl enables QTL mapping models with a versatile structure of random effects and efficient computation for sparse covariances. lme4qtl is available at https://github.com/variani/lme4qtl.

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.

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.

Risk Stratification in Ischemic Heart Failure Patients with Linear and Nonlinear Methods of Heart Rate Variability Analysis

Heart failure has a current prevalence of 14 million concerned people in Europe and is thus a major and escalating public health problem in the industrialized countries with ageing populations. A five-year mortality rate between 62–75% in men and 38–42% in women related to the initial diagnosis of heart failure was documented in the Framingham study. The aim of this study was to investigate the suitability of linear (according the Task Force recommendations) and nonlinear (symbolic dynamics - SD and detrended fluctuation analysis - DFA) methods of heart rate variability (HRV) analysis for risk stratification in patients with ischemic heart failure (IHF). From 221 low risk (LR: stable condition) and 35 high risk (HR: cardiac death) IHF patients HRV from 24h long-term BBI time series were analyzed. Seven measures from all applied methods revealed significant differences (p<0.05) discriminating LR and HR patients. These results suggest that HRV analysis according to the Task Force (only frequency domain), SD and DFA are useful methods for enhanced risk stratification in IHF patients.

Next generation sequencing to dissect the genetic architecture of KNG1 and F11 loci using factor XI levels as an intermediate phenotype of thrombosis

Venous thromboembolism is a complex disease with a high heritability. There are significant associations among Factor XI (FXI) levels and SNPs in the KNG1 and F11 loci. Our aim was to identify the genetic variation of KNG1 and F11 that might account for the variability of FXI levels. The KNG1 and F11 loci were sequenced completely in 110 unrelated individuals from the GAIT-2 (Genetic Analysis of Idiopathic Thrombophilia 2) Project using Next Generation Sequencing on an Illumina MiSeq. The GAIT-2 Project is a study of 935 individuals in 35 extended Spanish families selected through a proband with idiopathic thrombophilia. Among the 110 individuals, a subset of 40 individuals was chosen as a discovery sample for identifying variants. A total of 762 genetic variants were detected. Several significant associations were established among common variants and low-frequency variants sets in KNG1 and F11 with FXI levels using the PLINK and SKAT packages. Among these associations, those of rs710446 and five low-frequency variant sets in KNG1 with FXI level variation were significant after multiple testing correction and permutation. Also, two putative pathogenic mutations related to high and low FXI levels were identified by data filtering and in silico predictions. This study of KNG1 and F11 loci should help to understand the connection between genotypic variation and variation in FXI levels. The functional genetic variants should be useful as markers of thromboembolic risk.