Prioritizing genetic testing in patients with kallmann syndrome using clinical phenotypes.

Context: The complexity of genetic testing in Kallmann syndrome (KS) is growing and costly. Thus, it is important to leverage the clinical evaluations of KS patients to prioritize genetic screening. Objective: The objective of the study was to determine which reproductive and nonreproductive phenotypes of KS subjects have implications for specific gene mutations. Subjects: Two hundred nineteen KS patients were studied: 151 with identified rare sequence variants (RSVs) in 8 genes known to cause KS (KAL1, NELF, CHD7, HS6ST1, FGF8/FGFR1, or PROK2/PROKR2) and 68 KS subjects who remain RSV negative for all 8 genes. Main Outcome Measures: Reproductive and nonreproductive phenotypes within each genetic group were measured. Results: Male KS subjects with KAL1 RSVs displayed the most severe reproductive phenotype with testicular volumes (TVs) at presentation of 1.5 ± 0.1 mL vs 3.7 ± 0.3 mL, P < .05 vs all non-KAL1 probands. In both sexes, synkinesia was enriched but not unique to patients with KAL1 RSVs compared with KAL1-negative probands (43% vs 12%; P < .05). Similarly, dental agenesis and digital bone abnormalities were enriched in patients with RSVs in the FGF8/FGFR1 signaling pathway compared with all other gene groups combined (39% vs 4% and 23% vs 0%; P < .05, respectively). Hearing loss marked the probands with CHD7 RSVs (40% vs 13% in non-CHD7 probands; P < .05). Renal agenesis and cleft lip/palate did not emerge as statistically significant phenotypic predictors. Conclusions: Certain clinical features in men and women are highly associated with genetic causes of KS. Synkinesia (KAL1), dental agenesis (FGF8/FGFR1), digital bony abnormalities (FGF8/FGFR1), and hearing loss (CHD7) can be useful for prioritizing genetic screening.

Identification of different heart tissues from MRI C-SENC images using an unsupervised multi-stage fuzzy clustering technique.

PURPOSE: To objectively characterize different heart tissues from functional and viability images provided by composite-strain-encoding (C-SENC) MRI. MATERIALS AND METHODS: C-SENC is a new MRI technique for simultaneously acquiring cardiac functional and viability images. In this work, an unsupervised multi-stage fuzzy clustering method is proposed to identify different heart tissues in the C-SENC images. The method is based on sequential application of the fuzzy c-means (FCM) and iterative self-organizing data (ISODATA) clustering algorithms. The proposed method is tested on simulated heart images and on images from nine patients with and without myocardial infarction (MI). The resulting clustered images are compared with MRI delayed-enhancement (DE) viability images for determining MI. Also, Bland-Altman analysis is conducted between the two methods. RESULTS: Normal myocardium, infarcted myocardium, and blood are correctly identified using the proposed method. The clustered images correctly identified 90 +/- 4% of the pixels defined as infarct in the DE images. In addition, 89 +/- 5% of the pixels defined as infarct in the clustered images were also defined as infarct in DE images. The Bland-Altman results show no bias between the two methods in identifying MI. CONCLUSION: The proposed technique allows for objectively identifying divergent heart tissues, which would be potentially important for clinical decision-making in patients with MI.

Sex-linked genetic markers in the shrews of the Sorex araneus group : from free gene flow to speciation

SUMMARY: The shrews of the Sorex araneus group are morphologically .very similar, but have undergone a spectacular chromosomal evolution. Altogether, the shrews of this group present a complete array of every possible level of chromosomal and genetic differentiation. In South-Western Europe, four species are recognised: S. antiriorii, S. araneus, S. coronatus and S. granarius, which differ essentially by the amount and the composition of Robertsonian metacentric chromosomés. Additionally, several chromosome races of S. araneus are also present in the same region (i.e. Bretolet, Carlit, Cordon, Jura and Vaud). The objective of this thesis was to examine the genetic relationships between populations, races and /or species of the Sorex araneus group with a special emphasis onsex-specific markers (mtDNA and Y chromosome). We first investigate the evolutionary history of the shrews of the Sorex araneus group distributed in the South-Western Europe. The results of. these analyses confirmed the difficulty to draw a single dichotomic tree within this group. Incongruent mtDNA and Y chromosome phylogenies suggest further that genetic and chromosomal evolution are in this group partially independent processes and that the evolutionary history of the south-western European populations of the S. araneus group can only be understood if we consider secondary contacts between taxa, after their divergence (with genetic exchanges by means of hybridization and / or introgression). Using one male-inherited, one female inherited and eight biparentally inherited markers, we investigate the population genetic structure of the Valais shrew (Sorex antinorii). Overall there results suggest that two already well-differentiated genetic lineages colonized the Swiss Alps after the last glacial period and came into contact in the Rhône Valley. After the Valais shrew (Sorex antinorii) reached the Swiss Alps, it came into contact with the common shrew (Sorex araneus). When two species come into contact and hybridize, endogenous counter-selection of hybrids is usually first expressed as a reduced fertility or viability in hybrids of the heterogametic sex, a mechanism know as Haldane's rule (Haldane 1922). We first evaluated the extent of introgression for Y chromosome, mtDNA and autosomal markers in a hybrid zone between S. antinoriii and S. araneus. The overall level of genetic and karyotypic differentiation between the two species must be strong .enough to allow the detection asymmetric introgression. Secondly, we compared the levels of gene flow between chromosome common to both species and chromosome differently rearranged in each of them. We detected a significantly stronger genetic structure in rearranged chromosomes. Over a 10-year period, we even observed a decrease of genetic structure for common chromosomes. These results strongly support the role of chromosomal rearrangements in the reproductive barrier between S. araneus and S. anfinorii. Overall, this thesis underlines the need to use different inherited (paternally, maternally and / or biparentally) and chromosomally located (on common vs. on rearranged chromosomes) markers to obtain more accurate pictures of genetic relationships between populations or species. RÉSUMÉ: Les musaraignes du groupe Sorex araneus sont morphologiquement très proches, mais ont connu une spectaculaire évolution chromosomique. Prises dans leur ensemble, les musaraignes de ce groupe présentent tous les nivaux possibles de différenciation génétique et chromosomique. Dans le sud-ouest de l'Europe, quatre espèces appartenant à ce groupe sont présentes : S. antinorii, S. araneus, S. coronatus et S. granarius. Celles-ci diffèrent essentiellement par leur caryotype dont la variabilité est principalement due à des fusions Robertsoniennes. De plus, plusieurs races chromosomiques appartenant à S. araneus sont aussi présentes dans la même région (i.e. les races Bretolet, Carlit, Cordon, Jura et Vaud). L'objectif de cette thèse était d'examiner les relations génétiques entre populations, races et/ou espèces du groupe S. araneus, en utilisant particulièrement des marqueurs liés aux sexes (ADN mitochondrial et Chromosome Y). Nous avons dans un premier temps retracé l'histoire évolutive des musaraignes de ce groupe dans le sud-ouest de l'Europe. Les résultats dé ces analyses confirment qu'il est difficile de tracer un simple arbre dichotomique au sein de ce groupe. Les arbres phylogénétiques obtenus sur l'ADN mitochondrial et le chromosome Y sont incongruents et suggèrent de plus que l'évolution génétique et chromosomique sont des processus indépendants. L'histoire évolutive -des populations de ce groupe ne peut. être comprise qu'en considérant des contacts secondaires entre taxa postérieure à leur divergence et induisant des échanges génétiques par hybridation et/ou introgression. Par la suite, nous avons examiné la structure génétique des populations de la musaraigne du Valais, S. antinorii, en utilisant un marqueur transmis par les mâles, un marqueur transmis par les femelles et huit marqueurs transmis par les 2 sexes. Nos résultats suggèrent que deux lignées génétiquement bien différenciées aient colonisé les Alpes Suisses, après les dernières glaciations et entrent en contact dans là Vallée du Rhône. Après avoir franchi les Alpes Suisses, la musaraigne du Valais est entrée en contact avec là musaraigne commune (S. araneus). Lorsque deux espèces entrent en contact et s'hybrident, la sélection contre les hybrides implique habituellement une baisse de fertilité ou de viabilité des hybrides du sexe hétérogamétique (i.e. les mâles XY chez les mammifères). Ce mécanisme est connu sous le nom de règle de Haldane (Haldane 1922) et implique une plus forte structuration génétique de marqueurs males - spécifiques que des marqueurs femelles spécifiques. Nous avons donc évalué le degré d'introgression des marqueurs situés sur le chromosome Y, sur l'ADN mitochondrial et sur des autosomes dans une zone hybride entre S. araneus et S. antinorii. Le niveau de différenciation chromosomique et génétique entre les 2 espèces doit être suffisamment fort pour ne pas permettre la détection d'une introgression asymétrique entre les sexes. Dans un second temps, nous avons comparé les niveaux de flux de gênes mesurés à l'échelle du chromosome, pour des chromosomes communs aux deux espèces et pour des chromosomes différemment arrangées dans chacune des deux espèces. Nous avons détecté une structure génétique significativement plus forte sur les chromosomes réarrangés et comme la zone hybride a été étudiée à dix années d'intervalle, nous observons même une diminution de la structure génétique pour les chromosomes communs au cours du temps.. Ces résultats soutiennent fortement l'hypothèse d'un rôle des réarrangements chromosomiques dans l'établissement d'une barrière reproductive entre S. araneus et S. antinorii. Ainsi cette thèse souligne l'utilité d'utiliser des marqueurs génétiques avec différents modes de transmission. (par les mâles, par les femelles et/ou par les 2 sexes) ou localisés au niveau du chromosome (chromosomes communs vs chromosomes réarrangés) afin d'obtenir une image plus juste ou du moins plus complète des relations génétiques entre populations ou espèces.

Human epidermal Langerhans cells express the tight junction protein claudin-1 and are present in human genetic claudin-1 deficiency (NISCH syndrome).

Claudin-1 (CLDN1) is a structural tight junction (TJ) protein and is expressed in differentiating keratinocytes and Langerhans cells in the epidermis. Our objective was to identify immunoreactive CLDN1 in human epidermal Langerhans cells and to examine the pattern of epidermal Langerhans cells in genetic human CLDN1 deficiency [neonatal ichthyosis, sclerosing cholangitis (NISCH) syndrome]. Epidermal cells from healthy human skin labelled with CLDN1-specific antibodies were analysed by confocal laser immunofluorescence microscopy and flow cytometry. Skin biopsy sections of two patients with NISCH syndrome were stained with an antibody to CD1a expressed on epidermal Langerhans cells. Epidermal Langerhans cells and a subpopulation of keratinocytes from healthy skin were positive for CLDN1. The gross number and distribution of epidermal Langerhans cells of two patients with molecularly confirmed NISCH syndrome, however, was not grossly altered. Therefore, CLDN1 is unlikely to play a critical role in migration of Langerhans cells (or their precursors) to the epidermis or their positioning within the epidermis. Our findings do not exclude a role of this TJ molecule once Langerhans cells have left the epidermis for draining lymph nodes.

Stochastic models and numerical algorithms for a class of regulatory gene networks.

Regulatory gene networks contain generic modules, like those involving feedback loops, which are essential for the regulation of many biological functions (Guido et al. in Nature 439:856-860, 2006). We consider a class of self-regulated genes which are the building blocks of many regulatory gene networks, and study the steady-state distribution of the associated Gillespie algorithm by providing efficient numerical algorithms. We also study a regulatory gene network of interest in gene therapy, using mean-field models with time delays. Convergence of the related time-nonhomogeneous Markov chain is established for a class of linear catalytic networks with feedback loops.

A Multi-SNP Locus-Association Method Reveals a Substantial Fraction of the Missing Heritability.

There are many known examples of multiple semi-independent associations at individual loci; such associations might arise either because of true allelic heterogeneity or because of imperfect tagging of an unobserved causal variant. This phenomenon is of great importance in monogenic traits but has not yet been systematically investigated and quantified in complex-trait genome-wide association studies (GWASs). Here, we describe a multi-SNP association method that estimates the effect of loci harboring multiple association signals by using GWAS summary statistics. Applying the method to a large anthropometric GWAS meta-analysis (from the Genetic Investigation of Anthropometric Traits consortium study), we show that for height, body mass index (BMI), and waist-to-hip ratio (WHR), 3%, 2%, and 1%, respectively, of additional phenotypic variance can be explained on top of the previously reported 10% (height), 1.5% (BMI), and 1% (WHR). The method also permitted a substantial increase (by up to 50%) in the number of loci that replicate in a discovery-validation design. Specifically, we identified 74 loci at which the multi-SNP, a linear combination of SNPs, explains significantly more variance than does the best individual SNP. A detailed analysis of multi-SNPs shows that most of the additional variability explained is derived from SNPs that are not in linkage disequilibrium with the lead SNP, suggesting a major contribution of allelic heterogeneity to the missing heritability.

Epidemiology of cleft palate in Europe: implications for genetic research.

OBJECTIVE: To describe the epidemiology of cleft palate (CP) in Europe. DESIGN AND SETTING: A descriptive epidemiological study on 3852 cases of CP, identified (1980 through 1996) from more than 6 million births from the EUROCAT network of 30 registers in 16 European countries. RESULTS: Significant differences in prevalence in Europe between registries and within countries were observed. A total of 2112 (54.8%) CP cases occurred as isolated, 694 (18.0%) were associated with other defects such as multiple congenital anomalies, and 1046 (27.2%) were in recognized conditions. The study confirmed the tendency toward female prevalence (sex ratio [SR] = 0.83), particularly among isolated cases (SR = 0.78) even if SR inversion is reported in some registries. A specific association with neural tube defects (NTDs) in some registers is reported. CONCLUSION: The differences identified in Europe (prevalence, sex, associated anomalies) can be only partially explained by methodological reasons because a common methodology was shared among all registries for case ascertainment and collection, and CP is an easy detectable condition with few induced abortions. The complex model of inheritance and the frequently conflicting results in different populations on the role of genes that constitute risk factors suggest the presence of real biological differences. The association of CP/NTD in an area with a high prevalence of NTDs can identify a group of conditions that can be considered etiologically homogeneous. The epidemiological evaluation can guide genetic research to specify the role of etiological factors in each different population

Towards the identification of a genetic basis for Landau-Kleffner syndrome.

OBJECTIVE: To establish the genetic basis of Landau-Kleffner syndrome (LKS) in a cohort of two discordant monozygotic (MZ) twin pairs and 11 isolated cases. METHODS: We used a multifaceted approach to identify genetic risk factors for LKS. Array comparative genomic hybridization (CGH) was performed using the Agilent 180K array. Whole genome methylation profiling was undertaken in the two discordant twin pairs, three isolated LKS cases, and 12 control samples using the Illumina 27K array. Exome sequencing was undertaken in 13 patients with LKS including two sets of discordant MZ twins. Data were analyzed with respect to novel and rare variants, overlapping genes, variants in reported epilepsy genes, and pathway enrichment. RESULTS: A variant (cG1553A) was found in a single patient in the GRIN2A gene, causing an arginine to histidine change at site 518, a predicted glutamate binding site. Following copy number variation (CNV), methylation, and exome sequencing analysis, no single candidate gene was identified to cause LKS in the remaining cohort. However, a number of interesting additional candidate variants were identified including variants in RELN, BSN, EPHB2, and NID2. SIGNIFICANCE: A single mutation was identified in the GRIN2A gene. This study has identified a number of additional candidate genes including RELN, BSN, EPHB2, and NID2. A PowerPoint slide summarizing this article is available for download in the Supporting Information section here.

Heritability of blood pressure in the swiss population: the family-based skipogh study

Objective: Blood pressure is known to aggregate in families. Yet, heritability estimates are population-specific and no Swiss data have been published so far. Moreover, little is known on the heritability of the white-coat effect. We investigated the heritability of various blood pressure (BP) traits in a Swiss population-based sample. Methods: SKIPOGH (Swiss Kidney Project on Genes in Hypertension) is a family-based multi-centre (Lausanne, Bern, Geneva) cross-sectional study that examines the role of genes in determining BP levels. Office and 24-hour ambulatory BP were measured using validated devices (A&D UM-101 and Diasys Integra). We estimated the heritability of systolic BP (SBP), diastolic BP (DBP), heart rate (HR), pulse pressure (PP), proportional white-coat effect (i.e. [office BP-mean ambulatory daytime BP]/mean ambulatory daytime BP), and nocturnal BP dipping (difference between mean ambulatory daytime and night-time BP) using a maximum likelihood method implemented in the SAGE software. Analyses were adjusted for age, sex, body mass index (BMI), and study centre. Analyses involving PP were additionally adjusted for DBP. Results: The 517 men and 579 women included in this analysis had a mean (}SD) age of 46.8 (17.8) and 47.8 (17.1) years and a mean BMI of 26.0 (4.2) and 24.2 (4.6) kg/m2, respectively. Heritability estimates (}SE) for office SBP, DBP, HR, and PP were 0.20}0.07, 0.20}0.07, 0.39}0.08, and 0.16}0.07 (all P<0.01). Heritability estimates for 24-hour ambulatory SBP, DBP, HR, and PP were, respectively, 0.39}0.07, 0.30}.08, 0.19}0.09, and 0.25}0.08 (all P<0.05). The heritability of the white-coat effect was 0.29}0.07 for SBP and 0.31}0.07 for DBP (both P<0.001). The heritability of nocturnal BP dipping was 0.15}0.08 for SBP and 0.22}0.07 for DBP (both P<0.05). Conclusions: We found that the white-coat effect is significantly heritable. Our findings show that BP traits are moderately heritable in a multi-centric study in Switzerland, in line with previous population-based studies, justifying the ongoing search for genetic determinants in this field.

quantiNemo: an individual-based program to simulate quantitative traits with explicit genetic architecture in a dynamic metapopulation.

quantiNemo is an individual-based, genetically explicit stochastic simulation program. It was developed to investigate the effects of selection, mutation, recombination and drift on quantitative traits with varying architectures in structured populations connected by migration and located in a heterogeneous habitat. quantiNemo is highly flexible at various levels: population, selection, trait(s) architecture, genetic map for QTL and/or markers, environment, demography, mating system, etc. quantiNemo is coded in C++ using an object-oriented approach and runs on any computer platform. Availability: Executables for several platforms, user's manual, and source code are freely available under the GNU General Public License at http://www2.unil.ch/popgen/softwares/quantinemo.

Machine learning for geospatial data : algorithms, software tools and case studies

Résumé Cette thèse est consacrée à l'analyse, la modélisation et la visualisation de données environnementales à référence spatiale à l'aide d'algorithmes d'apprentissage automatique (Machine Learning). L'apprentissage automatique peut être considéré au sens large comme une sous-catégorie de l'intelligence artificielle qui concerne particulièrement le développement de techniques et d'algorithmes permettant à une machine d'apprendre à partir de données. Dans cette thèse, les algorithmes d'apprentissage automatique sont adaptés pour être appliqués à des données environnementales et à la prédiction spatiale. Pourquoi l'apprentissage automatique ? Parce que la majorité des algorithmes d'apprentissage automatiques sont universels, adaptatifs, non-linéaires, robustes et efficaces pour la modélisation. Ils peuvent résoudre des problèmes de classification, de régression et de modélisation de densité de probabilités dans des espaces à haute dimension, composés de variables informatives spatialisées (« géo-features ») en plus des coordonnées géographiques. De plus, ils sont idéaux pour être implémentés en tant qu'outils d'aide à la décision pour des questions environnementales allant de la reconnaissance de pattern à la modélisation et la prédiction en passant par la cartographie automatique. Leur efficacité est comparable au modèles géostatistiques dans l'espace des coordonnées géographiques, mais ils sont indispensables pour des données à hautes dimensions incluant des géo-features. Les algorithmes d'apprentissage automatique les plus importants et les plus populaires sont présentés théoriquement et implémentés sous forme de logiciels pour les sciences environnementales. Les principaux algorithmes décrits sont le Perceptron multicouches (MultiLayer Perceptron, MLP) - l'algorithme le plus connu dans l'intelligence artificielle, le réseau de neurones de régression généralisée (General Regression Neural Networks, GRNN), le réseau de neurones probabiliste (Probabilistic Neural Networks, PNN), les cartes auto-organisées (SelfOrganized Maps, SOM), les modèles à mixture Gaussiennes (Gaussian Mixture Models, GMM), les réseaux à fonctions de base radiales (Radial Basis Functions Networks, RBF) et les réseaux à mixture de densité (Mixture Density Networks, MDN). Cette gamme d'algorithmes permet de couvrir des tâches variées telle que la classification, la régression ou l'estimation de densité de probabilité. L'analyse exploratoire des données (Exploratory Data Analysis, EDA) est le premier pas de toute analyse de données. Dans cette thèse les concepts d'analyse exploratoire de données spatiales (Exploratory Spatial Data Analysis, ESDA) sont traités selon l'approche traditionnelle de la géostatistique avec la variographie expérimentale et selon les principes de l'apprentissage automatique. La variographie expérimentale, qui étudie les relations entre pairs de points, est un outil de base pour l'analyse géostatistique de corrélations spatiales anisotropiques qui permet de détecter la présence de patterns spatiaux descriptible par une statistique. L'approche de l'apprentissage automatique pour l'ESDA est présentée à travers l'application de la méthode des k plus proches voisins qui est très simple et possède d'excellentes qualités d'interprétation et de visualisation. Une part importante de la thèse traite de sujets d'actualité comme la cartographie automatique de données spatiales. Le réseau de neurones de régression généralisée est proposé pour résoudre cette tâche efficacement. Les performances du GRNN sont démontrées par des données de Comparaison d'Interpolation Spatiale (SIC) de 2004 pour lesquelles le GRNN bat significativement toutes les autres méthodes, particulièrement lors de situations d'urgence. La thèse est composée de quatre chapitres : théorie, applications, outils logiciels et des exemples guidés. Une partie importante du travail consiste en une collection de logiciels : Machine Learning Office. Cette collection de logiciels a été développée durant les 15 dernières années et a été utilisée pour l'enseignement de nombreux cours, dont des workshops internationaux en Chine, France, Italie, Irlande et Suisse ainsi que dans des projets de recherche fondamentaux et appliqués. Les cas d'études considérés couvrent un vaste spectre de problèmes géoenvironnementaux réels à basse et haute dimensionnalité, tels que la pollution de l'air, du sol et de l'eau par des produits radioactifs et des métaux lourds, la classification de types de sols et d'unités hydrogéologiques, la cartographie des incertitudes pour l'aide à la décision et l'estimation de risques naturels (glissements de terrain, avalanches). Des outils complémentaires pour l'analyse exploratoire des données et la visualisation ont également été développés en prenant soin de créer une interface conviviale et facile à l'utilisation. Machine Learning for geospatial data: algorithms, software tools and case studies Abstract The thesis is devoted to the analysis, modeling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense machine learning can be considered as a subfield of artificial intelligence. It mainly concerns with the development of techniques and algorithms that allow computers to learn from data. In this thesis machine learning algorithms are adapted to learn from spatial environmental data and to make spatial predictions. Why machine learning? In few words most of machine learning algorithms are universal, adaptive, nonlinear, robust and efficient modeling tools. They can find solutions for the classification, regression, and probability density modeling problems in high-dimensional geo-feature spaces, composed of geographical space and additional relevant spatially referenced features. They are well-suited to be implemented as predictive engines in decision support systems, for the purposes of environmental data mining including pattern recognition, modeling and predictions as well as automatic data mapping. They have competitive efficiency to the geostatistical models in low dimensional geographical spaces but are indispensable in high-dimensional geo-feature spaces. The most important and popular machine learning algorithms and models interesting for geo- and environmental sciences are presented in details: from theoretical description of the concepts to the software implementation. The main algorithms and models considered are the following: multi-layer perceptron (a workhorse of machine learning), general regression neural networks, probabilistic neural networks, self-organising (Kohonen) maps, Gaussian mixture models, radial basis functions networks, mixture density networks. This set of models covers machine learning tasks such as classification, regression, and density estimation. Exploratory data analysis (EDA) is initial and very important part of data analysis. In this thesis the concepts of exploratory spatial data analysis (ESDA) is considered using both traditional geostatistical approach such as_experimental variography and machine learning. Experimental variography is a basic tool for geostatistical analysis of anisotropic spatial correlations which helps to understand the presence of spatial patterns, at least described by two-point statistics. A machine learning approach for ESDA is presented by applying the k-nearest neighbors (k-NN) method which is simple and has very good interpretation and visualization properties. Important part of the thesis deals with a hot topic of nowadays, namely, an automatic mapping of geospatial data. General regression neural networks (GRNN) is proposed as efficient model to solve this task. Performance of the GRNN model is demonstrated on Spatial Interpolation Comparison (SIC) 2004 data where GRNN model significantly outperformed all other approaches, especially in case of emergency conditions. The thesis consists of four chapters and has the following structure: theory, applications, software tools, and how-to-do-it examples. An important part of the work is a collection of software tools - Machine Learning Office. Machine Learning Office tools were developed during last 15 years and was used both for many teaching courses, including international workshops in China, France, Italy, Ireland, Switzerland and for realizing fundamental and applied research projects. Case studies considered cover wide spectrum of the real-life low and high-dimensional geo- and environmental problems, such as air, soil and water pollution by radionuclides and heavy metals, soil types and hydro-geological units classification, decision-oriented mapping with uncertainties, natural hazards (landslides, avalanches) assessments and susceptibility mapping. Complementary tools useful for the exploratory data analysis and visualisation were developed as well. The software is user friendly and easy to use.

Common genetic loci influencing plasma homocysteine concentrations and their effect on risk of coronary artery disease.

BACKGROUND: The strong observational association between total homocysteine (tHcy) concentrations and risk of coronary artery disease (CAD) and the null associations in the homocysteine-lowering trials have prompted the need to identify genetic variants associated with homocysteine concentrations and risk of CAD. OBJECTIVE: We tested whether common genetic polymorphisms associated with variation in tHcy are also associated with CAD. DESIGN: We conducted a meta-analysis of genome-wide association studies (GWAS) on tHcy concentrations in 44,147 individuals of European descent. Polymorphisms associated with tHcy (P < 10(-8)) were tested for association with CAD in 31,400 cases and 92,927 controls. RESULTS: Common variants at 13 loci, explaining 5.9% of the variation in tHcy, were associated with tHcy concentrations, including 6 novel loci in or near MMACHC (2.1 Ã- 10(-9)), SLC17A3 (1.0 Ã- 10(-8)), GTPB10 (1.7 Ã- 10(-8)), CUBN (7.5 Ã- 10(-10)), HNF1A (1.2 Ã- 10(-12)), and FUT2 (6.6 Ã- 10(-9)), and variants previously reported at or near the MTHFR, MTR, CPS1, MUT, NOX4, DPEP1, and CBS genes. Individuals within the highest 10% of the genotype risk score (GRS) had 3-μmol/L higher mean tHcy concentrations than did those within the lowest 10% of the GRS (P = 1 Ã- 10(-36)). The GRS was not associated with risk of CAD (OR: 1.01; 95% CI: 0.98, 1.04; P = 0.49). CONCLUSIONS: We identified several novel loci that influence plasma tHcy concentrations. Overall, common genetic variants that influence plasma tHcy concentrations are not associated with risk of CAD in white populations, which further refutes the causal relevance of moderately elevated tHcy concentrations and tHcy-related pathways for CAD.