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Background Cardiometabolic diseases are highly comorbid, but their relationship with female‐specific or overwhelmingly female‐predominant health conditions (breast cancer, endometriosis, pregnancy complications) is understudied. This study aimed to estimate the cross‐trait genetic overlap and influence of genetic burden of cardiometabolic traits on health conditions unique to women. Methods and Results Using electronic health record data from 71 008 ancestrally diverse women, we examined relationships between 23 obstetrical/gynecological conditions and 4 cardiometabolic phenotypes (body mass index, coronary artery disease, type 2 diabetes, and hypertension) by performing 4 analyses: (1) cross‐trait genetic correlation analyses to compare genetic architecture, (2) polygenic risk score–based association tests to characterize shared genetic effects on disease risk, (3) Mendelian randomization for significant associations to assess cross‐trait causal relationships, and (4) chronology analyses to visualize the timeline of events unique to groups of women with high and low genetic burden for cardiometabolic traits and highlight the disease prevalence in risk groups by age. We observed 27 significant associations between cardiometabolic polygenic scores and obstetrical/gynecological conditions (body mass index and endometrial cancer, body mass index and polycystic ovarian syndrome, type 2 diabetes and gestational diabetes, type 2 diabetes and polycystic ovarian syndrome). Mendelian randomization analysis provided additional evidence of independent causal effects. We also identified an inverse association between coronary artery disease and breast cancer. High cardiometabolic polygenic scores were associated with early development of polycystic ovarian syndrome and gestational hypertension. Conclusions We conclude that polygenic susceptibility to cardiometabolic traits is associated with elevated risk of certain female‐specific health conditions.

Chronic Obstructive Pulmonary Disease (COPD) has a simple physiological diagnostic criterion but a wide range of clinical characteristics. The mechanisms underlying this variability in COPD phenotypes are unclear. To investigate the potential contribution of genetic variants to phenotypic heterogeneity, we examined the association of genome-wide associated lung function, COPD, and asthma variants with other phenotypes using phenome-wide association results derived in the UK Biobank. Our clustering analysis of the variants-phenotypes association matrix identified three clusters of genetic variants with different effects on white blood cell counts, height, and body mass index (BMI). To assess the potential clinical and molecular effects of these groups of variants, we investigated the association between cluster-specific genetic risk scores and phenotypes in the COPDGene cohort. We observed differences in steroid use, BMI, lymphocyte counts, chronic bronchitis, and differential gene and protein expression across the three genetic risk scores. Our results suggest that multi-phenotype analysis of obstructive lung disease-related risk variants may identify genetically driven phenotypic patterns in COPD.

Genome-wide association analysis of cohorts with thousands of phenotypes is computationally expensive, particularly when accounting for sample relatedness or population structure. Here we present a novel machine-learning method called REGENIE for fitting a whole-genome regression model for quantitative and binary phenotypes that is substantially faster than alternatives in multi-trait analyses while maintaining statistical efficiency. The method naturally accommodates parallel analysis of multiple phenotypes and requires only local segments of the genotype matrix to be loaded in memory, in contrast to existing alternatives, which must load genome-wide matrices into memory. This results in substantial savings in compute time and memory usage. We introduce a fast, approximate Firth logistic regression test for unbalanced case–control phenotypes. The method is ideally suited to take advantage of distributed computing frameworks. We demonstrate the accuracy and computational benefits of this approach using the UK Biobank dataset with up to 407,746 individuals. REGENIE is a whole-genome regression method based on ridge regression that enables highly parallelized analysis of quantitative and binary traits in biobank-scale data with reduced computational requirements.

BackgroundGenome-wide association studies (GWAS) uncovered a wealth of associations between common variants and human phenotypes. These results, widely shared across the scientific community as summary statistics, fostered a flurry of secondary analysis: heritability and genetic correlation assessment, pleiotropy characterization and multitrait association test. Amongst these secondary analyses, a rising new field is the decomposition of multitrait genetic effects into distinct profiles of pleiotropy.ResultsWe conducted an integrative analysis of GWAS summary statistics from 36 phenotypes to decipher multitrait genetic architecture and its link to biological mechanisms. We started by benchmarking multitrait association tests on a large panel of phenotype sets and established the Omnibus test as the most powerful in practice. We detected 322 new associations that were not previously reported by univariate screening. Using independent significant associations, we investigated the breakdown of genetic association into clusters of variants harboring similar multitrait association profile. Focusing on two subsets of immunity and metabolism phenotypes, we then demonstrate how SNPs within clusters can be mapped to biological pathways and disease mechanisms, providing a putative insight for numerous SNPs with unknown biological function. Finally, for the metabolism set, we investigate the link between gene cluster assignment and success of drug targets in random control trials. We report additional uninvestigated drug targets classified by clusters.ConclusionsMultitrait genetic signals can be decomposed into distinct pleiotropy profiles that reveal consistent with pathways databases and random control trials. We propose this method for the mapping of unannotated SNPs to putative pathways.

The effective sample size (ESS) is a quantity estimated in genome-wide association studies (GWAS) with related individuals and/or linear mixed models used in analysis. ESS originally measured relative power in family-based GWAS and has recently become important for correcting GWAS summary statistics in post-GWAS analyses. However, existing ESS approaches have been overlooked and based on empirical estimation. This work presents an analytical form of ESS in mixed-model GWAS of quantitative traits, which is derived using the expectation of quadratic form and validated in extensive simulations. We illustrate the performance and relevance of our ESS estimator in common GWAS scenarios and analytically show that (i) family-based studies are consistently underpowered compared to studies of unrelated individuals of the same sample size; (ii) conditioning on polygenic genetic effect by linear mixed models boosts power; and (iii) power of detecting gene-environment interaction can be substantially gained or lost in family-based designs depending on exposure distribution. We further analyze UK Biobank dataset in two samples of 336,347 unrelated and 68,910 related individuals. Analysis in unrelated individuals reveals a high accuracy of our ESS estimator compared to the existing empirical approach; and analysis of related individuals suggests that the loss in effective sample size due to relatedness is at most 0.94x. Overall, we provide an analytical form of ESS for guiding GWAS designs and processing summary statistics in post-GWAS analyses.

Despite the extensive literature on methods for assessing interactions between genetic and environmental factors, approaches for the joint analysis of multiple G-E interactions are surprisingly lacking. Kim et al. compare the power and robustness.... With growing human genetic and epidemiologic data, there has been increased interest for the study of gene-by-environment (G-E) interaction effects. Still, major questions remain on how to test jointly a large number of interactions between multiple SNPs and multiple exposures. In this study, we first compared the relative performance of four fixed-effect joint analysis approaches using simulated data, considering up to 10 exposures and 300 SNPs: (1) omnibus test, (2) multi-exposure and genetic risk score (GRS) test, (3) multi-SNP and environmental risk score (ERS) test, and (4) GRS-ERS test. Our simulations explored both linear and logistic regression while considering three statistics: the Wald test, the Score test, and the likelihood ratio test (LRT). We further applied the approaches to three large sets of human cohort data (n = 37,664), focusing on type 2 diabetes (T2D), obesity, hypertension, and coronary heart disease with smoking, physical activity, diets, and total energy intake. Overall, GRS-based approaches were the most robust, and had the highest power, especially when the G-E interaction effects were correlated with the marginal genetic and environmental effects. We also observed severe miscalibration of joint statistics in logistic models when the number of events per variable was too low when using either the Wald test or LRT test. Finally, our real data application detected nominally significant interaction effects for three outcomes (T2D, obesity, and hypertension), mainly from the GRS-ERS approach. In conclusion, this study provides guidelines for testing multiple interaction parameters in modern human cohorts including extensive genetic and environmental data.

Genome-wide association analysis of cohorts with thousands of phenotypes is computationally expensive, particularly when accounting for sample relatedness or population structure. Here we present a novel machine learning method called REGENIE for fitting a whole genome regression model that is orders of magnitude faster than alternatives, while maintaining statistical efficiency. The method naturally accommodates parallel analysis of multiple phenotypes, and only requires local segments of the genotype matrix to be loaded in memory, in contrast to existing alternatives which must load genomewide matrices into memory. This results in substantial savings in compute time and memory usage. The method is applicable to both quantitative and binary phenotypes, including rare variant analysis of binary traits with unbalanced case-control ratios where we introduce a fast, approximate Firth logistic regression test. The method is ideally suited to take advantage of distributed computing frameworks. We demonstrate the accuracy and computational benefits of this approach compared to several existing methods using quantitative and binary traits from the UK Biobank dataset with up to 407,746 individuals.

Admixture mapping has led to the discovery of many genes associated with differential disease risk by ancestry, highlighting the importance of ancestry-based approaches to association studies. However, the potential of admixture mapping in deciphering the interplay between genes and environment exposures has been seldom explored. Here, we performed a genome-wide screening of local ancestry-smoking interactions for five spirometric lung function phenotypes in 3,300 African Americans from the COPDGene study. To account for population structure and outcome heterogeneity across exposure groups, we developed a multi-component linear mixed model for mapping gene-environment interactions, and empirically showed its robustness and increased power. When applied to the COPDGene study, our approach identified two 11p15.2-3 and 2q37 loci, exhibiting local ancestry-smoking interactions at genome-wide significant level, that would have been missed by standard singlenucleotide polymorphism analyses. These two loci harbor the PARVA and RAB17 genes previously recognized to be involved in smoking behavior. Overall, our study provides the first evidence for potential synergistic effects between African ancestry and smoking on pulmonary function and underlines the importance of ethnic diversity in genetic studies.

Publisher's version (útgefin grein), Cutaneous squamous cell carcinoma (SCC) is one of the most common cancers in the United States. Previous genome-wide association studies (GWAS) have identified 14 single nucleotide polymorphisms (SNPs) associated with cutaneous SCC. Here, we report the largest cutaneous SCC meta-analysis to date, representing six international cohorts and totaling 19,149 SCC cases and 680,049 controls. We discover eight novel loci associated with SCC, confirm all previously associated loci, and perform fine mapping of causal variants. The novel SNPs occur within skin-specific regulatory elements and implicate loci involved in cancer development, immune regulation, and keratinocyte differentiation in SCC susceptibility. © 2020, The Author(s)., We thank the research participants and employees of 23andMe for contributing to this work. In addition, we thank the participants and staff of the Nurses’ Health Study and the Health Professionals Follow-up Study, for their valuable contributions, as well as the following state cancer registries for their help: A.L., A.Z., A.R., C.A., C.O., C.T., D.E., F.L., G.A., I.D., I.L., I.N., I.A., K.Y., L.A., M.E., M.D., M.A., M.I., N.E., N.H., N.J., N.Y., N.C., N.D., O.H., O.K., O.R., P.A., R.I., S.C., T.N., T.X., V.A., W.A. and W.Y. We assume full responsibility for analyses and interpretation of these data. EQTL data described in this paper were obtained from the GTEx Portal on 06/01/2018. We thank L. Tryggvadottir and G.H. Olafsdottir of the Icelandic Cancer Registry for assistance in the ascertainment of affected individuals. We thank D. Allain, V. Klee, and M. Bernhardt for ascertainment of cases and associated clinical data. The OSU Human Genetics Sample bank provided control samples. This work was supported in part by the National Human Genome Research Institute of the National Institutes of Health (grant number R44HG006981) and in part by NIH R01 CA49449, P01 CA87969, UM1 CA186107, UM1 CA167552, R03 CA219779, K23 CA211793 (K.Y.S.), and in part by Walther Cancer Foundation (J.H.). K.Y.S. is a Damon Runyon Clinical Investigator supported (in part) by the Damon Runyon Cancer Research Foundation. The Rotterdam Study is funded by Erasmus Medical Center and Erasmus University Rotterdam; Netherlands Organization for the Health Research and Development (ZonMw); the Research Institute for Diseases in the Elderly (RIDE); the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the European Commission (DG XII), and the Municipality of Rotterdam.

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.

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. (C) 2016 Elsevier Inc All rights reserved.

Venous thromboembolism(VTE) is a common disease that involves the interaction of genetic and environmental risk factors. Previous studies have estimated a heritability of approximately 60% for the risk of VTE. Using variance analysis, intermediate phenotypes related to thrombosis have been used to identify genetic risk factors. Erythrocyte intermediate phenotypes are good candidates for VTE risk factors. We investigated the relationship of erythrocyte-related phenotypes with the risk of VTE. Our study used the Genetic Analysis of Idiopathic Thrombophilia 2 (GAIT2 Project) sample, which consisted of 35 extended pedigrees (including a total of 935 individuals). The sample had 120 subjects with thromboembolism (86 with VTE, 47 with arterial thrombosis and 13 with both). The following risk factors were evaluated: Red blood cell parameters, reticulocytes, serum iron, total iron binding capacity, serumferritin, serum transferrin receptor (STFR), and haptoglobin. Using the variance component method, heritability (h(2)) and the phenotypic, genetic and environmental correlations with VTE were estimated. The h(2) of VTE was 0.67. Most erythrocyte parameters showed significantly high h2. In addition, VTE was genetically correlated with: hematocrit (rho(G) = 0.52, p = 0.01), red blood cell distribution width (RDW) (rho(G) = 0.28, p = 0.05), immature reticulocyte fraction (IRF) (rho(G) = 0.45, p = 0.008), transferrin saturation index (rho(G) = -0.7, p = 0.05), and STFR (rho(G) = 0.4, p = 0.006). In conclusion, a genetic relationship between erythrocyte intermediate phenotypes and VTE was demonstrated. Also, we identified 5 phenotypes as risk factors. These phenotypes are useful in the search for genes that affect VTE. (C) 2016 Elsevier Inc. All rights reserved.

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

An ever-increasing number of data analysis problems include more than one view of the data, i.e. differ- ent measurement approaches to the population under study. In consequence, pattern analysis methods that deal appropriately with multiview data are becoming increasingly useful. In this paper, a novel mul- tiview spectral clustering algorithm is presented (multiview spectral clustering by common eigenvectors, or MVSC-CEV), based on computing the common eigenvectors of the Laplacian matrices derived from the similarity matrices of the input data. This algorithm maintains the features of spectral clustering, while allowing the use of an arbitrary number of input views, possibly of a different nature (feature or graph space) and with different dimensions. The method has been tested on four standard multiview data sets (UCI’s Handwritten, BBC segmented news, Max Planck Institute’s Animal With Attributes and Reuters multilingual), and compared with seven methods in the state of the art. Seven standard clus- tering evaluation metrics have been used in the experiments. The quality of the clustering produced by MVSC-CEV is above those obtained by other state-of-the-art methods in the majority of evaluation met- rics and dataset combinations. The computation times of this method are approximately twice those of the baseline spectral clustering of the concatenated data views.

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.

Summary: 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

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.

IntroductionSeveral 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. MethodsWe 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. ResultsOur 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. DiscussionTo 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. A comprehensive analysis of the age and gender effects on the platelet phenotypes.A more reasonable age stratification was proposed: childhood and the mature group.The platelet counts, MPV and IPF levels vary over time.The PFA phenotypes were mantained invariably over time.

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.

The design of bioinspired systems for chemical sensing is an engaging line of research in machine olfaction. Developments in this line could increase the lifetime and sensitivity of artificial chemo-sensory systems. Such approach is based on the sensory systems known in live organisms, and the resulting developed artificial systems are targeted to reproduce the biological mechanisms to some extent. Sniffing behaviour, sampling odours actively, has been studied recently in neuroscience, and it has been suggested that the respiration frequency is an important parameter of the olfactory system, since the odour perception, especially in complex scenarios such as novel odourants exploration, depends on both the stimulus identity and the sampling method. In this work we propose a chemical sensing system based on an array of 16 metal-oxide gas sensors that we combined with an external mechanical ventilator to simulate the biological respiration cycle. The tested gas classes formed a relatively broad combination of two analytes, acetone and ethanol, in binary mixtures. Two sets of low-frequency and high-frequency features were extracted from the acquired signals to show that the high-frequency features contain information related to the gas class. In addition, such information is available at early stages of the measurement, which could make the technique suitable in early detection scenarios. The full data set is made publicly available to the community. (C) 2014 Elsevier B.V. All rights reserved.

Recent studies in neuroscience suggest that sniffing, namely sampling odors actively, plays an important role in olfactory system, especially in certain scenarios such as novel odorant detection. While the computational advantages of high frequency sampling have not been yet elucidated, here, in order to motivate further investigation in active sampling strategies, we share the data from an artificial olfactory system made of 16 MOX gas sensors under gas flow modulation. The data were acquired on a custom set up featured by an external mechanical ventilator that emulates the biological respiration cycle. 58 samples were recorded in response to a relatively broad set of 12 gas classes, defined from different binary mixtures of acetone and ethanol in air. The acquired time series show two dominant frequency bands: the low-frequency signal corresponds to a conventional response curve of a sensor in response to a gas pulse, and the high-frequency signal has a clear principal harmonic at the respiration frequency. The data are related to the study in [1], and the data analysis results reported there should be considered as a reference point.The data presented here have been deposited to the web site of The University of California at Irvine (UCI) Machine Learning Repository (https://archive.ics.uci.edu/ml/datasets/Gas+sensor+array+under+flow+modulation). The code repository for reproducible analysis applied to the data is hosted at the GutHub web site (https://github.com/variani/pulmon).The data and code can be used upon citation of [1].

Venous thromboembolism (VTE) is one of the major causes of cancer-associated mortality. Risk for developing VTE increases when in chemotherapy, mainly in the outpatient setting. Current risk scores for predicting chemotherapy-associated VTE have low/moderate discrimination capacity. These models use clinical parameters. ThromboinCode (TiC) is a new tool for VTE risk prediction using an algorithm that combines a genetic risk score (GRS) with subject's VTE clinical risk parameters (cancer type and cancer disease status "CDS", included).To evaluate whether TiC predicts better the risk for chemotherapy-associated VTE than Khorana score.A prospective, observational study including 251 patients with locally advanced or metastatic cancer (colon, stomach, pancreas and lung) receiving systemic outpatient chemotherapy. Patients are followed-up for 6 months. Three predictive models were compared: a) Khorana score; b) Khorana score plus CDS and c) TiC. Genetic variants included in TiC are FVL, PT, F5 rs118203906 and rs118203905, F12 rs1801020, F13 rs5985, SERPINC1 rs121909548, SERPINEA10 rs2232698 and A1 blood group rs8176719, rs7853989, rs8176743, rs8176750. Clinical risk factors in TiC are age, sex, family history of VTE, BMI, smoking, diabetes, type of cancer and CDS. Prediction capacity of each model was assessed in terms of the discrimination (area under the receiver operating characteristic curve, AUC).The incidence of VTE at 6 months was 23.11%. Korana score had an AUC of 0.550 (95% CI 0.485-0.613, p=0.2162), sensitivity 64.41, specificity 46.56, positive likelihood ratio of 1.21, negative likelihood ratio of 0.76. Khorana score+CDS prediction model had an AUC of 0.609 (95% CI 0.545-0.670, p=0.008), sensitivity 70.69, specificity 50.53, positive likelihood ratio of 1.25, negative likelihood ratio of 0.67. Discrimination increased significantly with TiC (AUC: 0.70; 95% CI 0.636 - 0.753, p0.0001), sensitivity 79.31, specificity 55.44, positive likelihood ratio of 1.78, negative likelihood ratio of 0.37. The discrimination with TiC was significantly higher than with the other two predictive models (p0.05).We conclude that TiC predicts chemotherapy-associated VTE risk significantly better than Khorana score with CDS.

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.

This manuscript introduces a software tool that allows for the design of synthetic experiments in machine olfaction. The proposed software package includes both, a virtual sensor array that reproduces the diversity and response of a polymer array and tools for data generation. The synthetic array of sensors allows for the generation of chemosensor data with a variety of characteristics: unlimited number of sensors, support of multicomponent gas mixtures and full parametric control of the noise in the system. The artificial sensor array is inspired from a reference database of seventeen polymeric sensors with concentration profiles for three analytes. The main features in the sensor data, like sensitivity, diversity, drift and sensor noise, are captured by a set of models under simplified assumptions. The generator of sensor signals can be used in applications related to test and benchmarking of signal processing methods, neuromorphic simulations in machine olfaction and educational tools. The software is implemented in R language and can be freely accessed at: http://chemosensors.r-forge.r-project.org/ .

This paper depicts a virtual sensor array that allows the user to generate gas sensor synthetic data while controlling a wide variety of the characteristics of the sensor array response: arbitrary number of sensors, support for multi‐component gas mixtures and full control of the noise in the system such as sensor drift or sensor aging. The artificial sensor array response is inspired on the response of 17 polymeric sensors for three analytes during 7 month. The main trends in the synthetic gas sensor array, such as sensitivity, diversity, drift and sensor noise, are user controlled. Sensor sensitivity is modeled by an optionally linear or nonlinear method (spline based). The toolbox on data generation is implemented in open source R language for statistical computing and can be freely accessed as an educational resource or benchmarking reference. The software package permits the design of scenarios with a very large number of sensors (over 10000 sensels), which are employed in the test and benchmarking o...

This work describes the data from navigation experiments with the mobile robot, equipped with the sensor array of three MOX gas sensors. Performed four series of measurements aim to explore the capabilities of sensor array to build the odour map with one or two odour sources in the wind tunnel space. It was demonstrated that the method based on Independent Component Analysis (ICA) is able to discriminate two odour sources, that in future can be used in the surge‐and‐cast robot navigation algorithm.

A new drift compensation method based on common principal component analysis (CPCA) is proposed. The drift variance in data is found as the principal components computed by CPCA. This method finds components that are common for all gasses in feature space. The method is compared in classification task with respect to the other approaches published where the drift direction is estimated through a principal component analysis (PCA) of a reference gas. The proposed new method – employing no specific reference gas, but information from all gases – has shown the same performance as the traditional approach with the best-fitted reference gas. Results are shown with data lasting 7 months including three gases at different concentrations for an array of 17 polymeric sensors.

The design of the signal and data processing algorithms requires a validation stage and some data relevant for a validation procedure. While the practice to share public data sets and make use of them is a recent and still on-going activity in the community, the synthetic benchmarks presented here are an option for the researches, who need data for testing and comparing the algorithms under development. The collection of synthetic benchmark data sets were generated for classification, segmentation and sensor damage scenarios, each defined at 5 difficulty levels. The published data are related to the data simulation tool, which was used to create a virtual array of 1020 sensors with a default set of parameters [1].

In this paper we propose a generic approach to the multiview clustering problem that can be applied to any number of data views and with different topologies, either continuous, discrete, graphs, or other. The proposed method is an extension of the well-established spectral clustering algorithm to integrate the information from several data views in the partition solution. The algorithm, therefore, resolves a joint cluster structure which could be present in all views, which enables researchers to better resolve data structures in data fusion problems The application of this novel clustering approach covers an extended number of machine learning unsupervised clustering problems including biomedical analysis or machine vision.