Dichotomous metabolic networks govern human ILC2 proliferation and function.

Group 2 innate lymphoid cells (ILC2s) represent innate homologs of type 2 helper T cells (TH2) that participate in immune defense and tissue homeostasis through production of type 2 cytokines. While T lymphocytes metabolically adapt to microenvironmental changes, knowledge of human ILC2 metabolism is limited, and its key regulators are unknown. Here, we show that circulating 'naive' ILC2s have an unexpected metabolic profile with a higher level of oxidative phosphorylation (OXPHOS) than natural killer (NK) cells. Accordingly, ILC2s are severely reduced in individuals with mitochondrial disease (MD) and impaired OXPHOS. Metabolomic and nutrient receptor analysis revealed ILC2 uptake of amino acids to sustain OXPHOS at steady state. Following activation with interleukin-33 (IL-33), ILC2s became highly proliferative, relying on glycolysis and mammalian target of rapamycin (mTOR) to produce IL-13 while continuing to fuel OXPHOS with amino acids to maintain cellular fitness and proliferation. Our results suggest that proliferation and function are metabolically uncoupled in human ILC2s, offering new strategies to target ILC2s in disease settings.

Integrating multiomics and prior knowledge: a study of the Graphnet penalty impact.

MOTIVATION: In the field of oncology, statistical models are used for the discovery of candidate factors that influence the development of the pathology or its outcome. These statistical models can be designed in a multiblock framework to study the relationship between different multiomic data, and variable selection is often achieved by imposing constraints on the model parameters. A priori graph constraints have been used in the literature as a way to improve feature selection in the model, yielding more interpretability. However, it is still unclear how these graphs interact with the models and how they impact the feature selection. Additionally, with the availability of different graphs encoding different information, one can wonder how the choice of the graph meaningfully impacts the results obtained. RESULTS: We proposed to study the graph penalty impact on a multiblock model. Specifically, we used the SGCCA as the multiblock framework. We studied the effect of the penalty on the model using the TCGA-LGG dataset. Our findings are 3-fold. We showed that the graph penalty increases the number of selected genes from this dataset, while selecting genes already identified in other works as pertinent biomarkers in the pathology. We demonstrated that using different graphs leads to different though consistent results, but that graph density is the main factor influencing the obtained results. Finally, we showed that the graph penalty increases the performance of the survival prediction from the model-derived components and the interpretability of the results. AVAILABILITY AND IMPLEMENTATION: Source code is freely available at https://github.com/neurospin/netSGCCA.

Host genetic control of natural killer cell diversity revealed in the Collaborative Cross.

Natural killer (NK) cells are innate effectors armed with cytotoxic and cytokine-secreting capacities whose spontaneous antitumor activity is key to numerous immunotherapeutic strategies. However, current mouse models fail to mirror the extensive immune system variation that exists in the human population which may impact on NK cell-based therapies. We performed a comprehensive profiling of NK cells in the Collaborative Cross (CC), a collection of novel recombinant inbred mouse strains whose genetic diversity matches that of humans, thereby providing a unique and highly diverse small animal model for the study of immune variation. We demonstrate that NK cells from CC strains displayed a breadth of phenotypic and functional variation reminiscent of that reported for humans with regards to cell numbers, key marker expression, and functional capacities. We took advantage of the vast genetic diversity of the CC and identified nine genomic loci through quantitative trait locus mapping driving these phenotypic variations. SNP haplotype patterns and variant effect analyses identified candidate genes associated with lung NK cell numbers, frequencies of CD94+ NK cells, and expression levels of NKp46. Thus, we demonstrate that the CC represents an outstanding resource to study NK cell diversity and its regulation by host genetics.

Multitrait GWAS to connect disease variants and biological mechanisms.

Genome-wide association studies (GWASs) have uncovered a wealth of associations between common variants and human phenotypes. Here, we present an integrative analysis of GWAS summary statistics from 36 phenotypes to decipher multitrait genetic architecture and its link with biological mechanisms. Our framework incorporates multitrait association mapping along with an investigation of the breakdown of genetic associations into clusters of variants harboring similar multitrait association profiles. Focusing on two subsets of immunity and metabolism phenotypes, we then demonstrate how genetic variants within clusters can be mapped to biological pathways and disease mechanisms. Finally, for the metabolism set, we investigate the link between gene cluster assignment and the success of drug targets in randomized controlled trials.

The genetic architecture of language functional connectivity.

Language is a unique trait of the human species, of which the genetic architecture remains largely unknown. Through language disorders studies, many candidate genes were identified. However, such complex and multifactorial trait is unlikely to be driven by only few genes and case-control studies, suffering from a lack of power, struggle to uncover significant variants. In parallel, neuroimaging has significantly contributed to the understanding of structural and functional aspects of language in the human brain and the recent availability of large scale cohorts like UK Biobank have made possible to study language via image-derived endophenotypes in the general population. Because of its strong relationship with task-based fMRI (tbfMRI) activations and its easiness of acquisition, resting-state functional MRI (rsfMRI) have been more popularised, making it a good surrogate of functional neuronal processes. Taking advantage of such a synergistic system by aggregating effects across spatially distributed traits, we performed a multivariate genome-wide association study (mvGWAS) between genetic variations and resting-state functional connectivity (FC) of classical brain language areas in the inferior frontal (pars opercularis, triangularis and orbitalis), temporal and inferior parietal lobes (angular and supramarginal gyri), in 32,186 participants from UK Biobank. Twenty genomic loci were found associated with language FCs, out of which three were replicated in an independent replication sample. A locus in 3p11.1, regulating EPHA3 gene expression, is found associated with FCs of the semantic component of the language network, while a locus in 15q14, regulating THBS1 gene expression is found associated with FCs of the perceptual-motor language processing, bringing novel insights into the neurobiology of language.

Immune response profiling of patients with spondyloarthritis reveals signalling networks mediating TNF-blocker function in vivo.

OBJECTIVES: Antitumour necrosis factor (TNF) therapy has revolutionised treatment of several chronic inflammatory diseases, including spondyloarthritis (SpA). However, TNF inhibitors (TNFi) are not effective in all patients and the biological basis for treatment failure remains unknown. We have analysed induced immune responses to define the mechanism of action of TNF blockers in SpA and to identify immunological correlates of responsiveness to TNFi. METHODS: Immune responses to microbial and pathway-specific stimuli were analysed in peripheral blood samples from 80 patients with axial SpA before and after TNFi treatment, using highly standardised whole-blood stimulation assays. Cytokines and chemokines were measured in a Clinical Laboratory Improvement Amendments (CLIA)-certified laboratory, and gene expression was monitored using nCounter assays. RESULTS: Anti-TNF therapy induced profound changes in patients' innate immune responses. TNFi action was selective, and had only minor effects on Th1/Th17 immunity. Modular transcriptional repertoire analysis identified prostaglandin E2 synthesis and signalling, leucocyte recirculation, macrophage polarisation, dectin and interleukin (IL)-1 signalling, as well as the nuclear factor kappa B (NF-kB) transcription factor family as key pathways targeted by TNF blockers in vivo. Analysis of induced immune responses before treatment initiation revealed that expression of molecules associated with leucocyte adhesion and invasion, chemotaxis and IL-1 signalling are correlated with therapeutic responses to anti-TNF. CONCLUSIONS: We show that TNFi target multiple immune cell pathways that cooperate to resolve inflammation. We propose that immune response profiling provides new insight into the biology of TNF-blocker action in patients and can identify signalling pathways associated with therapeutic responses to biological therapies.

A strategy for multimodal data integration: application to biomarkers identification in spinocerebellar ataxia.

The growing number of modalities (e.g. multi-omics, imaging and clinical data) characterizing a given disease provides physicians and statisticians with complementary facets reflecting the disease process but emphasizes the need for novel statistical methods of data analysis able to unify these views. Such data sets are indeed intrinsically structured in blocks, where each block represents a set of variables observed on a group of individuals. Therefore, classical statistical tools cannot be applied without altering their organization, with the risk of information loss. Regularized generalized canonical correlation analysis (RGCCA) and its sparse generalized canonical correlation analysis (SGCCA) counterpart are component-based methods for exploratory analyses of data sets structured in blocks of variables. Rather than operating sequentially on parts of the measurements, the RGCCA/SGCCA-based integrative analysis method aims at summarizing the relevant information between and within the blocks. It processes a priori information defining which blocks are supposed to be linked to one another, thus reflecting hypotheses about the biology underlying the data blocks. It also requires the setting of extra parameters that need to be carefully adjusted.Here, we provide practical guidelines for the use of RGCCA/SGCCA. We also illustrate the flexibility and usefulness of RGCCA/SGCCA on a unique cohort of patients with four genetic subtypes of spinocerebellar ataxia, in which we obtained multiple data sets from brain volumetry and magnetic resonance spectroscopy, and metabolomic and lipidomic analyses. As a first step toward the extraction of multimodal biomarkers, and through the reduction to a few meaningful components and the visualization of relevant variables, we identified possible markers of disease progression.

Adaptive human immunity drives remyelination in a mouse model of demyelination.

One major challenge in multiple sclerosis is to understand the cellular and molecular mechanisms leading to disease severity progression. The recently demonstrated correlation between disease severity and remyelination emphasizes the importance of identifying factors leading to a favourable outcome. Why remyelination fails or succeeds in multiple sclerosis patients remains largely unknown, mainly because remyelination has never been studied within a humanized pathological context that would recapitulate major events in plaque formation such as infiltration of inflammatory cells. Therefore, we developed a new paradigm by grafting healthy donor or multiple sclerosis patient lymphocytes in the demyelinated lesion of nude mice spinal cord. We show that lymphocytes play a major role in remyelination whose efficacy is significantly decreased in mice grafted with multiple sclerosis lymphocytes compared to those grafted with healthy donors lymphocytes. Mechanistically, we demonstrated in vitro that lymphocyte-derived mediators influenced differentiation of oligodendrocyte precursor cells through a crosstalk with microglial cells. Among mice grafted with lymphocytes from different patients, we observed diverse remyelination patterns reproducing for the first time the heterogeneity observed in multiple sclerosis patients. Comparing lymphocyte secretory profile from patients exhibiting high and low remyelination ability, we identified novel molecules involved in oligodendrocyte precursor cell differentiation and validated CCL19 as a target to improve remyelination. Specifically, exogenous CCL19 abolished oligodendrocyte precursor cell differentiation observed in patients with high remyelination pattern. Multiple sclerosis lymphocytes exhibit intrinsic capacities to coordinate myelin repair and further investigation on patients with high remyelination capacities will provide new pro-regenerative strategies.

Clinical-genetic model predicts incident impulse control disorders in Parkinson's disease.

OBJECTIVES: Impulse control disorders (ICD) are commonly associated with dopamine replacement therapy (DRT) in patients with Parkinson's disease (PD). Our aims were to estimate ICD heritability and to predict ICD by a candidate genetic multivariable panel in patients with PD. METHODS: Data from de novo patients with PD, drug-naïve and free of ICD behaviour at baseline, were obtained from the Parkinson's Progression Markers Initiative cohort. Incident ICD behaviour was defined as positive score on the Questionnaire for Impulsive-Compulsive Disorders in PD. ICD heritability was estimated by restricted maximum likelihood analysis on whole exome sequencing data. 13 candidate variants were selected from the DRD2, DRD3, DAT1, COMT, DDC, GRIN2B, ADRA2C, SERT, TPH2, HTR2A, OPRK1 and OPRM1 genes. ICD prediction was evaluated by the area under the curve (AUC) of receiver operating characteristic (ROC) curves. RESULTS: Among 276 patients with PD included in the analysis, 86% started DRT, 40% were on dopamine agonists (DA), 19% reported incident ICD behaviour during follow-up. We found heritability of this symptom to be 57%. Adding genotypes from the 13 candidate variants significantly increased ICD predictability (AUC=76%, 95% CI (70% to 83%)) compared to prediction based on clinical variables only (AUC=65%, 95% CI (58% to 73%), p=0.002). The clinical-genetic prediction model reached highest accuracy in patients initiating DA therapy (AUC=87%, 95% CI (80% to 93%)). OPRK1, HTR2A and DDC genotypes were the strongest genetic predictive factors. CONCLUSIONS: Our results show that adding a candidate genetic panel increases ICD predictability, suggesting potential for developing clinical-genetic models to identify patients with PD at increased risk of ICD development and guide DRT management.

Variable selection for generalized canonical correlation analysis.

Regularized generalized canonical correlation analysis (RGCCA) is a generalization of regularized canonical correlation analysis to 3 or more sets of variables. RGCCA is a component-based approach which aims to study the relationships between several sets of variables. The quality and interpretability of the RGCCA components are likely to be affected by the usefulness and relevance of the variables in each block. Therefore, it is an important issue to identify within each block which subsets of significant variables are active in the relationships between blocks. In this paper, RGCCA is extended to address the issue of variable selection. Specifically, sparse generalized canonical correlation analysis (SGCCA) is proposed to combine RGCCA with an [Formula: see text]-penalty in a unified framework. Within this framework, blocks are not necessarily fully connected, which makes SGCCA a flexible method for analyzing a wide variety of practical problems. Finally, the versatility and usefulness of SGCCA are illustrated on a simulated dataset and on a 3-block dataset which combine gene expression, comparative genomic hybridization, and a qualitative phenotype measured on a set of 53 children with glioma. SGCCA is available on CRAN as part of the RGCCA package.

Single-voxel (1)H spectroscopy in the human hippocampus at 3 T using the LASER sequence: characterization of neurochemical profile and reproducibility.

The hippocampus is crucial for long-term episodic memory and learning. It undergoes structural change in aging and is sensitive to neurodegenerative and psychiatric diseases. MRS studies have seldom been performed in the hippocampus due to technical challenges. The reproducibility of MRS in the hippocampus has not been evaluated at 3 T. The purpose of the present study was to quantify the concentration of metabolites in a small voxel placed in the hippocampus and evaluate the reproducibility of the quantification. Spectra were measured in a 2.4 mL voxel placed in the left hippocampus covering the body and most of the tail of the structure in 10 healthy subjects across three different sessions and quantified using LCModel. High-quality spectra were obtained, which allowed a reliable quantification of 10 metabolites including glutamate and glutamine. Reproducibility of MRS was evaluated with coefficient of variation, standard errors of measurement, and intraclass correlation coefficients. All of these measures showed improvement with increased number of averages. Changes of less than 5% in concentration of N-acetylaspartate, choline-containing compounds, and total creatine and of less than 10% in concentration of myo-inositol and the sum of glutamate and glutamine can be confidently detected between two measurements in a group of 20 subjects. A reliable and reproducible neurochemical profile of the human hippocampus was obtained using MRS at 3 T in a small hippocampal volume.

Hypomorphic variants of cationic amino acid transporter 3 in males with autism spectrum disorders.

Cationic amino acid transporters (CATs) mediate the entry of L-type cationic amino acids (arginine, ornithine and lysine) into the cells including neurons. CAT-3, encoded by the SLC7A3 gene on chromosome X, is one of the three CATs present in the human genome, with selective expression in brain. SLC7A3 is highly intolerant to variation in humans, as attested by the low frequency of deleterious variants in available databases, but the impact on variants in this gene in humans remains undefined. In this study, we identified a missense variant in SLC7A3, encoding the CAT-3 cationic amino acid transporter, on chromosome X by exome sequencing in two brothers with autism spectrum disorder (ASD). We then sequenced the SLC7A3 coding sequence in 148 male patients with ASD and identified three additional rare missense variants in unrelated patients. Functional analyses of the mutant transporters showed that two of the four identified variants cause severe or moderate loss of CAT-3 function due to altered protein stability or abnormal trafficking to the plasma membrane. The patient with the most deleterious SLC7A3 variant had high-functioning autism and epilepsy, and also carries a de novo 16p11.2 duplication possibly contributing to his phenotype. This study shows that rare hypomorphic variants of SLC7A3 exist in male individuals and suggest that SLC7A3 variants possibly contribute to the etiology of ASD in male subjects in association with other genetic factors.

Significant correlation between a set of genetic polymorphisms and a functional brain network revealed by feature selection and sparse Partial Least Squares.

Brain imaging is increasingly recognised as an intermediate phenotype to understand the complex path between genetics and behavioural or clinical phenotypes. In this context, a first goal is to propose methods to identify the part of genetic variability that explains some neuroimaging variability. Classical univariate approaches often ignore the potential joint effects that may exist between genes or the potential covariations between brain regions. In this paper, we propose instead to investigate an exploratory multivariate method in order to identify a set of Single Nucleotide Polymorphisms (SNPs) covarying with a set of neuroimaging phenotypes derived from functional Magnetic Resonance Imaging (fMRI). Recently, Partial Least Squares (PLS) regression or Canonical Correlation Analysis (CCA) have been proposed to analyse DNA and transcriptomics. Here, we propose to transpose this idea to the DNA vs. imaging context. However, in very high-dimensional settings like in imaging genetics studies, such multivariate methods may encounter overfitting issues. Thus we investigate the use of different strategies of regularisation and dimension reduction techniques combined with PLS or CCA to face the very high dimensionality of imaging genetics studies. We propose a comparison study of the different strategies on a simulated dataset first and then on a real dataset composed of 94 subjects, around 600,000 SNPs and 34 functional MRI lateralisation indexes computed from reading and speech comprehension contrast maps. We estimate the generalisability of the multivariate association with a cross-validation scheme and demonstrate the significance of this link, using a permutation procedure. Univariate selection appears to be necessary to reduce the dimensionality. However, the significant association uncovered by this two-step approach combining univariate filtering and L1-regularised PLS suggests that discovering meaningful genetic associations calls for a multivariate approach.

Distance between homologous chromosomes results from chromosome positioning constraints.

The organization of chromosomes is important for various biological processes and is involved in the formation of rearrangements often observed in cancer. In mammals, chromosomes are organized in territories that are radially positioned in the nucleus. However, it remains unclear whether chromosomes are organized relative to each other. Here, we examine the nuclear arrangement of 10 chromosomes in human epithelial cancer cells by three-dimensional FISH analysis. We show that their radial position correlates with the ratio of their gene density to chromosome size. We also observe that inter-homologue distances are generally larger than inter-heterologue distances. Using numerical simulations taking radial position constraints into account, we demonstrate that, for some chromosomes, radial position is enough to justify the inter-homologue distance, whereas for others additional constraints are involved. Among these constraints, we propose that nucleolar organizer regions participate in the internal positioning of the acrocentric chromosome HSA21, possibly through interactions with nucleoli. Maintaining distance between homologous chromosomes in human cells could participate in regulating genome stability and gene expression, both mechanisms that are key players in tumorigenesis.

Susceptibilities of CNS Cells towards Rabies Virus Infection Is Linked to Cellular Innate Immune Responses.

Rabies is caused by neurotropic rabies virus (RABV), contributing to 60,000 human deaths annually. Even though rabies leads to major public health concerns worldwide, we still do not fully understand factors determining RABV tropism and why glial cells are unable to clear RABV from the infected brain. Here, we compare susceptibilities and immune responses of CNS cell types to infection with two RABV strains, Tha and its attenuated variant Th2P-4M, mutated on phospho- (P-protein) and matrix protein (M-protein). We demonstrate that RABV replicates in human stem cell-derived neurons and astrocytes but fails to infect human iPSC-derived microglia. Additionally, we observed major differences in transcription profiles and quantification of intracellular protein levels between antiviral immune responses mediated by neurons, astrocytes (IFNB1, CCL5, CXCL10, IL1B, IL6, and LIF), and microglia (CCL5, CXCL10, ISG15, MX1, and IL6) upon Tha infection. We also show that P- and M-proteins of Tha mediate evasion of NF-κB- and JAK-STAT-controlled antiviral host responses in neuronal cell types in contrast to glial cells, potentially explaining the strong neuron-specific tropism of RABV. Further, Tha-infected astrocytes and microglia protect neurons from Tha infection via a filtrable and transferable agent. Overall, our study provides novel insights into RABV tropism, showing the interest in studying the interplay of CNS cell types during RABV infection.

Alterations of the axon initial segment in multiple sclerosis grey matter.

Grey matter damage has been established as a key contributor to disability progression in multiple sclerosis. Aside from neuronal loss and axonal transections, which predominate in cortical demyelinated lesions, synaptic alterations have been detected in both demyelinated plaques and normal-appearing grey matter, resulting in functional neuronal damage. The axon initial segment is a key element of neuronal function, responsible for action potential initiation and maintenance of neuronal polarity. Despite several reports of profound axon initial segment alterations in different pathological models, among which experimental auto-immune encephalomyelitis, whether the axon initial segment is affected in multiple sclerosis is still unknown. Using immunohistochemistry, we analysed axon initial segments from control and multiple sclerosis tissue, focusing on layer 5/6 pyramidal neurons in the neocortex and Purkinje cells in the cerebellum and performed analysis on the parameters known to control neuronal excitability, i.e. axon initial segment length and position. We found that the axon initial segment length was increased only in pyramidal neurons of inactive demyelinated lesions, compared with normal appearing grey matter tissue. In contrast, in both cell types, the axon initial segment position was altered, with an increased soma-axon initial segment gap, in both active and inactive demyelinated lesions. In addition, using a computational model, we show that this increased gap between soma and axon initial segment might increase neuronal excitability. Taken together, these results show, for the first time, changes of axon initial segments in multiple sclerosis, in active as well as inactive grey matter lesions in both neocortex and cerebellum, which might alter neuronal function.

High prevalence of small intestine bacteria overgrowth and asymptomatic carriage of enteric pathogens in stunted children in Antananarivo, Madagascar.

Environmental Enteric Dysfunction (EED) refers to an incompletely defined syndrome of inflammation, reduced absorptive capacity, and reduced barrier function in the small intestine. It is widespread among children and adults in low- and middle-income countries and is also associated with poor sanitation and certain gut infections possibly resulting in an abnormal gut microbiota, small intestinal bacterial overgrowth (SIBO) and stunting. We investigated bacterial pathogen exposure in stunted and non-stunted children in Antananarivo, Madagascar by collecting fecal samples from 464 children (96 severely stunted, 104 moderately stunted and 264 non-stunted) and the prevalence of SIBO in 109 duodenal aspirates from stunted children (61 from severely stunted and 48 from moderately stunted children). SIBO assessed by both aerobic and anaerobic plating techniques was very high: 85.3% when selecting a threshold of ≥105 CFU/ml of bacteria in the upper intestinal aspirates. Moreover, 58.7% of the children showed more than 106 bacteria/ml in these aspirates. The most prevalent cultivated genera recovered were Streptococcus, Neisseria, Staphylococcus, Rothia, Haemophilus, Pantoea and Branhamella. Feces screening by qPCR showed a high prevalence of bacterial enteropathogens, especially those categorized as being enteroinvasive or causing mucosal disruption, such as Shigella spp., enterotoxigenic Escherichia coli, enteropathogenic E. coli and enteroaggregative E. coli. These pathogens were detected at a similar rate in stunted children and controls, all showing no sign of severe diarrhea the day of inclusion but both living in a highly contaminated environment (slum-dwelling). Interestingly Shigella spp. was the most prevalent enteropathogen found in this study (83.3%) without overrepresentation in stunted children.

Over-optimism in bioinformatics: an illustration.

MOTIVATION: In statistical bioinformatics research, different optimization mechanisms potentially lead to 'over-optimism' in published papers. So far, however, a systematic critical study concerning the various sources underlying this over-optimism is lacking. RESULTS: We present an empirical study on over-optimism using high-dimensional classification as example. Specifically, we consider a 'promising' new classification algorithm, namely linear discriminant analysis incorporating prior knowledge on gene functional groups through an appropriate shrinkage of the within-group covariance matrix. While this approach yields poor results in terms of error rate, we quantitatively demonstrate that it can artificially seem superior to existing approaches if we 'fish for significance'. The investigated sources of over-optimism include the optimization of datasets, of settings, of competing methods and, most importantly, of the method's characteristics. We conclude that, if the improvement of a quantitative criterion such as the error rate is the main contribution of a paper, the superiority of new algorithms should always be demonstrated on independent validation data. AVAILABILITY: The R codes and relevant data can be downloaded from http://www.ibe.med.uni-muenchen.de/organisation/mitarbeiter/020_professuren/boulesteix/overoptimism/, such that the study is completely reproducible.

Use of pretransformation to cope with extreme values in important candidate features.

Extreme values in predictors often strongly affect the results of statistical analyses in high-dimensional settings. Although they frequently occur with most high-throughput techniques, the problem is often ignored in the literature. We suggest to use a very simple transformation, proposed before in a different context by Royston and Sauerbrei, as an intermediary step between array preprocessing and high-level statistical analysis. This straightforward univariate transformation identifies extreme values in continuous features and can thus be used as a diagnostic tool for outliers. The use of the transformation and its effects is demonstrated for diverse univariate and multivariate statistical analyses using nine publicly available microarray data sets.

JASS: command line and web interface for the joint analysis of GWAS results.

Genome-wide association study (GWAS) has been the driving force for identifying association between genetic variants and human phenotypes. Thousands of GWAS summary statistics covering a broad range of human traits and diseases are now publicly available. These GWAS have proven their utility for a range of secondary analyses, including in particular the joint analysis of multiple phenotypes to identify new associated genetic variants. However, although several methods have been proposed, there are very few large-scale applications published so far because of challenges in implementing these methods on real data. Here, we present JASS (Joint Analysis of Summary Statistics), a polyvalent Python package that addresses this need. Our package incorporates recently developed joint tests such as the omnibus approach and various weighted sum of Z-score tests while solving all practical and computational barriers for large-scale multivariate analysis of GWAS summary statistics. This includes data cleaning and harmonization tools, an efficient algorithm for fast derivation of joint statistics, an optimized data management process and a web interface for exploration purposes. Both benchmark analyses and real data applications demonstrated the robustness and strong potential of JASS for the detection of new associated genetic variants. Our package is freely available at https://gitlab.pasteur.fr/statistical-genetics/jass.