Mapping genome-wide neuropsychiatric mutation effects on functional brain connectivity : c opy number variants delineate dimensions contributing to autism and schizophrenia

Les recherches menées pour comprendre les troubles du spectre autistique (TSA) et la schizophrénie (SZ) ont communément utilisé une approche dite descendante, partant du diagnostic clinique pour investiguer des phénotypes intermédiaires cérébraux ainsi que des variations génétiques associées. Des études transdiagnostiques récentes ont remis en question ces frontières nosologiques, et suggèrent des mécanismes étiologiques imbriqués. L’approche montante propose de composer des groupes de porteurs d’un même variant génétique afin d’investiguer leur contribution aux conditions neuropsychiatriques (NPs) associées. Les variations du nombre de copies (CNV, perte ou gain d’un fragment d’ADN) figurent parmi les facteurs biologiques les plus associés aux NPs, et sont dès lors des candidats particulièrement appropriés. Les CNVs induisant un risque pour des conditions similaires, nous posons l’hypothèse que des classes entières de CNVs convergent sur des dimensions d’altérations cérébrales qui contribuent aux NPs. L’imagerie fonctionnelle au repos (rs-fMRI) s’est révélée un outil prometteur en psychiatrie, mais presqu’aucune étude n’a été menée pour comprendre l’impact des CNVs sur la connectivité fonctionnelle cérébrale (FC). Nos objectifs étaient de: 1) Caractériser l’effet des CNVs sur la FC; 2) Rechercher la présence des motifs conférés par ces signatures biologiques dans des conditions idiopathiques; 3) Tester si la suppression de gènes intolérants à l’haploinsuffisance réorganise la FC de manière indépendante à leur localisation dans le génome. Nous avons agrégé des données de rs-fMRI chez: 502 porteurs de 8 CNVs associées aux NPs (CNVs-NP), de 4 CNVs sans association établie, ainsi que de porteurs de CNVs-NPs éparses; 756 sujets ayant un diagnostic de TSA, de SZ, ou de trouble déficitaire de l’attention/hyperactivité (TDAH), et 5377 contrôles. Les analyses du connectome entier ont montré un effet de dosage génique positif pour les CNVs 22q11.2 et 1q21.1, et négatif pour le 16p11.2. La taille de l’effet des CNVs sur la FC était corrélée au niveau de risque psychiatrique conféré par le CNV. En accord avec leurs effets sur la cognition, l’effet des délétions sur la FC était plus élevé que celui des duplications. Nous avons identifié des similarités entre les motifs cérébraux conférés par les CNVs-NP, et l’architecture fonctionnelle des individus avec NPs. Le niveau de similarité était associé à la sévérité du CNV, et était plus fort avec la SZ et les TSA qu’avec les TDAH. La comparaison des motifs conférés par les délétions les plus sévères (16p11.2, 22q11.2) à l’échelle fonctionnelle, et d’expression génique, nous a confirmé l’existence présumée de relation entre les mutations elles-mêmes. À l’aide d’une mesure d’intolérance aux mutations (pLI), nous avons pu inclure tous les porteurs de CNVs disponibles, et ainsi identifier un profil d’haploinsuffisance impliquant le thalamus, le cortex antérieur cingulaire, et le réseau somato-moteur, associé à une diminution de mesure d’intelligence générale. Enfin, une analyse d’exploration factorielle nous a permis de confirmer la contribution de ces régions cérébrales à 3 composantes latentes partagées entre les CNVs et les NPs. Nos résultats ouvrent de nouvelles perspectives dans la compréhension des mécanismes polygéniques à l’oeuvre dans les maladies mentales, ainsi que des effets pléiotropiques des CNVs.Research on Autism Spectrum Disorder (ASD) and schizophrenia (SZ) has mainly adopted a ‘top-down’ approach, starting from psychiatric diagnosis, and moving to intermediate brain phenotypes and underlying genetic factors. Recent cross-disorder studies have raised questions about diagnostic boundaries and pleiotropic mechanisms. By contrast, the recruitment of groups based on the presence of a genetic risk factor allows for the investigation of molecular pathways related to a particular risk for neuropsychiatric conditions (NPs). Copy number variants (CNVs, loss or gain of a DNA segment), which confer high risk for NPs are natural candidates to conduct such bottom-up approaches. Because CNVs have a similar range of adverse effects on NPs, we hypothesized that entire classes of CNVs may converge upon shared connectivity dimensions contributing to mental illness. Resting-state functional MRI (rs-fMRI) studies have provided critical insight into the architecture of brain networks involved in NPs, but so far only a few studies have investigated networks modulated by CNVs. We aimed at 1) Delineating the effects of neuropsychiatric variants on functional connectivity (FC), 2) Investigating whether the alterations associated with CNVs are also found among idiopathic psychiatric populations, 3) Testing whether deletions reorganize FC along general dimensions, irrespective of their localization in the genome. We gathered rsfMRI data on 502 carriers of eight NP-CNVs (high-risk), four CNVs without prior association to NPs as well as carriers of eight scarcer NP-CNVs. We also analyzed 756 subjects with idiopathic ASD, SZ, and attention deficit hyperactivity disorder (ADHD), and 5,377 controls. Connectome-wide analyses showed a positive gene dosage effect for the 22q11.2 and 1q21.1 CNVs, and a negative association for the 16p11.2 CNV. The effect size of CNVs on relative FC (mean-connectivity adjusted) was correlated with the known level of NP-risk conferred by CNVs. Consistent with results on cognition, we also reported that deletions had a larger effect size on FC than duplications. We identified similarities between high-risk CNV profiles and the connectivity architecture of individuals with NPs. The level of similarity was associated with mutation severity and was strongest in SZ, followed by ASD, and ADHD. The similarity was driven by the thalamus, and the posterior cingulate cortex, previously identified as hubs in transdiagnostic psychiatric studies. These results raised questions about shared mechanisms across CNVs. By comparing deletions at the 16p11.2 and 22q11.2 loci, we identified similarities at the connectivity, and at the gene expression level. We extended this work by pooling all deletions available for analysis. We asked if connectivity alterations were associated with the severity of deletions scored using pLI, a measure of intolerance to haploinsufficiency. The haploinsufficiency profile involved the thalamus, anterior cingulate cortex, and somatomotor network and was correlated with lower general intelligence and higher autism severity scores in 3 unselected and disease cohorts. An exploratory factor analysis confirmed the contribution of these regions to three latent components shared across CNVs and NPs. Our results open new avenues for understanding polygenicity in psychiatric conditions, and the pleiotropic effect of CNVs on cognition and on risk for neuropsychiatric disorders

Evolving toward a human-cell based and multiscale approach to drug discovery for CNS disorders

A disruptive approach to therapeutic discovery and development is required in order to significantly improve the success rate of drug discovery for central nervous system (CNS) disorders. In this review, we first assess the key factors contributing to the frequent clinical failures for novel drugs. Second, we discuss cancer translational research paradigms that addressed key issues in drug discovery and development and have resulted in delivering drugs with significantly improved outcomes for patients. Finally, we discuss two emerging technologies that could improve the success rate of CNS therapies: human induced pluripotent stem cell (hiPSC)-based studies and multiscale biology models. Coincident with advances in cellular technologies that enable the generation of hiPSCs directly from patient blood or skin cells, together with methods to differentiate these hiPSC lines into specific neural cell types relevant to neurological disease, it is also now possible to combine data from large-scale forward genetics and post-mortem global epigenetic and expression studies in order to generate novel predictive models. The application of systems biology approaches to account for the multiscale nature of different data types, from genetic to molecular and cellular to clinical, can lead to new insights into human diseases that are emergent properties of biological networks, not the result of changes to single genes. Such studies have demonstrated the heterogeneity in etiological pathways and the need for studies on model systems that are patient-derived and thereby recapitulate neurological disease pathways with higher fidelity. In the context of two common and presumably representative neurological diseases, the neurodegenerative disease Alzheimer’s Disease (AD), and the psychiatric disorder schizophrenia (SZ), we propose the need for, and exemplify the impact of, a multiscale biology approach that can integrate panomic, clinical, imaging, and literature data in order to c

Genome-scale neurogenetics: methodology and meaning

Genetic analysis is currently offering glimpses into molecular mechanisms underlying such neuropsychiatric disorders as schizophrenia, bipolar disorder and autism. After years of frustration, success in identifying disease-associated DNA sequence variation has followed from new genomic technologies, new genome data resources, and global collaborations that could achieve the scale necessary to find the genes underlying highly polygenic disorders. Here we describe early results from genome-scale studies of large numbers of subjects and the emerging significance of these results for neurobiology

Complexity and integration. A philosophical analysis of how cancer complexity can be faced in the era of precision medicine

Complexity and integration are longstanding widely debated issues in philosophy of science and recent contributions have largely focused on biology and biomedicine. This paper specifically considers some methodological novelties in cancer research, motivated by various features of tumours as complex diseases, and shows how they encourage some rethinking of philosophical discourses on those topics. In particular, we discuss the integrative cluster approach, and analyse its potential in the epistemology of cancer. We suggest that, far from being the solution to tame cancer complexity, this approach offers a philosophically interesting new manner of considering integration, and show how it can help addressing the apparent contrast between a pluralistic and a unitary account

Translational studies on bipolar disorder and anorexia nervosa

Translational medicine aims at closing the gap between basic and clinical sciences in an integrative way. Psychiatry is one of the few medical specialties in which diagnosis is primarily based on clinical observation and all mental disorders are defined by abnormal behaviors and cognitions. The lack of biomarkers supporting diagnostic and therapeutic procedures has been a challenge in psychiatry. A better biological understanding is needed to move the field forward, it will enhance diagnostics and treatment, while reducing the stigma that surrounds mental disorders that are so poorly understood. Over the last years, advances in fundamental sciences like genetics and neuroscience have made it clear that there is shared biology between many psychiatric disorders and that integration of methods might lead to new understandings. The studies presented in this thesis focus on bipolar disorder (BD) and anorexia nervosa (AN), both severe mental disorders with high suicide rates, high heritability, and both lacking in biological understanding. BD, formerly known as manic-depressive disorder, is a mood disorder, characterized by manic or hypomanic episodes, often in combination with depressive episodes. AN is an eating disorder characterized by severe weight loss together with pathological behaviors. This thesis includes five main studies on the biology underlying these disorders, based on large, well characterized cohorts, covering several methods, including genetic, imaging and protein markers, as well as preliminary data on the establishment of in vitro models. Specifically, in study I, we attempted to replicate previously published findings on the association between subphenotypes of bipolar disorder and genetic variations in the AKT1 gene. Using frequentist and Bayesian approaches, as well as publicly available results from genome-wide association studies (GWAS), we were able to reject previously proposed associations. In study II, we explored the effects of genetic variations in genes involved in glutamate regulation on glutamate levels in two brain regions and their associations with other phenotypes. We found that the minor allele of rs3812778/rs3829280 in the 5’-untranslated region of SLC1A2, coding for a glutamate transporter, is associated (1) with increased glutamate levels in the anterior cingulate cortex, (2) with increased expression levels, in several brain regions, of the transmembrane receptor gene CD44, which is implicated in inflammation and brain development, as well as (3) with an increased risk for rapid-cycling in bipolar disorder, potentially linking CD44/SLC1A2 to a more severe phenotype of BD. In study III, we investigated the effects of clinical and genetic parameters on lithium pharmacokinetics in order to better understand lithium biology and improve lithium dose prediction models for bipolar patients, using the ratio between serum lithium and daily lithium intake, as outcome. We were able to confirm the association of several clinical predictors. Although no genome-wide significant locus was found, we report that genetic variation is important and might influence the outcome. Finally, based on the results obtained in the study, we developed a prediction algorithm that can be tested in the clinic. In study IV, we investigated the involvement of neuronal degeneration in AN by studying neurofilament light chain (NfL), a known marker of neurodegeneration, in a case-control setting and found increased levels of NfL in patients with active AN in two different cohorts. In study V, we studied the involvement of inflammation in AN, using a panel of 92 inflammatory markers in a case-control setting and report an aberrant inflammatory profile in patients with active AN, but not in patients that have recovered from AN. These studies exemplify possible approaches that can be taken in translational psychiatry. The integration of clinical, technical and analytical approaches illustrates important learning outcomes for an aspiring clinical scientist in psychiatr

Convergent Approaches for Defining Functional Imaging Endophenotypes in Schizophrenia

In complex genetic disorders such as schizophrenia, endophenotypes have potential utility both in identifying risk genes and in illuminating pathophysiology. This is due to their presumed status as closer in the etiopathological pathway to the causative genes than is the currently defining clinical phenomenology of the illness and thus their simpler genetic architecture than that of the full syndrome. There, many genes conferring slight individual risk are additive or epistatic (interactive) with regard to cumulative schizophrenia risk. In addition the use of endophenotypes has encouraged a conceptual shift away from the exclusive study of categorical diagnoses in manifestly ill patients, towards the study of quantitative traits in patients, unaffected relatives and healthy controls. A more recently employed strategy is thus to study unaffected first-degree relatives of schizophrenia patients, who share some of the genetic diathesis without illness-related confounds that may themselves impact fMRI task performance. Consistent with the multiple biological abnormalities associated with the disorder, many candidate endophenotypes have been advanced for schizophrenia, including measures derived from structural brain imaging, EEG, sensorimotor integration, eye movements and cognitive performance (Allen et al., 2009), but recent data derived from quantitative functional brain imaging measures present additional attractive putative endophenotypes. We will review two major, conceptually different approaches that use fMRI in this context. One, the dominant paradigm, employs defined cognitive tasks on which schizophrenia patients perform poorly as “cognitive stress tests”. The second uses very simple probes or “task-free” approaches where performance in patients and controls is equal. We explore the potential advantages and disadvantages of each method, the associated data analytic approaches and recent studies exploring their interface with the genetic risk architecture of schizophrenia

Any-way and Sparse Analyses for Multimodal Fusion and Imaging Genomics

This dissertation aims to develop new algorithms that leverage sparsity and mutual information across data modalities built upon the independent component analysis (ICA) framework to improve the performance of current ICA-based multimodal fusion approaches. These algorithms are further applied to both simulated data and real neuroimaging and genomic data to examine their performance. The identified neuroimaging and genomic patterns can help better delineate the pathology of mental disorders or brain development. To alleviate the signal-background separation difficulties in infomax-decomposed sources for genomic data, we propose a sparse infomax by enhancing a robust sparsity measure, the Hoyer index. Hoyer index is scale-invariant and well suited for ICA frameworks since the scale of decomposed sources is arbitrary. Simulation results demonstrate that sparse infomax increases the component detection accuracy for situations where the source signal-to-background (SBR) ratio is low, particularly for single nucleotide polymorphism (SNP) data. The proposed sparse infomax is further extended into two data modalities as a sparse parallel ICA for applications to imaging genomics in order to investigate the associations between brain imaging and genomics. Simulation results show that sparse parallel ICA outperforms parallel ICA with improved accuracy for structural magnetic resonance imaging (sMRI)-SNP association detection and component spatial map recovery, as well as with enhanced sparsity for sMRI and SNP components under noisy cases. Applying the proposed sparse parallel ICA to fuse the whole-brain sMRI and whole-genome SNP data of 24985 participants in the UK biobank, we identify three stable and replicable sMRI-SNP pairs. The identified sMRI components highlight frontal, parietal, and temporal regions and associate with multiple cognitive measures (with different association strengths in different age groups for the temporal component). Top SNPs in the identified SNP factor are enriched in inflammatory disease and inflammatory response pathways, which also regulate gene expression, isoform percentage, transcription expression, or methylation level in the frontal region, and the regulation effects are significantly enriched. Applying the proposed sparse parallel ICA to imaging genomics in attention-deficit/hyperactivity disorder (ADHD), we identify and replicate one SNP component related to gray matter volume (GMV) alterations in superior and middle frontal gyri underlying working memory deficit in adults and adolescents with ADHD. The association is more significant in ADHD families than controls and stronger in adults and older adolescents than younger ones. The identified SNP component highlights SNPs in long non-coding RNAs (lncRNAs) in chromosome 5 and in several protein-coding genes that are involved in ADHD, such as MEF2C, CADM2, and CADPS2. Top SNPs are enriched in human brain neuron cells and regulate gene expression, isoform percentage, transcription expression, or methylation level in the frontal region. Moreover, to increase the flexibility and robustness in mining multimodal data, we propose aNy-way ICA, which optimizes the entire correlation structure of linked components across any number of modalities via the Gaussian independent vector analysis and simultaneously optimizes independence via separate (parallel) ICAs. Simulation results demonstrate that aNy-way ICA recover sources and loadings, as well as the true covariance patterns with improved accuracy compared to existing multimodal fusion approaches, especially under noisy conditions. Applying the proposed aNy-way ICA to integrate structural MRI, fractal n-back, and emotion identification task functional MRIs collected in the Philadelphia Neurodevelopmental Cohort (PNC), we identify and replicate one linked GMV-threat-2-back component, and the threat and 2-back components are related to intelligence quotient (IQ) score in both discovery and replication samples. Lastly, we extend the proposed aNy-way ICA with a reference constraint to enable prior-guided multimodal fusion. Simulation results show that aNy-way ICA with reference recovers the designed linkages between reference and modalities, cross-modality correlations, as well as loading and component matrices with improved accuracy compared to multi-site canonical correlation analysis with reference (MCCAR)+joint ICA under noisy conditions. Applying aNy-way ICA with reference to supervise structural MRI, fractal n-back, and emotion identification task functional MRIs fusion in PNC with IQ as the reference, we identify and replicate one IQ-related GMV-threat-2-back component, and this component is significantly correlated across modalities in both discovery and replication samples.Ph.D

Plastic diagnostics : the remaking of disease and evidence in personalized medicine

Politically authorized reports on personalized and precision medicine stress an urgent need for finer-grained disease categories and faster taxonomic revision, through integration of genomic and phenotypic data. Developing a data-driven taxonomy is, however, not as simple as it sounds. It is often assumed that an integrated data infrastructure is relatively easy to implement in countries that already have highly centralized and digitalized health care systems. Our analysis of initiatives associated with the Danish National Genome Center, recently launched to bring Denmark to the forefront of personalized medicine, tells a different story. Through a “meta-taxonomy” of taxonomic revisions, we discuss what a genomics-based disease taxonomy entails, epistemically as well as organizationally. Whereas policy reports promote a vision of seamless data integration and standardization, we highlight how the envisioned strategy imposes significant changes on the organization of health care systems. Our analysis shows how persistent tensions in medicine between variation and standardization, and between change and continuity, remain obstacles for the production as well as the evaluation of genomics-based taxonomies of difference. We identify inherent conflicts between the ideal of dynamic revision and existing regulatory functions of disease categories in, for example, the organization and management of health care systems. Moreover, we raise concerns about shifts in the regulatory regime of evidence standards, where clinical care increasingly becomes a vehicle for biomedical research