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Cited by 430Bioinformatics and Genomic NetworksGenetics and Neurodevelopmental DisordersFunctional Brain Connectivity Studies

Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders

Parikshak, Neelroop N., Gandal, Michael J., Geschwind, Daniel H.·2015·Nature Reviews Genetics
DOI 10.1038/nrg3934PMC PMC4699316
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Abstract

Genetic and genomic approaches have implicated hundreds of genetic loci in neurodevelopmental disorders and neurodegeneration, but mechanistic understanding continues to lag behind the pace of gene discovery. Understanding the role of specific genetic variants in the brain involves dissecting a functional hierarchy that encompasses molecular pathways, diverse cell types, neural circuits and, ultimately, cognition and behaviour. With a focus on transcriptomics, this Review discusses how high-throughput molecular, integrative and network approaches inform disease biology by placing human genetics in a molecular systems and neurobiological context. We provide a framework for interpreting network biology studies and leveraging big genomics data sets in neurobiology.

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TL;DR

This review argues that transcriptomic and integrative network methods can connect genetic risk to molecular systems in neurodevelopmental and neurodegenerative disorders. It outlines network construction, annotation, and validation steps and offers design and reproducibility recommendations for CNS post-mortem and model studies.

Key findings

  • Genetic studies have linked hundreds to thousands of loci to these disorders, and more than 1,000 Mendelian neurodevelopmental forms are known; pleiotropy suggests disruption of constrained developmental programs rather than one-to-one gene–disorder mapping.
  • In ASD cortex, co-expression analysis found an upregulated module enriched for immune/microglial genes and a downregulated module enriched for synaptic/neuronal genes; the latter was proposed as potentially causal, the former reactive. Schizophrenia studies report dysregulation of GABAergic, oligodendrocyte/myelin, mitochondrial/energy, synaptic, and immune/inflammatory pathways, with a neuronal differentiation module failing normal age-related downregulation.
  • Network approaches can organize whole-tissue transcriptomes into modules, relate them to cell types, regions, and GWAS signals, and identify hubs; examples include fetal cortical glutamatergic neuron convergence for ASD risk and TYROBP as a microglial regulatory hub in Alzheimer disease validated in mice.
  • The review recommends sample sizes: at least 15 cases and 15 controls for initial post-mortem DGE, 20 independent samples for module-level reproducibility, a minimum of 20 per condition for comparative network studies, and at least 100 cases and controls for integrating genetics with co-expression in Alzheimer disease.

Methods

  • Narrative review of systems biology and gene network studies in neurodevelopmental and neurodegenerative disorders, focused on transcriptomics but generalizable to genomic, proteomic, and epigenomic data.
  • Synthesizes findings from post-mortem human brain, mouse, fly, iPSC-derived neuron, single-cell, PPI, eQTL, GWAS, and consortium resources such as BrainSpan, GTEx, and Roadmap Epigenomics.
  • Provides a five-step network framework (node and edge specification, module identification, annotation, validation) and experimental design and reproducibility guidelines.

Limitations

  • The paper states that CNS complexity (cell type, spatial, and temporal heterogeneity), scarce human tissue and imperfect model systems, and poorly defined intermediate phenotypes hamper molecular profiling; cell type definitions remain controversial and many phenotypes are clinical/behavioral rather than mechanistic.
  • It notes that post-mortem tissue reflects late disease consequences, literature-curated pathway and PPI databases are biased and often non-neuronal, and most molecular profiling studies cannot establish causality without follow-up experiments or genetic evidence.
  • Evident from the text: as a review, it presents no new primary data or systematic meta-analysis beyond illustrative examples; its recommendations are based on existing theoretical and empirical assessments and may not fit all CNS study designs.

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Integrity signals

Registry

Crossref · 10.1038/nrg3934

Cites

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Statistics

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Claims the paper makes

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  • Findingreasoning

    A network-based meta-analysis of brain transcriptional profiles from publicly available data in ASD, schizophrenia, and Alzheimer disease identifies shared and distinct biological processes across disorders.

    “FIGURE 3 describes an example of a network-based meta-analysis of brain transcriptional profiles from publicly available data in ASD, schizophrenia and Alzheimer disease, which identifies shared and distinct biological processes across disorders.”
    Quote in the paperSubstantial claim
  • Premisecitation

    The Allen Brain Institute and BrainSpan have evaluated spatial and temporal transcriptomes.

    “Recent important advances include the evaluation of spatial and temporal transcriptomes by the Allen Brain Institute and BrainSpan8,11–13”
    Quote in the paperNot a substantial claim0 of 4 sources checked
    • cites [8] Sunkin, S. M. et al. Allen Brain Atlas: an integrated spatio-temporal portal for exploring the central nervous system. Nucleic Acids Res. 41, D996–D1008 (2013). source

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  • Premisecitation

    Large-scale genetic association studies have identified hundreds to thousands of genetic loci contributing to risk of neurodevelopmental and neurodegenerative disorders.

    “Large-scale genetic association studies have begun to unravel the genetic architecture of neurodevelopmental and neurodegenerative disorders and have found that hundreds to thousands of genetic loci are involved in disease risk1.”
    • Also stated in: § Abstract · plain
      “Genetic and genomic approaches have implicated hundreds of genetic loci in neurodevelopmental disorders and neurodegeneration”
    Quote in the paperSubstantial claim0 of 1 sources checked
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  • Premisecitation

    Consortium efforts have mapped transcriptomic, epigenomic, and proteomic landscapes in the brain.

    “Collaborative and consortium-level efforts have made substantial progress by mapping transcriptomic, epigenomic and proteomic landscapes in the brain8–10.”
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    • cites [8] Sunkin, S. M. et al. Allen Brain Atlas: an integrated spatio-temporal portal for exploring the central nervous system. Nucleic Acids Res. 41, D996–D1008 (2013). source

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    Many biological networks have a hierarchical structure in which nodes can be organized into a relatively small collection of highly interconnected modules.

    “Many biological networks have a hierarchical structure such that their nodes can be organized into a relatively small collection of highly interconnected modules4,28,29”
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    • cites [4] Geschwind, D. H. & Konopka, G. Neuroscience in the era of functional genomics and systems biology. Nature 461, 908–915 (2009). source

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    Findingown data

    In that meta-analysis, several modules are shared by two of the three disorders, including a red module (ASD and schizophrenia) containing voltage-gated calcium channels and a green module (ASD and Alzheimer disease) containing microglial markers.

    “Several modules are shared by two of the three disorders, including the red module (ASD and schizophrenia), which contains voltage-gated calcium channels, and the green module (ASD and Alzheimer disease), which contains microglial markers (FIG. 3b–d).”
    Quote in the paperSubstantial claim

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    Methods are now available to measure large-scale molecular, cellular, and circuit-level phenotypes, with additional methods in development.

    “Methods have recently become available that permit the measurement of large-scale molecular4,5, cellular6 and circuit-level3 phenotypes, and additional methods are currently in development7.”
    Quote in the paperSubstantial claim0 of 4 sources checked
    • cites [4] Geschwind, D. H. & Konopka, G. Neuroscience in the era of functional genomics and systems biology. Nature 461, 908–915 (2009). source

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    The Roadmap Epigenomics Mapping Consortium quantified the epigenetic landscape in CNS tissue and cell types.

    “the quantification of the epigenetic landscape in CNS tissue and cell types by the Roadmap Epigenomics Mapping Consortium14”
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    Premisecitation

    Network analysis can identify multiple levels of molecular organization within the hierarchy of brain region, cell type, organelle and molecular pathways using only transcriptional data, and can enable integration with other information.

    “A major advantage of network analysis over DGE analysis is that it can identify multiple levels of molecular organization within the hierarchy of brain region, cell type, organelle and molecular pathways using only transcriptional data, and can thus enable integration with other information, such as known pathway annotations, protein interactions and other molecular profiling data11,12,26,27”
    Quote in the paperSubstantial claim0 of 4 sources checked
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    • cites [12] Hawrylycz, M. J. et al. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature 489, 391–399 (2012). source

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    Weighted networks are more reproducible and powerful than binary networks.

    “weighted networks are more reproducible and powerful than binary networks42”
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    The GTEx project integrated genetic variation with gene expression in the brain.

    “the integration of genetic variation with gene expression in the brain by the Genotype-Tissue Expression (GTEx) project15”
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    Premisecitation

    At currently available sample sizes, networks are most reproducible at module level, then hub gene level, and least at precise gene connectivity rankings or module memberships.

    “At currently available sample sizes, networks are most reproducible at a module level35,38,39, then at the hub gene level41,223 and, last, at the level of precise gene connectivity rankings or precise module memberships of genes40,49.”
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    In most experiments, biological variability is greater than technical variability, so biological replicates are of greater value than technical replicates.

    “In most experiments, biological variability is greater than technical variability, so biological replicates are of greater value than technical replicates174,175,219.”
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    Differential co-expression or connectivity methods are additional means for determining gene connectivity changes between conditions and can identify disruption or gain of function in pathways.

    “Differential co-expression or connectivity methods36,37 are additional means for determining gene connectivity changes between conditions and can identify disruption or gain of function in pathways.”
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    Premisecitation

    Network methods relate genes to each other using measured or predicted relationships and provide an organizing framework that places each gene in its molecular system.

    “Network methods (BOX 2) relate genes to each other using the measured or predicted relationships between them4 and provide an essential organizing framework that places each gene in the context of its molecular system.”
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    • cites [4] Geschwind, D. H. & Konopka, G. Neuroscience in the era of functional genomics and systems biology. Nature 461, 908–915 (2009). source

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    Hubs can be key molecular drivers, such as transcriptional regulators that drive co-expression, or may annotate a module by reflecting its predominant biological role.

    “Hubs can be key molecular drivers, such as transcriptional regulators that drive co-expression30,31, or they may annotate a module by reflecting the predominant biological role of the module.”
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    Premisecitation

    The definition of a cell type in the brain remains controversial.

    “the definition of a cell type in the brain remains controversial20,21”
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    To obtain module-level reproducibility, 20 independent samples are usually sufficient, but systematic and accurate reconstruction of specific edges may require hundreds of samples.

    “To obtain module-level reproducibility, 20 independent samples are usually sufficient40, but systematic and accurate reconstruction of specific edges, particularly for systematic regulatory relationship discovery, may require hundreds of samples49.”
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    • cites [40] Ballouz, S., Verleyen, W. & Gillis, J. Guidance for RNA-seq co-expression network construction and analysis: safety in numbers. Bioinformatics http://dx.doi.org/10.1093/bioinformatics/btv118 (2015). An evaluation of sample size and power for constructing co-expression networks with RNA-seq. source

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    PPI data sets are biased to highly studied gene categories and are generally incomplete.

    “Similar to pathway databases, these PPI data sets are biased to highly studied gene categories (for example, those implicated in cancer biology) and are still generally incomplete2,53 (TABLE 1).”
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    • cites TABLE 1

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    Premisecitation

    That study showed that in schizophrenia, as in ASD, the overall transcriptomic structure in control brains is intact, but a neural differentiation module associated with schizophrenia does not follow the normal trajectory of downregulation with age.

    “This study showed that, as in ASD, the overall transcriptomic structure that is observed in control brains is intact but that a neural differentiation module that is associated with schizophrenia does not follow the normal trajectory of downregulation with age.”
    Quote in the paperSubstantial claim0 of 1 sources checked
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    Themes across ASD post-mortem studies include increased expression of immune-microglial genes and decreased expression of synaptic genes in cerebral cortex.

    “Nevertheless, some themes have emerged across studies, including the increased expression of immune-microglial genes and the decreased expression of synaptic genes in the cerebral cortex.”
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    Another study confirmed that a dysregulated neuronal differentiation module was consistently observed in schizophrenia post-mortem brains and suggested the same pathways were involved in bipolar disorder.

    “Another study confirmed that a dysregulated neuronal differentiation module was consistently observed in schizophrenia post-mortem brains and suggested that the same pathways were involved in bipolar disorder108.”
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    Consistent findings across schizophrenia transcriptomic studies include dysregulation of GABAergic signalling, downregulation of oligodendrocyte/myelination, mitochondrial/energy metabolism, and synaptic genes, and upregulation of immune/inflammatory genes.

    “Despite variable results, consistent findings across studies can be identified, including dysregulation of GABAergic signalling102; downregulation of oligodendrocyte- and myelination-related genes103, mitochondrial function or energy metabolism104, and synaptic genes105; and upregulation of immune and inflammatory genes106.”
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    Computational approaches to predict physical interactions can partly circumvent bias but do not address tissue specificity and may have relatively low reproducibility across methods.

    “Computational approaches to predict physical interactions can partly circumvent bias (TABLE 1), but they do not address tissue specificity, and there may be relatively low reproducibility across different methods 58,59.”
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    • cites TABLE 1

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    Premisecitation

    One network study defined robust co-expression modules reproducible in independent data and identified five developmentally regulated co-expression modules enriched for PPIs and ASD risk genes.

    “One network study defined robust co-expression modules that were reproducible in independent data and identified five developmentally regulated co-expression modules that were enriched for PPIs and ASD risk genes27.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [27] Parikshak, N. N. et al. Integrative functional genomic analyses implicate specific molecular pathways and circuits in autism. Cell 155, 1008–1021 (2013). A study that constructs genome-wide co-expression networks to identify modules spanning prenatal human brain development and demonstrates how module structure can be validated with multiple data sources and how tissue-specific and temporally specific co-expression modules can provide new biological insights into genetic variants. source

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    That study found genes in these modules have dynamic developmental trajectories, demonstrating a role for ASD risk genes in neural development.

    “Moreover, this study found that genes in these modules have dynamic developmental trajectories, demonstrating a role for ASD risk genes in neural development.”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [12] Hawrylycz, M. J. et al. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature 489, 391–399 (2012). source

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    • cites [109] Ben-David, E. & Shifman, S. Networks of neuronal genes affected by common and rare variants in autism spectrum disorders. PLoS Genet. 8, e1002556 (2012). source

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    Premisecitation

    NETBAG combines multiple forms of literature-curated data in an integrated network approach that has been shown effective for predicting gene essentiality in yeast.

    “The Network-Based Analysis of Genes (NETBAG)127 approach combines multiple forms of literature-curated data using an integrated network approach that has been demonstrated to be effective for predicting gene essentiality in yeast128.”
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    The first study to map risk genes onto transcriptional networks identified co-expression modules with cell type-specific and region-specific expression patterns using nearly 1,000 adult brain regions, and identified neuronal gene-enriched modules containing ASD candidate genes and ASD GWAS signals.

    “The first study to do this identified co-expression modules that had cell type-specific and region-specific expression patterns using nearly 1,000 adult brain regions12,109, and identified neuronal gene-enriched modules containing ASD candidate genes and ASD GWAS signals.”
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    • cites [12] Hawrylycz, M. J. et al. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature 489, 391–399 (2012). source

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    Interpretationreasoning

    Biases in literature-curated data and lack of tissue specificity in PPI networks limit discovery of novel pathways or circuits.

    “However, the biases inherent to literature-curated data and the lack of tissue specificity in these PPI networks limit the identification of novel pathways or circuits with this approach (TABLE 1).”
    Quote in the paperSubstantial claim

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    Premisecitation

    The genome-wide approach assessed both ASD and intellectual disability genes and suggested that disruption of upper neocortical layers (layers 2-4) is related to ASD-like phenotypes and not intellectual disability.

    “Notably, the genome-wide approach27 assessed both ASD and intellectual disability genes, and further suggested that the disruption of the upper neocortical layers (layers 2–4) is related to ASD-like phenotypes and not intellectual disability.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [27] Parikshak, N. N. et al. Integrative functional genomic analyses implicate specific molecular pathways and circuits in autism. Cell 155, 1008–1021 (2013). A study that constructs genome-wide co-expression networks to identify modules spanning prenatal human brain development and demonstrates how module structure can be validated with multiple data sources and how tissue-specific and temporally specific co-expression modules can provide new biological insights into genetic variants. source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    There was no pathway or PPI enrichment in these modules, probably owing to inclusion of both positive and negative correlations when computing co-expression relationships (unsigned networks), which is less sensitive to pathway and protein interaction detection.

    “Interestingly, there was no pathway or PPI enrichment in these modules, probably owing to the inclusion of both positive and negative correlations when computing co-expression relationships (unsigned networks), which is a method that is less sensitive to pathway and protein interaction detection38,43.”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [38] Song, L., Langfelder, P. & Horvath, S. Comparison of co-expression measures: mutual information, correlation, and model based indices. BMC Bioinformatics 13, 328 (2012). source

      Not checked in the free run.

    • cites [43] Ramani, A. K. et al. A map of human protein interactions derived from co-expression of human mRNAs and their orthologs. Mol. Syst. Biol. 4, 180 (2008). source

      Not checked in the free run.

  • May combine two claims.

    Interpretationreasoning

    FMRP-related activity-dependent regulation during fetal brain development may be especially vulnerable to genetic perturbations, and severe mutations could disrupt developmental canalization.

    “FMRP-related activity-dependent regulation during fetal brain development might be particularly vulnerable to genetic perturbations, with severe mutations resulting in disruption of developmental canalization.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisecitation

    Other studies have also found that fetal cortical development and glutamatergic neurons are affected by mutations in ASD, suggesting it is a robust finding.

    “Other studies have also found that fetal cortical development and glutamatergic neurons are affected by mutations in ASD, suggesting that it is a robust finding11,113,114.”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites [11] Miller, J. A. et al. Transcriptional landscape of the prenatal human brain. Nature 508, 199–206 (2014). source

      Not checked in the free run.

    • cites [113] Stein, J. L. et al. A quantitative framework to evaluate modeling of cortical development by neural stem cells. Neuron 83, 69–86 (2014). source

      Not checked in the free run.

    • cites [114] Steinberg, J. & Webber, C. The roles of FMRP-regulated genes in autism spectrum disorder: single- and multiple-hit genetic etiologies. Am. J. Hum. Genet. 93, 825–839 (2013). source

      Not checked in the free run.

  • May combine two claims.

    Interpretationcitation

    These results demonstrated convergence among known disease-relevant genes at the PPI level and that evaluating tissue-specific isoforms can identify novel interactions.

    “These results further demonstrated convergence among known disease-relevant genes at the PPI level and also demonstrated that evaluating tissue-specific isoforms can be used to identify novel interactions.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisecitation

    By comparing these genes with intellectual disability genes, that study identified molecular processes preferentially disrupted by ASD risk genes, including transcriptional regulation, chromatin regulation and synaptic development, and disruption of BAF (SWI/SNF) complex-mediated neuronal development.

    “By comparing these genes with genes that cause intellectual disability, this study identified molecular processes that are preferentially disrupted by ASD risk genes, including transcriptional regulation, chromatin regulation and synaptic development, and it identified disruption of specific pathways, such as BAF (SWI/ SNF) complex-mediated neuronal development 30,110,111.”
    Quote in the paperSubstantial claim0 of 3 sources checked
    • cites [30] Voineagu, I. et al. Transcriptomic analysis of autistic brain reveals convergent molecular pathology. Nature 474, 380–384 (2011). This study identifies common transcriptomic changes in a heterogeneous neuropsychiatric disorder and illustrates how gene network modules can be validated experimentally and how network modules can be related to GWAS findings. source

      Not checked in the free run.

    • cites [110] Ronan, J. L., Wu, W. & Crabtree, G. R. From neural development to cognition: unexpected roles for chromatin. Nat. Rev. Genet. 14, 347–359 (2013). source

      Not checked in the free run.

    • cites [111] Basu, S. N., Kollu, R. & Banerjee-Basu, S. AutDB: a gene reference resource for autism research. Nucleic Acids Res. 37, D832–D836 (2009). source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Targeted sequencing of genes in that subnetwork in a larger cohort found more risk variants than expected by chance, showing PPI connectivity can predict ASD risk mutations.

    “Genes in this subnetwork were evaluated in a larger cohort in a targeted sequencing study120, which identified more risk variants compared with chance and demonstrated that PPI connectivity can be a predictor of ASD risk mutations.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [120] O'Roak, B. J. et al. Multiplex targeted sequencing identifies recurrently mutated genes in autism spectrum disorders. Science 338, 1619–1622 (2012). source

      Not checked in the free run.

  • May combine two claims.

    Interpretationreasoning

    Despite the same gene rarely being hit recurrently by rare de novo variants in ASD, this class of variation preferentially disrupts projection neurons.

    “Thus, despite the fact that the same gene is rarely hit recurrently by rare de novo variants in ASD, this class of variation preferentially disrupts projection neurons.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisecitation

    This analysis found a PPI module enriched for synaptic function genes and weakly enriched for mutations from ASD individuals.

    “This identified a PPI module that is enriched for genes related to synaptic function and weakly enriched for mutations from individuals with ASD.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [123] Li, J. et al. Integrated systems analysis reveals a molecular network underlying autism spectrum disorders. Mol. Syst. Biol. 10, 774–774 (2014). source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Both of these studies found convergence for rare de novo ASD-associated mutations during early fetal and mid-fetal development, with greatest enrichment for risk in genes found in cortical glutamatergic neurons.

    “Importantly, both of these studies found convergence for rare de novo ASD-associated mutations during early fetal and mid-fetal development, with the greatest enrichment for risk in genes found in cortical glutamatergic neurons.”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [27] Parikshak, N. N. et al. Integrative functional genomic analyses implicate specific molecular pathways and circuits in autism. Cell 155, 1008–1021 (2013). A study that constructs genome-wide co-expression networks to identify modules spanning prenatal human brain development and demonstrates how module structure can be validated with multiple data sources and how tissue-specific and temporally specific co-expression modules can provide new biological insights into genetic variants. source

      Not checked in the free run.

    • cites [112] Willsey, A. J. et al. Coexpression networks implicate human midfetal deep cortical projection neurons in the pathogenesis of autism. Cell 155, 997–1007 (2013). source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Forabosco and colleagues used network analysis to explore TREM2 function, suggesting a role for microglial function and implicating neuroinflammation in Alzheimer disease.

    “Forabosco and colleagues152 used network analysis to explore the function of TREM2 (triggering receptor expressed on myeloid cells 2), an Alzheimer disease risk gene, suggesting a role for microglial function and further implicating neuroinflammation in Alzheimer disease.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [152] Forabosco, P. et al. Insights into TREM2 biology by network analysis of human brain gene expression data. Neurobiol. Aging 34, 2699–2714 (2013). source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S4 (based).

    Interpretationreasoning

    Important disease-associated molecular changes can be confounded by environmental and clinical factors.

    “Important disease-associated molecular changes can be confounded by environmental and clinical factors.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    Y2H has been used to identify potential modulators of Huntington disease, where interactors of huntingtin might modulate disease severity.

    “This Y2H approach has also been used to identify potential modulators of Huntington disease166, in which it is thought that inter-actors of huntingtin (the causally mutated pathological protein) might modulate disease severity.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [166] Goehler, H. et al. A protein interaction network links GIT1, an enhancer of huntingtin aggregation, to Huntington's disease. Mol. Cell 15, 853–865 (2004). source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Interactors in that network were potential modifiers of disease progression, and subsequent work found that gain of function mediated by a newly identified protein complex mediates disease pathogenesis.

    “Importantly, interactors in the network were potential modifiers of disease progression, and, in subsequent work, gain of function mediated by a newly identified protein complex was found to mediate disease pathogenesis165.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [165] Lim, J. et al. Opposing effects of polyglutamine expansion on native protein complexes contribute to SCA1. Nature 452, 713–718 (2008). source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    MAGI identified modules containing functionally related genes enriched for deleterious mutations in ASD, many under strong purifying selection, and also found in epilepsy, schizophrenia, and intellectual disability.

    “MAGI identified modules containing functionally related genes enriched for deleterious mutations in ASD, many of which are under strong purifying selection, and are also found in epilepsy, schizophrenia and intellectual disability133.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [133] Hormozdiari, F., Penn, O., Borenstein, E. & Eichler, E. E. The discovery of integrated gene networks for autism and related disorders. Genome Res. 25, 142–154 (2015). This study uses a network analysis method that combines gene co-expression and PPIs to identify modules that are highly interconnected in the network but that are also more likely to be mutated in individuals with neurodevelopmental disorders compared with controls. source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    A mouse model study of FTD identified Npepps, and cross-species analyses in flies and humans confirmed its expression pattern and neuroprotective effect.

    “The first of two well-powered studies that applied this approach carried out transcriptomic analysis in a mouse model of FTD, identifying the gene Npepps138. Cross-species analyses in flies and humans confirmed the expression pattern and neuroprotective effect of NPEPPS138.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [138] Karsten, S. L. et al. A genomic screen for modifiers of tauopathy identifies puromycin-sensitive aminopeptidase as an inhibitor of tau-induced neurodegeneration. Neuron 51, 549–560 (2006). source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Another approach evaluating CNV duplications and deletions found an interconnected PPI network enriched for synaptic transmission proteins, validating that pathogenic CNVs affect similar gene networks.

    “Another approach130 has evaluated CNV duplications in addition to CNV deletions and also found an interconnected PPI network that was enriched for proteins involved in synaptic transmission, validating the observation that pathogenic CNVs affect similar gene networks127.”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [130] Noh, H. J. et al. Network topologies and convergent aetiologies arising from deletions and duplications observed in individuals with autism. PLoS Genet. 9, e1003523 (2013). source

      Not checked in the free run.

    • cites [127] Gilman, S. R. et al. Rare de novo variants associated with autism implicate a large functional network of genes involved in formation and function of synapses. Neuron 70, 898–907 (2011). This study applies a rigorous framework to integrate multiple levels of molecular data and evaluates whether genes affected by CNV in autism were functionally interconnected. source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    That work found major differences in dementia susceptibility genes between humans and mice, potentially explaining why some mouse models may not recapitulate human neuropathology.

    “This work also found major differences in dementia susceptibility genes between humans and mice, potentially identifying why some mouse models might not recapitulate human neuropathology.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [150] Miller, J. A., Horvath, S. & Geschwind, D. H. Divergence of human and mouse brain transcriptome highlights Alzheimer disease pathways. Proc. Natl Acad. Sci. USA 107, 12698–12703 (2010). source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    WGCNA identified spatially and temporally specific modules associated with mutant Htt, and hub proteins with high intra-modular connectivity modulated neurodegeneration in flies.

    “WGCNA identified spatially and temporally specific modules associated with mutant Htt (which encodes huntingtin); and proteins with high intra-modular connectivity (hub proteins) modulated neurodegeneration in flies.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [167] Shirasaki, D. I. et al. Network organization of the Huntingtin proteomic interactome in mammalian brain. Neuron 75, 41–57 (2012). Illustrates the power of network analysis for defining protein interaction modules across brain regions and time points to understand the huntingtin interactome. source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    The first large transcriptomic study (188 controls and 176 Alzheimer disease cases) connected genetic variation to expression changes using eQTL analysis and supported pathway-level findings related to transcriptional regulation and energy metabolism.

    “The first large transcriptomic study (involving 188 controls and 176 individuals with Alzheimer disease)147 connected genetic variation to expression changes by using expression quantitative trait locus analysis (eQTL analysis) in controls and Alzheimer disease, and further supported the pathway-level findings related to transcriptional regulation and energy metabolism in Alzheimer disease140,148.”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites [147] Webster, J. A. et al. Genetic control of human brain transcript expression in Alzheimer disease. Am. J. Hum. Genet. 84, 445–458 (2009). source

      Not checked in the free run.

    • cites [140] Blalock, E. M. et al. Incipient Alzheimer's disease: microarray correlation analyses reveal major transcriptional and tumor suppressor responses. Proc. Natl Acad. Sci. USA 101, 2173–2178 (2004). source

      Not checked in the free run.

    • cites [148] Liang, W. S. et al. Altered neuronal gene expression in brain regions differentially affected by Alzheimer's disease: a reference data set. Physiol. Genomics 33, 240–256 (2008). source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Many neurodegenerative diseases have known causes involving mutations in ubiquitously expressed proteins, yet they follow stereotyped degeneration patterns that selectively affect certain cell subsets, producing disease-specific spatial and temporal patterns.

    “Many of these diseases have known causes that involve mutations in ubiquitously expressed proteins134, but they follow stereotyped patterns of degeneration that selectively affect certain cell subsets more severely, resulting in disease-specific spatial and temporal patterns of degeneration135–137 (BOX 1).”
    Quote in the paperSubstantial claim0 of 4 sources checked
    • cites [134] Taylor, J. P. Toxic proteins in neurodegenerative disease. Science 296, 1991–1995 (2002). source

      Not checked in the free run.

    • cites [135] Seeley, W. W., Crawford, R. K., Zhou, J., Miller, B. L. & Greicius, M. D. Neurodegenerative diseases target large-scale human brain networks. Neuron 62, 42–52 (2009). source

      Not checked in the free run.

    • cites [136] Zhou, J., Gennatas, E. D., Kramer, J. H., Miller, B. L. & Seeley, W. W. Predicting regional neurodegeneration from the healthy brain functional connectome. Neuron 73, 1216–1227 (2012). source

      Not checked in the free run.

    • cites [137] Forman, M. S., Trojanowski, J. Q. & Lee, V. M.-Y. Neurodegenerative diseases: a decade of discoveries paves the way for therapeutic breakthroughs. Nat. Med. 10, 1055–1063 (2004). source

      Not checked in the free run.

  • May combine two claims.

    Interpretationreasoning

    Post-mortem samples reflect the consequence of years of ageing and disease progression, making causal mechanisms difficult to pinpoint.

    “but causal mechanisms are difficult to pinpoint, as post-mortem samples reflect the consequence of years of ageing and disease progression.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationreasoning

    Although positional cloning has identified genes and pathways involved in many neurodegenerative diseases, pathological mechanisms, modulators of pathogenesis, and disease biomarkers remain elusive, suggesting that genome-wide approaches are needed.

    “Additionally, although positional cloning has identified genes and pathways that are involved in many neurodegenerative diseases, pathological mechanisms, modulators of pathogenesis and disease biomarkers have remained elusive, suggesting that genome-wide approaches are needed.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationreasoning

    Integration of eQTLs can identify causality in transcriptomic studies of Alzheimer disease risk, adding a mechanistic element to post-mortem gene expression studies.

    “Integration of eQTLs can identify causality in transcriptomic studies in the context of Alzheimer disease risk, adding a crucial mechanistic element to studies of post-mortem gene expression.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisecitation

    A subsequent study incorporating more than 1,000 microarrays from mouse models and human Alzheimer disease reproduced and extended those results, identifying additional co-expression modules related to mitochondrial dysfunction and synaptic plasticity.

    “They subsequently150 incorporated more than 1,000 microarrays from mouse models of Alzheimer disease and human patients with Alzheimer disease from public databases to reproduce and extend these results, identifying additional co-expression modules that are related to mitochondrial dysfunction and synaptic plasticity.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [150] Miller, J. A., Horvath, S. & Geschwind, D. H. Divergence of human and mouse brain transcriptome highlights Alzheimer disease pathways. Proc. Natl Acad. Sci. USA 107, 12698–12703 (2010). source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Interpretationcitation

    In silico drug screening based on DGE or network modules has barely been explored in the CNS but has considerable promise.

    “This area of in silico drug screening based on DGE or network modules has barely been explored in the CNS, but it has considerable promise193.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [193] Qu, X. A. & Rajpal, D. K. Applications of Connectivity Map in drug discovery and development. Drug Discov. Today 17, 1289–1298 (2012). source

      Not checked in the free run.

  • May combine two claims.

    Interpretationreasoning

    These biases inflate enrichment results and can result in false positives.

    “These biases inflate enrichment results and can result in false positives”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisecitation

    The role of Tyrobp was experimentally validated in mice, showing that network structure is predictive, as previously demonstrated with co-expression networks.

    “The role of Tyrobp was experimentally validated in mice170, showing that network structure is predictive, as had previously been demonstrated with co-expression networks32.”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [170] Zhang, B. et al. Integrated systems approach identifies genetic nodes and networks in late-onset Alzheimer's disease. Cell 153, 707–720 (2013). This study combined network analysis in post-mortem tissue, eQTL mapping and Bayesian causal inference to identify a causal role for the gene TYROBP in Alzheimer disease. source

      Not checked in the free run.

    • cites [32] Winden, K. D. et al. The organization of the transcriptional network in specific neuronal classes. Mol. Syst. Biol. 5, 291 (2009). source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable), S4 (based).

    Interpretationnone given

    Gene networks provide an organizational framework that simplifies hypothesis generation and testing.

    “Although certainly vast and seemingly complex, gene networks provide an organizational framework that simplifies the process of hypothesis generation and testing.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    Multiple transcriptional modules were remodelled in Alzheimer disease: immune and neurogenesis pathways gained connectivity, while GABA signalling and myelination pathways lost connectivity.

    “They showed that multiple transcriptional modules were remodelled in Alzheimer disease: gain of connectivity was observed in immune and neurogenesis pathways, and loss of connectivity was predominant in pathways related to GABA signalling and myelination.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [170] Zhang, B. et al. Integrated systems approach identifies genetic nodes and networks in late-onset Alzheimer's disease. Cell 153, 707–720 (2013). This study combined network analysis in post-mortem tissue, eQTL mapping and Bayesian causal inference to identify a causal role for the gene TYROBP in Alzheimer disease. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S4 (based).

    Premisenone given

    Currently, high-throughput, high dynamic range spatial and temporal proteomic data from minute sample quantities are not available.

    “Currently, high-throughput, high dynamic range spatial and temporal data from minute sample quantities with proteomics are not available”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based).

    Premisenone given

    Much basic and translational neuroscience research is still focused on candidate genes and candidate hypotheses.

    “Currently, much basic and translational neuroscience research is still focused on candidate genes and candidate hypotheses”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable). May combine two claims.

    Interpretationreasoning

    Biological complexity cannot be ignored; genome-wide measurements, together with studying individual genes and pathways, are essential to address the true underlying mechanisms of neurodevelopmental and neurodegenerative disorders.

    “However, biological complexity cannot be ignored; genome-wide measurements, in conjunction with studying individual genes and pathways, are essential to address the true underlying mechanisms of neurodevelopmental and neurodegenerative disorders.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based).

    Interpretationnone given

    Genome-wide transcriptomic approaches provide a quantitative endophenotype or biomarker that genetic association studies can use to refine measurement of disease states.

    “Genome-wide transcriptomic approaches provide a quantitative endophenotype, or a biomarker, that genetic association studies can use to further refine the measurement of disease states.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationcitation

    Grouping genetic findings using disease- and tissue-relevant modules can increase power to detect genetic associations by combining signals reflecting similar biology while informing mechanism by functionally annotating genetic findings.

    “Grouping genetic findings using disease- and tissue-relevant modules can increase the power to detect genetic associations with disease by combining signals that reflect similar underlying biology while simultaneously informing biological mechanism by functionally annotating genetic findings30,109,122,183.”
    Quote in the paperSubstantial claim0 of 3 sources checked
    • cites [30] Voineagu, I. et al. Transcriptomic analysis of autistic brain reveals convergent molecular pathology. Nature 474, 380–384 (2011). This study identifies common transcriptomic changes in a heterogeneous neuropsychiatric disorder and illustrates how gene network modules can be validated experimentally and how network modules can be related to GWAS findings. source

      Not checked in the free run.

    • cites [109] Ben-David, E. & Shifman, S. Networks of neuronal genes affected by common and rare variants in autism spectrum disorders. PLoS Genet. 8, e1002556 (2012). source

      Not checked in the free run.

    • cites [122] Ben-David, E. & Shifman, S. Combined analysis of exome sequencing points toward a major role for transcription regulation during brain development in autism. Mol. Psychiatry 18, 1054–1056 (2012). source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable), S4 (based). May combine two claims.

    Interpretationnone given

    Well-designed, reproducible molecular profiling studies allow biologists to simultaneously evaluate hypotheses in an unbiased manner and generate new hypotheses.

    “Well-designed, reproducible molecular profiling studies allow biologists to simultaneously evaluate hypotheses in an unbiased manner and to generate new hypotheses.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationcitation

    Because gene expression changes are caused by genetic variation, these modules were suggested to be causally involved.

    “Given that gene expression changes are caused by genetic variation, this suggested these modules were causally involved171.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [171] Aten, J. E., Fuller, T. F., Lusis, A. J. & Horvath, S. Using genetic markers to orient the edges in quantitative trait networks: the NEO software. BMC Syst. Biol. 2, 34 (2008). source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationcitation

    One of the greatest challenges is to systematically infer causality in molecular networks with a systems genetics approach.

    “One of the greatest challenges is to systematically infer causality in molecular networks with a systems genetics approach168,169.”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [168] Chen, J. C. et al. Identification of causal genetic drivers of human disease through systems-level analysis of regulatory networks. Cell 159, 402–414 (2014). source

      Not checked in the free run.

    • cites [169] Civelek, M. & Lusis, A. J. Systems genetics approaches to understand complex traits. Nat. Rev. Genet. 15, 34–48 (2013). source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S4 (based).

    Interpretationnone given

    Developmental and cell subtype differences in the PSD are not well understood.

    “However, developmental and cell subtype differences in the PSD are not well understood”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationnone given

    Studies of proteomic data are highly complementary to co-expression data and have revealed a crucial level of organization and regulation at translational and post-translational levels.

    “As noted throughout this Review, studies of proteomic data are highly complementary to co-expression data and have revealed a crucial level of organization and regulation at the translational and post-translational levels.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationcitation

    Transcriptomic and other molecular systems measurements can be correlated with systems neuroscience phenotypes, such as MRI and functional MRI, or behavioural phenotypes to identify non-invasive indicators of disease state.

    “Transcriptomic and other molecular systems measurements can also be correlated with systems neuroscience phenotypes, such as MRI and functional MRI measurements, or behavioural phenotypes to identify non-invasive indicators of disease state4 (FIG. 1a).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [4] Geschwind, D. H. & Konopka, G. Neuroscience in the era of functional genomics and systems biology. Nature 461, 908–915 (2009). source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable), S4 (based). May combine two claims.

    Interpretationnone given

    The general paradigm of using correlational and physical interaction molecular networks in neurobiology to understand molecular systems changes can be applied across methodologies and enables investigation of relationships spanning multiple levels of analysis.

    “The general paradigm of using correlational and physical interaction molecular networks in neurobiology to understand molecular systems changes can be applied across methodologies and enables the investigation of relationships that span multiple levels of analysis.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationreasoning

    No single experimental design suits all aims.

    “There is no experimental design that suits all aims”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

    Interpretationcitation

    Optimal methodological choices and study designs ensure that biological signals from main factors are not confounded by unwanted variation.

    “Optimal methodological choices and study designs ensure that the biological signals from the main factors are not confounded by variation from unwanted factors178.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [178] Leek, J. T. et al. Tackling the widespread and critical impact of batch effects in high-throughput data. Nat. Rev. Genet. 11, 733–739 (2010). A must-read paper prior to pursuing the design or analysis of a high-throughput experiment, it contains advice and analyses for evaluating the contribution of technical and biological variation in data sets. source

      Not checked in the free run.

  • May combine two claims.

    Interpretationcitation

    Biological insights into the CNS require assessment of relevant behavioural and cognitive phenotypes, which are not well defined for most neuropsychiatric diseases and are rarely collected in large populations.

    “Even once we have the information from thousands of genomes, biological insights into the CNS require the assessment of relevant behavioural and cognitive phenotypes, which are not well defined for most neuropsychiatric diseases198,199 and are rarely collected in large populations.”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [198] Freimer, N. & Sabatti, C. The Human Phenome Project. Nat. Genet. 34, 15–21 (2003). source

      Not checked in the free run.

    • cites [199] Congdon, E., Poldrack, R. A. & Freimer, N. B. Neurocognitive phenotypes and genetic dissection of disorders of brain and behavior. Neuron 68, 218–230 (2010). source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Many guidelines for DGE and network analysis are based on theoretical models and empirical assessments, but most use data lacking the spatial, temporal, or disease-relevant complexities of CNS or post-mortem tissue.

    “For DGE and network analysis, there are many studies with guidelines that are based on theoretical models and empirical assessments40,173–176, but most studies use data from experiments that do not have the spatial, temporal or disease-relevant complexities that occur in studies of the CNS or post-mortem tissue.”
    Quote in the paperSubstantial claim0 of 5 sources checked
    • cites [40] Ballouz, S., Verleyen, W. & Gillis, J. Guidance for RNA-seq co-expression network construction and analysis: safety in numbers. Bioinformatics http://dx.doi.org/10.1093/bioinformatics/btv118 (2015). An evaluation of sample size and power for constructing co-expression networks with RNA-seq. source

      Not checked in the free run.

    • cites [173] Hart, S. N., Therneau, T. M., Zhang, Y., Poland, G. A. & Kocher, J.-P. Calculating sample size estimates for RNA sequencing data. J. Comput. Biol. 20, 970–978 (2013). source

      Not checked in the free run.

    • cites [174] Robles, J. A. et al. Efficient experimental design and analysis strategies for the detection of differential expression using RNA-sequencing. BMC Genomics 13, 484 (2012). source

      Not checked in the free run.

    • cites [175] Rapaport, F. et al. Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data. Genome Biol. 14, R95 (2013). source

      Not checked in the free run.

    • cites [176] Ching, T., Huang, S. & Garmire, L. X. Power analysis and sample size estimation for RNA-seq differential expression. RNA 20, 1684–1696 (2014). source

      Not checked in the free run.

70 of 70

  • The paper relies on this without support.

    Most neurodevelopmental and neurodegenerative disorders are defined by perturbations in specific cognitive and/or behavioural domains, pointing to selective vulnerability of specific cells.

    “Most neurodevelopmental and neurodegenerative disorders are defined by perturbations in specific cognitive and/or behavioural domains, pointing to a selective vulnerability of specific cells.”

  • The paper relies on this without support.

    Molecular signatures of specific cell types, finer-grained temporal dynamics, and causal or reactive alterations in CNS diseases remain mostly uncharacterized.

    “However, the molecular signatures of specific cell types, finer-grained temporal dynamics and causal or reactive alterations in CNS diseases remain mostly uncharacterized (FIG. 1c).”

  • The paper relies on this without support.

    In neurodevelopmental disorders, the specific brain regions, cell types or time points that are most affected remain poorly defined.

    “In neurodevelopmental disorders, the specific brain regions, cell types or time points that are most affected remain poorly defined and whole-tissue profiling still holds great value.”

  • The paper relies on this without support.

    The relationships of human disease phenotypes to developmental trajectories are relatively unknown.

    “the relationships of human disease phenotypes to developmental trajectories are relatively unknown”

  • The paper relies on this without support.

    Model systems in many neurobiological studies are often chosen on the basis of convenience and history.

    “model systems in many neurobiological studies are often chosen on the basis of convenience and history”

  • The paper relies on this without support.

    DGE analysis does not take into account the relationships between genes.

    “Differential gene expression analysis (DGE analysis) addresses this issue, albeit one gene at a time, but does not take into account the relationships between genes.”

  • The paper relies on this without support.

    Gene networks built on data containing even a small fraction of literature-curated components can contain substantial bias.

    “Gene networks that are built on data that contain even a small fraction of literature-curated components can contain substantial bias.”

  • The paper relies on this without support.

    Networks with edges derived from shared pathway membership can reflect cellular states that might not be found in the CNS and will lack many important CNS-specific relationships.

    “However, networks with edges that are derived from shared pathway membership can reflect cellular states that might not be found in the CNS, and they will certainly lack many important CNS-specific relationships.”

  • The paper relies on this without support.

    Most genome-wide PPI experiments use methods such as yeast two-hybrid screens or tandem affinity purification and are cell type agnostic.

    “Alternatively, most genome-wide PPI experiments use methods such as yeast two-hybrid (Y2H) screens or tandem affinity purification and are cell type agnostic.”

  • The paper relies on this without support.

    Transcriptomics, epigenomics and proteomics query unique levels of cellular or tissue organization.

    “When combining multiple molecular levels in networks, it is important to recognize that transcriptomics, epigenomics and proteomics all query unique levels of cellular or tissue organization.”

  • The paper relies on this without support.

    A shortcoming of studies using post-mortem brain tissue is that the tissue is usually obtained long after the disease-causing changes have occurred.

    “A shortcoming of studies using post-mortem brain tissue is that the tissue is usually obtained long after the disease-causing changes have occurred.”

  • The paper relies on this without support.

    Patients with schizophrenia have greater comorbidity of confounders such as smoking, alcohol and substance abuse than those with ASD.

    “However, patients with schizophrenia have greater comorbidity of confounders such as smoking, alcohol and substance abuse than those with ASD.”

  • The paper relies on this without support.

    Transcriptional studies of schizophrenia have benefited from considerably larger sample sizes than those of ASD.

    “Transcriptional studies of schizophrenia have benefited from considerably larger sample sizes than those of ASD.”

  • The paper relies on this without support.

    Combining multiple lines of evidence increases the power to detect disease-relevant interactions.

    “The idea that multiple lines of evidence may increase the power to detect disease-relevant interactions has motivated the integration of literature-curated, molecular and genetic evidence to support specific genes or pathways.”
14 of 14

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • In autism spectrum disorder, risk pathways involve regulation of transcription and chromatin structure during neurogenesis, and subsequent synaptic development and function during early fetal cortical development.

    “In ASD, these pathways involve regulation of transcription and chromatin structure during neurogenesis, and subsequent processes of synaptic development and function during early fetal cortical development (Parikshak et al., 2015).”

    From Defining the genetic, genomic, cellular, and diagnostic architectures of psychiatric disorders (2019) · cites this paper as Parikshak et al., 2015

  • Network analysis provides an organizing framework that can divide large gene sets into biologically coherent modules for prioritization or add power to genome-wide association studies.

    “network analysis provides an organizing framework that can divide large gene sets into biologically coherent modules for prioritization (Parikshak et al., 2015; Parikshak et al., 2013), or add power to GWAS (Horn et al., 2018)”

    From Defining the genetic, genomic, cellular, and diagnostic architectures of psychiatric disorders (2019) · cites this paper as Parikshak et al., 2015

  • Despite clear genetic heterogeneity, both autism spectrum disorder and schizophrenia risk converge on shared molecular pathways.

    “Despite clear genetic heterogeneity, both ASD and SCZ risk converge on shared molecular pathways (Network and Pathway Analysis Subgroup of Psychiatric Genomics Consortium, 2015; Parikshak et al., 2015).”

    From Defining the genetic, genomic, cellular, and diagnostic architectures of psychiatric disorders (2019) · cites this paper as Parikshak et al., 2015

  • Protein co-expression networks are conserved across BLSA and Emory brains, supporting the robustness of WGCNA in defining consistent patterns of protein co-expression representing shared biology.

    “Thus, protein co-expression networks are conserved across both BLSA and Emory brains, supporting the robustness of WGCNA in defining consistent patterns of protein co-expression representing shared biology (Parikshak et al., 2015).”

    From A Multi-network Approach Identifies Protein-Specific Co-expression in Asymptomatic and Symptomatic Alzheimer’s Disease (2017) · cites this paper as Parikshak et al., 2015

  • Network analyses of AD brain tissue demonstrate the most reproducibility at the module level, followed by the hub protein level, and then at the level of precise protein connectivity rankings and module protein membership.

    “Network analyses of AD brain tissue demonstrate the most reproducibility at the module level, followed by the hub protein level, and then finally at the level of precise protein connectivity rankings and module protein membership [61, 89–94].”

    From Systems-based proteomics to resolve the biology of Alzheimer’s disease beyond amyloid and tau (2021) · cites this paper as [61]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

  • NETBAG validation organism conflicts with cited reference

    Minor

    The text says the cited work demonstrated the approach in yeast, but reference 128 identifies its subject as Caenorhabditis elegans.

1 of 1

Cites

Flagged first, then by citations

Research works printed but not linked: 4

  1. Miller, J. A. & Geschwind, D. H. in Systems Biology for Signaling Networks (ed. Choi, S.) Ch. 25 611–643 (Springer, 2010). · DOI
  2. Butte, A. J. & Kohane, I. S. in Pacific Symposiumon Biocomputing 2000 (eds Altman, R. B. et al.) 418–429 (World Scientific, 2000).
  3. Horvath, S. Weighted Network Analysis: Applications in Genomics and Systems Biology (Springer, 2011). · DOI
  4. Good, P. I. Permutation, Parametric, and Bootstrap Tests of Hypotheses (Springer, 2010).

Cited by

Sorted by citations

Authors
Parikshak, N. N., Gandal, M. J., & Geschwind, D. H.
Affiliations
University of California, Los Angeles (US)
Corresponding author
Geschwind, Daniel H.
ORCID
Venue
Nature Reviews Genetics
Publisher
Springer Science and Business Media LLC
Published
2015-07-07
Volume
16
Issue
8
Pages
441-458
ISSN
  • 1471-0056 (ISSN-L)
  • 1471-0064
DOI
10.1038/nrg3934
PMC
PMC4699316
OpenAlex
W1508717233
Cited by
430
References
233
Citations by year
2026: 24 · 2025: 27 · 2024: 31 · 2023: 20 · 2022: 37 · 2021: 45 · 2020: 39 · 2019: 57 · 2018: 54 · 2017: 47 · 2016: 42 · 2015: 5 · 2014: 1
FWCI
4.81
Type
journal article
Language
en
Open access
closed
Licence
www.springer.com/tdm (tdm)
Locations
  • Nature Reviews Genetics (publishedVersion)
  • PubMed (publishedVersion)
  • eScholarship (California Digital Library) (submittedVersion)
Funders
  • National Institutes of Health (5r01mh094714-04)
  • National Institute of Mental Health (5R01MH094714, R01-MH094714, F30MH099886, R01MH100027, T32MH073526, R37MH060233)
  • National Institute of General Medical Sciences (T32GM008042)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetics and Neurodevelopmental Disorders
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
Keywords
neurodevelopmental disorders, neurodegenerative diseases, gene networks, systems biology, transcriptomics, genetic loci, network biology, molecular pathways, neural circuits
MeSH terms
Brain, Developmental Disabilities, Humans, Models, Neurological, Neurobiology, Neurodegenerative Diseases, Gene Expression Profiling, Systems Biology, Gene Regulatory Networks, High-Throughput Screening Assays

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-06.

Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-06.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

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