Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders

In the paper

“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).”

In the paper

“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.”

What the sources say

• Not checked yetcites [4] Geschwind, D. H. & Konopka, G. Neuroscience in the era of functional genomics and systems biology. Nature 461, 908–915 (2009). source
• Not checked yetcites [5] Grant, S. Systems biology in neuroscience: bridging genes to cognition. Curr. Opin. Neurobiol. 13, 577–582 (2003). source
• Not checked yetcites [6] Tian, L. et al. Imaging neural activity in worms, flies and mice with improved GCaMP calcium indicators. Nat. Methods 6, 875–881 (2009). source
• Not checked yetcites [3] Bullmore, E. & Sporns, O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 10, 186–198 (2009). source

In the paper

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

What the sources say

• Not checked yetcites [14] Roadmap Epigenomics Consortium. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).

In the paper

“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”

What the sources say

• Not checked yetcites [11] Miller, J. A. et al. Transcriptional landscape of the prenatal human brain. Nature 508, 199–206 (2014). source
• Not checked yetcites [12] Hawrylycz, M. J. et al. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature 489, 391–399 (2012). source
• Not checked yetcites [26] Oldham, M. C. et al. Functional organization of the transcriptome in human brain. Nat. Neurosci. 11, 1271–1282 (2008). source
• Not checked yetcites [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

In the paper

“weighted networks are more reproducible and powerful than binary networks42”

What the sources say

• Not checked yetcites [42] Zhang, B. & Horvath, S. A general framework for weighted gene co-expression network analysis. Stat. Appl. Genet. Mol. Biol. 4,17 (2005). This paper describes the value of weighted co-expression networks over binary co-expression networks and discusses the theory behind the widely used WGCNA package. source

In the paper

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

What the sources say

• Not checked yetcites [15] Lonsdale, J. et al. The Genotype-Tissue Expression (GTEx) project. Nat. Genet. 45, 580–585 (2013). source

In the paper

“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.”

What the sources say

• Not checked yetcites [35] Langfelder, P., Luo, R., Oldham, M. C. & Horvath, S. Is my network module preserved and reproducible? PLoS Comput. Biol. 7, e1001057 (2011). A description and comparison of multiple metrics for measuring modular structure in networks, which provides a statistical framework for demonstrating module preservation that is included in the R package WGCNA.[Au:OK?] source
• Not checked yetcites [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 yetcites [39] Allen, J. D., Xie, Y., Chen, M., Girard, L. & Xiao, G. Comparing statistical methods for constructing large scale gene networks. PLoS ONE 7, e29348 (2012). This study compares multiple approaches for constructing large-scale gene networks, including methods based on correlation and mutual information. source
• Not checked yetcites [41] Gaiteri, C., Ding, Y., French, B., Tseng, G. C. & Sibille, E. Beyond modules and hubs: the potential of gene coexpression networks for investigating molecular mechanisms of complex brain disorders. Genes Brain Behav. 13, 13–24 (2013). source
• Not checked yetcites [223] Langfelder, P., Mischel, P. S. & Horvath, S. When is hub gene selection better than standard meta-analysis? PLoS ONE 8, e61505 (2013). source
• Not checked yetcites [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 yetcites [49] Marbach, D. et al. Wisdom of crowds for robust gene network inference. Nat. Methods 9, 796–804 (2012). This paper, from the DREAM challenge on regulatory network reconstruction, describes the results of applying multiple regulatory network inference algorithms to three large data sets from bacteria and yeast. source

In the paper

“In most experiments, biological variability is greater than technical variability, so biological replicates are of greater value than technical replicates174,175,219.”

What the sources say

• Not checked yetcites [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 yetcites [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 yetcites [219] Liu, Y., Zhou, J. & White, K. P. RNA-seq differential expression studies: more sequence or more replication? Bioinformatics 30, 301–304 (2014). A comparison of multiple RNA-seq differential expression methodologies that demonstrated biological replicates are more important than technical replicates and provided guidelines on sequencing depth. source

In the paper

“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.”

What the sources say

• Not checked yetcites [36] Hudson, N. J., Reverter, A. & Dalrymple, B. P. A differential wiring analysis of expression data correctly identifies the gene containing the causal mutation. PLoS Comput. Biol. 5, e1000382 (2009). source
• Not checked yetcites [37] Choi, J. K., Yu, U., Yoo, O. J. & Kim, S. Differential coexpression analysis using microarray data and its application to human cancer. Bioinformatics 21, 4348–4355 (2005). source

In the paper

“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.”

What the sources say

• Not checked yetcites [4] Geschwind, D. H. & Konopka, G. Neuroscience in the era of functional genomics and systems biology. Nature 461, 908–915 (2009). source

In the paper

“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.”

What the sources say

• Not checked yetcites [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 yetcites [31] Horvath, S. et al. Analysis of oncogenic signaling networks in glioblastoma identifies ASPM as a molecular target. Proc. Natl Acad. Sci. USA 103, 17402–17407 (2006). source

In the paper

“the definition of a cell type in the brain remains controversial20,21”

What the sources say

• Not checked yetcites [20] Nelson, S. B., Sugino, K. & Hempel, C. M. The problem of neuronal cell types: a physiological genomics approach. Trends Neurosci. 29, 339–345 (2006). source
• Not checked yetcites [21] DeFelipe, J. et al. New insights into the classification and nomenclature of cortical GABAergic interneurons. Nat. Rev. Neurosci. 14, 202–216 (2013). source

In the paper

“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.”

What the sources say

• Not checked yetcites [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 yetcites [49] Marbach, D. et al. Wisdom of crowds for robust gene network inference. Nat. Methods 9, 796–804 (2012). This paper, from the DREAM challenge on regulatory network reconstruction, describes the results of applying multiple regulatory network inference algorithms to three large data sets from bacteria and yeast. source

In the paper

“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).”

What the sources say

• Not checked yetcites [2] Beyer, A., Bandyopadhyay, S. & Ideker, T. Integrating physical and genetic maps: from genomes to interaction networks. Nat. Rev. Genet. 8, 699–710 (2007). source
• Not checked yetcites [53] Hart, G. T., Ramani, A. K. & Marcotte, E. M. How complete are current yeast and human protein-interaction networks? Genome Biol. 7, 120 (2006). source
• Not checked yetcites TABLE 1

In the paper

“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.”

What the sources say

• Not checked yetcites [107] Torkamani, A., Dean, B., Schork, N. J. & Thomas, E. A. Coexpression network analysis of neural tissue reveals perturbations in developmental processes in schizophrenia. Genome Res. 20, 403–412 (2010). This work applies mutual information-based co-expression network analysis to transcriptomic data from post-mortem brains of individuals with schizophrenia to identify several schizophrenia-associated modules. source

In the paper

“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.”

What the sources say

• Not checked yetcites [92] Purcell, A. E., Jeon, O. H., Zimmerman, A. W., Blue, M. E. & Pevsner, J. Postmortem brain abnormalities of the glutamate neurotransmitter system in autism. Neurology 57, 1618–1628 (2001). source
• Not checked yetcites [93] Garbett, K. et al. Immune transcriptome alterations in the temporal cortex of subjects with autism. Neurobiol. Dis. 30, 303–311 (2008). source
• Not checked yetcites [94] Ginsberg, M. R., Rubin, R. A., Falcone, T., Ting, A. H. & Natowicz, M. R. Brain transcriptional and epigenetic associations with autism. PLoS ONE 7, e44736 (2012). source
• Not checked yetcites [95] Chow, M. L. et al. Age-dependent brain gene expression and copy number anomalies in autism suggest distinct pathological processes at young versus mature ages. PLoS Genet. 8, e1002592 (2012). source

In the paper

“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.”

What the sources say

• Not checked yetcites [108] Chen, C. et al. Two gene co-expression modules differentiate psychotics and controls. Mol. Psychiatry 18, 1308–1314 (2012). source

In the paper

“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.”

What the sources say

• Not checked yetcites [102] Hashimoto, T. et al. Alterations in GABA-related transcriptome in the dorsolateral prefrontal cortex of subjects with schizophrenia. Mol. Psychiatry 13, 147–161 (2007). source
• Not checked yetcites [103] Hakak, Y. et al. Genome-wide expression analysis reveals dysregulation of myelination-related genes in chronic schizophrenia. Proc. Natl Acad. Sci. USA 98, 4746–4751 (2001). source
• Not checked yetcites [104] Altar, C. A. et al. Deficient hippocampal neuron expression of proteasome, ubiquitin, and mitochondrial genes in multiple schizophrenia cohorts. Biol. Psychiatry 58, 85–96 (2005). source

In the paper

“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.”

What the sources say

• Not checked yetcites [58] Bartel, D. P. MicroRNAs: target recognition and regulatory functions. Cell 136, 215–233 (2009). source
• Not checked yetcites [59] Tompa, M. et al. Assessing computational tools for the discovery of transcription factor binding sites. Nat. Biotechnol. 23, 137–144 (2005). source
• Not checked yetcites TABLE 1

In the paper

“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.”

What the sources say

• Not checked yetcites [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

In the paper

“Moreover, this study found that genes in these modules have dynamic developmental trajectories, demonstrating a role for ASD risk genes in neural development.”

What the sources say

• Not checked yetcites [12] Hawrylycz, M. J. et al. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature 489, 391–399 (2012). source
• Not checked yetcites [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

In the paper

“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.”

What the sources say

• Not checked yetcites [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 yetcites [128] Lee, I. et al. A single gene network accurately predicts phenotypic effects of gene perturbation in Caenorhabditis elegans. Nat. Genet. 40, 181–188 (2008). source

In the paper

“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.”

What the sources say

• Not checked yetcites [12] Hawrylycz, M. J. et al. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature 489, 391–399 (2012). source
• Not checked yetcites [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

In the paper

“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).”

In the paper

“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.”

What the sources say

• Not checked yetcites [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

In the paper

“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.”

What the sources say

• Not checked yetcites [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 yetcites [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

In the paper

“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.”

In the paper

“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.”

What the sources say

• Not checked yetcites [11] Miller, J. A. et al. Transcriptional landscape of the prenatal human brain. Nature 508, 199–206 (2014). source
• Not checked yetcites [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 yetcites [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

In the paper

“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.”

In the paper

“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.”

What the sources say

• Not checked yetcites [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 yetcites [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 yetcites [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

In the paper

“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.”

What the sources say

• Not checked yetcites [120] O'Roak, B. J. et al. Multiplex targeted sequencing identifies recurrently mutated genes in autism spectrum disorders. Science 338, 1619–1622 (2012). source

In the paper

“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.”

In the paper

“This identified a PPI module that is enriched for genes related to synaptic function and weakly enriched for mutations from individuals with ASD.”

What the sources say

• Not checked yetcites [123] Li, J. et al. Integrated systems analysis reveals a molecular network underlying autism spectrum disorders. Mol. Syst. Biol. 10, 774–774 (2014). source

In the paper

“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.”

What the sources say

• Not checked yetcites [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 yetcites [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

In the paper

“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.”

What the sources say

• Not checked yetcites [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

In the paper

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

In the paper

“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.”

What the sources say

• Not checked yetcites [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

In the paper

“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.”

What the sources say

• Not checked yetcites [165] Lim, J. et al. Opposing effects of polyglutamine expansion on native protein complexes contribute to SCA1. Nature 452, 713–718 (2008). source

In the paper

“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.”

What the sources say

• Not checked yetcites [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

In the paper

“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.”

What the sources say

• Not checked yetcites [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

In the paper

“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.”

What the sources say

• Not checked yetcites [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 yetcites [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

In the paper

“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.”

What the sources say

• Not checked yetcites [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

In the paper

“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.”

What the sources say

• Not checked yetcites [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

In the paper

“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.”

What the sources say

• Not checked yetcites [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 yetcites [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 yetcites [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

In the paper

“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).”

What the sources say

• Not checked yetcites [134] Taylor, J. P. Toxic proteins in neurodegenerative disease. Science 296, 1991–1995 (2002). source
• Not checked yetcites [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 yetcites [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 yetcites [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

In the paper

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

In the paper

“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.”

In the paper

“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.”

In the paper

“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.”

What the sources say

• Not checked yetcites [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

In the paper

“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.”

What the sources say

• Not checked yetcites [193] Qu, X. A. & Rajpal, D. K. Applications of Connectivity Map in drug discovery and development. Drug Discov. Today 17, 1289–1298 (2012). source

In the paper

“These biases inflate enrichment results and can result in false positives”

In the paper

“The role of Tyrobp was experimentally validated in mice170, showing that network structure is predictive, as had previously been demonstrated with co-expression networks32.”

What the sources say

• Not checked yetcites [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 yetcites [32] Winden, K. D. et al. The organization of the transcriptional network in specific neuronal classes. Mol. Syst. Biol. 5, 291 (2009). source

In the paper

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

In the paper

“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.”

What the sources say

• Not checked yetcites [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

In the paper

“Currently, high-throughput, high dynamic range spatial and temporal data from minute sample quantities with proteomics are not available”

In the paper

“Currently, much basic and translational neuroscience research is still focused on candidate genes and candidate hypotheses”

In the paper

“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.”

In the paper

“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.”

In the paper

“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.”

What the sources say

• Not checked yetcites [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 yetcites [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 yetcites [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

In the paper

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

In the paper

“Given that gene expression changes are caused by genetic variation, this suggested these modules were causally involved171.”

What the sources say

• Not checked yetcites [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

In the paper

“One of the greatest challenges is to systematically infer causality in molecular networks with a systems genetics approach168,169.”

What the sources say

• Not checked yetcites [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 yetcites [169] Civelek, M. & Lusis, A. J. Systems genetics approaches to understand complex traits. Nat. Rev. Genet. 15, 34–48 (2013). source

In the paper

“However, developmental and cell subtype differences in the PSD are not well understood”

In the paper

“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.”

In the paper

“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).”

What the sources say

• Not checked yetcites [4] Geschwind, D. H. & Konopka, G. Neuroscience in the era of functional genomics and systems biology. Nature 461, 908–915 (2009). source

In the paper

“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.”

In the paper

“There is no experimental design that suits all aims”

In the paper

“Optimal methodological choices and study designs ensure that the biological signals from the main factors are not confounded by variation from unwanted factors178.”

What the sources say

• Not checked yetcites [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

In the paper

“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.”

What the sources say

• Not checked yetcites [198] Freimer, N. & Sabatti, C. The Human Phenome Project. Nat. Genet. 34, 15–21 (2003). source
• Not checked yetcites [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

In the paper

“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.”

What the sources say

• Not checked yetcites [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 yetcites [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 yetcites [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 yetcites [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 yetcites [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