Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders

In the paper

“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.”

In the paper

“Recent important advances include the evaluation of spatial and temporal transcriptomes by the Allen Brain Institute and BrainSpan8,11–13”

What the sources say

• Not checked yetcites [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
• 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 [13] Kang, H. J. et al. Spatio-temporal transcriptome of the human brain. Nature 478, 483–489 (2011). source

In the paper

“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”

What the sources say

• Not checked yetcites [1] Gratten, J., Wray, N. R., Keller, M. C. & Visscher, P. M. Large-scale genomics unveils the genetic architecture of psychiatric disorders. Nat. Neurosci. 17, 782–790 (2014). A comprehensive review of GWASs and exome studies across major neuropsychiatric disorders. It discusses the role of common variants and rare variants across disorders, and the concept of explaining heritability. source

In the paper

“Collaborative and consortium-level efforts have made substantial progress by mapping transcriptomic, epigenomic and proteomic landscapes in the brain8–10.”

What the sources say

• Not checked yetcites [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
• Not checked yetcites [9] Oh, S. W. et al. A mesoscale connectome of the mouse brain. Nature 508, 207–214 (2014). source
• Not checked yetcites [10] The ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012). source

In the paper

“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”

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 [28] Mitra, K., Carvunis, A.-R., Ramesh, S. K. & Ideker, T. Integrative approaches for finding modular structure in biological networks. Nat. Rev. Genet. 14, 719–732 (2013). source
• Not checked yetcites [29] Carter, H., Hofree, M. & Ideker, T. Genotype to phenotype via network analysis. Curr. Opin. Genet. Dev. 23, 611–621 (2013). source

In the paper

“Network approaches are particularly useful for relating whole-tissue-level changes to data from these high-resolution experiments11–13,26 (FIG. 2a).”

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 [13] Kang, H. J. et al. Spatio-temporal transcriptome of the human brain. Nature 478, 483–489 (2011). 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

In the paper

“Appropriately transformed and normalized sequencing data can be treated similarly to microarray data as far as statistical modelling and multiple corrections are concerned175,220.”

What the sources say

• 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 [220] Soneson, C. & Delorenzi, M. A comparison of methods for differential expression analysis of RNA-seq data. BMC Bioinformatics 14, 91 (2013). source

In the paper

“For gene expression studies, co-expression network analysis leverages the fact that gene expression reflects the state of the cellular or tissue system that is being analysed25.”

What the sources say

• Not checked yetcites [25] Carter, S. L., Brechbühler, C. M., Griffin, M. & Bond, A. T. Gene co-expression network topology provides a framework for molecular characterization of cellular state. Bioinformatics 20, 2242–2250 (2004). source

In the paper

“Regional and cellular heterogeneity pose obstacles for transcriptomic studies in the central nervous system (CNS)100,200”

What the sources say

• Not checked yetcites [100] Mirnics, K. & Pevsner, J. Progress in the use of microarray technology to study the neurobiology of disease. Nat. Neurosci. 7, 434–439 (2004). source
• Not checked yetcites [200] Coppola, G. & Geschwind, D. H. Technology Insight: querying the genome with microarrays — progress and hope for neurological disease. Nat. Clin. Pract. Neurol. 2, 147–158 (2006). source

In the paper

“edge relationships using mutual information or other association measures might be necessary to accurately detect modules in time-series data, which can be non-monotonic46–48.”

What the sources say

• Not checked yetcites [46] Wexler, E. M. et al. Genome-wide analysis of a Wnt1-regulated transcriptional network implicates neurodegenerative pathways. Sci. Signal. 4, ra65 (2011). source
• Not checked yetcites [47] Ernst, J. & Bar-Joseph, Z. STEM: a tool for the analysis of short time series gene expression data. BMC Bioinformatics 7, 191 (2006). source
• Not checked yetcites [48] Zoppoli, P., Morganella, S. & Ceccarelli, M. TimeDelay-ARACNE: reverse engineering of gene networks from time-course data by an information theoretic approach. BMC Bioinformatics 11, 154 (2010). source

In the paper

“most phenotypes are based on clinical and behavioural symptomatology rather than on biological mechanism or aetiology22–24.”

What the sources say

• Not checked yetcites [22] Casey, B. J. et al. DSM-5 and RDoC: progress in psychiatry research? Nat. Rev. Neurosci. 14, 810–814 (2013). source
• Not checked yetcites [23] Geschwind, D. H. Autism: many genes, common pathways? Cell 135, 391–395 (2008). source
• Not checked yetcites [24] Insel, T. et al. Research domain criteria (RDoC): toward a new classification framework for research on mental disorders. Am. J. Psychiatry 167, 748–751 (2010). source

In the paper

“These efforts have provided the first systematic view of the immensely complex molecular landscape across brain development, between brain regions and among major cell types (FIG. 1b).”

In the paper

“weighted networks constructed with the topological overlap of correlation (for example, by weighted gene co-expression network analysis (WGCNA)42,44) have similar sensitivity and specificity for detecting true network structure for experiments involving monotonic relationships as do networks constructed with nonlinear association measures such as mutual information (for example, by the Algorithm”

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 [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

In the paper

“The crucial issue of reproducibility is addressed by validating network observations in independent data or experiments (BOX 3; TABLE 1).”

What the sources say

• Not checked yetcites BOX 3
• Not checked yetcites TABLE 1

In the paper

“signed networks are more predictive of protein interactions and shared pathway relationships than unsigned networks38,42,43”

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 [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
• 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

“The DREAM challenge identified that the integration of multiple network methods yields the most robust regulatory relationship predictions49.”

What the sources say

• 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

“For example, when evaluating co-expression across brain regions, hubs in modules that are associated with specific regions, such as the cerebellum, are usually markers for predominant cell types, such as granule cells11,12,26,32.”

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 [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

“Notably, edges are susceptible to ascertainment biases52,214,215 and confounding factors that can induce spurious relationships178 (FIG. 2b).”

What the sources say

• Not checked yetcites [52] Hakes, L., Pinney, J. W., Robertson, D. L. & Lovell, S. C. Protein–protein interaction networks and biology — what's the connection? Nat. Biotechnol. 26, 69–72 (2008). source
• Not checked yetcites [214] Rossin, E. J. et al. Proteins encoded in genomic regions associated with immune-mediated disease physically interact and suggest underlying biology. PLoS Genet. 7, e1001273 (2011). source
• Not checked yetcites [215] Lee, I. & Marcotte, E. M. Effects of functional bias on supervised learning of a gene network model. Methods Mol. Biol. 541, 463–475 (2009). source
• 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

“Thus, gene products that are involved in an unknown cell type or biological process can be annotated on the basis of their proximity to marker genes of known function (‘guilt by association’)26,33,34.”

What the sources say

• 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 [33] Wolfe, C. J., Kohane, I. S. & Butte, A. J. Systematic survey reveals general applicability of 'guilt-by-association' within gene coexpression networks. BMC Bioinformatics 6, 227 (2005). source
• Not checked yetcites [34] Dougherty, J. D. et al. PBK/TOPK, a proliferating neural progenitor-specific mitogen-activated protein kinase kinase. J. Neurosci. 25, 10773–10785 (2005). source

In the paper

“Although reliant on data from non-neuronal tissue, pathway databases such as the Gene Ontology (GO50), the Kyoto Encyclopaedia of Genes and Genome Elements (KEGG51), Ingenuity Pathway Analyses and MetaCore are valuable for evaluating specific genes and pathways.”

What the sources say

• Not checked yetcites [50] Ashburner, M. et al. Gene Ontology: tool for the unification of biology. Nat. Genet. 25, 25–29 (2000). source
• Not checked yetcites [51] Ogata, H. et al. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 27, 29–34 (1999). source

In the paper

“Thus, many severe mutations do not converge on one specific phenotype but instead seem to cause a range of clinical disorders74,76,80,81,84,87,91.”

What the sources say

• Not checked yetcites [74] Abrahams, B. S. & Geschwind, D. H. Advances in autism genetics: on the threshold of a new neurobiology. Nat. Rev. Genet. 9, 341–355 (2008). source
• Not checked yetcites [76] Iossifov, I. et al. The contribution of de novo coding mutations to autism spectrum disorder. Nature 515, 216–221 (2014). source
• Not checked yetcites [80] Fromer, M. et al. De novo mutations in schizophrenia implicate synaptic networks. Nature 506, 179–184 (2014). source
• Not checked yetcites [81] Purcell, S. M. et al. A polygenic burden of rare disruptive mutations in schizophrenia. Nature 506, 185–190 (2014). source
• Not checked yetcites [84] Cross-Disorder, Group of the Psychiatric Genomics Consortium. Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs. Nat. Genet. 45, 984–994 (2013). source
• Not checked yetcites [87] Samocha, K. E. et al. A framework for the interpretation of de novo mutation in human disease. Nat. Genet. 46, 944–950 (2014). source
• Not checked yetcites [91] Devlin, B. & Scherer, S. W. Genetic architecture in autism spectrum disorder. Curr. Opin. Genet. Dev. 22, 229–237 (2012). source

In the paper

“Furthermore, when data are from non-neuronal tissue, the database may contain relationships not found in neural tissues (TABLE 1).”

What the sources say

• Not checked yetcites TABLE 1

In the paper

“Although diagnosis is usually made in late adolescence or early adulthood, extensive evidence indicates a neurodevelopmental origin99.”

What the sources say

• Not checked yetcites [99] Weinberger, D. R. Implications of normal brain development for the pathogenesis of schizophrenia. Arch. Gen. Psychiatry 44, 660–669 (1987). source

In the paper

“A particularly salient example of the utility of defining tissue-relevant networks is the power obtained by using PPIs derived from cardiac tissue to identify new human loci for long QT syndrome54.”

What the sources say

• Not checked yetcites [54] Lundby, A. et al. Annotation of loci from genome-wide association studies using tissue-specific quantitative interaction proteomics. Nat. Methods 11, 868–874 (2014). This study experimentally defines PPIs specific to cardiac tissue for four genes known to cause long QT syndrome and demonstrates how tissue-relevant PPI networks can be used to prioritize genetic association signals. source

In the paper

“As more genetic risk variants for these disorders have been discovered, studies have found remarkable pleiotropy1,1,83,84.”

What the sources say

• Not checked yetcites [1] Gratten, J., Wray, N. R., Keller, M. C. & Visscher, P. M. Large-scale genomics unveils the genetic architecture of psychiatric disorders. Nat. Neurosci. 17, 782–790 (2014). A comprehensive review of GWASs and exome studies across major neuropsychiatric disorders. It discusses the role of common variants and rare variants across disorders, and the concept of explaining heritability. source
• Not checked yetcites [83] Zhu, X., Need, A. C., Petrovski, S. & Goldstein, D. B. One gene, many neuropsychiatric disorders: lessons from Mendelian diseases. Nat. Neurosci. 17, 773–781 (2014). source
• Not checked yetcites [84] Cross-Disorder, Group of the Psychiatric Genomics Consortium. Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs. Nat. Genet. 45, 984–994 (2013). source

In the paper

“There is compelling evidence that using DNase hypersensitivity or ATAC-seq data to infer open chromatin, followed by combining transcription factor binding with open chromatin footprinting, can provide a powerful and comprehensive way to identify tissue-specific transcription factor regulation60,61.”

What the sources say

• Not checked yetcites [60] Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y. & Greenleaf, W. J. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10, 1213–1218 (2013). source
• Not checked yetcites [61] Pique-Regi, R. et al. Accurate inference of transcription factor binding from DNA sequence and chromatin accessibility data. Genome Res. 21, 447–455 (2011). source

In the paper

“We frame this issue using the concept of developmental canalization88, whereby natural selection on developmental programmes in humans has led to robustness in a range of genetic or environmental perturbations89,90: typical development occurs along a ‘track’ (FIG. 1c).”

What the sources say

• Not checked yetcites [88] Waddington, C. H. Canalization of development and the inheritance of acquired characters. Nature 150, 563–565 (1942). source
• Not checked yetcites [89] Masel, J. & Siegal, M. L. Robustness: mechanisms and consequences. Trends Genet. 25, 395–403 (2009). This paper discusses the concept of canalization and its implications for molecular biology and evolution. source
• Not checked yetcites [90] Suliman, R., Ben-David, E. & Shifman, S. Chromatin regulators, phenotypic robustness, and autism risk. Front. Genet. 5, 81 (2014). source

In the paper

“Various genetic approaches have been successful in identifying the causes of more than 1,000 Mendelian, and fewer non-Mendelian, forms of neurodevelopmental disorders: prototypical examples are intellectual disability62–68, autism spectrum disorder (ASD)69–77, epilepsy78,79 and schizophrenia80–82.”

What the sources say

• Not checked yetcites [62] Ropers, H. H. Genetics of intellectual disability. Curr. Opin. Genet. Dev. 18, 241–250 (2008). source
• Not checked yetcites [63] van Bokhoven, H. Genetic and epigenetic networks in intellectual disabilities. Annu. Rev. Genet. 45, 81–104 (2011). source
• Not checked yetcites [64] Matson, J. L. & Shoemaker, M. Intellectual disability and its relationship to autism spectrum disorders. Res. Dev. Disabil. 30, 1107–1114 (2009). source
• Not checked yetcites [65] Lubs, H. A., Stevenson, R. E. & Schwartz, C. E. Fragile X and X-linked intellectual disability: four decades of discovery. Am. J. Hum. Genet. 90, 579–590 (2012). source
• Not checked yetcites [66] de Ligt, J. et al. Diagnostic exome sequencing in persons with severe intellectual disability. N. Engl. J. Med. 367, 1921–1929 (2012). source
• Not checked yetcites [67] Rauch, A. et al. Range of genetic mutations associated with severe non-syndromic sporadic intellectual disability: an exome sequencing study. Lancet 380, 1674–1682 (2012). source
• Not checked yetcites [68] Gilissen, C. et al. Genome sequencing identifies major causes of severe intellectual disability. Nature 511, 344–347 (2014). source
• Not checked yetcites [69] Jamain, S. et al. Mutations of the X-linked genes encoding neuroligins NLGN3 and NLGN4 are associated with autism. Nat. Genet. 34, 27–29 (2003). source
• Not checked yetcites [70] Sanders, S. J. et al. De novo mutations revealed by whole-exome sequencing are strongly associated with autism. Nature 485, 237–241 (2012). source
• Not checked yetcites [71] O'Roak, B. J. et al. Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations. Nature 485, 246–250 (2012). source

In the paper

“Synaptic and microglial modules have been replicated in ASD cortex using RNA-seq in larger independent cohorts97.”

What the sources say

• Not checked yetcites [97] Gupta, S. et al. Transcriptome analysis reveals dysregulation of innate immune response genes and neuronal activity-dependent genes in autism. Nat. Commun. 5, 5748 (2014). source

In the paper

“One of the first studies to put schizophrenia transcriptomics into a genome-wide co-expression network used mutual information and WGCNA107.”

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

“Under this framework, the observed pleiotropy is consistent with the notion that disrupting highly evolutionarily constrained genes leads to the ‘derailment’ of typical development off this track, rather than setting the brain on a path to a specific clinically defined disorder (FIG. 1c).”

In the paper

“The first ASD study to identify reproducible, genome-wide findings used WGCNA42 to identify two modules, one containing upregulated genes and another containing downregulated genes that defined coherent biological processes in ASD brains30.”

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
• 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

In the paper

“More transcriptional studies of ASD post-mortem brains have been limited by the paucity of available tissue, which has made them underpowered to identify reproducible pathways with statistical rigour92–95.”

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

“Several rare, highly penetrant mutations in evolutionarily constrained fetal brain-expressed genes are associated with ASD, schizophrenia and intellectual disability, as well as epilepsy83,85–87.”

What the sources say

• Not checked yetcites [83] Zhu, X., Need, A. C., Petrovski, S. & Goldstein, D. B. One gene, many neuropsychiatric disorders: lessons from Mendelian diseases. Nat. Neurosci. 17, 773–781 (2014). source
• Not checked yetcites [85] Doherty, J. L. & Owen, M. J. Genomic insights into the overlap between psychiatric disorders: implications for research and clinical practice. Genome Med. 6, 29 (2014). source
• Not checked yetcites [86] Hoischen, A., Krumm, N. & Eichler, E. E. Prioritization of neurodevelopmental disease genes by discovery of new mutations. Nat. Neurosci. 17, 764–772 (2014). source
• Not checked yetcites [87] Samocha, K. E. et al. A framework for the interpretation of de novo mutation in human disease. Nat. Genet. 46, 944–950 (2014). source

In the paper

“Moreover, genome-wide association study (GWAS) signal enrichment analysis30 found that common variants associated with schizophrenia and bipolar disorder were enriched in the neuronal differentiation module, suggesting that disorders sharing a genetic architecture84 may also share functional transcriptional alterations: a hypothesis that warrants rigorous testing.”

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 [84] Cross-Disorder, Group of the Psychiatric Genomics Consortium. Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs. Nat. Genet. 45, 984–994 (2013). source

In the paper

“The increasing availability of large amounts of relevant data sets within the public domain10,14 now permits the evaluation of network modules for complex regulatory relationships by combining network edges from statistical associations, time-series data, physical binding and computational predictions (FIG. 2b).”

What the sources say

• Not checked yetcites [10] The ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012). source
• Not checked yetcites [14] Roadmap Epigenomics Consortium. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).

In the paper

“Integration of ChIP–seq, CHD8 knockdown and gene co-expression suggested that CHD8 directly regulates co-expression modules that are enriched for rare de novo mutations and genes found in the proliferating layers of the fetal cortex27.”

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

“Another study applied a similar approach but evaluated CHD8 targets in the fetal brain in vivo57. This study identified stronger enrichment for ASD mutations”

What the sources say

• Not checked yetcites [57] Cotney, J. et al. The autism-associated chromatin modifier CHD8 regulates other autism risk genes during human neurodevelopment. Nat. Commun. 6, 6404 (2015). source

In the paper

“Additionally, whole-exome sequencing studies of other neurodevelopmental disorders have found enrichment for FMRP targets in rare mutations in schizophrenia81, intellectual disability68 and epilepsy78.”

What the sources say

• Not checked yetcites [81] Purcell, S. M. et al. A polygenic burden of rare disruptive mutations in schizophrenia. Nature 506, 185–190 (2014). source
• Not checked yetcites [68] Gilissen, C. et al. Genome sequencing identifies major causes of severe intellectual disability. Nature 511, 344–347 (2014). source
• Not checked yetcites [78] Epi4K Consortium & Epilepsy Phenome/Genome Project. De novo mutations in epileptic encephalopathies. Nature 501, 217–221 (2013). source

In the paper

“As many FMRP targets are highly conserved and are under purifying selection72,87,117”

What the sources say

• Not checked yetcites [72] Iossifov, I. et al. De novo gene disruptions in children on the autistic spectrum. Neuron 74, 285–299 (2012). source
• Not checked yetcites [87] Samocha, K. E. et al. A framework for the interpretation of de novo mutation in human disease. Nat. Genet. 46, 944–950 (2014). source
• Not checked yetcites [117] Ronemus, M., Iossifov, I., Levy, D. & Wigler, M. The role of de novo mutations in the genetics of autism spectrum disorders. Nat. Rev. Genet. 15, 133–141 (2014). source

In the paper

“This approach was applied to identify a highly interconnected PPI subnetwork among rare de novo variants in ASD71.”

What the sources say

• Not checked yetcites [71] O'Roak, B. J. et al. Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations. Nature 485, 246–250 (2012). source

In the paper

“showed that the PPI network seeded around these 35 genes was indeed highly interconnected.”

What the sources say

• Not checked yetcites [124] Sakai, Y. et al. Protein interactome reveals converging molecular pathways among autism disorders. Sci. Transl Med. 3, 86ra49 (2011). source

In the paper

“suggesting that ChIP–seq in the human brain at the right time point identifies interactions that are more disease relevant57.”

What the sources say

• Not checked yetcites [57] Cotney, J. et al. The autism-associated chromatin modifier CHD8 regulates other autism risk genes during human neurodevelopment. Nat. Commun. 6, 6404 (2015). source

In the paper

“They identified three spatiotemporal combinations that passed stringent correction for multiple testing: frontal cortical regions during the fetal period, and thalamic and cerebellar regions from birth to 6 years of postnatal age.”

What the sources say

• 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

“Recently, one study used global literature-curated PPI interactions in a genome-wide network analysis to identify modules that are enriched for ASD-associated genes123.”

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

“Given that the human brain transcriptome has a reproducible structure12,26”

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 [26] Oldham, M. C. et al. Functional organization of the transcriptome in human brain. Nat. Neurosci. 11, 1271–1282 (2008). source

In the paper

“Integration with transcriptomics annotated the module as highly expressed in oligodendrocytes and the corpus callosum”

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

“chromodomain helicase DNA-binding protein 8 (CHD8)118, which is the gene most frequently affected by ASD-associated rare de novo variation72,119–121.”

What the sources say

• Not checked yetcites [72] Iossifov, I. et al. De novo gene disruptions in children on the autistic spectrum. Neuron 74, 285–299 (2012). source
• Not checked yetcites [119] Talkowski, M. E. et al. Sequencing chromosomal abnormalities reveals neurodevelopmental loci that confer risk across diagnostic boundaries. Cell 149, 525–537 (2012). source
• 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
• Not checked yetcites [121] Bernier, R. et al. Disruptive CHD8 mutations define a subtype of autism early in development. Cell 158, 263–276 (2014). source

In the paper

“However, even with knowledge of isoform-specific interactions, the tissue environment for interaction cannot be efficiently recapitulated with current PPI approaches at a genome-wide scale (TABLE 1).”

In the paper

“Edges in NETBAG are predominantly derived from multiple PPI databases, GO50 and KEGG51, which are all literature-curated databases, and thus NETBAG is susceptible to the biases discussed above.”

What the sources say

• Not checked yetcites [50] Ashburner, M. et al. Gene Ontology: tool for the unification of biology. Nat. Genet. 25, 25–29 (2000). source
• Not checked yetcites [51] Ogata, H. et al. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 27, 29–34 (1999). source

In the paper

“A complementary study identified developmental co-expression networks enriched for ASD risk genes seeded around nine genes with the highest ASD association signal from whole-exome sequencing112.”

What the sources say

• 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

“demonstrating that tissue-specific data are essential for a neurobiological interpretation of PPI modules123.”

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

“Using CLIP–seq, investigators identified the RNA binding targets of the translational regulator fragile X mental retardation protein (FMRP)116, and a subsequent analysis found that these targets are highly enriched for de novo mutations in ASD72.”

What the sources say

• Not checked yetcites [116] Darnell, J. C. et al. FMRP stalls ribosomal translocation on mRNAs linked to synaptic function and autism. Cell 146, 247–261 (2011). source
• Not checked yetcites [72] Iossifov, I. et al. De novo gene disruptions in children on the autistic spectrum. Neuron 74, 285–299 (2012). source

In the paper

“Sakai and colleagues124 carried out a Y2H screen of 35 syndromic or candidate ASD genes and identified many novel PPIs.”

What the sources say

• Not checked yetcites [124] Sakai, Y. et al. Protein interactome reveals converging molecular pathways among autism disorders. Sci. Transl Med. 3, 86ra49 (2011). source

In the paper

“A seeded co-expression approach has also been used to identify risk convergence in schizophrenia, identifying fetal development of the prefrontal cortex as a point of convergence for de novo mutations115.”

What the sources say

• Not checked yetcites [115] Gulsuner, S. et al. Spatial and temporal mapping of de novo mutations in schizophrenia to a fetal prefrontal cortical network. Cell 154, 518–529 (2013). source

In the paper

“Both genome-wide27 and seeded114 co-expression network analysis further connected FMRP targets with multiple forms of ASD genetic risk, including copy number variations (CNVs)114.”

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 [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

“Genetic investigations in ASD have constructed seed-based networks with literature-curated PPIs to identify the convergence of ASD risk genes71,73.”

What the sources say

• Not checked yetcites [71] O'Roak, B. J. et al. Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations. Nature 485, 246–250 (2012). source
• Not checked yetcites [73] Neale, B. M. et al. Patterns and rates of exonic de novo mutations in autism spectrum disorders. Nature 485, 242–245 (2012). source

In the paper

“Furthermore, genes in CNVs from females contributed more to the module connectivity than those from males”

What the sources say

• Not checked yetcites [129] Levy, D. et al. Rare de novo and transmitted copy-number variation in autistic spectrum disorders. Neuron 70, 886–897 (2011). source

In the paper

“Lim and colleagues164 used a seed-based approach based on a Y2H screen to identify interactors of the protein products of multiple causal and candidate genes in inherited ataxias.”

What the sources say

• Not checked yetcites [164] Lim, J. et al. A protein–protein interaction network for human inherited ataxias and disorders of Purkinje cell degeneration. Cell 125, 801–814 (2006). source

In the paper

“Although transcriptomic studies have furthered our understanding of disease mechanisms beyond neuropathology and single genes, the effects of cell type loss have not been completely accounted for in most studies.”

In the paper

“Experimental validation of predictions from these networks showed that this cross-species approach can identify consistent, high-confidence perturbations in neurodegenerative disease46,153.”

What the sources say

• Not checked yetcites [46] Wexler, E. M. et al. Genome-wide analysis of a Wnt1-regulated transcriptional network implicates neurodegenerative pathways. Sci. Signal. 4, ra65 (2011). source
• Not checked yetcites [153] Rosen, E. Y. et al. Functional genomic analyses identify pathways dysregulated by progranulin deficiency, implicating Wnt signaling. Neuron 71, 1030–1042 (2011). source

In the paper

“Interestingly, in vivo PPI screening by large-scale co-immunoprecipitation and mass spectrometry provided tissue- and time-specific information that was not found by Y2H studies167.”

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

“In FTD, transcriptional signatures related to differential regional vulnerability have helped to identify modulators of neurodegeneration.”

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
• Not checked yetcites [139] Chen-Plotkin, A. S. et al. Variations in the progranulin gene affect global gene expression in frontotemporal lobar degeneration. Hum. Mol. Genet. 17, 1349–1362 (2008). source

In the paper

“Both of these studies in FTD demonstrate the value of using selective vulnerability and differential genetic risk in study design.”

In the paper

“Miller and colleagues149 applied network analysis to compare the transcriptome in normal ageing and Alzheimer disease, finding many shared features that were downregulated in Alzheimer disease and normal human ageing149.”

What the sources say

• Not checked yetcites [149] Miller, J. A., Oldham, M. C. & Geschwind, D. H. A. Systems level analysis of transcriptional changes in Alzheimer's disease and normal aging. J. Neurosci. 28, 1410–1420 (2008). source

In the paper

“Purifying cell populations or carrying out transcriptional analyses on single cells158–163 can identify important changes that are not apparent in whole-tissue transcriptomes141,142.”

What the sources say

• Not checked yetcites [141] Miller, J. A., Woltjer, R. L., Goodenbour, J. M., Horvath, S. & Geschwind, D. H. Genes and pathways underlying regional and cell type changes in Alzheimer's disease. Genome Med. 5, 48 (2013). source
• Not checked yetcites [142] Kuhn, A., Thu, D., Waldvogel, H. J., Faull, R. L. M. & Luthi-Carter, R. Population-specific expression analysis (PSEA) reveals molecular changes in diseased brain. Nat. Methods 8, 945–947 (2011). source
• Not checked yetcites [158] Ginsberg, S. D. et al. Single-cell gene expression analysis: implications for neurodegenerative and neuropsychiatric disorders. Neurochem. Res. 29, 1053–1064 (2004). source
• Not checked yetcites [159] Lobo, M. K., Karsten, S. L., Gray, M., Geschwind, D. H. & Yang, X. W. FACS-array profiling of striatal projection neuron subtypes in juvenile and adult mouse brains. Nat. Neurosci. 9, 443–452 (2006). source
• Not checked yetcites [160] Doyle, J. P. et al. Application of a translational profiling approach for the comparative analysis of CNS cell types. Cell 135, 749–762 (2008). source
• Not checked yetcites [161] Gong, S. et al. A gene expression atlas of the central nervous system based on bacterial artificial chromosomes. Nature 425, 917–925 (2003). source
• Not checked yetcites [162] Zhang, Y. et al. An RNA-sequencing transcriptome and splicing database of glia, neurons, and vascular cells of the cerebral cortex. J. Neurosci. 34, 11929–11947 (2014). An RNA-seq database of gene expression and splicing differences between major cell types in the mouse CNS that provides cell type-specific profiles that can be used to query cell type specificity in other studies. source
• Not checked yetcites [163] Cahoy, J. D. et al. A transcriptome database for astrocytes, neurons, and oligodendrocytes: a new resource for understanding brain development and function. J. Neurosci. 28, 264–278 (2008). source

In the paper

“Both involved extensive bioinformatic analyses of expression data from in vitro neural progenitor models and identified transcriptomic changes shared across the post-mortem human brain, human neural cell lines and the mouse brain.”

What the sources say

• Not checked yetcites [153] Rosen, E. Y. et al. Functional genomic analyses identify pathways dysregulated by progranulin deficiency, implicating Wnt signaling. Neuron 71, 1030–1042 (2011). source
• Not checked yetcites [154] Wexler, E. M., Paucer, A., Kornblum, H. I., Palmer, T. D. & Geschwind, D. H. Endogenous Wnt signaling maintains neural progenitor cell potency. Stem Cells 27, 1130–1141 (2009). source

In the paper

“The involvement of this pathway in Alzheimer disease was experimentally validated146.”

What the sources say

• Not checked yetcites [146] Muhammad, A. et al. Retromer deficiency observed in Alzheimer's disease causes hippocampal dysfunction, neurodegeneration, and Aβ accumulation. Proc. Natl Acad. Sci. USA 105, 7327–7332 (2008). source

In the paper

“One particularly powerful approach involved the use of the pattern of regional vulnerability to guide a microarray study that identified a defect in the retromer complex, which is responsible for endosome-mediated recycling of membrane proteins145.”

What the sources say

• Not checked yetcites [145] Small, S. A. et al. Model-guided microarray implicates the retromer complex in Alzheimer's disease. Ann. Neurol. 58, 909–919 (2005). source

In the paper

“The major challenge in Alzheimer disease and FTD transcriptomics has been the identification of changes that are independent of alterations in cell type proportions, which accompany cell death and inflammation.”

In the paper

“In FTD, two studies re-analysed published transcriptome data139 to discover a role for WNT signalling in GRN-mediated FTD153,154.”

What the sources say

• Not checked yetcites [139] Chen-Plotkin, A. S. et al. Variations in the progranulin gene affect global gene expression in frontotemporal lobar degeneration. Hum. Mol. Genet. 17, 1349–1362 (2008). source
• Not checked yetcites [153] Rosen, E. Y. et al. Functional genomic analyses identify pathways dysregulated by progranulin deficiency, implicating Wnt signaling. Neuron 71, 1030–1042 (2011). source
• Not checked yetcites [154] Wexler, E. M., Paucer, A., Kornblum, H. I., Palmer, T. D. & Geschwind, D. H. Endogenous Wnt signaling maintains neural progenitor cell potency. Stem Cells 27, 1130–1141 (2009). source

In the paper

“The findings demonstrated that GRN-positive individuals were a transcriptomically distinct group from those with sporadic FTD139.”

What the sources say

• Not checked yetcites [139] Chen-Plotkin, A. S. et al. Variations in the progranulin gene affect global gene expression in frontotemporal lobar degeneration. Hum. Mol. Genet. 17, 1349–1362 (2008). source

In the paper

“suggesting that females are affected by more severe genetic hits in ASD, an observation that has been replicated in exome-sequencing studies76,117.”

What the sources say

• Not checked yetcites [129] Levy, D. et al. Rare de novo and transmitted copy-number variation in autistic spectrum disorders. Neuron 70, 886–897 (2011). source
• Not checked yetcites [76] Iossifov, I. et al. The contribution of de novo coding mutations to autism spectrum disorder. Nature 515, 216–221 (2014). source
• Not checked yetcites [117] Ronemus, M., Iossifov, I., Levy, D. & Wigler, M. The role of de novo mutations in the genetics of autism spectrum disorders. Nat. Rev. Genet. 15, 133–141 (2014). source

In the paper

“Analysis of the resultant PPI network identified an interconnected network of proteins related to inherited ataxias.”

What the sources say

• Not checked yetcites [164] Lim, J. et al. A protein–protein interaction network for human inherited ataxias and disorders of Purkinje cell degeneration. Cell 125, 801–814 (2006). source

In the paper

“The second study139 compared post-mortem tissue from patients with FTD harbouring dominant mutations in the pro-granulin (GRN) gene, patients who had FTD but who did not have a known family history or mutations, and control individuals. This study also leveraged regional vulnerability by comparing transcriptome profiles in the frontal cortex, hippocampus and cerebellum, identifying a diminishing”

What the sources say

• Not checked yetcites [139] Chen-Plotkin, A. S. et al. Variations in the progranulin gene affect global gene expression in frontotemporal lobar degeneration. Hum. Mol. Genet. 17, 1349–1362 (2008). source

In the paper

“Another study used similar methods to identify overlap in transcriptional networks between vascular disease (a major risk factor for dementia) and Alzheimer disease, identifying potential molecular mechanisms that might underlie their co-occurrence151.”

What the sources say

• Not checked yetcites [151] Ray, M., Ruan, J. & Zhang, W. Variations in the transcriptome of Alzheimer's disease reveal molecular networks involved in cardiovascular diseases. Genome Biol. 9, R148 (2008). source

In the paper

“The first study with NETBAG evaluated CNV-hit genes implicated in ASD and found a highly interconnected module related to synaptic function129.”

What the sources say

• Not checked yetcites [129] Levy, D. et al. Rare de novo and transmitted copy-number variation in autistic spectrum disorders. Neuron 70, 886–897 (2011). source

In the paper

“Large studies using quantitative metrics of severity140 and differentially vulnerable regions144 have identified pathway-level changes in transcriptional regulation, apoptosis, cell proliferation, energy metabolism and synaptic transmission.”

What the sources say

• 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 [144] Liang, W. S. et al. Alzheimer's disease is associated with reduced expression of energy metabolism genes in posterior cingulate neurons. Proc. Natl Acad. Sci. USA 105, 4441–4446 (2008). source

In the paper

“An extension of the NETBAG approach (dubbed NETBAG+) has also been applied to simultaneously evaluate large sets of single-nucleotide variants (SNVs) and CNVs in schizophrenia131 and ASD132, confirming the convergence of disease genes onto shared pathways.”

What the sources say

• Not checked yetcites [131] Gilman, S. R. et al. Diverse types of genetic variation converge on functional gene networks involved in schizophrenia. Nat. Neurosci. 15, 1723–1728 (2012). source
• Not checked yetcites [132] Chang, J., Gilman, S. R., Chiang, A. H., Sanders, S. J. & Vitkup, D. Genotype to phenotype relationships in autism spectrum disorders. Nat. Neurosci. 18, 191–198 (2015). source

In the paper

“Mapping genetic variants to gene expression and PPI networks has been fruitful, but most disease-associated variation in complex diseases lies in the noncoding regulatory regions of the genome188.”

What the sources say

• Not checked yetcites [188] Gusev, A. et al. Partitioning heritability of regulatory and cell-type-specific variants across 11 common diseases. Am. J. Hum. Genet. 95, 535–552 (2014). source

In the paper

“This work further emphasizes the importance of considering tissue context in the studies examining disease-relevant protein associations.”

In the paper

“As shown by multiple studies, gene network analyses can aid in understanding genetic association studies.”

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

“The researchers then applied Bayesian network analysis to evaluate causal relationships in an Alzheimer disease-related microglial module, implicating TYROBP (TYRO protein tyrosine kinase-binding protein) as a regulatory hub.”

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

“For example, longer genes are more likely to be implicated by CNVs and SNVs87,184,185”

What the sources say

• Not checked yetcites [87] Samocha, K. E. et al. A framework for the interpretation of de novo mutation in human disease. Nat. Genet. 46, 944–950 (2014). source
• Not checked yetcites [184] Shohat, S. & Shifman, S. Bias towards large genes in autism. Nature 512, E1–E2 (2014). source
• Not checked yetcites [185] Wang, L., Jia, P., Wolfinger, R. D., Chen, X. & Zhao, Z. Gene set analysis of genome-wide association studies: methodological issues and perspectives. Genomics 98, 1–8 (2011). source

In the paper

“Many of the most influential studies using gene networks to probe neuropsychiatric disease mechanisms integrate multiple data types (for example, RNA expression, GWAS signals and PPI) or data sets (for example, human post-mortem, mouse and in vitro), emphasizing the value of publicly available data sets.”

In the paper

“This demonstrates how cross-disorder analyses can systematically reveal shared and distinct biological processes among disorders, even when the data are from different studies”

In the paper

“and genes highly expressed in the brain, particularly those involved in synaptic function, are longer on average than other genes186.”

What the sources say

• Not checked yetcites [186] Raychaudhuri, S. et al. Accurately assessing the risk of schizophrenia conferred by rare copy-number variation affecting genes with brain function. PLoS Genet. 6, e1001097 (2010). source

In the paper

“Zhang and colleagues170 applied WGCNA to hundreds of post-mortem brain samples from individuals with Alzheimer disease, other neurodegenerative diseases and controls.”

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

“A recent study shows that this goal is possible in the CNS by using genetic variation as a causal anchor to define genetically driven network-level changes in Alzheimer disease and to provide experimental validation for network predictions170.”

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

“A particularly salient example is the synaptic signalling apparatus, more specifically the postsynaptic density (PSD), which has been extensively characterized at the protein level in humans and mice, showing key areas of overlap and divergence191,192.”

What the sources say

• Not checked yetcites [191] Bayés, À. et al. Characterization of the proteome, diseases and evolution of the human postsynaptic density. Nat. Neurosci. 14, 19–21 (2010). source
• Not checked yetcites [192] Bayés, À. et al. Comparative study of human and mouse postsynaptic proteomes finds high compositional conservation and abundance differences for key synaptic proteins. PLoS ONE 7, e46683 (2012). source

In the paper

“An eQTL analysis followed by module-level genetic signal enrichment identified several modules in which genetic association signals were enriched.”

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

“Notably, molecular profiles in post-mortem gene expression studies are affected by RNA degradation and post-mortem intervals179”

What the sources say

• Not checked yetcites [179] Trabzuni, D. et al. Quality control parameters on a large dataset of regionally dissected human control brains for whole genome expression studies. J. Neurochem. 120, 473–473 (2011). source

In the paper

“As far as causality is concerned, most molecular profiling studies, especially those using post-mortem tissue, cannot show causality without follow-up controlled experiments or genetic evidence169–171.”

What the sources say

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

“Given the promise of molecular systems and integrative network approaches, it is perhaps surprising that there are few universally agreed on metrics, power analysis tools or methodological comparisons to guide experimental design, execution and analysis, such as there are for genetic association studies172.”

What the sources say

• Not checked yetcites [172] Sham, P. C. & Purcell, S. M. Statistical power and significance testing in large-scale genetic studies. Nat. Rev. Genet. 15, 335–346 (2014). source

In the paper

“However, studies have demonstrated biases in gene length, gene mutability and other factors that can drive gene set enrichment instead of a true biological signal.”

What the sources say

• Not checked yetcites [87] Samocha, K. E. et al. A framework for the interpretation of de novo mutation in human disease. Nat. Genet. 46, 944–950 (2014). source
• Not checked yetcites [184] Shohat, S. & Shifman, S. Bias towards large genes in autism. Nature 512, E1–E2 (2014). source
• Not checked yetcites [185] Wang, L., Jia, P., Wolfinger, R. D., Chen, X. & Zhao, Z. Gene set analysis of genome-wide association studies: methodological issues and perspectives. Genomics 98, 1–8 (2011). source