Review Article | Published:

Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders

Nature Reviews Genetics volume 16, pages 441458 (2015) | Download Citation

Abstract

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

Key points

  • When applying high-throughput molecular methods to the study of neurodevelopmental disorders, major challenges include the spatial and temporal heterogeneity of the brain, a lack of appropriate tissue available for studies and poorly defined phenotypes.

  • Transcriptomics assays are currently the most widely used functional genomic assays in neurobiology owing to their ability to efficiently capture tissue-specific spatial and temporal heterogeneity in a high-throughput manner. Principles from transcriptomic studies will aid in evaluating additional molecular and cellular levels of regulation.

  • We review the principles of network analysis and describe how gene networks provide a framework to organize, integrate and analyse large-scale genomic data sets in neurobiology.

  • We review representative differential expression and gene network studies in neurodevelopmental disorders and neurodegenerative diseases and identify some next steps in data generation and integration that are necessary for progress in the field.

  • We provide guidelines for designing, analysing and evaluating high-throughput transcriptomic studies in the brain in order to improve study quality and reproducibility.

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Acknowledgements

The authors thank L. de la Torre-Ubieta and H. Won for assistance with Figure 1, as well as members of the Geschwind laboratory and K. Lage for critical reading of the manuscript. This work is supported by the US National Institute of Mental Health grants (5R37MH060233 and 5R01MH094714, D.H.G.), an Autism Center for Excellence network grant (9R01MH100027), the Simons Foundation (SFARI 206744, D.H.G.), NIMH Training and NRSA Fellowships (T32MH073526 and F30MH099886, N.N.P.), and the Medical Scientist Training Program at University of California, Los Angeles (UCLA).

Author information

Affiliations

  1. Program in Neurobehavioral Genetics, Semel Institute, and Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, California 90095, USA.

    • Neelroop N. Parikshak
    • , Michael J. Gandal
    •  & Daniel H. Geschwind
  2. Interdepartmental Program in Neuroscience, University of California, Los Angeles, California 90095, USA.

    • Neelroop N. Parikshak
    •  & Daniel H. Geschwind
  3. Center for Autism Treatment and Research, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, California 90095, USA.

    • Michael J. Gandal
    •  & Daniel H. Geschwind
  4. Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, California 90095, USA.

    • Daniel H. Geschwind

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Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Daniel H. Geschwind.

Supplementary information

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  1. 1.

    Supplementary information S1 (box)

    Methodology and analysis for cross-disorder transcriptomic analysis.

Glossary

Genetic architecture

For genetic variants, the relationship among allele frequency, effect size, number of contributing variants and how they quantitatively influence a given trait.

Molecular systems or integrative network approach

Systems biology methods that use high-throughput quantification, analysis and interpretation of the molecular relationships within and across molecular levels, including the genome, transcriptome, epigenome, proteome and other 'omes'.

Systems neuroscience

An area of neuroscience that focuses on short- and long-range circuits that are usually related to specific behavioral or cognitive functions (vision, motor function, attention and so on).

Gene network

A graph consisting of genes as nodes connected by edges that represent relationships between genes.

Differential gene expression analysis

(DGE analysis). An approach commonly used in transcriptomic studies that serially compares thousands of genes between groups (for example, disease and controls) to evaluate the mean difference and its significance for each gene independently.

Modules

Also known as clusters, cliques and communities. Highly interconnected subsets of genes in a gene network; for example, genes in a transcriptomic network sharing highly similar patterns of gene expression.

Nodes

Molecular entities that constitute a network; for example, genes in a gene network or proteins in a protein interaction network.

Edges

The relationships between nodes in a network delineating some measure of shared function; for example, correlations or physical interactions.

Mutual information

A measure of dependence between two variables that can capture complex relationships, including nonlinear and nonmonotonic patterns, that could be missed by linear correlation measures.

Hubs

Genes in a network or module that are highly connected; that is, they have a relatively high number of edges compared with other genes.

RNA sequencing

(RNA-seq). An assay for measuring RNA transcript levels in a genome-wide manner that involves the extraction of RNA followed by construction of cDNA libraries that undergo high-throughput sequencing.

Weighted networks

Networks in which the edges have continuous values, with higher values reflecting an increased strength or probability of connectivity.

Binary networks

Networks in which the edges are all or nothing, either because this is inherent to the edge measurement (for example, physically interacting or not) or because a cut-off or threshold has been applied to a continuous measurement (for example, by applying a rule that all correlation values ≥0.7 are 1, all others are 0).

Signed networks

Networks in which the direction of association is taken into consideration in addition to the magnitude of the correlation; for example, in a signed correlation network, high positive correlations are assigned high edge values, but high negative correlations are assigned low edge values.

Unsigned networks

Networks in which any high magnitude association is assigned a high edge value regardless of the direction of the association.

Topological overlap

A computation on direct edge relationships in a network that transforms them into indirect edge values that reflect the sharing of neighbourhoods between genes.

Seeded (prior-based) networks

Network analysis approaches in which edges are 'grown' around 'seed' genes that are selected on the basis of previous experiments or prior hypotheses, and the network structure is dependent on these seed genes.

Unseeded (genome-wide) networks

Network analysis approaches in which edges are evaluated in a genome-wide manner, and network structure is not dependent on prior knowledge of a particular set of genes.

Adjacency matrix

A matrix of pairwise node–node relationships that quantifies all possible edges in a network. Edge relationships may be determined from one data type or by weighting the contribution from multiple types of data.

CLIP-seq

An assay for measuring the binding sites of a protein on RNA transcripts in a genome-wide manner that involves crosslinking immunoprecipitation followed by high-throughput sequencing.

ChIP-seq

An assay for measuring the binding sites of a protein on DNA across the genome that involves chromatin immunoprecipitation followed by high-throughput sequencing.

DNase hypersensitivity or ATAC-seq

Sequencing methods that infer regions of the genome in a particular cell or tissue with open chromatin by exploiting the fact that these regions are preferentially accessible to the DNase I enzyme or a transposase.

Eigengenes

Module-level summaries of expression utilized in co-expression networks calculated by taking the first principal component of the expression levels of genes in a module.

Psychosis

A mental state defined by a loss of contact with reality and characterized by exaggerations or distortions of normal perception.

Negative symptoms

Symptoms involving a loss of normal emotional responses, including a lack of motivation, an inability to experience pleasure and reduced expression through speech.

Unsupervised methods

Analysis approaches that use the intrinsic variation in data to define shared patterns without explicit prior knowledge of how the data should be grouped (for example, hierarchical clustering). This can identify novel clusters or groupings of data points.

Expression quantitative trait locus analysis

(eQTL analysis). A specific case of genotype-to-phenotype association that uses RNA transcript levels as the phenotype in order to identify genetic loci that regulate RNA levels.

Selective vulnerability

The relative susceptibility of specific brain regions, cell populations or time points to genetic or environmental insults that result in disease.

Causal anchor

A causal factor, such as genetic variation, that can be used to orient edges to transform an undirected correlational network to a directed causal network.

Gene set enrichment

An analysis approach that assesses the statistical significance of the overlap between two gene sets; one set is usually an annotated reference set, and the other is an unannotated set of interest.

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DOI

https://doi.org/10.1038/nrg3934

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