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Keywords:

  • network medicine;
  • omics;
  • systems biology

Abstract

  1. Top of page
  2. Abstract
  3. COMPLEXITIES IN PSYCHIATRIC GENETICS
  4. SINGULAR “OMICS” APPROACHES TO PSYCHIATRIC GENETICS
  5. OMICS DATA TYPES
  6. “OMICS” LIMITATIONS IN PSYCHIATRY
  7. REDUCTIONIST VERSES HOLISTIC APPROACHES
  8. NETWORK MEDICINE
  9. METHODOLOGICAL APPROACHES TOWARDS NETWORK MEDICINE
  10. APPLICATIONS OF NETWORK MEDICINE IN PSYCHIATRIC GENETICS
  11. CONCLUSION
  12. REFERENCES

The major psychiatric disorders are complex in nature, meaning that they are influenced by multiple environmental and genetic exposures that perturb the intricate cellular network, resulting in disease. In general, psychiatric diseases are highly heritable but also have important environmental etiologies. Environmental influences include neonatal exposures, social environments, psychological mechanisms, and abnormal functioning of the neurotransmitter system. Molecular influences can be identified using many data types including genomics, epigenomics, transcriptomics, metabolomics, and proteomics. The emerging field of network medicine offers a new approach to explore the complexities of disease development in a framework that considers a holistic, rather than a reductionist viewpoint. In this review we explain a general framework of how the network medicine approach can provide valuable insight into understanding important molecular mechanisms that contribute to the pathogenesis of psychiatric disorders. © 2013 Wiley Periodicals, Inc.


COMPLEXITIES IN PSYCHIATRIC GENETICS

  1. Top of page
  2. Abstract
  3. COMPLEXITIES IN PSYCHIATRIC GENETICS
  4. SINGULAR “OMICS” APPROACHES TO PSYCHIATRIC GENETICS
  5. OMICS DATA TYPES
  6. “OMICS” LIMITATIONS IN PSYCHIATRY
  7. REDUCTIONIST VERSES HOLISTIC APPROACHES
  8. NETWORK MEDICINE
  9. METHODOLOGICAL APPROACHES TOWARDS NETWORK MEDICINE
  10. APPLICATIONS OF NETWORK MEDICINE IN PSYCHIATRIC GENETICS
  11. CONCLUSION
  12. REFERENCES

Heritability estimates for psychiatric disorders such as bipolar (85%), unipolar disorder (59%), alcoholism (50–60%), attention deficit hyperactivity disorder (ADHD; 76%), and schizophrenia (85%) [Anon, 1983; McGuffin et al., 2003; Faraone et al., 2005; Husted et al., 2006; Stacey et al., 2009; Rommelse et al., 2010; Hallmayer et al., 2011] are notably higher than for other complex diseases such as cancer and several measures of heart disease, which typically have heritability estimates less than 60% (25–57%) [Austin et al., 1987; Sluijter et al., 1989; Page et al., 1997]. Traditional statistical approaches to gene identification for psychiatric disorders have used genetic markers to identify genetic variants that are associated with disease. This started with single markers, expanded to 100's of markers in linkage analysis, evolved to 100's of thousands of markers in genome-wide association, and has now become whole genome and exome sequencing. This approach has had some success for psychiatric disorders, most notably leading to the identification of APOE for Alzheimer's disease [Corder et al., 1995]. Both common [Stefansson et al., 2009; Jia et al., 2010; Ripke et al., 2011] and rare [Stefansson et al., 2008] genetic variants have also been identified for schizophrenia while primarily common genetic variants have been identified for bipolar disorder [Sklar et al., 2011]. However, meta-analyses for both major depressive disorder and ADHD have found no genetic variants that meet genome-wide significance using genome-wide association approaches [Neale et al., 2010; Ripke et al., 2013]. Even given the identified genetic variants for psychiatric disorders to date, these findings explain a shockingly small about of the total genetic variation [Manolio et al., 2009]. In addition, the understanding of the functional role that these variants have played in the pathogenesis of disease is most often not understood, making it difficult for any of these polymorphisms to have a clear therapeutic impact on the disorder. By itself, it is clear that the single marker approach is limited in the knowledge it can provide towards a complete understanding of the molecular mechanisms underlying psychiatric diseases for several reasons:

  • Small genetic effect: To date the identified psychiatric genetic variants have most often only had very modest effect sizes (odd ratio less than 1.5). Therefore, any one variant has a minimal impact on the disease. These variants are also often difficult to identify using the GWAS approach, where many statistical tests often require stringent thresholds for declaring statistical significance.
  • Rare genetic variants: Rare genetic single nucleotide polymorphisms (SNPs) are often defined as those variants with an allele frequency less than one percent. Research suggests that rare genetic variants often have greater overall genetic effects on psychiatric disorders [Frazer et al., 2009]. In fact some consistent genetic associations have been observed with rare genetic variants and several psychiatric disorders [Stefansson et al., 2008; Walsh et al., 2008]. Rare variants tend to be difficult to identify by the simple fact that few individuals have these variants, making large sample sizes necessary for variant identification.
  • Copy number variants (CNVs): CNVs are alterations of the DNA of a genome that result in the cell having an abnormal number of copies of one or more sections of the DNA. As SNPs are alterations in a single nucleotide of genetic variants, CNVs are alterations of larger segments of genetic material. CNVs account for over 12% of the variation in the human genome [Stankiewicz and Lupski, 2010]. Some CNVs has been successfully identified for psychiatric disorders such as schizophrenia [Vrijenhoek et al., 2008], but these variants are also often rare, thereby limiting their identifiability.
  • Incomplete genomic coverage: Although the costs for whole genomic sequencing are decreasing rapidly, to date there are few psychiatric genetic studies with whole genome sequencing on large numbers of individuals. As such, the GWAS and imputed data that are commonly being used may not often identify the true causative variants. In addition there may be no genotyped genetic variant that is in strong linkage disequilibrium with the actual causative variant. Therefore using current methodologies, causative variants often remain unidentified.
  • Incomplete assessment of interactions: The single SNP approach is also limited in its ability to fully assess the interactions between multiple genetic variants. Seeing that many of the genetic variants that contribute to the pathogenesis of psychiatric disorders are likely interacting in a complex molecular network, it is likely that several genetic variants act together to “turn on” or “turn off” molecular pathways that increase or decrease disease risk. Therefore, by studying each genetic variant independently, the potential large impact that multiple genetic variants may have when acting in concert together is completely overlooked. In addition, it is highly likely that several of these genetic variants are impacted by known environmental risk factors (e.g., smoking, in-utero conditions) and when studied independently, these gene by environment interactions are also ignored.
  • Heterogeneity of phenotypes: An inherent problem for complex disorders is the inevitable heterogeneity that exists within each diagnosis. For psychiatric disorders, this is exemplified by the subtype classifications for many disorders. For example, an individual diagnosed with bipolar disorder may have a sub-classification of bipolar 1, bipolar 2, bipolar no otherwise specified, or cyclothymic disorder. In genetic analyses, it is a constant deliberation whether to reduce the population to those individuals with a very specific, more homogeneous disorder or to include a larger number of individuals with a more heterogeneous disease. Regardless, the variation in outward manifestations of a given psychiatric disorder likely correlate with increased genetic heterogeneity, which will decrease our overall power to detect genetic associations.

In sum, psychiatric genetics is a complex field that has had some successes in identifying genetic variants that contribute to disease. Despite this, the majority of molecular determinants for most of these disorders remain to be discovered. We propose that an integrative “omics” approach offers promise to identify a portion of the “missing heritability” [Manolio et al., 2009] for these disorders.

SINGULAR “OMICS” APPROACHES TO PSYCHIATRIC GENETICS

  1. Top of page
  2. Abstract
  3. COMPLEXITIES IN PSYCHIATRIC GENETICS
  4. SINGULAR “OMICS” APPROACHES TO PSYCHIATRIC GENETICS
  5. OMICS DATA TYPES
  6. “OMICS” LIMITATIONS IN PSYCHIATRY
  7. REDUCTIONIST VERSES HOLISTIC APPROACHES
  8. NETWORK MEDICINE
  9. METHODOLOGICAL APPROACHES TOWARDS NETWORK MEDICINE
  10. APPLICATIONS OF NETWORK MEDICINE IN PSYCHIATRIC GENETICS
  11. CONCLUSION
  12. REFERENCES

While it is true that several of the psychiatric disorders have high heritability estimates, using disease phenotypes in genetic analyses continues to result in a substantial amount of “missing heritability [Manolio et al., 2009],” leaving much to be discovered. There is a long developmental pathway between genetic risk variants and complex disease phenotypes with several intermediate steps. “Omics” data represent some of the intermediate phenotypes between the genetic variant and the disease outcome. More recently, various “omics” approaches have been used to better understand the molecular determinants behind complex diseases. The use of “omics” data offers promise in explaining the missing heritability as these measures bridge the gap between molecular make-up and clinical phenotype. Furthermore integrating multiple forms of relevant data will inform the analysis more than any single “omics” approach can by itself. We now present a review of several types of “omics” data, including transcriptomics, epigenomics, proteomics, and metabolomics. Genomics, the study of SNP level variation throughout the genome, has been described above.

OMICS DATA TYPES

  1. Top of page
  2. Abstract
  3. COMPLEXITIES IN PSYCHIATRIC GENETICS
  4. SINGULAR “OMICS” APPROACHES TO PSYCHIATRIC GENETICS
  5. OMICS DATA TYPES
  6. “OMICS” LIMITATIONS IN PSYCHIATRY
  7. REDUCTIONIST VERSES HOLISTIC APPROACHES
  8. NETWORK MEDICINE
  9. METHODOLOGICAL APPROACHES TOWARDS NETWORK MEDICINE
  10. APPLICATIONS OF NETWORK MEDICINE IN PSYCHIATRIC GENETICS
  11. CONCLUSION
  12. REFERENCES

Transcriptomics studies the set of all RNA molecules, including mRNA, rRNA, tRNA, and other non-coding RNA produced in the human genome. One of the key advantages to using transcriptomic approach in psychiatric genetics is that the heritability of these variables is typically very high. This is because these measurements are closer to the underlying biology than other phenotypes. Therefore, these data can often identify key determinants that are not identified through typical genetic variants. Unfortunately, one of the biggest drawbacks for psychiatry is the gene expression measurements are tissue specific. For most psychiatric disorders, the tissue of interest is human brain tissue, which is difficult to obtain in sufficiently large samples. One alternative to this approach is the use of gene expression measurements from the brains of murine models. Extensive gene expression samples are available for various psychiatric disorders using animal models and success in the identification of key genes for various psychiatric disorders have already been identified through transcriptomic data [Schijndel and Martens, 2010; Liu et al., 2011; Sazonova et al., 2011; Zhang-James et al., 2011; Lempp et al., 2013].

RNA-Seq is a technology that profiles the transcriptome using deep-sequencing. RNA-seq has demonstrated the increased complexity of human transcriptome and provided more accurate measurements of transcripts and their isoforms, thereby advancing the overall ability to characterize the human transcriptome. MicroRNAs are a class of post-transcriptional regulators that can be identified using RNA-seq. They are short noncoding RNA sequences that bind to complementary sequences in target mRNAs, usually resulting in their silencing [Chen and Rajewsky, 2007]. MicroRNAs target roughly 60% of all genes, are abundantly present in all human cells and are able to repress hundreds of targets each [Bartel, 2009]. These features suggest they are a vital part of genetic regulation that represent an untapped area of psychiatric genetics research. Resources such as the Encyclopedia of DNA Elements (ENCODE) are now available and contain information on functional elements in the human genome, including elements that act at the protein and RNA levels, and regulatory elements that control cells.

Epigenomics is the heritable and possibly reversible modifications in gene expression that do not depend on changes to the DNA sequence [Heijmans et al., 2009]. One such epigenetic mechanism is DNA methylation. As biomarkers may be a reflection of individual/cumulative risk factors, methylation signatures are well suited as biomarkers for prenatal and other environmental effects on programming of psychiatric disorders. This is therefore a powerful took as it can incorporate molecular as well as environmental influences on disease. Epigenetics has been implicated in the pathogenesis of several psychiatric disorders, including alcoholism [Starkman et al., 2012], drug addiction [Nielsen et al., 2012], ADHD [Archer et al., 2011], and schizophrenia [Ekstrom et al., 2012], among others.

Proteomics refers to the entire set of proteins and their modifications, produced by the genome. This is largely because the “omics” approaches more accurately describe biologic states that can be disease specific. This is expected to be extremely informative in psychiatric genetics [Taurines et al., 2011; Martins-de-Souza, 2012]. Little work has been done in proteomics with regard to psychiatric disorders, although some proteomic research has begun and shows promise towards identifying important variants for psychiatric diseases such as schizophrenia [Kim et al., 2012] and ADHD [Hirano et al., 2008]. Therefore, this represents an untapped resource with rich information for psychiatric genetics research.

Metabolomics is the study of chemical processes involving metabolites, the chemicals left behind from metabolism. This is an emerging field so limited studies of metabolomics and psychiatry exist. One study identified associations with schizophrenia and metabolic abnormalities related to glucoregulatory processes and proline metabolism [Oresic et al., 2011]. Metabolomics is a powerful resource for providing information about the physiology underlying disease, which can provide information on new biological mechanisms that contribute to disease pathogenesis. The understanding metabolites and psychiatric disorders is still in its infancy with much knowledge to be learned.

“OMICS” LIMITATIONS IN PSYCHIATRY

  1. Top of page
  2. Abstract
  3. COMPLEXITIES IN PSYCHIATRIC GENETICS
  4. SINGULAR “OMICS” APPROACHES TO PSYCHIATRIC GENETICS
  5. OMICS DATA TYPES
  6. “OMICS” LIMITATIONS IN PSYCHIATRY
  7. REDUCTIONIST VERSES HOLISTIC APPROACHES
  8. NETWORK MEDICINE
  9. METHODOLOGICAL APPROACHES TOWARDS NETWORK MEDICINE
  10. APPLICATIONS OF NETWORK MEDICINE IN PSYCHIATRIC GENETICS
  11. CONCLUSION
  12. REFERENCES

A notable difficulty in applying these approaches to psychiatric disorders is that many forms of “omics” data are tissue specific and the primary tissue of interest for psychiatric disorders is brain tissue, which often cannot be obtained. The most obvious way to circumvent this issue is through the use of animal models in which brain tissue is more readily available. Another option is to use post-mortem brain tissue. Although limited in quantity, such resources are publically available through Gene Expression Omnibus (GEO), a public repository that archives and freely distributes microarray, next-generation sequencing, and other forms of high-throughput functional genomic data submitted by the scientific community. Applications using postmortem brain tissue have already been successfully applied to some psychiatric diseases [Zhang et al., 2013].

In summary, “omics” approaches show promise in identifying and understanding new molecular pathways and variants that contribute to the pathogenesis of psychiatric disorders. They also represent an intermediate link between genetic variants and disease phenotypes that can be capitalized upon. While each “omics” resource individually is valuable for identifying new molecular contributors for disease pathogenesis, individually each “omics” type ignores the interplay that exists among “omics” data. This interplay is represents a more realistic portrayal of how psychiatric illness occurs. Recently, these “omics” approaches have been integrated together to identify disease phenotypes. By combining multiple “omics” resources together, there is increased statistical power to identify important molecular determinants and pathways for disease phenotypes.

REDUCTIONIST VERSES HOLISTIC APPROACHES

  1. Top of page
  2. Abstract
  3. COMPLEXITIES IN PSYCHIATRIC GENETICS
  4. SINGULAR “OMICS” APPROACHES TO PSYCHIATRIC GENETICS
  5. OMICS DATA TYPES
  6. “OMICS” LIMITATIONS IN PSYCHIATRY
  7. REDUCTIONIST VERSES HOLISTIC APPROACHES
  8. NETWORK MEDICINE
  9. METHODOLOGICAL APPROACHES TOWARDS NETWORK MEDICINE
  10. APPLICATIONS OF NETWORK MEDICINE IN PSYCHIATRIC GENETICS
  11. CONCLUSION
  12. REFERENCES

The approaches described up to this point represent a reductionist view of science whereby very specific hypotheses are interrogated alone. With the initial genetic studies, individual SNPs are most often evaluated for genetic associations alone. With the introduction of the genome-wide association era, the reductionist viewpoint remains in use, albeit on a genome scale. Specifically, the same statistical tests are used over and over again on different measurements throughout the genome, while the interaction of any given set of these networks remains largely ignored. Over hundreds of years, the reductionist approach has served science well and has resulted in many new discoveries; specific hypotheses are made and tested. This approach has also been a key component in both the development of the scientific method and the traditional Frequentist approach to statistical testing. It is important to understand when this reductionistic approach works well and when it does not. In psychiatric genetics, there were early successes using simply genetic association studies and linkage analyses, most notably the identification of APOE for Alzheimer's disease [Anon, 1995]. As described above, this approach had also been quite successful in identifying single SNPs that are consistently associated with psychiatric conditions, particularly in the context of using very large sample sizes. Where the reductionist's approach falls short is the evaluation of biological models where multiple genetic variants and intermediate bi-products work together to cause a given psychiatric diagnosis. As is evidenced by the missing heritability for most psychiatric disorders, we suggest a novel approach to interrogation and identification of key variants and pathways that contribute to psychiatric disorders. We propose taking a holistic approach to disease pathogenesis where biological networks are generated through the use of multiple sources of “omics” data. Although the holistic philosophy has been the norm in some areas of science, such as specific branches of physics [Anderson, 1972], it has not been the norm in genetics research. We now describe the newly emerging field of network medicine and how this approach can incorporate information from multiple “omics” platforms.

NETWORK MEDICINE

  1. Top of page
  2. Abstract
  3. COMPLEXITIES IN PSYCHIATRIC GENETICS
  4. SINGULAR “OMICS” APPROACHES TO PSYCHIATRIC GENETICS
  5. OMICS DATA TYPES
  6. “OMICS” LIMITATIONS IN PSYCHIATRY
  7. REDUCTIONIST VERSES HOLISTIC APPROACHES
  8. NETWORK MEDICINE
  9. METHODOLOGICAL APPROACHES TOWARDS NETWORK MEDICINE
  10. APPLICATIONS OF NETWORK MEDICINE IN PSYCHIATRIC GENETICS
  11. CONCLUSION
  12. REFERENCES

Recently, the field of network medicine has evolved through leveraging advances in the area of general network theory and modeling [Barabasi et al., 2011]. A major implication of the network approach is that if a few disease components are identified, other disease-related components are likely to be found in their network-based vicinity. Thus, network-based approaches enable characterization of disease modules for specific pathophenotypes of interest, uncovering previously unsuspected molecular interactions, which, in turn, motivate new directions for mechanistic investigation, disease prediction models, and drug development [Vidal et al., 2011].

Network medicine is an emerging field based on the principles of network theory that applies systems biology methods to develop biological models that describe human disease outcomes [Barabasi et al., 2011] (Fig. 1). Network theory suggests that networks operating in biology and can be characterized by a set of organizing principles [Barabasi et al., 2011]. An interactome network describes the complete set of macromolecular interactions for genes and their bi-products that result in the disease [Vidal et al., 2011]. The disease module hypothesis states that genes associated with the same disease should form a connected sub graph in an interactome network. Nodes in these networks represent genes and their bi-products (CpG sites, SNPs, genes, proteins, gene expression levels) and edges between any two nodes represent a relationship of some sort (interactions, causation, correlations, transcriptional activity, etc.) [Silverman and Loscalzo, 2012]. As explained above, single genetic variants alone are unlikely to completely explain disease pathogenesis. This is because a key characteristic to disease modules is their robustness to small perturbations in the network. This is because most of the nodes in a network can be removed with a minimal result. There are highly connected nodes that are fundamental to the connectivity of a network called hubs. Hubs are essential to the operation of the network because many nodes are connected to it. The connectivity of nodes within a network follow a scale free distribution [Barabasi and Bonabeau, 2003], that is most nodes have very few edges and a few nodes (i.e., hubs) are highly connected. The scale free property explains why perturbations in single genetic variants are unlikely to have a high impact on complex disease phenotypes. It is perturbations of biological networks, not isolated genes or proteins, that confer disease risk. This robustness to perturbation is a key feature of biological networks. Removal of key genes (i.e., hubs) will result in the demolition of the network, therefore hub identification is essential towards understanding how psychiatric diseases occur.

image

Figure 1. Figure 1 presents a schematic of a disease module that is commonly used in network medicine, where nodes represent genes or their bi-products, edges represent relationships that exist between nodes, and hubs represent key nodes that are essential to the operation of the network.

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Figure 1 presents a schematic of a disease module that is commonly used in network medicine, where nodes represent genes or their bi-products, edges represent relationships that exist between nodes, and hubs represent key nodes that are essential to the operation of the network.

METHODOLOGICAL APPROACHES TOWARDS NETWORK MEDICINE

  1. Top of page
  2. Abstract
  3. COMPLEXITIES IN PSYCHIATRIC GENETICS
  4. SINGULAR “OMICS” APPROACHES TO PSYCHIATRIC GENETICS
  5. OMICS DATA TYPES
  6. “OMICS” LIMITATIONS IN PSYCHIATRY
  7. REDUCTIONIST VERSES HOLISTIC APPROACHES
  8. NETWORK MEDICINE
  9. METHODOLOGICAL APPROACHES TOWARDS NETWORK MEDICINE
  10. APPLICATIONS OF NETWORK MEDICINE IN PSYCHIATRIC GENETICS
  11. CONCLUSION
  12. REFERENCES

The statistical methodology for network approaches is rapidly evolving. We provide a brief summary of some of the methodological approaches that are currently being used. Weighted Correlation Network Analysis (WGCNA) [Horvath, 2013] is a method for describing a disease network that uses correlation patterns across various types of “omics” data. WGCNA can be used for (1) defining networks and modules (i.e., clusters of genes); (2) determining the preservation (i.e., similarities and differences) across networks; and (3) relating the modules within a network to external sample traits (e.g., phenotypes and SNPs). Because networks identify important pathways likely to impact disease, they are an effective method to identify candidate biomarkers or therapeutic targets. WGCNA has been successfully applied in various biological contexts, (e.g., cancer, mouse genetics, yeast genetics, and analysis of brain imaging data) [Carlson et al., 2006; Ghazalpour et al., 2006; Horvath et al., 2006; Oldham et al., 2008], but has not yet been applied to psychiatric diseases. The WGCNA software package contains a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis, including functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, and visualization. Another network medicine approach developed by Hu et al. [2005] integrates several biological networks through data mining methods that identify consistent patterns among multiple co-expression networks. The identification of the common patterns among co-expression networks can be obtained through the use of the efficient computer program, CODENSE (coherent dense subgraphs) [Hu et al., 2005]. Other network methods do not have direct computer programs that are publically available for use, but follow general computer algorithms. The DiseAse Module Detection (DIAMOnD) algorithm first constructs a general network using data high-throughput data available through databases [Barabasi et al., 2011]. This is followed by the identification of a disease module that is then validated and refined using external data. Key disease pathways are then defined and additional “omics” can then be integrated to further inform the disease module. This represents a few of the many and rapidly evolving integrative methods currently available for network medicine.

Once networks are developed, annotating these networks becomes crucial in understanding the underlying biology of identified networks. As such, biological network databases have become available for various “omics” data. A summary of several of these databases is provided in Table I.

Table I. Table I represents a non-exhaustive list of network database resources that can be used to annotate networks and identify key genes and/or pathways that are enriched within a given network. These databases are rapidly increasing in size and becoming more comprehensive in nature
Network typeDatabases
Protein–proteinMűnich Information Center for Protein Sequence (MIPS)
 The Biomolecular Interaction Network Database (BIND)
 The Database of Interacting Proteins (DIP)
 The Molecular Interaction database (MINT)
 The protein Interaction database (IntAct)
 The Biological General Repository for Interaction Datasets (BioGRID)
 The Human Protein Reference Database (HPRD), STRING
MetabolicKyoto Encyclopedia of Genes and Genomes (KEGG)
 Biochemical Genetic and Genomics knowledgebase (BIGG)
 DAVID Bioinformatics Resources
 Gene ontology (GO)
 GeneMania
 Gene Signature DataBase (GeneSigDB)
 SKAT (rare variant)
 GSEA
 Geneanswers
RegulatoryUniversal Protein Binding Microarray Resource for Oligonucleotide Binding Evaluation (UniPROBE)
 JASPAR
 TRANSFAC
 B-cell interactome (BCI). Human post-translational modifications can be found in databases such as Phospho.ELM, PhosphoSite, phosphorylation site database (PHOSIDA), NetPhorest, and the CBS prediction database
RNATargetScan, PicTar, microRNA, miRBase, and miRDB
 The number of experimentally supported targets is also increasing, and they are now compiled in databases such as TarBase and miRecords

APPLICATIONS OF NETWORK MEDICINE IN PSYCHIATRIC GENETICS

  1. Top of page
  2. Abstract
  3. COMPLEXITIES IN PSYCHIATRIC GENETICS
  4. SINGULAR “OMICS” APPROACHES TO PSYCHIATRIC GENETICS
  5. OMICS DATA TYPES
  6. “OMICS” LIMITATIONS IN PSYCHIATRY
  7. REDUCTIONIST VERSES HOLISTIC APPROACHES
  8. NETWORK MEDICINE
  9. METHODOLOGICAL APPROACHES TOWARDS NETWORK MEDICINE
  10. APPLICATIONS OF NETWORK MEDICINE IN PSYCHIATRIC GENETICS
  11. CONCLUSION
  12. REFERENCES

Recent research in psychiatry has started integrating “omics” data using various network approaches. In a study that was aimed to identify risk loci for five psychiatric disorders, GWAS for these five disorders were used to identify initial common genetic variants. This analysis was followed by an expression quantitative trait loci (eQTL) analysis that incorporates gene expression data from post-mortem tissues [Smoller et al., 2013]. This study identified SNP that were associated with the diseases and enriched for brain eQTL markers [Smoller et al., 2013]. An even more integrative network approach was recently successfully applied to late-onset Alzheimer's disease. An integrative gene network was generated using WCGNA in 1,647 postmortem brain tissues from late onset Alzheimer's disease patients and nondemented subjects. The structures within this network were then rank-ordered according to the pathological relevance for late onset Alzheimer's disease. An immune- and microglia-specific module was identified that contained several genes involved in pathogen phagocytosis. One of these genes was TYROBP that was upregulated in late onset Alzheimer's disease individuals. Further expression research using mouse microglia cells demonstrated overlap in the expression changes that are observed in the human brain TYROBP network. These studies demonstrate the potential utility of using network approaches in an effective manner to increase our understanding of important molecular contributors to psychiatric diseases [Zhang et al., 2013].

CONCLUSION

  1. Top of page
  2. Abstract
  3. COMPLEXITIES IN PSYCHIATRIC GENETICS
  4. SINGULAR “OMICS” APPROACHES TO PSYCHIATRIC GENETICS
  5. OMICS DATA TYPES
  6. “OMICS” LIMITATIONS IN PSYCHIATRY
  7. REDUCTIONIST VERSES HOLISTIC APPROACHES
  8. NETWORK MEDICINE
  9. METHODOLOGICAL APPROACHES TOWARDS NETWORK MEDICINE
  10. APPLICATIONS OF NETWORK MEDICINE IN PSYCHIATRIC GENETICS
  11. CONCLUSION
  12. REFERENCES

Psychiatric genetics is a rapidly evolving field. To date there have been some successes in identifying genetic variants that contribute to psychiatric disorders, however there remains much to be discovered. A holistic approach to psychiatric genetics, that incorporates multiple “omics” data, holds great promise in identifying larger contributions to the overall heritability of psychiatric disorders. One such holistic approach is network medicine, which uses the principles of network theory and applies systems biology methods to develop biological models for diseases. Network medicine holds promise to more comprehensively interrogate the complex biological processes that have multiple genetic variants and intermediate bi-products contributing to the pathogenesis of psychiatric disorders.

REFERENCES

  1. Top of page
  2. Abstract
  3. COMPLEXITIES IN PSYCHIATRIC GENETICS
  4. SINGULAR “OMICS” APPROACHES TO PSYCHIATRIC GENETICS
  5. OMICS DATA TYPES
  6. “OMICS” LIMITATIONS IN PSYCHIATRY
  7. REDUCTIONIST VERSES HOLISTIC APPROACHES
  8. NETWORK MEDICINE
  9. METHODOLOGICAL APPROACHES TOWARDS NETWORK MEDICINE
  10. APPLICATIONS OF NETWORK MEDICINE IN PSYCHIATRIC GENETICS
  11. CONCLUSION
  12. REFERENCES
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