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Deciphering Themes for Schizophrenia’s Genetic Variation

16 November 2012. Many of the diverse genes emerging for schizophrenia are bound together by a common role in brain development, according to a network analysis published online November 11 in Nature Neuroscience. Led by Dennis Vitkup of Columbia University in New York, the study deciphers biological themes underlying a variety of genes linked to schizophrenia through studies of common and rare variations. Within a background network estimating the phenotypic relationships among all human genes, 47 schizophrenia-related genes formed a cluster related to axon guidance and cell migration that is highly expressed in the brain during prenatal development.

Recent genetic studies of schizophrenia point to a large number of loci—the newest GWAS report identifies more than 60 (see SRF related news story), and a recent study estimates over 850 genes involved (see SRF related news story). Researchers have speculated that these diverse genes converge on related biological pathways, which comprise many different molecules working together to do something for a cell. But testing this idea requires a comprehensive description of all pathways, which biologists are still grappling to understand in single-celled organisms, let alone the human brain (Alberts, 2012). The computational approach designed by Vitkup and colleagues attempts to fill this gap with a network that describes the chance that any two human genes contribute to the same phenotype. In it, the strength of the connection between any two genes is based on multiple sources of information, including gene ontology classifications, functional annotations, evolutionary similarities, and physical interactions with other molecules. Genes that share a similar profile are strongly connected, whereas those with dissimilar profiles would be connected weakly or not at all, thus producing something like an airline flight map showing the varying traffic patterns between cities. Vitkup’s network differs from others built from more limited datasets, which, for example, take into account only molecular interactions.

Vitkup has already used this approach in an effort to describe the roles of genes contained within CNVs found in autism (Gilman et al., 2012). In the new study, he teamed up with Joseph Gogos and Maria Karayiorgou, also of Columbia, to analyze a wider collection of genes associated with schizophrenia through de novo CNVs, de novo protein-altering point mutations detected by exome sequencing, and genomewide association studies (GWAS). Considering all of these genes, rather than merely a subset deemed as more important to schizophrenia, provided an unbiased look at any shared roles among them.

Clusters of connections
First author Sarah Gilman and colleagues analyzed a total of 1,044 genes with links to schizophrenia: 173 came from regions within 250 kb of single nucleotide polymorphisms flagged by GWAS; 712 from genes contained within de novo CNVs; and 159 hit by de novo protein-altering point mutations found by exome sequencing, including data from Gogos and Karayiorgou’s most recent study (see SRF related news story). After mapping these genes to the background network, the researchers then asked whether they formed groups of well-connected genes, termed clusters. To do this, they first scored each possible cluster based on the connection strengths between the genes within the cluster, and plucked out the highest-scoring ones.

This process identified one cluster composed of 47 putative schizophrenia genes, which were enriched for genes with roles in brain development and intracellular signaling. As a control, the researchers used random genes with the same average connection strengths as the schizophrenia-related genes, and found these formed more fragmented clusters within the background network. A second, marginally significant schizophrenia gene cluster was also identified (p = 0.071), which contained genes related to chromosomal remodeling. Clustering worked best when combining information from all sources of genetic data: when the input consisted of only the 159 genes hit by point mutations, a smaller, marginally significant cluster emerged. This suggests that clues coming from studies of common and rare variations in schizophrenia mutually reinforce each other.

Because even healthy people carry rare variants, the researchers also tested whether the genes impacted by de novo point mutations or CNVs in controls or in unaffected siblings of people with autism (which gives a measure of background de novo mutation; see SRF related news story) formed clusters. They did not, which argues that the clusters emerging from the schizophrenia-derived input have something to do with the disorder.

Same pathways, different disorders
Further characterizing their two clusters using the Human Brain Transcriptome database (see SRF related news story), the researchers found that these cluster genes had high levels of expression in the brain, particularly during prenatal development. Also, the functional categories given to their cluster genes overlapped with those ascribed to differentially expressed genes in neurons grown from pluripotent stem cells derived from people with schizophrenia (see SRF related news story), which offers further validation for the cluster genes.

The researchers then grappled with the emerging evidence for genetic overlaps between schizophrenia and other disorders. When considering gene sets already implicated in autism or intellectual disability, they found these were significantly connected with the schizophrenia cluster genes; in contrast, genes hit by de novo mutations in healthy controls, or by synonymous mutations (which do not alter protein structure) in schizophrenia were not. This suggests that genetic glitches involved in schizophrenia, autism, and intellectual disability involve similar biological pathways.

But hitting the same pathway does not necessarily result in the same consequences, and the researchers suggest that the clinical phenotype may depend on the types of mutation involved. In their previous study of autism, the researchers found that cluster genes derived from CNVs were likely to increase the growth of dendrites, the specialized structures on the receiving end of synapses (Gilman et al., 2011). Using a similar analysis, the researchers reported that, of the schizophrenia cluster genes derived from CNV data, those with known phenotypes tended to decrease dendritic growth when hit by a CNV in someone with schizophrenia. Although the story is likely to be more complicated, the analysis reminds us that there are different ways to break a biological pathway, and the details in how they are perturbed may explain different outcomes.—Michele Solis.

Reference:
Gilman SR, Chang J, Xu B, Bawa TS, Gogos, JA, Karayiorgou M, Vitkup D. Diverse types of genetic variation converge on functional gene networks involved in schizophrenia. Nat Neurosci. 2012 Nov 11. Abstract

Comments on News and Primary Papers
Comment by:  Patrick Sullivan, SRF AdvisorDanielle Posthuma
Submitted 16 November 2012
Posted 16 November 2012

Gilman et al. pose exceptionally important and salient questions: given that increasingly detailed genomic data have established that many genes are now strongly implicated in the etiology of schizophrenia, how do we understand this? How can these different components of the “parts list” for schizophrenia be pieced together to derive a cogent etiological hypothesis for further testing?

The authors use a new computational approach to address these questions, and derive lists related to axon guidance, neuronal cell mobility, synaptic function, and chromosomal remodeling. Additional analyses suggest the coherence of their lists. These are good clues that deserve further evaluation.

It was intriguing that the authors included multiple types of genetic variation—rare but potent copy number variants (e.g., Kirov et al., 2012), rare exonic mutations (Xu et al., 2012), and common variations from genomewide association studies (Ripke et al., 2011)—as most authors have tended to conduct these analyses separately.

In sum, a nice contribution to the literature and initial steps towards tackling a tough problem in human genetics. But, there are four issues for readers to bear in mind in evaluating the results.

First, we hope that the authors make their program freely available. This is the standard in the field. Many of us are interested in evaluating the capacities of their program. To our knowledge, it is not now available, although it has been used in multiple published papers. We could find no link in the paper or on the senior author’s lab page.

Second, readers need to remember that this was an in-silico analysis. It produces hypotheses but does not (and cannot) provide proof. The methods are subject to multiple biases, and it was not clear how well these were controlled (see point 4 as well). We wondered whether known biases like gene size and LD patterns were well controlled.

Third, we would have liked to see greater scholarship. There is an unfortunate trend for computational biologists to produce tools without benchmarking them against existing tools or rigorously determining power and error rates. The lack of finding significant clusters in control sets is insufficient in showing the validity of their program. Are the authors’ claims that their new tool represents superiority truly justified?

Moreover, there are a lot of tools for performing analyses of these sorts (e.g., INRICH, FORGE, MAGENTA, Ingenuity, ALIGATOR, among many others). Indeed, these sorts of analyses are in the toolkits of most psychiatric genetics groups and are routinely applied. Given that there are many papers reporting results, a scholarly treatment of how their results compare to those of others and what the added value of their program is would have been useful.

Fourth, and most importantly, pathway analysis is completely dependent on the input—the genetic findings and the pathways. The findings that the authors used had issues. The CNV list is likely to change soon as the PGC CNV group completes its integrated analyses of tens of thousands of subjects. The exome list was based on a small and atypical sample, and much larger studies are in preparation (see SRF comment). The authors did not seem to confront the issue that all humans contain a lot of deleterious exonic variation. And (spoiler alert), the GWAS list is soon to increase markedly. More and more precise findings are sure to alter the results.

The pathways used were pretty standard—GO, KEGG, protein-protein interaction databases. Unfortunately, although widely used, these pathways have multiple issues. The content of many GO annotations and KEGG pathways have not been constructed by experts in the area. As one salient example, synaptic gene lists in standard pathway databases were quite imperfectly related to lists created by experts (Ruano et al., 2010). The authors also relied somewhat uncritically on the PPI databases. These have multiple issues, and some (unpublished) data suggest substantial error (i.e., large fractions of the predicted interactions are not, in fact, real or biologically meaningful). The fraction of the proteome screened adequately by these methods is small. Some interactions in these databases are non-specific, or occur between molecules that are never in the same place at the same time.

Indeed, the genes overrepresented in PPI databases were selected due to disease relevance or biological importance (e.g., there is a lot of work on P53). In general, the more a gene is investigated, the more interactions are found.

Still, this is a key paper, albeit a snapshot based on imperfect input data, and we look forward to seeing whether additional analyses confirm a role in schizophrenia of the networks identified currently with their program.

References:

Kirov G, Pocklington AJ, Holmans P, Ivanov D, Ikeda M, Ruderfer D, Moran J, Chambert K, Toncheva D, Georgieva L, Grozeva D, Fjodorova M, Wollerton R, Rees E, Nikolov I, van de Lagemaat LN, Bayés A, Fernandez E, Olason PI, Böttcher Y, Komiyama NH, Collins MO, Choudhary J, Stefansson K, Stefansson H, Grant SG, Purcell S, Sklar P, O'Donovan MC, Owen MJ. De novo CNV analysis implicates specific abnormalities of postsynaptic signalling complexes in the pathogenesis of schizophrenia. Mol Psychiatry. 2012 Feb; 17(2):142-53. Abstract

Xu B, Ionita-Laza I, Roos JL, Boone B, Woodrick S, Sun Y, Levy S, Gogos JA, Karayiorgou M. De novo gene mutations highlight patterns of genetic and neural complexity in schizophrenia. Nat Genet. 2012 Oct 3. Abstract

Ripke S, Sanders AR, Kendler KS, Levinson DF, Sklar P, Holmans PA, Lin DY, Duan J, Ophoff RA, Andreassen OA, Scolnick E, Cichon S, St Clair D, Corvin A, Gurling H, Werge T, Rujescu D, Blackwood DH, Pato CN, Malhotra AK, Purcell S, Dudbridge F, Neale BM, Rossin L, Visscher PM, Posthuma D, Ruderfer DM, Fanous A, Stefansson H, Steinberg S, Mowry BJ, Golimbet V, de Hert M, Jönsson EG, Bitter I, Pietiläinen OP, Collier DA, Tosato S, Agartz I, Albus M, Alexander M, Amdur RL, Amin F, Bass N, Bergen SE, Black DW, Børglum AD, Brown MA, Bruggeman R, Buccola NG, Byerley WF, Cahn W, Cantor RM, Carr VJ, Catts SV, Choudhury K, Cloninger CR, Cormican P, Craddock N, Danoy PA, Datta S, de Haan L, Demontis D, Dikeos D, Djurovic S, Donnelly P, Donohoe G, Duong L, Dwyer S, Fink-Jensen A, Freedman R, Freimer NB, Friedl M, Georgieva L, Giegling I, Gill M, Glenthøj B, Godard S, Hamshere M, Hansen M, Hansen T, Hartmann AM, Henskens FA, Hougaard DM, Hultman CM, Ingason A, Jablensky AV, Jakobsen KD, Jay M, Jürgens G, Kahn RS, Keller MC, Kenis G, Kenny E, Kim Y, Kirov GK, Konnerth H, Konte B, Krabbendam L, Krasucki R, Lasseter VK, Laurent C, Lawrence J, Lencz T, Lerer FB, Liang KY, Lichtenstein P, Lieberman JA, Linszen DH, Lönnqvist J, Loughland CM, Maclean AW, Maher BS, Maier W, Mallet J, Malloy P, Mattheisen M, Mattingsdal M, McGhee KA, McGrath JJ, McIntosh A, McLean DE, McQuillin A, Melle I, Michie PT, Milanova V, Morris DW, Mors O, Mortensen PB, Moskvina V, Muglia P, Myin-Germeys I, Nertney DA, Nestadt G, Nielsen J, Nikolov I, Nordentoft M, Norton N, Nöthen MM, O'Dushlaine CT, Olincy A, Olsen L, O'Neill FA, Orntoft TF, Owen MJ, Pantelis C, Papadimitriou G, Pato MT, Peltonen L, Petursson H, Pickard B, Pimm J, Pulver AE, Puri V, Quested D, Quinn EM, Rasmussen HB, Réthelyi JM, Ribble R, Rietschel M, Riley BP, Ruggeri M, Schall U, Schulze TG, Schwab SG, Scott RJ, Shi J, Sigurdsson E, Silverman JM, Spencer CC, Stefansson K, Strange A, Strengman E, Stroup TS, Suvisaari J, Terenius L, Thirumalai S, Thygesen JH, Timm S, Toncheva D, van den Oord E, van Os J, van Winkel R, Veldink J, Walsh D, Wang AG, Wiersma D, Wildenauer DB, Williams HJ, Williams NM, Wormley B, Zammit S, Sullivan PF, O'Donovan MC, Daly MJ, Gejman PV. Genome-wide association study identifies five new schizophrenia loci. Nat Genet. 2011 Oct ; 43(10):969-76. Abstract

Ruano D, Abecasis GR, Glaser B, Lips ES, Cornelisse LN, de Jong AP, Evans DM, Davey Smith G, Timpson NJ, Smit AB, Heutink P, Verhage M, Posthuma D. Functional gene group analysis reveals a role of synaptic heterotrimeric G proteins in cognitive ability. Am J Hum Genet. 2010 Feb 12;86(2):113-25. Abstract

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Comments on Related News


Related News: Researchers Model Susceptibility to Schizophrenia in a Petri Dish

Comment by:  Alan Mackay-Sim
Submitted 13 April 2011
Posted 13 April 2011

With a heritability of 50 percent, schizophrenia is very clearly a disease of disturbed biology, but to dissect the biological contribution to its etiology, researchers need relevant, patient-derived cell models. Ideally, we need cell models that can tell us how schizophrenia cell biology leads to an altered brain. Induced pluripotent stem (iPS) cells are genetically engineered cells, from a patient's cells (e.g., fibroblasts), that resemble embryonic stem cells, that can be used to generate neurons. There is much excitement that they will be useful as models for many brain disorders and diseases. Two new papers in Molecular Psychiatry and Nature report on applying iPS cell technology to schizophrenia by generating iPS cells from patients with a DISC1 mutation (Chiang et al., 2011) and from patients selected with a high likelihood of a genetic component to disease (Brennand et al., 2011).

When specific genes are implicated, then animal models can provide breakthroughs by determining the cellular functions of the implicated genes and their mutations. Although schizophrenia lacks single commonly mutated genes of large effect, some candidate genes, such as DISC1, are being identified in some families. This is now a very hot area for research that is identifying the role of this gene at the cellular level and in animal models. As such candidate genes are identified and their functions are ascertained, it will be essential to demonstrate their direct relevance in schizophrenia through patient-derived cellular models. In this regard, a new tool has emerged in the recent letter to Molecular Psychiatry reporting the generation of induced pluripotent cells from two patients with DISC1 mutation (Chiang et al., 2011). This preliminary study did not report a disease-associated phenotype in these iPS cells.

A disease-associated phenotype is best identified by comparing iPS cells from patients and controls, as now demonstrated by Brennand et al. (2011). This work is a significant new contribution to the field because it has demonstrated differences in the biology of neurons derived from patients and controls. As proof of principle, they have identified differences in the way patient neurons branch (they have fewer branches) and connect with each other (they connect to fewer other neurons). Most importantly, the patient neurons had normal physiological properties. That is to say, their physiology was not different from controls. These are interesting and important distinctions that are a reassuring proof of principle for this model, suggesting that the etiology of schizophrenia derives from altered connectivity of neuronal circuits and not from basic neuronal functions. This fits with the postulated “neurodevelopmental hypothesis” of schizophrenia. Patient neurons also had decreased levels of synaptic proteins (PSD95, glutamate receptor), which is consistent with “synaptic hypotheses” of schizophrenia. These are early days yet, but this cell model already demonstrates how a relevant cell model can provide a path for unifying etiological hypotheses.

Another aim for developing cell models of schizophrenia is to use them for drug discovery. Patient-control differences in cell functions can be the basis for screening chemical compounds that ameliorate this difference. Here, too, Brennand et al. (2011) demonstrate proof of principle by showing that loxapine treatment of the patient neurons increased their connectivity towards control levels. Only loxapine, of five antipsychotic drugs tested, had this effect, but the results are a clear sign of the utility of such cells for drug screening to find new potential drug candidates.

These two papers are a great start to using iPS cells as models of schizophrenia.

References:

Chiang CH, Su1Y, Wen Z, Yoritomo N, Ross CA, Margolis RL, Song H, Ming G-I. (2011) Integration-free induced pluripotent stem cells derived from schizophrenia patients with a DISC1 mutation. Molecular Psychiatry advance online publication, 22 February 2011. Abstract

Brennand KJ, Simone A, Jou1 J, Gelboin-Burkhart C, Tran N, Sangar S, Li Y, Mu Y, Chen G, Yu D, McCarthy S, Sebat J, Gage FH (2011). Modeling schizophrenia using human induced pluripotent stem cells. Nature.

View all comments by Alan Mackay-Sim

Related News: Researchers Model Susceptibility to Schizophrenia in a Petri Dish

Comment by:  Akira Sawa, SRF Advisor
Submitted 13 April 2011
Posted 13 April 2011

I fully appreciate the efforts of Brennand and colleagues as pioneers. Indeed, this is great work. Like any pioneering work, this paper will be both applauded and criticized. The strength of the paper is in providing ways for us to analyze iPS cells and derived neurons. The multifaceted approach taken in this study will be a great platform for many investigators.

Schizophrenia is, clinically, a very heterogeneous condition, but for the past several years, basic scientists have tended to oversimplify the disorder. It is also true that this trend makes the neurobiology of schizophrenia move productively forward in some ways. I believe that the new tools for studying the biology of schizophrenia, such as iPSC-derived neurons, will teach us how difficult it is to draw simplified pathways for the disorder. Nonetheless, some common pathway(s) may be identified in the future, I optimistically hope.

Based on the great experimental procedures that this paper provides, many other groups may need to address whether or not these data are reproducible or not in “general” cases of schizophrenia. In such studies, the most important issue is to examine detailed clinical information of the subjects in comparison with this study.

View all comments by Akira Sawa

Related News: The Life and Times of the Human Brain Transcriptome

Comment by:  Karoly Mirnics, SRF Advisor
Submitted 31 October 2011
Posted 31 October 2011

Well done! Finally, some systematic transcriptome profiling of the human brain on a large scale. If we are ever going to crack neurodevelopmental disorders, such datasets will be absolutely critical. Exon-level transcriptome and associated genotyping data, brain regions, gender differences, developmental trajectories—this manuscript has it all. However, this is only a start, a catalogue of molecular events that begs to be explored. We see the complexity contained within the dataset, and it is simply mind-boggling. How do we make sense out of all this? Which changes are characteristic of interneurons, and which trajectories are projection neuron derived? How are the changes related to maturation of layers or various diseases? The mining of this dataset is far from over. It will be interesting to see what a WGCNA type of analysis will uncover in this proverbial gold mine. We need new ideas, we need new bioinformatic tools to look at this.

In addition, based on the presented data, we need to form precise, testable hypotheses. And then will come the hardest part—we need to test these hypotheses, and this will be incredibly time consuming and very low throughput. From in-vitro systems, transgenic models, electrophysiology, neurochemistry to imaging, we should use everything at our disposal.

While the generation of this dataset is clearly long overdue, I also must note the enormous price tag that these experiments carry. Very few laboratories/groups in the world have resources to perform such studies, and such fishing expeditions/dataset-generation projects are poorly suited to regular NIH-funded mechanisms.

View all comments by Karoly Mirnics

Related News: The Life and Times of the Human Brain Transcriptome

Comment by:  Paul Harrison
Submitted 2 November 2011
Posted 3 November 2011
  I recommend the Primary Papers

The Nature papers by Colantuoni et al. (2011) and Kang et al. (2011) are landmark studies, not only because of the wealth of data about the human brain transcriptome across the lifespan that they contain, but as a resource for other researchers to dip into or mine as they wish. Both papers represent the culmination of extensive research programs, and are based ultimately on the crucial, sensitive, and often unappreciated task of collecting a sufficient number of well-characterized brains (Deep-Soboslay et al., 2011). In turn (as noted by Karoly Mirnics in his comment), they also attest to the importance of having funding schemes which permit this kind of ambitious, long-term, large-scale—and expensive—research. The papers set a new gold standard for human brain studies in terms of size and scope. They also illustrate the renaissance of postmortem brain research, and provide confirmation (if any was needed) that human brain diseases need direct study of human brains—including normative analyses across the lifespan—if their genetic, neurodevelopmental, and molecular aspects are to be understood (Kleinman et al., 2011).

The papers will take time to digest fully. Early impressions reveal several findings of particular interest and relevance to schizophrenia.

1. It's striking just how dramatic are the transcriptional changes, even across a restricted fetal time period. Simple notions of a "second trimester" origin of a disorder need to become more nuanced.

2. The flow of alterations between fetal and infant life, and the infant-aging similarities and differences also speak to the dynamic temporal nature of the transcriptome, its regulation, refinement, and recapitulation.

3. The extent of regional (and sex) differences in gene expression and exon usage—and the interactions of these with development—found by Kang et al. are noteworthy, too, again attesting to the sheer complexity of the transcriptomic landscape.

4. The eQTL data in both studies emphasize the importance of cis variation in regulation of gene expression, especially for SNPs around transcriptional start sites; the P value of 10-78 (Fig. 3b in Colantuoni et al.) must be a record for a human brain study!

The data provide a much more detailed (albeit more complex) context within which to interpret deviations from the normal transcriptional profile in those with, or at risk of, schizophrenia. Notwithstanding the huge number of data in these papers, many questions remain unanswered. There is a relative gap across mid-childhood—for obvious reasons—which later studies can fill in (c.f. the accompanying Nature editorial on the need to collect more brains from children). Future studies will also hopefully move to sequencing methods, extend to other brain regions, and address the daunting task of protein-based equivalent studies. Finally, as the authors of both papers note, the current data are from tissue homogenates, and so cannot reveal differential changes in one cell type from another. We can expect these last differences to be as complicated and fascinating as the temporal and regional profiles reported here.

A key issue for researchers interested in the neurobiology of genes involved in schizophrenia is how deep to dig when investigating the expression of a gene (as one aspect of its function or pathology) before deciding enough is enough. The data in these papers indicate that the answer is probably "very deep." Stretching the metaphor, the data also highlight that there may need to be several digs, across time and space, in looking for different kinds of molecular treasure.

References:

Deep-Soboslay A, Benes FM, Haroutunian V, Ellis JK, Kleinman JE, Hyde TM. Psychiatric brain banking: three perspectives on current trends and future directions. Biol Psychiatry . 2011 Jan 15 ; 69(2):104-12. Abstract

Kleinman JE, Law AJ, Lipska BK, Hyde TM, Ellis JK, Harrison PJ, Weinberger DR. Genetic neuropathology of schizophrenia: new approaches to an old question and new uses for postmortem human brains. Biol Psychiatry . 2011 Jan 15 ; 69(2):140-5. Abstract

View all comments by Paul Harrison

Related News: The Life and Times of the Human Brain Transcriptome

Comment by:  Marquis Vawter
Submitted 9 November 2011
Posted 10 November 2011
  I recommend the Primary Papers

Just a passing comment. I believe the study by Kang et al. shows an interesting change in gene expression of the MIR137, which was strongly implicated by GWAS.

Both of these papers are extremely useful, and welcomed for the study of eQTLs in human brain.

View all comments by Marquis Vawter

Related News: The Life and Times of the Human Brain Transcriptome

Comment by:  Yasue Horiuchi, Shin-ichi Kano, Akira Sawa (SRF Advisor)Ashley Wilson
Submitted 1 December 2011
Posted 1 December 2011

These two new papers show the spatial and temporal regulation of gene expression in the human brain across various ages. Although it is not novel to observe various patterns of gene expression during human brain development, systematic bioinformatics approaches using such enormous sample sizes will lead us to a new level of understanding the complexity of the transcriptome during development.

Both groups showed that age is a very strong contributor to global differences in gene expression compared to other variables such as sex, ethnicity, and inter-individual variation. Thus, transcriptional differences and changes are most pronounced during early development, gradually slowing through infancy, adolescence, and into adulthood—each stage having a clear transcriptional profile. Kang et al. further showed that gene expression is also spatially regulated. Furthermore, they found many co-expressed gene groups that were spatially and temporally regulated. They also reported sex-biased gene expression.

Our group, like many other laboratories, is trying to approach molecular mechanism(s) underlying schizophrenia by using patient-derived cells, especially induced pluripotent stem cells (Dolmetsch and Geschwind, 2011) and immature neurons obtained from nasal biopsy (Sawa and Cascella, 2009). The challenge in this approach has been the shortage of information on gene expression patterns during the neurodevelopmental trajectory. In this sense, these two outstanding papers provide all of us with useful information. If any future studies can address the spatial and temporal regulation of gene expression in each “specific” type of brain cell, this will be of further help to the field. Laser-captured microdissection could be a useful tool to obtain enriched populations of different cell types from tissue (Goswami et al., 2010; Tajinda et al., 2010). Such encyclopedia-type efforts may also be applied to reveal the epigenetic landscape of the brain in the future (Cheung et al., 2010).

References:

Dolmetsch R, Geschwind DH. The human brain in a dish: the promise of iPSC-derived neurons. Cell . 2011 Jun 10 ; 145(6):831-4. Abstract

Sawa A, Cascella NG. Peripheral olfactory system for clinical and basic psychiatry: a promising entry point to the mystery of brain mechanism and biomarker identification in schizophrenia. Am J Psychiatry . 2009 Feb 1 ; 166(2):137-9. Abstract

Goswami DB, May WL, Stockmeier CA, Austin MC. Transcriptional expression of serotonergic regulators in laser-captured microdissected dorsal raphe neurons of subjects with major depressive disorder: sex-specific differences. J Neurochem . 2010 Jan 1 ; 112(2):397-409. Abstract

Tajinda K, Ishizuka K, Colantuoni C, Morita M, Winicki J, Le C, Lin S, Schretlen D, Sawa A, Cascella NG. Neuronal biomarkers from patients with mental illnesses: a novel method through nasal biopsy combined with laser-captured microdissection. Mol Psychiatry . 2010 Mar 1 ; 15(3):231-2. Abstract

Cheung I, Shulha HP, Jiang Y, Matevossian A, Wang J, Weng Z, Akbarian S. Developmental regulation and individual differences of neuronal H3K4me3 epigenomes in the prefrontal cortex. Proc Natl Acad Sci U S A . 2010 May 11 ; 107(19):8824-9. Abstract

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Related News: Autism Exome: Lessons for Schizophrenia?

Comment by:  Patrick Sullivan, SRF Advisor
Submitted 20 April 2012
Posted 23 April 2012
  I recommend the Primary Papers

Fascinating papers that likely presage work in the pipeline from multiple groups for schizophrenia. Truly groundbreaking work by some of the best groups in the business. Required reading for those interested in psychiatric genomics.

The identified loci provide important new windows into the neurobiology of ASD.

The results also pertain to the longstanding debate about the nature of ASD: does it result from many individually rare, Mendelian-like variants (potentially a different one in each person) and/or from the summation of the effects of many different common variants of subtle effects?

The multiple rare variant model now seems unlikely for ASD as, contrary to the expectations of some, ASD did not readily resolve into a handful of Mendelian-like diseases. (This comment is of course qualified by the limits of the technologies - which have, however, identified causal mutations for many monogenetic disorders.)

Readers might also want to read Ben Neale's comments on these papers at the Genomes Unzipped website.

View all comments by Patrick Sullivan

Related News: Exome Sequencing Hints at Prenatal Genes in Schizophrenia

Comment by:  Sven CichonMarcella RietschelMarkus M. Nöthen
Submitted 5 October 2012
Posted 5 October 2012

The new exome sequencing study by Xu et al. confirms previous results by the same research group (Xu et al., 2011) and by an independent group (Girard et al., 2011) that a significantly higher frequency of protein-altering de novo single nucleotide variants (SNVs) and in/dels is found in sporadic patients with schizophrenia. It is certainly reassuring that this observation has now been confirmed in an independent and considerably larger sample (134 patient-parent trios and 34 control-parent trios).

A closer look also reveals differences between this study and the study by Girard et al.: Xu et al. do not find a significantly higher overall de novo mutation rate per base per generation when comparing schizophrenia and control trios (1.73 x 10-08 vs. 1.28 x 10-08). In contrast, the Girard study found 2.59 x 10-08 de novo mutations in schizophrenia trios as opposed to the 1.1 x 10-08 events reported in the general population by the 1000 Genomes Project. The larger sample size in the new study by Xu et al., however, suggests that their estimation of the de novo mutation rates may be more precise now.

What eventually seems to count is the quality of the de novo mutations in the sporadic schizophrenia patients. The function of the genes hit by the non-synonymous/deleterious (as defined by in-silico scores) mutations is diverse and shows similarity with functions reported for common risk genes for schizophrenia identified by GWAS. Interestingly, there is an overrepresentation of genes that are predominantly expressed during embryogenesis, strongly highlighting a possible effect of neurodevelopmental disturbances in the etiology of schizophrenia (and nicely supporting what has already been concluded from GWAS).

It would probably be very interesting to estimate the penetrance of such de novo mutations to get a feeling for their individual impact on the development of the disease. In the absence of a reasonable number of individuals with the same mutation, however, this will be a difficult task.

Another aspect that is missing in the current paper, but is accessible to investigation, is the frequency/quality of de novo mutations in trios with a family history of schizophrenia and comparison to the figures seen in the sporadic trios. That might (or might not) support the authors’ conclusion that de novo events play a strong role in sporadic cases (and not in familial cases).

References:

Xu B, Roos JL, Dexheimer P, Boone B, Plummer B, Levy S, Gogos JA, Karayiorgou M. Exome sequencing supports a de novo mutational paradigm for schizophrenia. Nat Genet . 2011 Sep ; 43(9):864-8. Abstract

Girard SL, Gauthier J, Noreau A, Xiong L, Zhou S, Jouan L, Dionne-Laporte A, Spiegelman D, Henrion E, Diallo O, Thibodeau P, Bachand I, Bao JY, Tong AH, Lin CH, Millet B, Jaafari N, Joober R, Dion PA, Lok S, Krebs MO, Rouleau GA. Increased exonic de novo mutation rate in individuals with schizophrenia. Nat Genet . 2011 Sep ; 43(9):860-3. Abstract

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Related News: Exome Sequencing Hints at Prenatal Genes in Schizophrenia

Comment by:  Patrick Sullivan, SRF Advisor
Submitted 5 October 2012
Posted 5 October 2012

This paper by the productive group at Columbia increases our knowledge of the role of rare exon mutations in schizophrenia. The authors applied exome sequencing—a newish high-throughput sequencing technology—to trios consisting of both parents plus an offspring with schizophrenia. The authors focused on a subset of the genome (the “exome,” genetic regions believed to code for protein) on a subset of genetic variants (SNPs and insertion/deletion variants) of predicted functional significance, and on one type of inheritance (“de novo“ mutations, those absent in both parents and present in the offspring with schizophrenia).

The sample sizes are the largest yet reported for schizophrenia—231 affected trios and 34 controls. About 28 percent of these samples were reported in 2011 (Xu et al., 2011). A recent schizophrenia sequencing study (N = 166) from the Duke group was unrevealing (Need et al., 2012). The numbers in the Xu, 2012 paper are small compared to the three Nature trio studies for autism (see SRF related news story), an approximately threefold larger trio study for schizophrenia (in preparation), a case-control exome sequencing study for schizophrenia (total N ~5,000, in preparation), and a case-control exome chip study for schizophrenia (total N ~11,000, in preparation).

The authors reported:

more mutations with older fathers, as has been reported before (see SRF related news story). Note that advanced paternal age is an established risk factor for schizophrenia.

more de novo/predicted functional/exonic mutations in schizophrenia than in controls. However, the difference was slight, one-sided P = 0.03. One can quibble with the use of a one-tailed test (should never be used, in my opinion), but it is difficult to interpret this result unless paternal age is included as a covariate in this critical test.

an impressive set of bioinformatic and integrative analyses—see the paper for the large amount of work they did.

as might be predicted given the small sample size and the rarity of these sorts of mutations, there was no statistically significant pile-up of variants in specific genes. Hence, to my reading, the authors do not compellingly implicate any specific genes in the pathophysiology of schizophrenia. This conclusion is consistent with Need et al., 2012, and I note that the autism work implicated only a few genes (e.g., CHD8 and KATNAL2).

Note that the authors would disagree with the above, as they chose to focus on a set of genes that they thought stood out (reporting an aggregate P of 0.002), and the last third of the paper focuses on these genes. However, the human genetics community now insists on two critical points for implicating specific genes in associations with a disorder. The first is statistical significance, and the critical P value for an exome sequencing study is on the order of 1E-6. The second is replication. In my view, neither of these standards are achieved. However, their observations are intriguing, and may well eventually move us forward.

The key observation in this paper is the increased rate of de novo variation in schizophrenia cases. Is the increased rate indeed part of an etiological process? In other words, older fathers have an increased chance of exonic mutations, and these, in turn, increase risk for schizophrenia? Or are these merely hitch-hikers of no particularly biological import?

A major issue with exome studies is that there are so many predicted functional variants in apparently normal people. We all carry on the order of 100 exonic variants of predicted functional consequences with on the order of 20 genes that are probable knockouts. If part of the risk for schizophrenia indeed resides in the exome, very large studies will be required to identify such loci confidently. Moreover, published work on autism and unpublished work for type 2 diabetes, coronary artery disease, and schizophrenia suggest that this will require very large sample sizes, on the order of 100 times more than reported here. And, it is possible that the exome is not all that important for schizophrenia.

References:

Xu B, Roos JL, Dexheimer P, Boone B, Plummer B, Levy S, Gogos JA, Karayiorgou M. Exome sequencing supports a de novo mutational paradigm for schizophrenia. Nat Genet . 2011 Sep ; 43(9):864-8. Abstract

Need AC, McEvoy JP, Gennarelli M, Heinzen EL, Ge D, Maia JM, Shianna KV, He M, Cirulli ET, Gumbs CE, Zhao Q, Campbell CR, Hong L, Rosenquist P, Putkonen A, Hallikainen T, Repo-Tiihonen E, Tiihonen J, Levy DL, Meltzer HY, Goldstein DB. Exome sequencing followed by large-scale genotyping suggests a limited role for moderately rare risk factors of strong effect in schizophrenia. Am J Hum Genet . 2012 Aug 10 ; 91(2):303-12. Abstract

View all comments by Patrick Sullivan

Related News: Ambitious Genetic Integration Analysis of Schizophrenia Points to Early Brain Development

Comment by:  Roger Boshes
Submitted 10 August 2013
Posted 20 August 2013

These data suggest a "stem" circuit that may be common to many patients with schizophrenia, but subsequent de novo mutations may explain the protean manifestations of the disorder. Alternatively, this prefrontal perturbation may be related to a heritable, i.e., not a somatic, mutation that explains 80 percent heritability but not the protean phenotypic expression of the condition. Finally, it may be the link between schizophrenia and some flavors of autism.

References:

Boshes RA, Manschreck TC, Konigsberg W. Genetics of the schizophrenias: a model accounting for their persistence and myriad phenotypes. Harv Rev Psychiatry. 2012 May-Jun; 20(3):119-29. Abstract

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