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Sweeping SchizophreniaGene Study Applies New Criteria to Finger Suspects

2 July 2008. We at the Schizophrenia Research Forum won’t pretend to be impartial about a particular paper in the July issue of Nature Genetics. The excitement that permeates our virtual walls comes from seeing Lars Bertram and colleagues at Massachusetts General Hospital report findings based on their SchizophreniaGene, which resides at SRF. Their meta-analyses point to associations between schizophrenia and 24 genetic variants in 16 different genes, but only four of those associations—namely, those involving DRD1 rs4532, DTNBP1 rs1011313, MTHFR rs1801131, and TPH1 rs1800532—earned high marks for epidemiologic credibility by new criteria proposed by the Human Genome Epidemiology Network, or HuGENet. These results, and the methods used to find them, may speed the search for schizophrenia genes.

The deluge of papers claiming to either incriminate or absolve specific genes in schizophrenia has taken interested parties on a wild ride. “For health care providers, researchers, and the general public, the accumulating information is increasingly difficult to follow, evaluate, and interpret,” the SZGene researchers write. Furthermore, they note, “No single gene or genetic variant has been established as a bona fide schizophrenia susceptibility gene, at least not with the confidence accorded to other genes associated with susceptibility to complex disease.”

Forging a new path
To contain the chaos, first author Nicole C. Allen, now at Columbia University, and associates created SZGene, a public database of genetic association studies in schizophrenia. They update it regularly in an effort to include every relevant English-language study published in a peer-reviewed journal. SZGene grew out of the AlzGene database, which Bertram and colleagues developed with help from our friends at the Alzheimer Research Forum (see Bertram et al., 2007).

Allen and colleagues performed meta-analyses of studies indexed in SZGene. They consider their work a step forward not only due to its scope, but also because “it systematically examines sources of biases and assigns a score for the epidemiologic credibility of the findings.” In addition, it scrutinized whole-genome as well as candidate gene studies.

The researchers began with the studies indexed in SZGene as of April 30, 2007, when it listed 1,179 papers that presented data on 3,608 genetic variants in 516 different genes. Only variants for which they had data from at least four independent case-control samples received further scrutiny, narrowing the focus of analysis to 118 variants in 52 genes. “This nearly doubles the number of meta-analyses thus far published in the field,” the authors write.

The new meta-analyses identified 24 variants in 16 genes as potential schizophrenia risk factors, with polymorphisms in APOE, COMT, DAO, DRD1, DRD2, DRD4, DTNBP1, GABRB2, GRIN2B, HP, IL1B, MTHFR, PLXNA2, SLC6A4, TP53, and TPH1 distinguishing cases from controls. For variants fingered as risk-raisers, odds ratios averaged 1.23; for potentially protective ones, they averaged 0.82.

“Notably, we identified significant risk-modifying effects in seven genes (DAO, DRD1, DTNBP1, GABRB2, HP, PLXNA2, and TP53) for which, to the best of our knowledge, no previous meta-analyses had been published,” the authors write. Contrary to past meta-analyses, the new one found no connection between schizophrenia and certain variants in BDNF, DRD3, NRG1, DAOA, or COMT.

Only two genomewide association studies (see SRF related news story) had been published before the cutoff date. One found an association between schizophrenia and variants in plexin A2 (PLXNA2) in individuals of European descent that a study of a Japanese sample could not replicate. The odds ratio found by Allen and colleagues, 0.82, hinted at a protective role for the gene. The other whole-genome study produced leads involving variants in CSF2RA and IL3RA, but too few samples existed for meta-analysis.

Making the grade
To evaluate the “epidemiological credibility” of the significant meta-analysis results, the researchers applied criteria proposed by the Human Genome Epidemiology Network (HuGENet). The benchmarks, drafted at a meeting in Venice, Italy, led by study coauthor John Ioannidis of the University of Ioannina (Ioannidis et al., 2008; Ioannidis et al., 2006), facilitate the grading of associations on the amount of relevant evidence, their consistency of replication, and their protection from bias.

Each association received an A, B, or C grade on each of the three dimensions assessing the evidence in support of an association. The grade for the amount of evidence reflected the number of minor alleles in the sample subjected to meta-analysis (e.g., an “A” grade for >1,000 minor alleles in cases and controls combined). The replication grade considered the degree of heterogeneity of odds ratios across all studies included in the meta-analysis. The protection from bias grade took into account potential biases due to genotyping, confounding, and selective reporting. Associations were deemed “strong” if they received three A grades, “moderate” if at least one B and no C grade, and “weak” if one or more C grades.

Only four associations, involving variants on DRD1, DTNBP1, MTHFR, and TPH1, earned straight A’s. “On the basis of the current data, these genes seem to be the best contenders to contain genuine susceptibility alleles modifying disease risk within the whole domain of genetic epidemiology in schizophrenia,” the researchers write.

DRD1 encodes the most plentiful dopamine receptor in the central nervous system. (For information about the dopamine hypothesis in schizophrenia, see SRF Current Hypotheses). Allen and colleagues write, “This receptor is thought to have a role in regulation of cognitive functions in the prefrontal cortex, possibly through interactions with NMDA-mediated neurotransmission, and to be involved in the action of clozapine.” DTNBP1, a top schizophrenia gene candidate, encodes dysbindin, a protein that may be expressed abnormally in schizophrenia and which may play a role in modulating neurotransmission via dopamine D2 receptors (see SRF related news story). MTHFR produces methylenetetrahydrofolate reductase, indirectly affecting homocysteine metabolism and within-cell methylation processes that may be related to schizophrenia; some evidence suggests that it plays a role in schizophrenia symptoms. TPH1 makes tryptophan hydroxylase 1, the rate-limiting enzyme in serotonin production. Many atypical antipsychotic drugs act on serotonin receptors (see SRF related news story).

As for the remaining genes, four showed modest epidemiologic credibility (i.e., they received at least one B grade, but no C’s) and 16, weak credibility (at least one C grade). Their lackluster grades do not rule them out as schizophrenia genes, any more than an A grade designates a surefire one. They simply say whether, based on the assumptions made in the guidelines, enough strong evidence exists at this time to posit a connection.

According to Allen and colleagues, “Our project represents the first comprehensive online resource for systematically synthesized and graded evidence of genetic association studies in schizophrenia.” However, they urge caution in interpreting its findings pending confirmation by future studies and clarification of the molecular pathways by which potential schizophrenia genes might affect risk. Even if updates to SZGene change the list of hot gene prospects, the methods used in this study could raise the bar for meta-analyses of schizophrenia genes. Furthermore, the researchers write, “The approach presented here can be easily adapted to genetic association studies of other common diseases of public health significance.”—Victoria L. Wilcox.

Reference:
Allen NC, Bagade S, McQueen MB, Ioannidis JPA, Kavvoura FK, Khoury MJ, Tanzi RE, Bertram L. Systematic meta-analyses and field synopsis of genetic association studies in schizophrenia: The SzGene database. Nature Genetics. 2008 July;40(7):499-506. Abstract

Comments on News and Primary Papers
Comment by:  Stephen J. Glatt
Submitted 17 July 2008
Posted 21 July 2008
  I recommend the Primary Papers

The paper by Allen et al. is a tremendously useful addition to the fields of schizophrenia research, psychiatric genetics, and medical genetics. By efficiently summarizing a tremendous amount of work, Allen et al. have endeavored to provide a "state-of-the-art" summary that most of us, as individuals, struggle to accomplish; they have largely succeeded in their attempt. This manuscript, and the continual availability of the SZGene database, should long serve as invaluable resources for the increasingly complex task of building polygenic models of risk for schizophrenia. Furthermore, these methods, which were initially implemented in the AlzGene database, have clearly generalized quite successfully to SZGene and thus, should be easy enough to scale up to cover many other psychiatric disorders as well. In this way, the contribution to psychiatric genetics, and possibly other disorders outside of psychiatry, is crystalline.

Aside from the database, the contribution of the recent manuscript to the field of schizophrenia research is also tremendous. As pointed out by the authors, several of the significantly associated genes identified by their meta-analyses were never before studied in this manner, so a whole new set of top candidate genes was identified. This work also served to confirm the results of prior meta-analyses from my group and others, which is always reassuring. Application of the HuGENet criteria to grading the detected associations is useful as a heuristic, but it must be kept in mind that that while these criteria reflect a consensus, they also reflect a moving target. One difficulty in implementing grades (especially the "overall" grade) is analogous to difficulties often encountered in meta-analyses when rating the quality of studies, and that is the ambiguity of ratings. Thus, on a seven-point quality scale (or a three-letter-grade scale), a score can be arrived at by a variety of combinations of flaws or strengths, but similar scores may not (often do not) reflect identical strengths and weaknesses of the graded studies. For example, I, for one, am not certain that having a relatively low number of minor alleles reflected in a meta-analytic result (especially if it is a rare variant) is as big a decrement as the pooled OR dropping from significance when the initial study is omitted.

Nevertheless, I reiterate that the use of this heuristic grading system is helpful, but should be taken with a grain of salt. Overall, the paper and its conclusions are a great contribution to this field and warrant mass attention. The ultimate question, not yet addressed here but apparently on the horizon, is how well the emerging GWASs detect these "positive control" associations, or we might say how well these hypothesis-driven results stack up against new candidates to emerge from the high-throughput generation of novel hypotheses....

View all comments by Stephen J. Glatt

Comments on Related News


Related News: WCPG 2007—Schizophrenia, Bipolar GWA Results Prompt Calls for Bigger Samples

Comment by:  William Carpenter, SRF Advisor (Disclosure)
Submitted 7 November 2007
Posted 8 November 2007

Terrific update and summary for those of us not attending the meeting.

View all comments by William Carpenter

Related News: Studies Suggest Potential Roles for Dysbindin in Schizophrenia

Comment by:  Philip Seeman (Disclosure)
Submitted 29 November 2007
Posted 29 November 2007
  I recommend the Primary Papers

The publication by Iizuka and colleagues is an important advance toward unraveling the basic biology of psychosis in general, and schizophrenia in particular. This is because they have found that a pathway known to be genetically associated with schizophrenia can alter the surface expression of dopamine D2 receptors. D2 continues to be the main target for all antipsychotic drugs (including aripiprazole and even the new Lilly glutamate agonists that have a potent affinity for D2High receptors).

In fact, the authors of this excellent study may do well to go one step further by testing whether the downregulation of dysbindin actually increases the proportion of D2 receptors that are in the high-affinity state, namely D2High. This is because all schizophrenia animal models markedly increase the proportion of D2High receptors by 100 to 900 percent (Seeman et al., 2005; Seeman et al., 2006). This generalization holds for animal models based on brain lesions, sensitization by amphetamine, phencyclidine, cocaine, caffeine or corticosterone, birth injury, social isolation, and more than 15 gene deletions in pathways for glutamate (NMDA), dopamine, GABA, acetylcholine, and norepinephrine. Although the proportion of D2High receptors invariably increases markedly, the total number of D2 receptors is generally unchanged, slightly reduced, or modestly elevated.

This publication for the first time bridges the hitherto wide gap between genetics and the antipsychotic targeting of the main cause of psychotic signs and symptoms, which is excessive D2 activity, presumably that of D2High, the functional component of D2.

References:

Seeman P, Weinshenker D, Quirion R, Srivastava LK, Bhardwaj SK, Grandy DK, Premont RT, Sotnikova TD, Boksa P, El-Ghundi M, O'dowd BF, George SR, Perreault ML, Männistö PT, Robinson S, Palmiter RD, Tallerico T. Dopamine supersensitivity correlates with D2High states, implying many paths to psychosis. Proc Natl Acad Sci U S A. 2005 Mar 1;102(9):3513-8. Epub 2005 Feb 16. Abstract

Seeman P, Schwarz J, Chen JF, Szechtman H, Perreault M, McKnight GS, Roder JC, Quirion R, Boksa P, Srivastava LK, Yanai K, Weinshenker D, Sumiyoshi T. Psychosis pathways converge via D2high dopamine receptors. Synapse. 2006 Sep 15;60(4):319-46. Review. Abstract

View all comments by Philip Seeman

Related News: Studies Suggest Potential Roles for Dysbindin in Schizophrenia

Comment by:  Christoph Kellendonk
Submitted 4 December 2007
Posted 4 December 2007

The study by Iizuka and colleagues is indeed very interesting. It suggests that one of the most promising risk genes for schizophrenia, the dysbindin gene, may functionally interact with dopamine D2 receptors. The D2 receptor itself is an old candidate in the study of schizophrenia, mostly because until very recently all antipsychotic medication had been directed against D2 receptors. But in addition, PET imaging studies have shown that the density and occupancy of D2 receptors is increased in drug-free and drug-naïve patients with schizophrenia.

How could this increase arise? In a subpopulation of patients it may be due to a polymorphism in the D2 receptor gene, the C957T polymorphism. The C-allele increases mRNA stability and has been found to be associated with schizophrenia, though obviously not all patients carry the C-allele. Iizuka and colleagues found an independent way in which the genetic risk factor dysbindin may upregulate D2 receptor signaling. Because dysbindin is downregulated in the brains of patients with schizophrenia, they used siRNA technology to study the molecular consequences of decreased dysbindin levels in cell culture.

They found that downregulation of dysbindin increases D2 receptor density in the outer cell membrane, suppresses dopamine-induced D2 receptor internalization, and increases D2 receptor signaling. The study is very promising but requires further confirmation.

How specific are the observed effects for D2 receptors? Because dysbindin is involved in both membrane trafficking and degradation of synaptic vesicles, knocking down dysbindin in growing cells may affect many physiological processes, one of them being D2 receptor signaling. Does quinpirole treatment differentially affect GTPgS incorporation in siRNA and control cells? This would be a more immediate way of looking at D2 signaling than measuring CREB phosphorylation. And, of course, the most important question is, What will happen in vivo? Maybe the sandy mouse, which carries a deletion in the dysbindin gene, could be of help here. Using these mice for a similar line of experiments may answer this question.

Iizuka and colleagues found an exciting new functional interaction between two major molecules involved in schizophrenia. I believe that these are the kind of interactions we have to look for if we want to understand complex genetic disorders such as schizophrenia.

View all comments by Christoph Kellendonk

Related News: Hidden Complexity Seen in Serotonin Signaling

Comment by:  Patricia Estani
Submitted 23 February 2008
Posted 26 February 2008
  I recommend the Primary Papers

Related News: Hidden Complexity Seen in Serotonin Signaling

Comment by:  Atheir Abbas
Submitted 25 February 2008
Posted 27 February 2008
  I recommend the Primary Papers

Implicit in the findings of Schmid et al. is the idea that the relationship among ligand, receptor signaling, and cellular context is an extremely complex one that will take a great deal more work to tease out. Thus, Dr. Bryan Roth has proposed on a number of occasions (see, for example, Gray and Roth, 2007; Abbas and Roth, 2005) that novel approaches for drug discovery may prove more effective in producing schizophrenia drugs that have greater therapeutic efficacy with lower side effect liability. Since it will likely be many years before the field has a detailed understanding of the "nitty-gritty" of the receptor signaling and trafficking relevant to schizophrenia and its treatment, we have suggested a number of approaches that are less reliant on such information.

For example, approaches based on screening for drugs that either mimic the gene expression profiles of gold standard drugs such as clozapine or normalize schizophrenia-associated changes in gene expression are being explored. Another approach is behavior-based screening, in which targeted screens are performed with drugs to find those that have efficacy in animal disease models. A further related approach, exemplified by Psychogenics' Smartcube(TM) (the associated database is called Smartbase[TM]) involves injecting drugs and monitoring the resulting behavior using computer-based machine learning to generate a multidimensional behavioral signature for gold standard drugs. Drugs can then be screened to look for those that mimic gold standard drugs in terms of their signatures. Though Psychogenics does not appear to have done much (at least publicly) with this approach, it represents the sort of innovative thinking that may prove fruitful in future behavior-based drug discovery efforts since it is not dependent on knowing anything about the mechanism. In the end, at least in the near future, we believe such approaches may prove extremely useful in drug discovery efforts since they do not rely on extensive mechanistic knowledge of the processes underlying schizophrenia.

References:

Gray JA, Roth BL. The pipeline and future of drug development in schizophrenia. Mol Psychiatry. 2007 Oct ;12(10):904-22. Abstract

Abbas A, Roth B. Progress towards better understanding and treatment of major psychiatric illnesses. Drug Discov Today. 2005 Jul 15;10(14):960-2. Abstract

View all comments by Atheir Abbas