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Schizophrenia Genes - Famine to Feast
EDITORIAL
Schizophrenia Genes - Famine to Feast It is difficult to escape the conclusion that genetic research about mental disorders, especially schizophrenia, has generated a quite credible list of potential susceptibility genes. As the evidence grows that some of the important genes for schizophrenia have been identified, our understanding of this disorder will change dramatically. We think it already has. Genes are the first objective clues to the primary causes of mental disorders and to the mechanisms of cellular pathogenesis. DNA, RNA and protein isoforms are not phenomenology, and as such, they show what the diseases are at a basic cellular level. This is not to say that genes are the only etiologic factors in the pathogenesis of schizophrenia, as certainly the environment is important and epigenetic processes may also play a role. But genes are bounded and objective and their biological effects can be more readily identified and quantitated. The discovery of genes for schizophrenia is difficult work because it is complicated work, and it has been the subject of heated debate and disagreement in the literature, at conferences, and in the laboratory. There remain some voices that argue that the effort has not been successful, for example, that all linkage and association results are weak and therefore suspect, that genes have not been found because there are “too many” differences in the alleles and haplotypes between studies claiming replication, that clearly functional “mutations” are generally missing, and that the mechanisms of the genetic effects are entirely obscure. Many of these arguments have traditionally been rebutted by familiar refrains about complex genetics; for instance, that such inconsistencies should be expected in the context of allelic and locus heterogeneity, reduced penetrance, small sample sizes, epistasis, and environmental interactions. However, this debate, which was at the forefront of psychiatric genetics only a few years ago, is now of lesser priority, because the research is so rapidly advancing and the data have finally become where the action is. Indeed, only a few years ago, prior to the publication in mid 2002 of the initial papers of association with dysbindin (DTNBP1), neuregulin (NRG1) and D-amino acid oxidase activator (DAOA; formerly G72/G30), genes that were discovered based on the results from genome scans for linkage, there was a fairly widespread pessimism. Many people were wondering aloud if genes for psychiatric disorders would be found only in the distant future. And yet here we are just four years later and a dozen or more genes have been found and replicated, and we are already starting to get at least a glimpse of the potential mechanisms by which these genes (see Table 1) influence the risk of disease. Most of these genes were found via linkage analysis, and surely genome wide association studies will dramatically accelerate the rate of gene discovery. We think that the nature of much of the genetic variation associated with schizophrenia will be clarified within the next decade and at least some of the cellular mechanisms will be elucidated, and that this applies to bipolar disease and unipolar depression as well. All sides in this debate agree that the field is in its infancy and that much more research over many years is required. It is also an inescapable conclusion that the benefits from this effort will be enormous. In this issue of the Journal, we have called on seasoned experts in our field to give us their assessments on particular topics. Thus, assembled here is an intentionally varied and somewhat eclectic collection of overviews - snapshots if you will 0006-3223/06/$32.00 doi:10.1016/j.biopsych.2006.06.002
- circa early 2006. Most are reviews of the current knowledge on individual genes, but others are on strategy and methodology and needless to say, we think the authors have done a superb job on each. The last issue of this Journal devoted to schizophrenia genes (Volume 45, Issue 5, March 1999) focused primarily on methodologies for gene discovery; this issue is more about pathogenic mechanisms related to those genes that look at this juncture to be valid susceptibility genes. This change in emphasis illustrates how far research in this area has come in such a short time. Before releasing our readers to explore these informative and thought provoking reviews, we would like to stress a few points that we think highlight the state of the art. Genetics is often surprising One of the surprises that has emerged in the past few years is that linkage identified regions of the genome where susceptibility genes have been found. The linkage strategy is ideal for finding regions containing major effect genes that follow identifiable inheritance patterns in families. For complex disorders, involving multiple small effect genes that are heterogeneous across families, theory predicted that linkage would stand little chance of success. Yet, after over a decade of linkage studies in schizophrenia families, including an early period in which “replication” seemed impossible, a sizable set of confirmed linkage regions has emerged, some of which are strong enough to withstand rigorous correction for whole genome analysis (e.g. 1q, 6p, 6q, 15q,) and others have been supported by meta-analysis (8p, 13q, 22q etc). Why has linkage worked? Are the genes of largest effects found in linkage regions? The answer appears to be no or at least probably not. Genes first identified from linkage, such as dysbindin (6p), NRG1 (8p), DAOA (G72/G30; 13q), do not appear to have greater effect sizes or odds ratios across samples than other genes (e.g. DISC1, AKT1, GAD1) that are not in linked regions. Do genes identified in linkage peaks show clear inheritance patterns within families? This also appears not to be the case. Likewise, genetic heterogeneity across samples is obvious. It appears that linkage has been fruitful at least in part because linked regions contain multiple susceptibility genes, and thus within a linkage sample, different genes segregating in different families can contribute to the linkage signal for that region. The known advantage of linkage over association is that a region gets “credit” for different alleles being transmitted to affected individuals more often than to unaffected individuals. But an added and quite unexpected bonus in gene hunting seems to be that the linkage approach also benefited greatly from the presence of multiple susceptibility genes being present, even up to 20 centimorgans apart. A striking example of this seems to be on chromosome 6p22-24, where dysbindin, MUTED (Straub et al 2005), and MRDS1 (OFCC1; Matsumoto et al 2002; Straub et al 2003) are all located within 10 megabases of each other. We presume that now that genome wide association scans are becoming affordable, association methods may soon make low density linkage scans unnecessary except in special cases. Another surprise is that even among some of the most consistently replicated genes (e.g. dysbindin NRG1, DAOA), the associated alleles and haplotypes (and presumably diplotypes) differ considerably across studies. This point is highlighted in the reviews in this issue by Harrison and Law (2006) concerning NRG1 and by Detera-Wadleigh and McMahon (2006) concerning DAOA. Oddly enough, there is still some controversy about whether differences between studies in the associated alleles and BIOL PSYCHIATRY 2006;60:81– 83 © 2006 Society of Biological Psychiatry
82 BIOL PSYCHIATRY 2006;60:81– 83 Table 1. Schizophrenia susceptibility genes and the strength of evidence in four domains Strength of evidence (0 to 5⫹)
COMT DTNBP1 NRG1 RGS4 GRM3 DISC1 DAOA (G72/G30) DAAO PPP3CC CHRNA7 PRODH2 AKT1 GAD1 ERBB4 FEZ1 MUTED MRDS1 (OFCC1)
22q11 6p22 8p12-21 1q21-22 7q21-22 1q42 13q32-34 12q24 8p21 15q13-14 22q11 14q22-32 2q31.1 2q34 11q24.2 6p24.3 6p24.3
Association with schizophrenia
Linkage to gene locus
Biological plausibility
Altered expression in schizophrenia
⫹⫹⫹ ⫹⫹⫹⫹⫹ ⫹⫹⫹⫹⫹ ⫹⫹⫹ ⫹⫹⫹ ⫹⫹⫹ ⫹⫹⫹ ⫹⫹ ⫹ ⫹ ⫹ ⫹ ⫹⫹ ⫹⫹ ⫹⫹ ⫹⫹⫹⫹ ⫹⫹
⫹⫹⫹⫹ ⫹⫹⫹⫹ ⫹⫹⫹⫹ ⫹⫹⫹ ⫹ ⫹⫹ ⫹⫹ ⫹ ⫹⫹⫹⫹ ⫹⫹ ⫹⫹⫹⫹ ⫹
⫹⫹⫹⫹ ⫹⫹ ⫹⫹⫹ ⫹⫹⫹ ⫹⫹⫹⫹ ⫹⫹ ⫹⫹ ⫹⫹⫹⫹ ⫹⫹⫹⫹ ⫹⫹⫹ ⫹⫹ ⫹⫹ ⫹⫹
Yes, ⫹ Yes, ⫹⫹ Yes, ⫹ Yes, ⫹⫹ No, ⫹⫹ Not known Not known Not known Yes, ⫹ Yes, ⫹⫹⫹ No, ⫹ Yes, ⫹⫹ Yes, ⫹⫹⫹ Yes, ⫹⫹ Yes, ⫹⫹ Yes Not known
⫹⫹⫹⫹ ⫹⫹⫹⫹
⫹⫹⫹ ⫹⫹⫹ ⫹
Revised after: Harrison and Weinberger (2005).
haplotypes necessarily constitute “discrepancies” and therefore represent a failure of replication. This may be due primarily to inadequate definition of the hypothesis, since indeed a failure to replicate a particular allele/haplotype may be embedded in data that has succeeded in replicating the association of the gene with the illness. Allelic diversity in complex disorders remains a mystery largely unsolved, but it cannot be emphasized enough that both allelic and locus heterogeneity is the overwhelming rule in Mendelian disorders. For example, in the cystic fibrosis mutation database (http://www.genet.sickkids.on.ca/cftr/), there are currently 1439 mutations associated with this disease in the CFTR gene. Why would we not expect similar levels of diversity to be found in complex disorder genes? Complex genetics is not simple There are many reasons that differences might exist in genetic association results involving different populations other than that the positive findings are spurious. Recent studies both in the USA and in Europe have demonstrated that population stratification is not just a theoretical problem of minor importance. Population stratification refers to unbalanced representation in samples of cases and controls of subpopulations having differences in allele and haplotype frequencies. Such differences can result in both false positive and false negative results. Even in Iceland, a country founded by a small population just over 1000 years ago and remaining relatively isolated ever since, significantly different allele frequencies are found across different regions of the country (Helgason et al 2005) The implications of such ascertainment differences in samples across Europe or across urban centers in the US are clear. Future studies will have to monitor and control for such effects, using approaches such as selected ancestry informative marker panels or whole genome association panels, in addition to paying greater attention to phenotypic and demographic variables. Incomplete knowledge of a gene, and its transcripts and protein products is surely at the root of some of the current differences in results across studies. Very few genes have been subjected to extensive resequencing or exhaustive genotyping, and while HAPMAP tagging SNPs are a good place to start, as they capture a substantial fraction of the common genetic www.sobp.org/journal
variation in most genes, surely they will miss much of what we seek. Very few genes have had a comprehensive survey across many brain regions of their alternatively spliced isoforms, much less their various post-translationally modified protein products. Invariably, the closer we look at these genes, for example COMT (Tunbridge et al 2006, in this issue) and GRM3 (Sartorius et al 2006), the more their complexity becomes evident. However, it also could be true that focusing too intently on particular haplotypes or diplotypes, is often not optimal. We think that a unifying and simplifying approach, based on characterizing the functional state of the gene, is going to be one of the most productive ways to evaluate genetic effects. As reviewed by Tunbridge et al herein, this is illustrated rather dramatically in the case of COMT, a gene that has defied consistent association to its common functional coding val/met variant, likely because other functional variants in the gene have evolved to balance the effect of this polymorphism. Many of the genes for schizophrenia identified so far appear to impact biologically on some of the most basic processes of brain development, involving neuronal differentiation (eg. Tunbridge et al 2006; Porteous et al 2006, in this issue), synapse biology (eg. Harrison and Law 2006, in this issue) and various processes involved in neuronal plasticity. This should not be too surprising, since the list of genes not expressed during development remains quite small. These processes are also central to cellular adaptation to the environment, and so gene-environment interactions will be required for a full understanding of the genetic pathogenesis of mental disorders. An illustrative example of this is the report of the interaction between COMT val/met genotype and early adolescent cannabis use (Caspi et al 2005). In that study, while COMT genotype alone had no predictive effect on the emergence of schizophreniform illness by age 25, in combination with early marijuana use, the risk odds ratio for the val/val genotype was ten-fold greater than in the general population. In addition to gene-environment interactions, quantifying gene-gene interactions (Cordell 2002; Thornton-Wells et al 2004) will be critically important in defining the risk architecture of any individual. Genes interact with each other, and with the environment, to modify their individual effects. This can lead to exag-
BIOL PSYCHIATRY 2006;60:81– 83 83 gerated, dampened, or even novel effects. There is increasing evidence, again, that COMT interacts with other genes to modify their effects (Nicodemus et al 2005). A recent report involving NRG1 suggests an important interaction with one of its receptors, ErbB4 (Norton et al 2006). There are two additional biological mechanisms that will surely add to the complexity, and stir up this fascinating but frustrating stew. The first is epigenetic effects. After one puts aside the fact that epigenetic effects are in vogue, it is at present unclear whether these effects will turn out to be a major factor in the risk of mental disease or just emerge as modifiers for a small subset of susceptibility genes. The second mechanism, antisense regulation of gene expression and translation, for example microRNAs, is likely to be more important and this deserves to be a very active area of investigation. In the larger perspective, it is not of great importance if a few of the 17 schizophrenia susceptibility genes we have put in the table turn out to be false positives or even if most of them turn out to be just minor effect size players in the underlying genetic architecture, or if we have left a couple of equally worthy genes out. Their discovery has already led to the extremely rapid elaboration of risk pathways (eg. NRG1/ERBB4, DISC1/FEZ1 and the BLOC-1 complex which includes dysbindin and MUTED) and this will surely accelerate with the advent of genome wide association studies coupled with next generation expression arrays. It is not genes, but rather pathways, and their interactions that are most relevant to the development of better therapeutics and early diagnosis. As a field, we should be a bit intimidated and humbled by the complexity implied by these initial discoveries, but we should not be inhibited in utilizing them or from proceeding with full force. The biology of genes that impact on the complex nature of human perception, cognition and behavior would not be simple. Why would we have expected otherwise? Richard E. Straub Daniel R. Weinberger Genes Cognition and Psychosis Program National Institute of Mental Health Caspi A, Moffitt TE, Cannon M, McClay J, Murray R, Harrington H, et al (2005): Moderation of the effect of adolescent-onset cannabis use on adult psychosis by a functional polymorphism in the catechol-O-methyltrans-
ferase gene: longitudinal evidence of a gene X environment interaction. Biol Psychiatry 57:1117–1127. Cordell HJ (2002): Epistasis: what it means, what it doesn’t mean, and statistical methods to detect it in humans. Hum Mol Genet 11:2463– 2468. Detera-Wadleigh SD, McMahon FJ (2006): G72/G30 in schizophrenia and bipolar disorder: review and meta-analysis. Biol Psychiatry 60:106 –114. Harrison PJ, Weinberger DR (2005): Schizophrenia genes, gene expression, and neuropathology: on the matter of their convergence. Mol Psychiatry 10:40 – 68. Harrison PJ, Law AJ (2006): Neuregalin 1 and schizophrenia: genetics, gene expression, and neurobiology. Biol Psych 60:132–140. Helgason A, Yngvadottir B, Hrafnkelsson B, Gulcher J, Stefansson K (2005): An Icelandic example of the impact of population structure on association studies. Nat Genet 37:90 –95. Matsumoto M, Weinberger DR, Straub RE (2002): Molecular cloning, sequencing, and characterization of a novel 500 kilobase gene (MRDS1) from 6p24, a schizophrenia candidate region. Am J Med Genet B: Neuropsychiatr Genet 114B:857. Nicodemus KK, Straub RE, Egan MF, Weinberger DR (2005): Evidence for statistical epistasis between (COMT) Val158Met polymorphism and multiple putative schizophrenia susceptibility genes. Am J Med Genet B: Neuropsychiatr Genet 138B:130 –131. Norton N, Moskvina V, Morris DW, Bray NJ, Zammit S, Williams NM, et al (2006): Evidence that interaction between neuregulin 1 and its receptor erbB4 increases susceptibility to schizophrenia. Am J Med Genet B: Neuropsychiatr Genet 141:96 –101. Porteous DJ, Thomson P, Brandon NJ, Miller JK (2006): The genetics and biology of Disc1—an emerging role in psychosis and cognition. Biol Psychiatry 60:123–131. Sartorius LJ, Nagappan G, Lipska BK, Lu B, Sei Y, Ren-Patterson R, et al (2006): Alternative splicing of human metabotropic glutamate receptor 3. J Neurochem 96:1139 –1148. Straub RE, Matsumoto M, Egan MF, Goldberg TE, Callicott JH, Hariri A, et al (2003): MRDS1 (6p24.3) is associated with schizophrenia in both adult onset and childhood onset schizophrenia families. Am J Med Genet B: Neuropsychiatr Genet 122B:18. Straub RE, Mayhew MB, Vakkalanka RK, Kolachana B, Goldberg TE, Egan MF, Weinberger DR (2005): MUTED, a protein that binds to dysbindin (DTNBP1), is associated with schizophrenia. A J Med Genet B: Neuropsychiatr Genet 138B:136. Thornton-Wells TA, Moore JH, Haines JL (2004): Genetics, statistics and human disease: analytical retooling for complexity. Trends Genet 20:640 – 647. Tunbridge EM, Harrison PJ, Weinberger DR (2006): Catechol-o-methyltransferase, cognition, and psychosis: val158met and beyond. Biol Psych 60: 141–151.
www.sobp.org/journal
Schizophrenia Genes - Famine to Feast It is difficult to escape the conclusion that genetic research about mental disorders, especially schizophrenia, has generated a quite credible list of potential susceptibility genes. As the evidence grows that some of the important genes for schizophrenia have been identified, our understanding of this disorder will change dramatically. We think it already has. Genes are the first objective clues to the primary causes of mental disorders and to the mechanisms of cellular pathogenesis. DNA, RNA and protein isoforms are not phenomenology, and as such, they show what the diseases are at a basic cellular level. This is not to say that genes are the only etiologic factors in the pathogenesis of schizophrenia, as certainly the environment is important and epigenetic processes may also play a role. But genes are bounded and objective and their biological effects can be more readily identified and quantitated. The discovery of genes for schizophrenia is difficult work because it is complicated work, and it has been the subject of heated debate and disagreement in the literature, at conferences, and in the laboratory. There remain some voices that argue that the effort has not been successful, for example, that all linkage and association results are weak and therefore suspect, that genes have not been found because there are “too many” differences in the alleles and haplotypes between studies claiming replication, that clearly functional “mutations” are generally missing, and that the mechanisms of the genetic effects are entirely obscure. Many of these arguments have traditionally been rebutted by familiar refrains about complex genetics; for instance, that such inconsistencies should be expected in the context of allelic and locus heterogeneity, reduced penetrance, small sample sizes, epistasis, and environmental interactions. However, this debate, which was at the forefront of psychiatric genetics only a few years ago, is now of lesser priority, because the research is so rapidly advancing and the data have finally become where the action is. Indeed, only a few years ago, prior to the publication in mid 2002 of the initial papers of association with dysbindin (DTNBP1), neuregulin (NRG1) and D-amino acid oxidase activator (DAOA; formerly G72/G30), genes that were discovered based on the results from genome scans for linkage, there was a fairly widespread pessimism. Many people were wondering aloud if genes for psychiatric disorders would be found only in the distant future. And yet here we are just four years later and a dozen or more genes have been found and replicated, and we are already starting to get at least a glimpse of the potential mechanisms by which these genes (see Table 1) influence the risk of disease. Most of these genes were found via linkage analysis, and surely genome wide association studies will dramatically accelerate the rate of gene discovery. We think that the nature of much of the genetic variation associated with schizophrenia will be clarified within the next decade and at least some of the cellular mechanisms will be elucidated, and that this applies to bipolar disease and unipolar depression as well. All sides in this debate agree that the field is in its infancy and that much more research over many years is required. It is also an inescapable conclusion that the benefits from this effort will be enormous. In this issue of the Journal, we have called on seasoned experts in our field to give us their assessments on particular topics. Thus, assembled here is an intentionally varied and somewhat eclectic collection of overviews - snapshots if you will 0006-3223/06/$32.00 doi:10.1016/j.biopsych.2006.06.002
- circa early 2006. Most are reviews of the current knowledge on individual genes, but others are on strategy and methodology and needless to say, we think the authors have done a superb job on each. The last issue of this Journal devoted to schizophrenia genes (Volume 45, Issue 5, March 1999) focused primarily on methodologies for gene discovery; this issue is more about pathogenic mechanisms related to those genes that look at this juncture to be valid susceptibility genes. This change in emphasis illustrates how far research in this area has come in such a short time. Before releasing our readers to explore these informative and thought provoking reviews, we would like to stress a few points that we think highlight the state of the art. Genetics is often surprising One of the surprises that has emerged in the past few years is that linkage identified regions of the genome where susceptibility genes have been found. The linkage strategy is ideal for finding regions containing major effect genes that follow identifiable inheritance patterns in families. For complex disorders, involving multiple small effect genes that are heterogeneous across families, theory predicted that linkage would stand little chance of success. Yet, after over a decade of linkage studies in schizophrenia families, including an early period in which “replication” seemed impossible, a sizable set of confirmed linkage regions has emerged, some of which are strong enough to withstand rigorous correction for whole genome analysis (e.g. 1q, 6p, 6q, 15q,) and others have been supported by meta-analysis (8p, 13q, 22q etc). Why has linkage worked? Are the genes of largest effects found in linkage regions? The answer appears to be no or at least probably not. Genes first identified from linkage, such as dysbindin (6p), NRG1 (8p), DAOA (G72/G30; 13q), do not appear to have greater effect sizes or odds ratios across samples than other genes (e.g. DISC1, AKT1, GAD1) that are not in linked regions. Do genes identified in linkage peaks show clear inheritance patterns within families? This also appears not to be the case. Likewise, genetic heterogeneity across samples is obvious. It appears that linkage has been fruitful at least in part because linked regions contain multiple susceptibility genes, and thus within a linkage sample, different genes segregating in different families can contribute to the linkage signal for that region. The known advantage of linkage over association is that a region gets “credit” for different alleles being transmitted to affected individuals more often than to unaffected individuals. But an added and quite unexpected bonus in gene hunting seems to be that the linkage approach also benefited greatly from the presence of multiple susceptibility genes being present, even up to 20 centimorgans apart. A striking example of this seems to be on chromosome 6p22-24, where dysbindin, MUTED (Straub et al 2005), and MRDS1 (OFCC1; Matsumoto et al 2002; Straub et al 2003) are all located within 10 megabases of each other. We presume that now that genome wide association scans are becoming affordable, association methods may soon make low density linkage scans unnecessary except in special cases. Another surprise is that even among some of the most consistently replicated genes (e.g. dysbindin NRG1, DAOA), the associated alleles and haplotypes (and presumably diplotypes) differ considerably across studies. This point is highlighted in the reviews in this issue by Harrison and Law (2006) concerning NRG1 and by Detera-Wadleigh and McMahon (2006) concerning DAOA. Oddly enough, there is still some controversy about whether differences between studies in the associated alleles and BIOL PSYCHIATRY 2006;60:81– 83 © 2006 Society of Biological Psychiatry
82 BIOL PSYCHIATRY 2006;60:81– 83 Table 1. Schizophrenia susceptibility genes and the strength of evidence in four domains Strength of evidence (0 to 5⫹)
COMT DTNBP1 NRG1 RGS4 GRM3 DISC1 DAOA (G72/G30) DAAO PPP3CC CHRNA7 PRODH2 AKT1 GAD1 ERBB4 FEZ1 MUTED MRDS1 (OFCC1)
22q11 6p22 8p12-21 1q21-22 7q21-22 1q42 13q32-34 12q24 8p21 15q13-14 22q11 14q22-32 2q31.1 2q34 11q24.2 6p24.3 6p24.3
Association with schizophrenia
Linkage to gene locus
Biological plausibility
Altered expression in schizophrenia
⫹⫹⫹ ⫹⫹⫹⫹⫹ ⫹⫹⫹⫹⫹ ⫹⫹⫹ ⫹⫹⫹ ⫹⫹⫹ ⫹⫹⫹ ⫹⫹ ⫹ ⫹ ⫹ ⫹ ⫹⫹ ⫹⫹ ⫹⫹ ⫹⫹⫹⫹ ⫹⫹
⫹⫹⫹⫹ ⫹⫹⫹⫹ ⫹⫹⫹⫹ ⫹⫹⫹ ⫹ ⫹⫹ ⫹⫹ ⫹ ⫹⫹⫹⫹ ⫹⫹ ⫹⫹⫹⫹ ⫹
⫹⫹⫹⫹ ⫹⫹ ⫹⫹⫹ ⫹⫹⫹ ⫹⫹⫹⫹ ⫹⫹ ⫹⫹ ⫹⫹⫹⫹ ⫹⫹⫹⫹ ⫹⫹⫹ ⫹⫹ ⫹⫹ ⫹⫹
Yes, ⫹ Yes, ⫹⫹ Yes, ⫹ Yes, ⫹⫹ No, ⫹⫹ Not known Not known Not known Yes, ⫹ Yes, ⫹⫹⫹ No, ⫹ Yes, ⫹⫹ Yes, ⫹⫹⫹ Yes, ⫹⫹ Yes, ⫹⫹ Yes Not known
⫹⫹⫹⫹ ⫹⫹⫹⫹
⫹⫹⫹ ⫹⫹⫹ ⫹
Revised after: Harrison and Weinberger (2005).
haplotypes necessarily constitute “discrepancies” and therefore represent a failure of replication. This may be due primarily to inadequate definition of the hypothesis, since indeed a failure to replicate a particular allele/haplotype may be embedded in data that has succeeded in replicating the association of the gene with the illness. Allelic diversity in complex disorders remains a mystery largely unsolved, but it cannot be emphasized enough that both allelic and locus heterogeneity is the overwhelming rule in Mendelian disorders. For example, in the cystic fibrosis mutation database (http://www.genet.sickkids.on.ca/cftr/), there are currently 1439 mutations associated with this disease in the CFTR gene. Why would we not expect similar levels of diversity to be found in complex disorder genes? Complex genetics is not simple There are many reasons that differences might exist in genetic association results involving different populations other than that the positive findings are spurious. Recent studies both in the USA and in Europe have demonstrated that population stratification is not just a theoretical problem of minor importance. Population stratification refers to unbalanced representation in samples of cases and controls of subpopulations having differences in allele and haplotype frequencies. Such differences can result in both false positive and false negative results. Even in Iceland, a country founded by a small population just over 1000 years ago and remaining relatively isolated ever since, significantly different allele frequencies are found across different regions of the country (Helgason et al 2005) The implications of such ascertainment differences in samples across Europe or across urban centers in the US are clear. Future studies will have to monitor and control for such effects, using approaches such as selected ancestry informative marker panels or whole genome association panels, in addition to paying greater attention to phenotypic and demographic variables. Incomplete knowledge of a gene, and its transcripts and protein products is surely at the root of some of the current differences in results across studies. Very few genes have been subjected to extensive resequencing or exhaustive genotyping, and while HAPMAP tagging SNPs are a good place to start, as they capture a substantial fraction of the common genetic www.sobp.org/journal
variation in most genes, surely they will miss much of what we seek. Very few genes have had a comprehensive survey across many brain regions of their alternatively spliced isoforms, much less their various post-translationally modified protein products. Invariably, the closer we look at these genes, for example COMT (Tunbridge et al 2006, in this issue) and GRM3 (Sartorius et al 2006), the more their complexity becomes evident. However, it also could be true that focusing too intently on particular haplotypes or diplotypes, is often not optimal. We think that a unifying and simplifying approach, based on characterizing the functional state of the gene, is going to be one of the most productive ways to evaluate genetic effects. As reviewed by Tunbridge et al herein, this is illustrated rather dramatically in the case of COMT, a gene that has defied consistent association to its common functional coding val/met variant, likely because other functional variants in the gene have evolved to balance the effect of this polymorphism. Many of the genes for schizophrenia identified so far appear to impact biologically on some of the most basic processes of brain development, involving neuronal differentiation (eg. Tunbridge et al 2006; Porteous et al 2006, in this issue), synapse biology (eg. Harrison and Law 2006, in this issue) and various processes involved in neuronal plasticity. This should not be too surprising, since the list of genes not expressed during development remains quite small. These processes are also central to cellular adaptation to the environment, and so gene-environment interactions will be required for a full understanding of the genetic pathogenesis of mental disorders. An illustrative example of this is the report of the interaction between COMT val/met genotype and early adolescent cannabis use (Caspi et al 2005). In that study, while COMT genotype alone had no predictive effect on the emergence of schizophreniform illness by age 25, in combination with early marijuana use, the risk odds ratio for the val/val genotype was ten-fold greater than in the general population. In addition to gene-environment interactions, quantifying gene-gene interactions (Cordell 2002; Thornton-Wells et al 2004) will be critically important in defining the risk architecture of any individual. Genes interact with each other, and with the environment, to modify their individual effects. This can lead to exag-
BIOL PSYCHIATRY 2006;60:81– 83 83 gerated, dampened, or even novel effects. There is increasing evidence, again, that COMT interacts with other genes to modify their effects (Nicodemus et al 2005). A recent report involving NRG1 suggests an important interaction with one of its receptors, ErbB4 (Norton et al 2006). There are two additional biological mechanisms that will surely add to the complexity, and stir up this fascinating but frustrating stew. The first is epigenetic effects. After one puts aside the fact that epigenetic effects are in vogue, it is at present unclear whether these effects will turn out to be a major factor in the risk of mental disease or just emerge as modifiers for a small subset of susceptibility genes. The second mechanism, antisense regulation of gene expression and translation, for example microRNAs, is likely to be more important and this deserves to be a very active area of investigation. In the larger perspective, it is not of great importance if a few of the 17 schizophrenia susceptibility genes we have put in the table turn out to be false positives or even if most of them turn out to be just minor effect size players in the underlying genetic architecture, or if we have left a couple of equally worthy genes out. Their discovery has already led to the extremely rapid elaboration of risk pathways (eg. NRG1/ERBB4, DISC1/FEZ1 and the BLOC-1 complex which includes dysbindin and MUTED) and this will surely accelerate with the advent of genome wide association studies coupled with next generation expression arrays. It is not genes, but rather pathways, and their interactions that are most relevant to the development of better therapeutics and early diagnosis. As a field, we should be a bit intimidated and humbled by the complexity implied by these initial discoveries, but we should not be inhibited in utilizing them or from proceeding with full force. The biology of genes that impact on the complex nature of human perception, cognition and behavior would not be simple. Why would we have expected otherwise? Richard E. Straub Daniel R. Weinberger Genes Cognition and Psychosis Program National Institute of Mental Health Caspi A, Moffitt TE, Cannon M, McClay J, Murray R, Harrington H, et al (2005): Moderation of the effect of adolescent-onset cannabis use on adult psychosis by a functional polymorphism in the catechol-O-methyltrans-
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