Some commentators in their reflections take a rather negative view on what has been achieved through the application of GWAS technology to schizophrenia and psychiatric disorders more generally. We strongly disagree with this position. Below, we give examples of a number of statements that can be made about the aetiology of schizophrenia and bipolar disorder that could not be made at high levels of confidence even two years ago that are based upon evidence deriving from the application of GWAS.
1. We know with confidence that the role of rare copy number variants in schizophrenia is not limited to 22q11DS (VCFS) (reviewed recently in O’Donovan et al., 2009). We do not yet know how much of a contribution, but we know the identity of an increasing number of these. Most span multiple genes so it may prove problematic as it has in 22q11DS to identify the relevant molecular mechanisms. However, for one locus, the CNVs are limited to a single gene: Neurexin1 (Kirov et al., [...continued] 2008; Rujescu et al., 2009). Genetic findings are merely the start of the journey to a deeper biological understanding, but no doubt many neurobiological researchers have already embarked on that journey in respect of neurexin1.
2. Although we have argued in this forum that some of the major pre-GWAS findings in
schizophrenia very likely reflect true susceptibility genes (DTNBP1, NRG1, etc), we now have at
least 4 novel loci where the evidence is more definitive (ZNF804A, MHC, NRGN, TCF4),
(O’Donovan et al., 2008a; ISC, 2009; Shi et al., 2009; Stefansson et al., 2009) and two novel loci (Ferreira et al., 2008) in bipolar disorder (ANK3 and CACNA1C), at least one of which (CACNA1C) additionally confers risk of schizophrenia (Green et al., 2009). This is obviously a small part of the picture, but it is certainly better than no picture at all. These findings also offer a much more secure foundation than the earlier findings upon which to build follow up studies, for example brain imaging, and cognitive phenotypes (Esslinger et al., 2009), and even candidate gene studies. We would not regard the first convincing evidence that altered calcium channel function is a primary aetiological event in at least some forms of psychosis as a trivial gain in knowledge.
3. We can say with confidence that common alleles of small effect are abundant in
schizophrenia, and that they contribute to a substantial part of the population risk (ISC, 2009).
Identifying any one of these at stringent levels of statistical significance may be challenging in
terms of sample sizes. As we have pointed out before, merging multiple datasets may indeed obscure some true associations because of sometimes unpredictable relationships between risk alleles and those assayed indirectly in GWAS studies (Moskvina and O’Donovan, 2007). Nevertheless, that many of the same alleles are overrepresented in multiple independent GWAS datasets from different countries (ISC, 2009) means that larger samples offer the prospect of identifying many more of these. This is not to say that large samples are the only approach; genetic heterogeneity may well underpin some aspects of clinical heterogeneity (Craddock et al., 2009a). However, with the exception of individual large pedigrees, it is not yet evident which type of clinical sample one should base a small scale study on. It should also be self-evident that the analysis of multiple samples, each with a different phenotypic structure, will pose major problems in respect of multiple testing and subsequent replication. Moreover, ascertaining special samples that represent putative subtypes of the clinical (and endophenotypic) spectrum of psychosis will first require large samples to be carefully assessed and the relevant subjects extracted. Subsequently, downstream, evaluation of specific genotype-phenotype relationships will require the remainder of the clinical population to be genotyped in a suitably powered way to show that those effects are specific to some clinical features of the disorder. Increasingly, it is ascertainment and assessment that dominate the cost of GWAS studies so it is not clear this approach will achieve any economies. We must also remember that after a GWAS study, there remains the opportunity to look in a controlled manner for relatively specific associations in the context of the heterogeneous clinical picture. For example we are aware of a number of papers in development that will exploit the sorts of multi-locus tests reported by the ISC to refine diagnostics, and no doubt many other applications of this will emerge in the next year or so.
Critics should bear in mind that the GWAS data are not just there for the ‘headline’ genome-wide findings, but that the data will be available to mine for years to come. The findings reported to date are based on only the simplest analyses.
4. If it were the case that the thousands of SNPs of small effect were randomly distributed across biological systems, none being of more relevance to pathophysiology than another, identifying them would probably be a pointless endeavour. However, there is no reason to believe this will be the case. We have recently shown that in bipolar disorder, the GWAS signals are enriched in particular biological pathways (Holmans et al., 2009) and we also published strong evidence for a relatively selective involvement of the GABAergic system in schizoaffective disorder (Craddock et al., 2009b). We are aware of an as-yet unpublished independent sample with similar findings. We would not regard the first convincing evidence that altered GABA function is a primary aetiological event in at least some forms of psychosis as a trivial gain in knowledge.
Incidentally it is a common misconception that the identification of risk alleles of small effect necessarily confers no useful insights into pathogenesis and possible drug targets. For example, common alleles in PPARG and KCNJ11 have been robustly shown to confer risk to Type 2 diabetes (T2D) but with odds ratios in the region of only 1.14 (of similar magnitude to those revealed by GWAS of schizophrenia). PPARG encodes the target for the thiazolidinedione class of drugs used to treat T2D. KCNJ11 encodes part of the target for another class of diabetes drug, the sulphonylureas (Prokopenko et al., 2008). Moreover, studies of novel associated variants identified in T2D GWAS in healthy, non-diabetic, populations have demonstrated that for most, the primary effect on T2D susceptibility is mediated through deleterious effects on insulin secretion, rather than insulin action (Prokopenko et al., 2008). Further examples of insights into the biology of common diseases coming from the identification of loci of small effect are the implication of the complement system in age-related macular degeneration and autophagy in Crohn’s disease (Hirschhorn, 2009). Already, efforts are under way to translate the new recognition of the role of autophagy in Crohn’s disease into new therapeutic leads (Hirschhorn, 2009). Of course many of the loci identified in GWAS implicate genes whose functions are as yet largely or completely unknown, and determining those functions is a prerequisite of translating those findings. Nevertheless, we believe that the greatest benefits that will accrue from the continued discovery of risk loci through GWAS will come from the assembly of that information into novel disease pathways leading to novel therapeutic targets.
5. We can say with confidence that bipolar disorder and schizophrenia substantially overlap, at least in terms of polygenic risk (ISC, 2009). As clinicians, we do not regard that knowledge as a trivial achievement.
6. We can say with confidence from studies of CNVs that schizophrenia and autism share at least some risk factors in common (O’Donovan et al., 2009). We believe that is also an important insight.
The above are major achievements in what we expect to be a long but accelerating process of picking apart the origins of schizophrenia and other psychotic disorders. We do not think that any other research discipline in psychiatry has done more to advance that knowledge in the past 100 years.
Like that other common familial diseases, the genetics of schizophrenia and bipolar disorder is a “mixed economy” of common alleles of small effect and rare alleles of large and small effects, including CNVs. Those who are concerned at the cost of collecting large samples for GWAS studies must bear in mind that the robust identification of both types of mutation will require similarly large samples; we will just have to get used to that fact if we want to make progress. Collecting samples like this may be expensive, but as clinicians, we know those costs are trivial compared with the human and economic costs of psychotic disorders.
The question of phenotype definition is one which we have repeatedly addressed (Craddock et al., 2009a). Unquestionably, if we knew the true pathophysiological basis of these disorders, we could do better. The fact is that we don’t. Given that, it must be extremely encouraging that despite the problems, risk loci can be robustly identified by GWAS using samples defined by current diagnostic criteria. Moreover, armed with GWAS data in these heterogeneous populations, additional risk genes can be identified through strategies aimed at refining the phenotype that are not constrained by the current dichotomous view of the functional psychoses. We have shown at least one way in which this might be achieved without imposing a further burden of multiple testing (Craddock et al., 2009b), and have little doubt that others will emerge. We agree that approaches to phenotyping that more directly index underlying pathophysiology are highly appealing, and will ultimately be necessary for understanding the mechanistic relationships between gene and disorder. However, the two cardinal assumptions upon which the use of endophenotypes is predicated for gene discovery are questionable. First, there is little good evidence that putative endophenotypes are substantially simpler genetically than “exophenotypes” (Flint and Munafo, 2007). Second, there is rarely good evidence that the current crop of popular putative endophenotypes lie on the disease pathway, indeed there seems to be substantial pleiotropy in the genetics of complex traits, psychosis included (Prokopenko et al., 2008; O’Donovan et al., 2008b).
Finally, we reiterate that while only small parts of the heritability of any complex disorder have been accounted for, large-scale genetic approaches have been extremely successful in studies of non-psychiatric diseases (Manolio et al., 2008) and have led to substantial advances in our understanding of pathogenesis, even for diseases like Crohn’s disease where there was already prior knowledge of pathogenesis from other research methods (Mathew, 2008).
Psychiatry starts from a situation in which there is no robust prior knowledge of pathogenesis for the major phenotypes. Recent findings suggest that mental illness may be the medical field that will actually benefit most over the coming years from application of these powerful molecular genetic technologies.
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