Dissecting the phenotype in genome-wide association studies of psychiatric illness

Summary

Over the last two years genome-wide association studies (GWAS) have made major contributions to understanding the genetic architecture of many common human diseases. This editorial outlines the development of GWAS in psychiatry and highlights the opportunities for advancing understanding of the biological underpinnings and nosological structure of psychiatric disorders.

Genome-wide association studies (GWAS) involve genotyping hundreds of thousands of common DNA variants (single nucleotide polymorphisms; “SNPs”) spread throughout the genome in large numbers of individuals with illness and a similar number of comparison individuals with a low prevalence of illness (“controls”) (1). Over the last two years GWAS have made major contributions to advancing our understanding of many common diseases including diabetes, heart disease, inflammatory bowel disease, various cancers and rheumatoid arthritis (2). In Crohn’s disease, 30 different genes have already been robustly shown to influence risk and this has pointed to novel biological pathways involved in illness pathogenesis (3).

This editorial outlines the development of GWAS approaches in psychiatry and highlights opportunities for advancing knowledge of the biological underpinnings and nosological structure of psychiatric disorders.

GWAS in psychiatric illness

Although relatively few studies have so far been published, large-scale collaborative GWAS have started to deliver genome-wide significant genetic associations for bipolar disorder and schizophrenia. Studies of approximately 10,000 individuals have shown strong evidence for association with susceptibility to bipolar disorder at variants within two genes involved in ion channel function: ANK3 (encoding the protein Ankyrin G) and CACNA1C (encoding the alpha-1C subunit of the L-type voltage-gated calcium channel) (4). A similar study in close to 20,000 individuals has shown strong evidence for association with susceptibility to schizophrenia at a variant within ZNF804A (encoding a zinc finger transcription factor) (5). Recent independent GWAS data provide further support for involvement of ANK3 in bipolar disorder and suggest the existence of at least 2 distinct susceptibility variants at this locus (6). Although further study and replication of the findings is important, these initial experiences suggest that the study of even larger samples will identify additional reliable associations and thereby extend knowledge of the proteins and biological pathways involved in illness.

Psychiatric GWAS Consortium (PGC)

One of the key lessons from GWAS conducted to date is the necessity for very large samples (ie. thousands of cases and controls) (7). Investigators and funders have recognized that scientists need to cooperate and share data rather than compete and restrict data access and consequently collaborative consortia have been forming. In psychiatry, the largest amongst these includes a large proportion of the world’s research groups working with psychiatric GWAS data: the Psychiatric GWAS Consortium (PGC: http://pgc.unc.edu). The disorders that are currently represented in the PGC are ADHD, autism, bipolar disorder, major depressive disorder, and schizophrenia. It is expected that during 2009, the PGC will include over 80,000 individuals each with approximately 500,000 SNP genotypes. The aims of PGC are to coordinate and facilitate the necessary large-scale collaborative analyses using both (a) traditional disorder categories, and (b) non-traditional analyses that cut across diagnostic categories. This latter aim is the focus of this editorial.

The phenotype is important in GWAS

The importance of phenotype definition and selection on GWAS findings is demonstrated strikingly by work on type 2 diabetes where the gene FTO was robustly associated with illness in a collaborative meta-analysis (8). However, association at FTO was not present at all in one of the 3 samples in the meta-analysis whilst being highly significant in one of the other samples of similar size. The difference was caused by phenotypic heterogeneity: in the sample showing no association, cases were not included if they were obese. No such exclusion criterion was present in the sample with the strong effect. Subsequent work showed that FTO influences diabetes risk through an effect on body mass (9). This demonstrates that phenotype variation can be critical to the ability to identify susceptibility variants. Furthermore, taking account of phenotype variation across samples can provide critical information about the mode of action of a susceptibility locus.

Psychiatric scenarios that might produce results similar to the obesity-diabetes story include presence or absence of prominent psychotic features in bipolar disorder or prominence of anxiety in recurrent depression.

Genetic dissection of psychiatric phenotypes

What about psychiatric phenotypes? Psychiatric diagnoses can be considered “the weak component of modern research” (10), defined solely by descriptive, usually behavioural, criteria. Although these phenotype definitions are highly heritable, and hence, are valid and sensible starting points for genetic research, it is generally agreed that the most useful biological categories and/or dimensional definitions and measures are still unknown. The strikingly high level of co-occurrence of different diagnoses within the same individual (“co-morbidity”) almost certainly reflects a substantial overlap in the underlying biology of currently defined syndromes. For example, the five psychiatric phenotypes within PGC are unlikely to identify completely distinct disease entities and there may be overlaps in genetic susceptibility across the disorders. Justification for this assertion includes (a) the existence of clinical symptom/item overlap across several of the phenotypes, (b) the non-independence of multiple diagnoses within the same individuals (11), and (c) the observation that the same structural genetic variants have been described in association with differing phenotypes. For example, deletion of chromosome 22q11 has been described associated with childhood autism and ADHD as well as adult mood disorders and psychosis (12).

Molecular genetics will not provide a simple, gene-based classification of psychiatric illness (as it will not for other common familial illnesses) (13). The notion that there is a “gene for …” one or more psychiatric disorders is inappropriate and unhelpful. Rather, there is a complex relationship between genotype and phenotype that involves multiple genes and environmental factors, together with stochastic variation. Nonetheless, molecular genetic findings can be expected to help delineate the relationship between specific biological pathways/systems and broad patterns, or domains, of psychopathology (14). A precedent for such insights from genetic studies is already emerging from GWAS in other areas of medicine that have revealed unforeseen biological relationships among different autoimmune diseases (15). We anticipate that genetic findings will not map cleanly onto current diagnostic categories and that genetic associations may point to more useful and valid nosological entities. To address this, within PGC a Cross-disorder Phenotype Group has been established to coordinate, develop and lead different types of analyses both across and within the different phenotype sample sets.

Types of analyses that may be relevant

We here briefly consider a range of analytic approaches that may be relevant to understanding the relationship between genotype and phenotype for psychiatric traits (16) (table 1). These include approaches designed to both discover new pathologically relevant genetic variants and also to characterise the phenotypic spectrum associated with robustly associated variants.

Table 1. Types of analysis to delineate the relationship between genotype and phenotype.

Here, phenotype refers to the measurable clinical characteristics of individuals which may be considered at several levels (eg. Disorder; syndrome; factors or domains of psychopathology; or individual symptom items) and genotype refers to the measured genetic variation which may also be considered at several levels (eg. Individual allele; individual polymorphism (SNP); gene; gene family; biological pathway; or other large set of polymorphisms [including polygenic “signature”]). It is possible to (a) start with phenotype(s) and seek associated genotype(s) (the traditional approach), (b) start with genotype(s) and seek correlated phenotype(s) (the “reverse phenotyping” or “phenotype refinement” approach), or (c) consider all phenotype and genotype data together and seek patterns of genotype-phenotype correlation (an approach that makes minimal prior assumptions about both nosology and pathogenesis)

Phenotype → Genotype ○ Seek susceptibility across traditional diagnostic categories ▪ Uses combinations of disorders v. Controls ○ Seek susceptibility to specific domains of psychopathology ▪ Uses cases with specific clinical feature v. Controls ○ Seek modifier genes for specific clinical features ▪ Uses cases with specific clinical feature v. Cases without specific clinical feature ○ Look for patterns (or signatures) of large numbers of associated SNPs that can then be compared across samples or diagnoses Genotype → Phenotype ○ Identify the phenotypic spectrum associated with a specific genotype of interest Genotype ↔ Phenotype ○ Look for patterns of correlation in data with minimal prior assumptions (ie. Seek novel, genetically valid diagnostic entities)

First, we can explore whether individual genetic variants increase risk across multiple diagnostic categories. For example, genes may exist that alter risk for both schizophrenia and autism, or for schizophrenia and bipolar disorder or for bipolar illness and recurrent depression. Second, we can attempt to identify risk genes for psychosis, depressed mood or some other domain of psychopathology regardless of the syndrome in which they occur. Third, we can look for disease-modifying effects. For example, genes may exist which do not influence risk for the diagnostic category of schizophrenia but, when an individual has this diagnosis, alters the probability that they have auditory hallucinations or early onset (17). Fourth, instead of starting with phenotypes and then looking at genotypes, we could reverse the order (16,18). We might start with a single gene or genotype of interest and study its phenotypic profile. Fifth, we could apply one of a range of advanced statistical tools to define novel diagnostic entities (whether they are categories or dimensions) that would “make more sense” from a genetic perspective. Sixth, instead of focusing on single genetic variants, we could consider a large set of polymorphisms (perhaps tens of thousands) and use aggregate measures of their overall contribution to phenotypic susceptibility to seek to define “signatures” of genetic variants, the patterns of which could be compared across phenotypes. This approach, which will be particularly useful if psychiatric phenotypes are highly polygenic (ie. many, many risk genes, each of small effect on risk) has recently been used to demonstrate a substantial overlap in polygenic contribution to schizophrenia and bipolar disorder (19).

Such large-scale analytic approaches have not been undertaken previously so an important task for the PGC CDG will be developing and validating the methodology and reporting standards for these analyses. The general framework outlined will accommodate analysis of additional psychiatric phenotypes when they become available to the PGC.

The challenges ahead

There are, of course, substantial challenges to be overcome in undertaking the types of analyses in table 1. An obvious, but crucial, logistical issue is ensuring the comparability of the data used across different component sample collections. Where possible, analyses should be robust to the inevitable variations in clinical measurement. Important statistical considerations include making efficient use of samples with incomplete data and taking account of multiple testing. This requires both a sensible analytic design to minimize the number of tests, together with an appropriate control for false positive findings that might emerge simply from conducting many tests. Inevitably any exploratory analyses will require independent replication of positive findings.

Conclusion

The ongoing major investments of time and money in GWAS for psychiatric disorders has the potential to contribute to the identification of pathways involved in illness and help psychiatry move towards approaches to diagnosis and treatment that are grounded in a better understanding of pathogenesis. This would be of great benefit to patients.