The phenomics strategy can be seen as an extension of the endophenotype approach that embraces multi-level modeling. This strategy acknowledges that many of the putative endophenotypes or intermediate phenotypes being investigated in biological psychiatry today may not possess much simpler genetic architecture than do the highest level syndromal phenotypes (Flint and Munafo, 2007
). While this strategy does not replace many elements of previously suggested qualities important for phenotype prioritization (Gottesman and Gould, 2003
, Bearden and Freimer, 2006
), it shifts the emphases (see ).
Considerations for Phenotype Selection in Phenomics Research
For example, heritability remains important, but the suggestion that a valuable endophenotype should associate with illness and show familial cosegregation with illness may be less critical, depending on how “illness” is defined. The critical point is that the most valuable phenotypes for phenomics research may well be those that cross boundaries of the current diagnostic taxonomy. Indeed, if it is true that substantial genetic correlations exist for neuropsychiatric syndromes, it may be most fruitful to study phenotypes that are shared across multiple diagnostic syndromes, and that by limiting research to more narrowly defined diagnostic groups, the genetic signals from the strongest genetic contributions may be lost amidst other less important diagnosis-specific “noise.” It is further unclear how helpful conventional heritability statistics are to prioritizing phenotypes, given that many well defined behavioral traits possess heritabilities exceeding 40% (Sabb et al., 2008
), and higher heritability does not seem to assure a simpler genetic architecture. Since the principal value of identifying high heritability is to assure that the phenotypic trait is likely to be meaningfully related to genetic variation (i.e., that it possesses some evidence of genetic validity
), it may also be valuable in phenotype selection to have evidence that the phenotype is significantly associated with known genetic variants. Further, to leverage the power of GWAS using quantitative trait loci, it is helpful if the genetic variant is common in human populations (although it should be recognized that with the advent of ever-increased capacity for genotyping, it may soon be possible for phenotypes associated with rare variants to be detected). It is also helpful if the genetic variant is associated with known functional effects at translational or transcriptional levels, in order to foster greater traction in molecular biology research.
Among the desiderata for phenotype selection, it is difficult to overemphasize the importance of fundamental measurement properties. Given that the upper limit on validity will be imposed by reliability, it is critical for research to focus on those measures that show adequate internal consistency (helping assure that a meaningfully coherent construct is being measured in the first place). If that is true, it is further important to identify those that are relatively stable over time and organismic states, or if there is some fluctuation that this fluctuation itself is a part of the phenotypic assay. The importance of phenotypic stability has been emphasized by others; in brief it is logical to propose that our fixed genetic inheritance will be most easily associated with features that are stable. But there also may be important phenotypic features that have instability as their signature, in which case it is precisely this variability that needs to be assessed. Finally, it is valuable to assure that the sensitivity of phenotype measurement is strong across widely varying levels of phenotype expression. For example, a cognitive phenotype is likely best measured by a test that shows strong measurement properties at both higher and lower levels of that ability, and if the measurement is biased towards either higher or lower levels of ability, then meaningful genetic effects may never be detected not because these do not exist but rather because the test is too insensitive.
A critical aspect of understanding the measurement properties of specific phenotypes is determining how amenable these are to high throughput phenotyping. Given the changing finances of genomics research, it has become clear that the rate-limiting step in advancing knowledge about human disease has shifted dramatically from genetics to phenomics. Phenotyping now far exceeds the time and financial costs of genotyping. Until “high throughput phenotyping” methods are developed, accrual of knowledge in phenomics will be thwarted. Development of detailed costing models is advocated to identify the most cost effective methods for addressing key phenomic targets. High throughput collection of behavioral data is virtually impossible using traditional laboratory based methods. For example, a “comprehensive” examination of cognitive abilities may require ten to twenty hours, and briefer assessments routinely fail to provide broad phenotypic coverage, or fail to specify adequately the phenotypes of interest, or both.
There are two major hopes in advancing high throughput phenotyping. One hope is based on leveraging advances in modern psychometric theory. Using item response theory and computerized adaptive testing algorithms, it is now routinely possible to increase efficiency in psychological assessment by a factor of two. Thus one can either specify the construct with double the precision, or decrease testing time for the same measurement precision in half the time of the original test. There are multiple challenges in using these methods, including: (a) the construct itself needs to be well operationalized in advance; and (b) relatively large samples (i.e., 500 or more with complete response sets) are suggested to apply these methods. A second hope is based on the incredible growth in use of the internet, with more than 100 million people in the United States using the web daily. If we can provide well structured phenotyping tools, enabling widespread access for individuals to provide meaningful data about themselves, the results could rapidly revolutionize behavioral genomics. A paradigm shift that could accelerate discovery would involve dynamic phenotyping, by which we mean the iterative refinement of phenotype assays based on prior genotype-phenotype associations. There remains considerable skepticism among scientists about both sampling biases and validity of data collected using web-based rather than conventional laboratory-based methods, despite demonstrations to the contrary (Krantz et al., 1997
, Buchanan and Smith, 1999
, Krantz and Dalal, 2000
, Andersson et al., 2006
, Chiasson et al., 2006
, Cunningham et al., 2006
, Graham et al., 2006
). While much work remains to address these concerns in a compelling manner, the potential increases in throughput may dramatically outweigh, and even provide solutions to the sampling and quality control issues. Given that conventional laboratory studies running for 5 years often have difficulty ascertaining and examining a few thousand individuals, the opportunities for sub-sampling and data cleaning with several hundred thousand individuals may appear increasingly attractive, particularly as we increasingly recognize the value of samples with tens of thousands of individuals.
There are some features of phenotypes that may be less important to advance genetics research, but may be prioritized because of their relevance to other human applications. Biomedical research ultimately seeks to alleviate human suffering, and thus those phenotypes that can be meaningfully related to clinical morbidity, or the clinical effectiveness and outcomes of treatments, may be important targets for study. Those phenotypes that are suitable for application in clinical trials may be particularly valuable given the possible opportunity to link phenotypic variation to putative effects at the levels of cellular systems or signaling pathways. Consistent with and extending the points made earlier about the value of cross-disorder phenotypes, those phenotypes that are relevant across multiple categories of neuropsychiatric illness may have even broader public health significance than those that do not. For example, finding the genetic bases of “normal” variation in memory or attentional control may be of greater value than identifying genetic associations with Alzheimer’s disease or ADHD.
The phenomics strategy also prioritizes phenotypes that are relevant for translational investigation
, and thus those phenotypes that cross species boundaries. This is not intended to discount the importance of uniquely human phenotypes, and focusing on these is an alternate approach with great potential but different goals. For example, it may be possible to gain traction on the genetic bases of language by examining differences in gene expression between human and chimpanzee (Oldham and Geschwind, 2006
). But the phenomics approach suggests that the “low hanging fruit” in neuropsychiatric genetics may be picked most readily by focusing on phenotypes that are generally well conserved phylogenetically. The primary rationale for this is that by examining phylogenetically conserved phenotypes, we increase the opportunity to gain traction on intervening biology using basic science models.
Of particular value today are transgenic models that can help illuminate the cellular systems and signaling pathways affected by genetic variants, and therefore a priority may be given to phenotypes that can be studied in both mouse and human. For phenotypes that have reasonable homologies from mouse to man, there is a high likelihood that there will be even more robust homologies in larger rodents and non-human primates, enabling more detailed analysis of higher level neuropsychopharmacological effects and neural systems level phenotypes.
Linked to prioritization of cross-species phenotypes is the idea that phenotypes selected for study may be most useful if embedded in plausible mechanistic hypotheses. The better fleshed out the biological mechanisms, the higher the likelihood that meaningful connections will be established with other biological knowledge. Thus, other factors being equal, a phenotype for which there exists already a set of mechanistic models may be preferable to one that does not possess similar evidence. While all such mechanistic models are so far incomplete, working within a framework that includes relevant empirical science may help link findings to existing evidence and either further develop more effective models, or help prune these and better specify superior models for the future.
A critical point to consider in any phenotype prioritization effort is that examining “criteria” does not enable generation of some figure-of-merit for phenotype selection. In the CNP, we had started with the aim of generating a phenotype selection algorithm, and on more careful consideration recognized that at best it might be possible to generate a phenotype “profiling tool” capable of characterizing the strengths and weaknesses of a given phenotype. Some of the features noted above are likely to conflict directly with others (for example, validation with respect to molecular targets may run counter to validation with respect to outcomes).
There is further a substantial risk of phenotype reification that can actively interfere with discovery. For example, a phenotype prioritized because it is influenced reliably by a known molecular entity (a drug) might be seen as valuable by virtue of links to signaling pathways in a mechanistic model, but this might be misleading if the mechanistic model is not well understood. A case in point is the phenotype involving induction of catalepsy in rodents, used for decades as a screening test for antipsychotic drug development. This may have canalized drug discovery towards agents more likely to produce extrapyramidal symptoms than antipsychotic efficacy.
Finally, the phenomics strategy emphasizes that phenotype selection should weigh cautiously evidence from candidate gene strategies. There may be value in research that uses a ‘bottom-up’ approach, starting with functional genetic variation, through identification of proteins affected, through identification of signaling and other cellular processes affected, through neural systems function to behavior. But there should also be appropriate concern that this approach can undermine discovery of novel gene-phene correlations. It may be an impediment to discovery to be tied to known genomic ‘hot spots’, many of which were identified using phenotypes that are acknowledged to be suboptimal, and also may be subject to various other methodological problems (including population stratification and linkage disequilibrium) that may have led to false positive identification of regions that are distant from the driving functional genomic regions of greater interest.