Psychophysiology, 51 (2014), 1205–1206. Wiley Periodicals, Inc. Printed in the USA. Published 2014. This article is a U.S. Government work and is in the public domain in the USA. DOI: 10.1111/psyp.12342
PERSPECTIVE
Translating intermediate phenotypes to psychopathology: The NIMH Research Domain Criteria
BRUCE N. CUTHBERT National Institute of Mental Health, Bethesda, Maryland, USA
Abstract The Research Domain Criteria project (RDoC) was initiated by the National Institute of Mental Health in early 2009 to “develop, for research purposes, new ways of classifying mental disorders based on dimensions of observable behavior and neurobiological measures.” RDoC provides a framework for psychopathology research intended to explicate specific aspects of functional impairment by studying relevant brain-behavior relationships, in contrast to the current heterogeneous categories of mental disorders defined by various groupings of symptoms. Endophenotypes fit naturally into the RDoC context since they are typically conceived to be closer to fundamental neural and psychological mechanisms than more abstracted disorder categories. Consequently, the genomic aspects of endophenotypes take on particular significance for understanding genetic risk architectures in such an approach to psychopathology. Descriptors: Research Domain Criteria, RDoC, Intermediate phenotypes, Endophenotypes, Psychopathology
Disorders) approach have seldom received favorable consideration in peer review because there is no alternative standard to reflect other research approaches that might be based upon emerging literatures in neuroscience and behavioral science. The National Institute of Mental Health (NIMH) initiated the Research Domain Criteria project (RDoC) to address this problem. RDoC’s core aim is to free investigators from the contemporary nosological hegemony of the grant-review system and encourage studies based upon neuroscience and specific aspects of functioning that can lead to new diagnostic and treatment innovations. What is the relationship of RDoC to psychophysiology and to the topic of this special issue, genome-wide scans of psychophysiological endophenotypes? First, RDoC involves an inherently translational and dimensional approach. The framework is organized into five major domains, each incorporating a set of related functional constructs. Importantly, the constructs represent basic dimensions of functioning (such as fear or working memory), with psychopathology viewed as varying degrees of dysregulation relative to the normal range of these functions (as opposed to the classic approach of starting with clinically defined disorders and seeking behavioral or biological “underpinnings”). Second, the RDoC conception builds upon a fundamentally psychophysiological outlook that emphasizes the simultaneous recording of behavioral responses and physiological activity; the intent is for constructs to be studied across multiple units of analysis ranging from genetics through neural systems to measures of behavior and symptoms (see Cuthbert, 2014, for a more detailed description). Thus, the RDoC framework is highly relevant to both basic and clinical psychophysiologists. RDoC constructs could be regarded as intermediate phenotypes (that are more fine grained and homogeneous than International
Thanks to recent advances in neuroscience and behavioral sciences, mental disorders are now understood to be complex syndromes that have roots in polygenic risk factors and eventuate from ongoing interactions between myriad environmental events and the stages of neurodevelopment at which they occur (Bale et al., 2010; McCarroll & Hyman, 2013). Further, mental illnesses are now typically viewed as disorders of neural circuits and networks, disrupting normal processing within and among brain systems (Hyman, 2000). This view, however, does not align well with current assessment and treatment. Disorders are assessed primarily on the basis of presenting symptoms, and it is increasingly recognized that current diagnostic categories, conceived largely to address critical problems of reliability, do not identify valid disease entities (Hyman, 2010). Problems of heterogeneity and excessive comorbidity frustrate attempts to develop more efficient treatments, which are effective in only 30 to 50 percent of patients for all therapeutic modalities (e.g., Wong, Yocca, Smith, & Lee, 2010). While these problems are often acknowledged in the literature, the vast majority of research grant applications are constrained to a single disorder and exclude patients with comorbid diagnoses. The difficulty is that the current categorical nosology has become the de facto criterion for evaluating research grant applications, journal publications, and treatment development. Grant applications that embody a non-DSM (Diagnostic and Statistical Manual of Mental
bs_bs_banner
Thanks are expressed to Dr. William Iacono at the University of Minnesota–Twin Cities for the opportunity to contribute this commentary. Address correspondence to: Bruce Cuthbert, National Institute of Mental Health, 6001 Executive Blvd., Room 7121, MSC 9632, Bethesda, MD 20892-9632, USA. E-mail: [email protected] 1205
1206
B.N. Cuthbert
Classification of Diseases (ICD)/DSM categories), and many of the measures incorporated into the “units of analysis” columns of the RDoC matrix might correspondingly be considered endophenotypes for the construct (including all of those addressed in this special issue). Ideally, such a perspective should facilitate the study of endophenotypes in psychopathology since the focus is upon a particular function rather than a highly abstracted disorder syndrome. In practice, however, the shift from disorder-based to construct-based views of endophenotypes will present its own difficulties, particularly in integrating basic and disordered aspects of response. (See Miller & Rockstroh, 2013, for a thoughtful consideration of endophenotypes vis-à-vis the RDoC framework.) The search for genetic aspects of endophenotypes has particular appeal due to the narrower focus. For traditional categories such as schizophrenia and autism, markedly increased sample sizes in the last few years have begun to provide hints of the promise that genome-wide analysis studies (GWAS) hold for understanding the genetic architecture of disorders (e.g., Ripke et al., 2013). These studies indicate that there is substantial noise in the phenotypic signal in addition to the considerable degree of polygenicity, thus requiring very large samples to detect significant associations. Given the increasing importance of endophenotypes in psychopathology research, relevant GWASs offer the potential to understand genetic risks at a lower and more tractable level as compared to current disorder categories and to expedite the parsing of phenotypic variance that will be required to develop new treatments based upon genetic risk patterns (McCarroll & Hyman, 2013). Thus, the papers in this special issue represent a significant advance in psychopathology research. However, as also revealed in the results, discovery of particular genetic variants for documented and candidate endophenotypes may be as difficult as for traditional disorders. Biometric analyses revealed significant genetic contributions to nearly all of the measures that were examined; however, few specific common or rare variants emerged as significant in the analyses. This is clearly no fault of the methods, which involved cutting-edge data cleaning and analysis procedures.
While this outcome is discouraging at first blush, there are good reasons to see the glass as half full rather than half empty. First, as noted in the various papers, the sample sizes are impressive for a psychophysiological experiment but small compared to current disorder-based studies; for example, the number of patients in the latest schizophrenia GWAS is nearly an order of magnitude higher than the sample sizes analyzed for this issue (Ripke, 2013). Second, most of the individuals in a community-based sample such as the Minnesota Twin Family Study will reflect the center-weighted pattern of a normal distribution, while extreme values provide more power for GWAS analyses; thus, addition of a relatively small number of cases at the tails of the distribution (perhaps, in part, in samples with psychopathology of various sorts) may contribute to more significant “hits.” Moving forward, modern “big data” approaches should generate larger sample sizes in this area that can foster statistical power. For instance, the NIMH is currently implementing an RDoC “information commons” (based upon the National Database for Autism Research) in which investigators will be strongly encouraged to share data of all types including genomics and neurophysiological data. It will take some years to accumulate data with common endophenotypic measures of the type used in these papers; however, this process, and other comparable database efforts, should eventually yield the sample sizes needed to reveal the nature of genomic variance related to endophenotypes. In turn, emerging data on functional gene networks, now being pioneered for traditional disorders (e.g., Gilman et al., 2013), may provide more coherent genetic signals against which to assess the functional significance of various endophenotypic measures. Overall, the studies reported in this special section provide unequivocal confirmation for the genetic basis of endophenotypes, yet identify scant specific exemplars with the current sampling frame and size. In this regard, these papers define a research agenda that has important implications not only for endophenotypes, but also for the RDoC approach to precision diagnostics in mental disorders.
References Bale, T. L., Baram, T. Z., Brown, A. S., Goldstein, J. M., Insel, T. R., . . . Nestler, E. J. (2010). Early life programming and neurodevelopmental disorders. Biological Psychiatry, 68, 314–319. Cuthbert, B. N. (2014). The RDoC framework: Facilitating transition from ICD/DSM to dimensional approaches that integrate neuroscience and psychopathology. World Psychiatry, 13, 28–35. Gilman, S. R., Chang, J., Xu, B., Bawa, T. S., Gogos, J. A., Karayiorgou, M., & Vitkup, D. (2013). Diverse types of genetic variation converge on functional gene networks involved in schizophrenia. Nature Neuroscience, 15, 1723–1728. Hyman S. (2000). Mental illness: Genetically complex disorders of neural circuitry and neural communication. Neuron, 28, 321–323. Hyman, S. E. (2010). Diagnosis of mental disorders: The problem of reification. Annual Review of Clinical Psychology, 6, 155–179. McCarroll, S. A., & Hyman, S. E. (2013). Progress in the genetics of polygenic brain disorders: Significant new challenges for neurobiology. Neuron, 80, 578–587.
Miller, G. A., & Rockstroh, B. (2013). Endophenotypes in psychopathology research: Where do we stand? Annual Review of Clinical Psychology, 9, 177–213. Ripke, S. (2013). Psychiatric genomics consortium quadruples schizophrenia GWAS sample-size to 35,000 cases and 47,000 controls. Symposium conducted at the 21st World Congress of Psychiatric Genetics, Boston, MA. Ripke, S., O’Dushlaine, C., Chambert, K., Moran, J. L., Kähler, A. K., . . . Sullivan, P. F. (2013). Genome-wide association analysis identifies 13 new risk loci for schizophrenia. Nature Genetics, 45, 1150– 1159. Wong, E. H., Yocca, F., Smith, M. A., & Lee, C.-M. (2010). Challenges and opportunities for drug discovery in psychiatric disorders: The drug hunters’ perspective. International Journal of Neuropsychopharmacology, 13, 1269–1284.
PERSPECTIVE
Translating intermediate phenotypes to psychopathology: The NIMH Research Domain Criteria
BRUCE N. CUTHBERT National Institute of Mental Health, Bethesda, Maryland, USA
Abstract The Research Domain Criteria project (RDoC) was initiated by the National Institute of Mental Health in early 2009 to “develop, for research purposes, new ways of classifying mental disorders based on dimensions of observable behavior and neurobiological measures.” RDoC provides a framework for psychopathology research intended to explicate specific aspects of functional impairment by studying relevant brain-behavior relationships, in contrast to the current heterogeneous categories of mental disorders defined by various groupings of symptoms. Endophenotypes fit naturally into the RDoC context since they are typically conceived to be closer to fundamental neural and psychological mechanisms than more abstracted disorder categories. Consequently, the genomic aspects of endophenotypes take on particular significance for understanding genetic risk architectures in such an approach to psychopathology. Descriptors: Research Domain Criteria, RDoC, Intermediate phenotypes, Endophenotypes, Psychopathology
Disorders) approach have seldom received favorable consideration in peer review because there is no alternative standard to reflect other research approaches that might be based upon emerging literatures in neuroscience and behavioral science. The National Institute of Mental Health (NIMH) initiated the Research Domain Criteria project (RDoC) to address this problem. RDoC’s core aim is to free investigators from the contemporary nosological hegemony of the grant-review system and encourage studies based upon neuroscience and specific aspects of functioning that can lead to new diagnostic and treatment innovations. What is the relationship of RDoC to psychophysiology and to the topic of this special issue, genome-wide scans of psychophysiological endophenotypes? First, RDoC involves an inherently translational and dimensional approach. The framework is organized into five major domains, each incorporating a set of related functional constructs. Importantly, the constructs represent basic dimensions of functioning (such as fear or working memory), with psychopathology viewed as varying degrees of dysregulation relative to the normal range of these functions (as opposed to the classic approach of starting with clinically defined disorders and seeking behavioral or biological “underpinnings”). Second, the RDoC conception builds upon a fundamentally psychophysiological outlook that emphasizes the simultaneous recording of behavioral responses and physiological activity; the intent is for constructs to be studied across multiple units of analysis ranging from genetics through neural systems to measures of behavior and symptoms (see Cuthbert, 2014, for a more detailed description). Thus, the RDoC framework is highly relevant to both basic and clinical psychophysiologists. RDoC constructs could be regarded as intermediate phenotypes (that are more fine grained and homogeneous than International
Thanks to recent advances in neuroscience and behavioral sciences, mental disorders are now understood to be complex syndromes that have roots in polygenic risk factors and eventuate from ongoing interactions between myriad environmental events and the stages of neurodevelopment at which they occur (Bale et al., 2010; McCarroll & Hyman, 2013). Further, mental illnesses are now typically viewed as disorders of neural circuits and networks, disrupting normal processing within and among brain systems (Hyman, 2000). This view, however, does not align well with current assessment and treatment. Disorders are assessed primarily on the basis of presenting symptoms, and it is increasingly recognized that current diagnostic categories, conceived largely to address critical problems of reliability, do not identify valid disease entities (Hyman, 2010). Problems of heterogeneity and excessive comorbidity frustrate attempts to develop more efficient treatments, which are effective in only 30 to 50 percent of patients for all therapeutic modalities (e.g., Wong, Yocca, Smith, & Lee, 2010). While these problems are often acknowledged in the literature, the vast majority of research grant applications are constrained to a single disorder and exclude patients with comorbid diagnoses. The difficulty is that the current categorical nosology has become the de facto criterion for evaluating research grant applications, journal publications, and treatment development. Grant applications that embody a non-DSM (Diagnostic and Statistical Manual of Mental
bs_bs_banner
Thanks are expressed to Dr. William Iacono at the University of Minnesota–Twin Cities for the opportunity to contribute this commentary. Address correspondence to: Bruce Cuthbert, National Institute of Mental Health, 6001 Executive Blvd., Room 7121, MSC 9632, Bethesda, MD 20892-9632, USA. E-mail: [email protected] 1205
1206
B.N. Cuthbert
Classification of Diseases (ICD)/DSM categories), and many of the measures incorporated into the “units of analysis” columns of the RDoC matrix might correspondingly be considered endophenotypes for the construct (including all of those addressed in this special issue). Ideally, such a perspective should facilitate the study of endophenotypes in psychopathology since the focus is upon a particular function rather than a highly abstracted disorder syndrome. In practice, however, the shift from disorder-based to construct-based views of endophenotypes will present its own difficulties, particularly in integrating basic and disordered aspects of response. (See Miller & Rockstroh, 2013, for a thoughtful consideration of endophenotypes vis-à-vis the RDoC framework.) The search for genetic aspects of endophenotypes has particular appeal due to the narrower focus. For traditional categories such as schizophrenia and autism, markedly increased sample sizes in the last few years have begun to provide hints of the promise that genome-wide analysis studies (GWAS) hold for understanding the genetic architecture of disorders (e.g., Ripke et al., 2013). These studies indicate that there is substantial noise in the phenotypic signal in addition to the considerable degree of polygenicity, thus requiring very large samples to detect significant associations. Given the increasing importance of endophenotypes in psychopathology research, relevant GWASs offer the potential to understand genetic risks at a lower and more tractable level as compared to current disorder categories and to expedite the parsing of phenotypic variance that will be required to develop new treatments based upon genetic risk patterns (McCarroll & Hyman, 2013). Thus, the papers in this special issue represent a significant advance in psychopathology research. However, as also revealed in the results, discovery of particular genetic variants for documented and candidate endophenotypes may be as difficult as for traditional disorders. Biometric analyses revealed significant genetic contributions to nearly all of the measures that were examined; however, few specific common or rare variants emerged as significant in the analyses. This is clearly no fault of the methods, which involved cutting-edge data cleaning and analysis procedures.
While this outcome is discouraging at first blush, there are good reasons to see the glass as half full rather than half empty. First, as noted in the various papers, the sample sizes are impressive for a psychophysiological experiment but small compared to current disorder-based studies; for example, the number of patients in the latest schizophrenia GWAS is nearly an order of magnitude higher than the sample sizes analyzed for this issue (Ripke, 2013). Second, most of the individuals in a community-based sample such as the Minnesota Twin Family Study will reflect the center-weighted pattern of a normal distribution, while extreme values provide more power for GWAS analyses; thus, addition of a relatively small number of cases at the tails of the distribution (perhaps, in part, in samples with psychopathology of various sorts) may contribute to more significant “hits.” Moving forward, modern “big data” approaches should generate larger sample sizes in this area that can foster statistical power. For instance, the NIMH is currently implementing an RDoC “information commons” (based upon the National Database for Autism Research) in which investigators will be strongly encouraged to share data of all types including genomics and neurophysiological data. It will take some years to accumulate data with common endophenotypic measures of the type used in these papers; however, this process, and other comparable database efforts, should eventually yield the sample sizes needed to reveal the nature of genomic variance related to endophenotypes. In turn, emerging data on functional gene networks, now being pioneered for traditional disorders (e.g., Gilman et al., 2013), may provide more coherent genetic signals against which to assess the functional significance of various endophenotypic measures. Overall, the studies reported in this special section provide unequivocal confirmation for the genetic basis of endophenotypes, yet identify scant specific exemplars with the current sampling frame and size. In this regard, these papers define a research agenda that has important implications not only for endophenotypes, but also for the RDoC approach to precision diagnostics in mental disorders.
References Bale, T. L., Baram, T. Z., Brown, A. S., Goldstein, J. M., Insel, T. R., . . . Nestler, E. J. (2010). Early life programming and neurodevelopmental disorders. Biological Psychiatry, 68, 314–319. Cuthbert, B. N. (2014). The RDoC framework: Facilitating transition from ICD/DSM to dimensional approaches that integrate neuroscience and psychopathology. World Psychiatry, 13, 28–35. Gilman, S. R., Chang, J., Xu, B., Bawa, T. S., Gogos, J. A., Karayiorgou, M., & Vitkup, D. (2013). Diverse types of genetic variation converge on functional gene networks involved in schizophrenia. Nature Neuroscience, 15, 1723–1728. Hyman S. (2000). Mental illness: Genetically complex disorders of neural circuitry and neural communication. Neuron, 28, 321–323. Hyman, S. E. (2010). Diagnosis of mental disorders: The problem of reification. Annual Review of Clinical Psychology, 6, 155–179. McCarroll, S. A., & Hyman, S. E. (2013). Progress in the genetics of polygenic brain disorders: Significant new challenges for neurobiology. Neuron, 80, 578–587.
Miller, G. A., & Rockstroh, B. (2013). Endophenotypes in psychopathology research: Where do we stand? Annual Review of Clinical Psychology, 9, 177–213. Ripke, S. (2013). Psychiatric genomics consortium quadruples schizophrenia GWAS sample-size to 35,000 cases and 47,000 controls. Symposium conducted at the 21st World Congress of Psychiatric Genetics, Boston, MA. Ripke, S., O’Dushlaine, C., Chambert, K., Moran, J. L., Kähler, A. K., . . . Sullivan, P. F. (2013). Genome-wide association analysis identifies 13 new risk loci for schizophrenia. Nature Genetics, 45, 1150– 1159. Wong, E. H., Yocca, F., Smith, M. A., & Lee, C.-M. (2010). Challenges and opportunities for drug discovery in psychiatric disorders: The drug hunters’ perspective. International Journal of Neuropsychopharmacology, 13, 1269–1284.
