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Affiliative Behaviors and Beyond: It's the Phenotype, Stupid
COMMENTARY
Affiliative Behaviors and Beyond: It’s the Phenotype, Stupid James S. Sutcliffe
B
eginning with twin studies in the late 1970s, data have consistently revealed a predominantly genetic etiology for autism. However, the observed genetic contribution has proved to be highly complex, and unambiguous identification of predisposing or causal factors has been difficult (1). However, there is light at the end of the tunnel. The last several years have witnessed substantial gains in our understanding of the genetic architecture of the autism spectrum (1). Wide agreement now exists that further advances in unraveling the genetics requires decomposition of the phenotype into its heritable components (2). These in turn are more likely to be informative for detecting predisposing common alleles. In this issue of Biological Psychiatry, Yrigollen et al. (3) provide an elegant illustration of this idea, constructing phenotypes derived from diagnostic and behavioral instruments used to index facets of the disorder. The authors tested the hypothesis that genes, known from animal studies to influence variability in social traits such as social recognition and bonding, parental care, and aggression, contribute to the social impairments seen in autism. The authors investigate the oxytocin and prolactin peptide hormone systems and two genes, dopamine -hydroxylase (DBH) and FOSB, animal models for which show marked deficits in affiliative behaviors. Oxytocin plays a central role in regulating affiliation and other social behaviors (4 – 6). Seminal behavioral and molecular studies of vole species starkly differing in social behaviors and structures provide insights into the neurobiology underlying affiliation (4,5). Prairie voles are monogamous, with males and females forming long-term bonds and participating in the rearing of offspring. Montane and meadow voles, however, do not form pair bonds, and males do not participate in rearing their offspring. These are but some of the behaviors, differences in which are attributed to differential expression and distribution of receptors for oxytocin and another neuropeptide vasopressin in reward centers in the brain. A genetic link to the behavioral differences in voles is found in a microsatellite repeat upstream of the arginine vasopressin receptor gene (Avpr1a); interspecific variation in this repeat is responsible for driving the Avpr1a expression differences between prairie and montane or meadow voles. This model of genetically encoded social behaviors has long been thought to point to a system relevant to social impairments in autism (4,5). Prolactin also plays an important role in affiliative behaviors. Both prolactin and oxytocin regulate milk production, with the former promoting milk production and the latter milk ejection in response to suckling. A variety of reports suggest alterations in prolactin signaling in autism (reviewed in ref. 3). Another connection between oxytocin and prolactin involves the hypothalamic-pituitary-adrenal (HPA) axis–mediated stress response. From the Center for Molecular Neuroscience and Vanderbilt Kennedy Center, Vanderbilt University, Nashville, Tennessee. Address reprint requests to James S. Sutcliffe, Ph.D., Associate Professor, Center for Molecular Neuroscience and Vanderbilt Kennedy Center, Vanderbilt University, Nashville, TN 37232; e-mail: [email protected]. Received March 31, 2008; accepted March 31, 2008.
0006-3223/08/$34.00 doi:10.1016/j.biopsych.2008.03.027
Oxytocin has a dampening effect on HPA axis activity, and suckling during the postpartum period is correlated with decreased HPA axis activity (6). Administration of exogenous oxytocin in humans and rodents leads to reduced basal and stimulated levels of adrenocorticotrophic hormone (ACTH) and cortisol (6). Indeed researchers speculate that by toning down stress reactivity, oxytocin may represent a physiologic mechanism by which the beneficial effects of social interaction and support are reinforced. An important clue to involvement of altered stress reactivity in autism is the finding that a significant proportion of affected subjects are nonresponsive to dexamethasone-mediated suppression of the HPA-mediated stress response (7). Taken together, evidence for the roles played by oxytocin and prolactin in regulating social behaviors and related physiological systems compels investigation of these networks for involvement of genetic variation in mediating social and other impairments seen in autism. Unfortunately, candidate gene association studies in autism, typical of other psychiatric disorders, have yielded maddeningly inconsistent results (1). Negative findings from association studies can result from limitations in several areas: limited power due to small sample size relative to locus-specific risk, locus heterogeneity, allelic heterogeneity or the presence of multiple variants at a locus, and a combination of these factors. Negative results can also indicate that our hypothesis regarding a specific gene was incorrect. In taking an unbiased approach, ongoing genomewide association studies (GWASs) hope to overcome our limited knowledge regarding the biology underlying these conditions. GWAS, like any association study, remains subject to the potential limitations just described. This is where choosing phenotypes becomes critical. For disorders such as autism with substantial clinical heterogeneity, we assume that locus-specific contributions to susceptibility are likely to have their greatest influence on a particular dimension of the phenotype rather than be averaged over the conglomeration of traits that defines the syndrome. How then to index the dimensionality of a complex, behaviorally defined psychiatric condition? Studies to date of autism subphenotypes have largely used discrete measures taken from diagnostic instruments such as the Autism Diagnostic Interview— Revised (ADI-R), a caregiver interview that captures information from across the various behavioral and functional domains in autism. Age at first word and phrased speech delay are two such variables, and examples of their utility include identification of significant quantitative trait loci (QTL) on chromosomes 7q and 2q. More elaborate subphenotypes or “derived traits” have been developed from factor or principal components analyses of items from the ADI-R, although their use in genetic studies has been relatively limited to date (8). The Social Reciprocity Scale (SRS) typifies other instruments that focus on a specific domain of impairment to index dimensions across that domain (9). Unfortunately, existing large autism family collections do not have such data for most of their sample, severely limiting their utility in the short term. In their article, Yrigollen and colleagues exploit data that are routinely collected during standard autism diagnostic evaluations and obtained 16 “indicators,” with five from the ADI/ADI-R, four BIOL PSYCHIATRY 2008;63:909 –910 © 2008 Society of Biological Psychiatry
910 BIOL PSYCHIATRY 2008;63:909 –910 each from the Autism Diagnostic Observation Schedule (ADOS) and Vineland Adaptive Behavior Scales, and clinical diagnosis. These partially overlapping measures were then combined for an analysis of seven multivariate phenotypes. The first (“All Diagnoses”) reflects the convergence of diagnostic information from the ADI, ADOS and clinical diagnosis. Synthesis of individual indicators from the three instruments (ADI, ADOS, and VABS) constitute phenotypes 2– 4, respectively. The final three phenotypes combine domain-specific indicators across ADI, ADOS, and VABS for social and communication skills and repetitive behaviors. Thus the authors have taken a comprehensive approach to extract data from these common instruments—not merely from social skills but from across the autism spectrum. The focus on developing these derived traits seems to have paid off, with the identification of significant allelic associations implicating each of the four gene classes. The most impressive findings emerge from the loci encoding prolactin (PRL) and its receptor (PRLR), for which associations were detected for the combined ADI and Communication Skills multivariate phenotypes. Indeed, although associations were observed across oxytocin, prolactin, DBH, and FOSB genes for many of the phenotypes, they were not limited to those that index social impairments. If other groups replicate these findings, it would suggest that these genes may play broader roles to influence susceptibility. The findings of Yrigollen et al. are consistent with several studies that report associations in genes for the oxytocin receptor (OXTR) and AVPR1A (reviewed in refs. 6 and 10) and reports of decreased DBH in probands and parents and potential maternal genotype effects (reviewed in ref. 3). The most important lesson from these studies may be that creative analysis of available phenotype data in genetic studies of autism, or any complex disorder, will offer opportunities to detect genetic effects that might otherwise escape notice when considering only categorical diagnoses. The autism genetics field is painfully aware of the need for better phenotypes. There is wide agreement that we must extract genetically relevant phenotypes from available data (e.g., ADI-R) while developing new family collections (e.g., the Autism Genome Project and Simons
www.sobp.org/journal
Commentary Simplex Collections) that place an emphasis on measures that will provide better quantitative and qualitative traits for finding susceptibility genes. Another lesson here is the importance of carefully exploring gene networks strongly implicated by intersecting lines of investigation. Studies of autism biomarkers, neuropathology, animal models, and potential etiologic connections revealed from studies of other conditions (e.g., fragile X syndrome, obsessive-compulsive disorder) are examples of such science. As we evaluate candidate genes and pathways, now armed with better phenotypes, larger and well-characterized samples, and dramatically improved genetic and statistical tools, we may well find that following the biology makes sense after all. Dr. Sutcliffe reports no biomedical financial interests or potential conflicts of interest. 1. O’Roak BJ, State MW (2008): Autism genetics: Strategies, challenges, and opportunities. Autism Res 1:4 –17. 2. Szatmari P, Merette C, Emond C, Zwaigenbaum L, Jones MB, Maziade M, et al. (2008): Decomposing the autism phenotype into familial dimensions. Am J Med Genet B Neuropsychiatr Genet 147:3–9. 3. Yrigollen CM, Han SS, Kochetkova A, Babitz T, Chang JT, Volkmar FR, et al. (2008): Genes controlling affiliative behaviors as candidate genes for autism. Biol Psychiatry 63:911–916. 4. Lim MM, Young LJ (2006): Neuropeptidergic regulation of affiliative behavior and social bonding in animals. Horm Behav 50:506 –517. 5. Hammock EA, Young LJ (2006): Oxytocin, vasopressin and pair bonding: Implications for autism. Philos Trans R Soc Lond B Biol Sci 361:2187–2198. 6. Bartz JA, Hollander E (2006): The neuroscience of affiliation: Forging links between basic and clinical research on neuropeptides and social behavior. Horm Behav 50:518 –528. 7. Cook EH (1990): Autism: Review of neurochemical investigation. Synapse 6:292–308. 8. Hus V, Pickles A, Cook EH, Jr., Risi S, Lord C (2007): Using the autism diagnostic interview—revised to increase phenotypic homogeneity in genetic studies of autism. Biol Psychiatry 61:438 – 448. 9. Constantino JN (2002): The Social Responsiveness Scale. Los Angeles: Western Psychological Services. 10. Jacob S, Brune CW, Carter CS, Leventhal BL, Lord C, Cook EH Jr. (2007): Association of the oxytocin receptor gene (OXTR) in Caucasian children and adolescents with autism. Neurosci Lett 417:6 –9.
Affiliative Behaviors and Beyond: It’s the Phenotype, Stupid James S. Sutcliffe
B
eginning with twin studies in the late 1970s, data have consistently revealed a predominantly genetic etiology for autism. However, the observed genetic contribution has proved to be highly complex, and unambiguous identification of predisposing or causal factors has been difficult (1). However, there is light at the end of the tunnel. The last several years have witnessed substantial gains in our understanding of the genetic architecture of the autism spectrum (1). Wide agreement now exists that further advances in unraveling the genetics requires decomposition of the phenotype into its heritable components (2). These in turn are more likely to be informative for detecting predisposing common alleles. In this issue of Biological Psychiatry, Yrigollen et al. (3) provide an elegant illustration of this idea, constructing phenotypes derived from diagnostic and behavioral instruments used to index facets of the disorder. The authors tested the hypothesis that genes, known from animal studies to influence variability in social traits such as social recognition and bonding, parental care, and aggression, contribute to the social impairments seen in autism. The authors investigate the oxytocin and prolactin peptide hormone systems and two genes, dopamine -hydroxylase (DBH) and FOSB, animal models for which show marked deficits in affiliative behaviors. Oxytocin plays a central role in regulating affiliation and other social behaviors (4 – 6). Seminal behavioral and molecular studies of vole species starkly differing in social behaviors and structures provide insights into the neurobiology underlying affiliation (4,5). Prairie voles are monogamous, with males and females forming long-term bonds and participating in the rearing of offspring. Montane and meadow voles, however, do not form pair bonds, and males do not participate in rearing their offspring. These are but some of the behaviors, differences in which are attributed to differential expression and distribution of receptors for oxytocin and another neuropeptide vasopressin in reward centers in the brain. A genetic link to the behavioral differences in voles is found in a microsatellite repeat upstream of the arginine vasopressin receptor gene (Avpr1a); interspecific variation in this repeat is responsible for driving the Avpr1a expression differences between prairie and montane or meadow voles. This model of genetically encoded social behaviors has long been thought to point to a system relevant to social impairments in autism (4,5). Prolactin also plays an important role in affiliative behaviors. Both prolactin and oxytocin regulate milk production, with the former promoting milk production and the latter milk ejection in response to suckling. A variety of reports suggest alterations in prolactin signaling in autism (reviewed in ref. 3). Another connection between oxytocin and prolactin involves the hypothalamic-pituitary-adrenal (HPA) axis–mediated stress response. From the Center for Molecular Neuroscience and Vanderbilt Kennedy Center, Vanderbilt University, Nashville, Tennessee. Address reprint requests to James S. Sutcliffe, Ph.D., Associate Professor, Center for Molecular Neuroscience and Vanderbilt Kennedy Center, Vanderbilt University, Nashville, TN 37232; e-mail: [email protected]. Received March 31, 2008; accepted March 31, 2008.
0006-3223/08/$34.00 doi:10.1016/j.biopsych.2008.03.027
Oxytocin has a dampening effect on HPA axis activity, and suckling during the postpartum period is correlated with decreased HPA axis activity (6). Administration of exogenous oxytocin in humans and rodents leads to reduced basal and stimulated levels of adrenocorticotrophic hormone (ACTH) and cortisol (6). Indeed researchers speculate that by toning down stress reactivity, oxytocin may represent a physiologic mechanism by which the beneficial effects of social interaction and support are reinforced. An important clue to involvement of altered stress reactivity in autism is the finding that a significant proportion of affected subjects are nonresponsive to dexamethasone-mediated suppression of the HPA-mediated stress response (7). Taken together, evidence for the roles played by oxytocin and prolactin in regulating social behaviors and related physiological systems compels investigation of these networks for involvement of genetic variation in mediating social and other impairments seen in autism. Unfortunately, candidate gene association studies in autism, typical of other psychiatric disorders, have yielded maddeningly inconsistent results (1). Negative findings from association studies can result from limitations in several areas: limited power due to small sample size relative to locus-specific risk, locus heterogeneity, allelic heterogeneity or the presence of multiple variants at a locus, and a combination of these factors. Negative results can also indicate that our hypothesis regarding a specific gene was incorrect. In taking an unbiased approach, ongoing genomewide association studies (GWASs) hope to overcome our limited knowledge regarding the biology underlying these conditions. GWAS, like any association study, remains subject to the potential limitations just described. This is where choosing phenotypes becomes critical. For disorders such as autism with substantial clinical heterogeneity, we assume that locus-specific contributions to susceptibility are likely to have their greatest influence on a particular dimension of the phenotype rather than be averaged over the conglomeration of traits that defines the syndrome. How then to index the dimensionality of a complex, behaviorally defined psychiatric condition? Studies to date of autism subphenotypes have largely used discrete measures taken from diagnostic instruments such as the Autism Diagnostic Interview— Revised (ADI-R), a caregiver interview that captures information from across the various behavioral and functional domains in autism. Age at first word and phrased speech delay are two such variables, and examples of their utility include identification of significant quantitative trait loci (QTL) on chromosomes 7q and 2q. More elaborate subphenotypes or “derived traits” have been developed from factor or principal components analyses of items from the ADI-R, although their use in genetic studies has been relatively limited to date (8). The Social Reciprocity Scale (SRS) typifies other instruments that focus on a specific domain of impairment to index dimensions across that domain (9). Unfortunately, existing large autism family collections do not have such data for most of their sample, severely limiting their utility in the short term. In their article, Yrigollen and colleagues exploit data that are routinely collected during standard autism diagnostic evaluations and obtained 16 “indicators,” with five from the ADI/ADI-R, four BIOL PSYCHIATRY 2008;63:909 –910 © 2008 Society of Biological Psychiatry
910 BIOL PSYCHIATRY 2008;63:909 –910 each from the Autism Diagnostic Observation Schedule (ADOS) and Vineland Adaptive Behavior Scales, and clinical diagnosis. These partially overlapping measures were then combined for an analysis of seven multivariate phenotypes. The first (“All Diagnoses”) reflects the convergence of diagnostic information from the ADI, ADOS and clinical diagnosis. Synthesis of individual indicators from the three instruments (ADI, ADOS, and VABS) constitute phenotypes 2– 4, respectively. The final three phenotypes combine domain-specific indicators across ADI, ADOS, and VABS for social and communication skills and repetitive behaviors. Thus the authors have taken a comprehensive approach to extract data from these common instruments—not merely from social skills but from across the autism spectrum. The focus on developing these derived traits seems to have paid off, with the identification of significant allelic associations implicating each of the four gene classes. The most impressive findings emerge from the loci encoding prolactin (PRL) and its receptor (PRLR), for which associations were detected for the combined ADI and Communication Skills multivariate phenotypes. Indeed, although associations were observed across oxytocin, prolactin, DBH, and FOSB genes for many of the phenotypes, they were not limited to those that index social impairments. If other groups replicate these findings, it would suggest that these genes may play broader roles to influence susceptibility. The findings of Yrigollen et al. are consistent with several studies that report associations in genes for the oxytocin receptor (OXTR) and AVPR1A (reviewed in refs. 6 and 10) and reports of decreased DBH in probands and parents and potential maternal genotype effects (reviewed in ref. 3). The most important lesson from these studies may be that creative analysis of available phenotype data in genetic studies of autism, or any complex disorder, will offer opportunities to detect genetic effects that might otherwise escape notice when considering only categorical diagnoses. The autism genetics field is painfully aware of the need for better phenotypes. There is wide agreement that we must extract genetically relevant phenotypes from available data (e.g., ADI-R) while developing new family collections (e.g., the Autism Genome Project and Simons
www.sobp.org/journal
Commentary Simplex Collections) that place an emphasis on measures that will provide better quantitative and qualitative traits for finding susceptibility genes. Another lesson here is the importance of carefully exploring gene networks strongly implicated by intersecting lines of investigation. Studies of autism biomarkers, neuropathology, animal models, and potential etiologic connections revealed from studies of other conditions (e.g., fragile X syndrome, obsessive-compulsive disorder) are examples of such science. As we evaluate candidate genes and pathways, now armed with better phenotypes, larger and well-characterized samples, and dramatically improved genetic and statistical tools, we may well find that following the biology makes sense after all. Dr. Sutcliffe reports no biomedical financial interests or potential conflicts of interest. 1. O’Roak BJ, State MW (2008): Autism genetics: Strategies, challenges, and opportunities. Autism Res 1:4 –17. 2. Szatmari P, Merette C, Emond C, Zwaigenbaum L, Jones MB, Maziade M, et al. (2008): Decomposing the autism phenotype into familial dimensions. Am J Med Genet B Neuropsychiatr Genet 147:3–9. 3. Yrigollen CM, Han SS, Kochetkova A, Babitz T, Chang JT, Volkmar FR, et al. (2008): Genes controlling affiliative behaviors as candidate genes for autism. Biol Psychiatry 63:911–916. 4. Lim MM, Young LJ (2006): Neuropeptidergic regulation of affiliative behavior and social bonding in animals. Horm Behav 50:506 –517. 5. Hammock EA, Young LJ (2006): Oxytocin, vasopressin and pair bonding: Implications for autism. Philos Trans R Soc Lond B Biol Sci 361:2187–2198. 6. Bartz JA, Hollander E (2006): The neuroscience of affiliation: Forging links between basic and clinical research on neuropeptides and social behavior. Horm Behav 50:518 –528. 7. Cook EH (1990): Autism: Review of neurochemical investigation. Synapse 6:292–308. 8. Hus V, Pickles A, Cook EH, Jr., Risi S, Lord C (2007): Using the autism diagnostic interview—revised to increase phenotypic homogeneity in genetic studies of autism. Biol Psychiatry 61:438 – 448. 9. Constantino JN (2002): The Social Responsiveness Scale. Los Angeles: Western Psychological Services. 10. Jacob S, Brune CW, Carter CS, Leventhal BL, Lord C, Cook EH Jr. (2007): Association of the oxytocin receptor gene (OXTR) in Caucasian children and adolescents with autism. Neurosci Lett 417:6 –9.