Commentary
Biological Psychiatry
DSM-5 and Psychiatric Genetics — Round Hole, Meet Square Peg Joseph D. Buxbaum It is useful to consider genetic variation as either common (e.g., an allele found in .5% of the population) or rare because these categories of variation are analyzed using different approaches and have different properties. Most importantly, for neurodevelopmental disorders (NDDs), common genetic variation (e.g., single nucleotide polymorphism [SNP]) is associated with very small effect sizes given evolution constraints on deleterious variation (negative or purifying selection), whereas rare variation can be associated with a much wider range of effect sizes. Chaste et al. (1) look at common variation in autism spectrum disorder (ASD) to answer questions about relationships between clinical and genetic heterogeneity. Enormous advances have been made in understanding of the genomic architecture of ASD, including the role of common and rare variation. There is compelling evidence from multiple studies that common variation represents the major proportion of the genetic risk for ASD (see (2) and citations therein). However, no SNP has been reliably associated with ASD to date because of small effect sizes. Until studies include many thousands of cases, it is exceedingly unlikely that many replicated common variation findings will be made in ASD. There has been much more success with gene discovery in ASD when focusing on rare variation. This increased success is due partly to the fact that there is a significant amount of de novo mutation in ASD: with de novo mutation being quite rare, even a few cases with de novo deleterious variation in a given gene are sufficient to provide statistically significant support for that gene in ASD (see (3) and citations therein). Although the effect size for discoverable rare variation is higher than that of common variation, the total variance explained by rare variation is quite low. Gaugler et al. (2) showed that within a given family with ASD, a rare de novo copy number variant (CNV) or single nucleotide variant can often be the difference between an ASD diagnosis or no ASD diagnosis; however, there must be a “genetic background” in the family, defined by multiple inherited SNPs and other genetic variation, that is a critical part of the architecture in that family. In other words, there appears to be risk in the family in most cases in the form of a multiplicity of common variation, and higher risk rare variation pushes an individual in that family over a liability threshold to manifest with an NDD. This model can explain why risk of family recurrence is high in ASD, while, at the same time, affected sibling or relative pairs within a family may have different rare risk variants, as first shown with CNV and, more recently, with single nucleotide variant (4,5). Chaste et al. study a large (N = 2576 families) and behaviorally well-phenotyped ASD sample (the Simons Simplex Collection) to ask whether subgrouping to enhance phenotypic homogeneity increases the ability to make SNP
findings in ASD. The results provide a quite clear “no.” The authors show that subphenotyping by many reasonable criteria, including IQ, extent of repetitive behaviors, insistence on sameness, and more severe ASD, failed to increase power substantially, and the subgroups showed very similar heritability estimates (.4). Most importantly, allele scores from the entire sample predicted case status equally well regardless of subgroup. In short, reducing phenotypic heterogeneity did nothing to increase genetic homogeneity. Although Chaste et al. focus on common variation, similar findings are being made with rare variation. From the very first studies of recurrent CNV, there was already abundant evidence that many were associated with a multiplicity of phenotypes. For example, DiGeorge/velocardiofacial syndrome (also known as 22q11 deletion syndrome) is associated with intellectual disability (ID), epilepsy, autism, schizophrenia, and congenital heart disease. Most genomic and genetic disorders show both variable expressivity and pleiotropy (6). The recent completion of two very large whole-exome sequencing studies in ASD, comprising .20,000 samples with 4000 ASD cases, has identified .30 rare ASD risk genes with a false discovery rate , .05 and .50 with a false discovery rate , .1 (7,8). Many of the top genes have already been associated with diverse phenotypes, including all those noted earlier for DiGeorge syndrome (Figure 1). The associated phenotypes extend beyond NDDs: for example, the findings supporting a major role for chromatin remodeling genes in ASD (7) overlap to a stunning degree with more recent findings in congenital heart disease (9). One area that is of specific interest in ASD is the question of ASD with or without ID. There is a persistent view that ASD without ID is genetically distinct from ASD with ID and that findings made in this latter group may be related to the ID phenotype. The data from Chaste et al. challenge this view, as do analyses in the Autism Genome Project cohort (2), which includes a broader representation of IQ. Although it has not been looked at as extensively with rare variation, it is clear that many rare risk genes and CNV associated with ID are frequently also associated with ASD and vice versa (Figure 1) (6). However, it may be that diagnosing ASD in the context of severe ID may reflect a different disorder than what has sometimes been called “Kanner autism” because social impairments can arise either because of a lack of social drive (Kanner autism) or because of other factors that inhibit effective social interactions. There is little doubt that severely affected individuals experience multiple barriers to social communication, even in the presence of intact social drive. How do we understand this failure of well-defined psychiatric phenotypes to map unambiguously to defined genetic loci and vice versa (Figure 1)? Also, how can we optimally pursue
SEE CORRESPONDING ARTICLE ON PAGE 775 & 2015 Society of Biological Psychiatry Biological Psychiatry May 1, 2015; 77:766–768 www.sobp.org/journal
766
ISSN: 0006-3223
Biological Psychiatry
Commentary
CHD8
ASD
SCN2A SYNGAP1 ARID1B
ID
DYRK1A ANK2 KDM5B
SCZ
ADNP POGZ
EPI
SUV420H1 DSCAM GRIN2B
CHD
TBR1 CHD2 PTEN
MCA
Figure 1. Many-to-many relationships in the genetics of neurodevelopmental disorders. Many ( 1000) genes can contribute to risk of autism spectrum disorder, and many phenotypes are already shown to be associated with known autism spectrum disorder genes. The top-ranked autism spectrum disorder genes, identified in recent whole-exome sequencing analyses, are shown together with various phenotypes associated with each. ASD, autism spectrum disorder; CHD, congenital heart disease; EPI, epilepsy; ID, intellectual disability; MCA, multiple congenital anomalies (which can include congenital heart disease); SCZ, schizophrenia.
enhanced treatment of neuropsychiatric disorders in light of this issue? First, it is possible that, when we know much more about common and rare risk loci, we may be able to begin to make better predictions regarding expected diagnosis. In this scenario, the “genetic background” in a family may be specifically increasing risk for ASD, and a high-risk recent variant (which can result in any of several phenotypes) occurring within that family leads to the manifestation of ASD. In addition, although certain genetically defined syndromes may be associated with specific features, the results provided by Chaste et al. and others suggest that most NDD risk genes are not associated with such features. One caveat to this, which applies to the Simons Simplex Collection and most other ASD samples, is that the phenotyping is almost exclusively behavioral, whereas distinct features in genetic syndromes are typically not behavioral. Recontacting individuals carrying rare variants of major effect for detailed phenotyping may identify unique features associated with some genes.
From both a research and a clinical perspective, we should consider a broad array of neurodevelopmental disorders as possible outcomes from specific genetic and genomic abnormalities. Moreno-De-Luca et al. (6) addressed the diverse phenotypes associated with various high-risk genetic and genomic findings by proposing to classify a large group of disorders under a broad category of “developmental brain dysfunction.” Similarly, Gillberg (10) used the acronym ESSENCE (Early Symptomatic Syndromes Eliciting Neurodevelopmental Clinical Examination) to capture a similar idea, which is meant to encourage clinical interventions at the earliest, nonspecific manifestations of NDDs. An immediate outcome from such a perspective is that penetrance estimates for certain genetic variants increase when one considers developmental brain dysfunction/ESSENCE as the outcome, rather than any specific diagnosis (6). It is a mistake to look for very specific behavioral phenotypes in rodent models of NDDs: because there is little evidence that the known NDD genes map singularly to specific phenotypes, an exclusive emphasis on such phenotypes in rodent models is not evidence based. However, rodent models have frequently been challenged if they do not show specific deficits. Similarly, it appears unlikely that investments in deep phenotyping for gene discovery in NDDs will lead to much increased yields. Neurobiologically, understanding how specific insults produce diverse phenotypes and how specific phenotypes can map to diverse insults is an exciting challenge. One potentially relevant concept, developed by Waddington during the 1940s–1960s, is that of canalization in brain development. In this scheme, brain development occurs along well-defined, robust “canals,” but the absence or inappropriate presence of critical factors at key junctures leads to a switch to alternate “canals.” The degree to which this construct will prove useful in clinical neuroscience remains to be seen. In conclusion, Chaste et al. and others show that genetic and genomic findings do not fit neatly into categorical psychiatric diagnoses. Developmental brain dysfunction and ESSENCE are useful concepts in advancing translational research on NDDs: with shared genes across multiple NDDs, jointly leveraging ongoing findings in, for example, ASD, ID, and epilepsy is certain to lead to more rapid identification of genes and pathways (molecular and neurodevelopmental) in these disorders. Ultimately, the sharing of pathways across diverse disorders may simplify the identification of novel therapeutics with the broadest impact.
Acknowledgments and Disclosures This work was supported by the National Institute of Mental Health Grant Nos. MH097849 and MH100233 and the Seaver Foundation. I thank Drs. Catalina Betancur and Silvia De Rubeis for comments on the manuscript and Dr. De Rubeis for creating the figure. The author reports no biomedical financial interests or potential conflicts of interest.
Article Information From the Seaver Autism Center for Research and Treatment, Departments of Psychiatry, Neuroscience, and Genetics and Genomic Sciences, Friedman Brain Institute and Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, New York.
Biological Psychiatry May 1, 2015; 77:766–768 www.sobp.org/journal
767
Biological Psychiatry
Commentary
Address correspondence to Joseph D. Buxbaum, Ph.D., Department of Psychiatry, Icahn School of Medicine at Mount Sinai, One Gustave Levy Place, Box 1668, New York, NY 10029; E-mail: [email protected]. Received Feb 25, 2015; accepted Feb 25, 2015.
5.
6.
References 1.
2.
3.
4.
768
Chaste P, Klei L, Sanders SJ, Hus V, Murtha MT, Lowe JK, et al. (2015): A genomewide association study of autism using the Simons Simplex Collection: Does reducing phenotypic heterogeneity in autism increase genetic homogeneity? Biol Psychiatry 77:775–784. Gaugler T, Klei L, Sanders SJ, Bodea CA, Goldberg AP, Lee AB, et al. (2014): Most genetic risk for autism resides with common variation. Nat Genet 46:881–885. Buxbaum JD, Daly MJ, Devlin B, Lehner T, Roeder K, State MW (2012): The autism sequencing consortium: Large-scale, high-throughput sequencing in autism spectrum disorders. Neuron 76:1052–1056. Consortium AGP, Szatmari P, Paterson AD, Zwaigenbaum L, Roberts W, Brian J, et al. (2007): Mapping autism risk loci using genetic linkage and chromosomal rearrangements. Nat Genet 39:319–328.
7.
8.
9.
10.
Biological Psychiatry May 1, 2015; 77:766–768 www.sobp.org/journal
Yuen RK, Thiruvahindrapuram B, Merico D, Walker S, Tammimies K, Hoang N, et al. (2015): Whole-genome sequencing of quartet families with autism spectrum disorder. Nat Med 21:185–191. Moreno-De-Luca A, Myers SM, Challman TD, Moreno-De-Luca D, Evans DW, Ledbetter DH (2013): Developmental brain dysfunction: Revival and expansion of old concepts based on new genetic evidence. Lancet Neurol 12:406–414. De Rubeis S, He X, Goldberg AP, Poultney CS, Samocha K, Ercument Cicek A, et al. (2014): Synaptic, transcriptional and chromatin genes disrupted in autism. Nature 515:209–215. Iossifov I, OʼRoak BJ, Sanders SJ, Ronemus M, Krumm N, Levy D, et al. (2014): The contribution of de novo coding mutations to autism spectrum disorder. Nature 515:216–221. Zaidi S, Choi M, Wakimoto H, Ma L, Jiang J, Overton JD, et al. (2013): De novo mutations in histone-modifying genes in congenital heart disease. Nature 498:220–223. Gillberg C (2010): The ESSENCE in child psychiatry: Early Symptomatic Syndromes Eliciting Neurodevelopmental Clinical Examinations. Res Dev Disabil 31:1543–1551.
Biological Psychiatry
DSM-5 and Psychiatric Genetics — Round Hole, Meet Square Peg Joseph D. Buxbaum It is useful to consider genetic variation as either common (e.g., an allele found in .5% of the population) or rare because these categories of variation are analyzed using different approaches and have different properties. Most importantly, for neurodevelopmental disorders (NDDs), common genetic variation (e.g., single nucleotide polymorphism [SNP]) is associated with very small effect sizes given evolution constraints on deleterious variation (negative or purifying selection), whereas rare variation can be associated with a much wider range of effect sizes. Chaste et al. (1) look at common variation in autism spectrum disorder (ASD) to answer questions about relationships between clinical and genetic heterogeneity. Enormous advances have been made in understanding of the genomic architecture of ASD, including the role of common and rare variation. There is compelling evidence from multiple studies that common variation represents the major proportion of the genetic risk for ASD (see (2) and citations therein). However, no SNP has been reliably associated with ASD to date because of small effect sizes. Until studies include many thousands of cases, it is exceedingly unlikely that many replicated common variation findings will be made in ASD. There has been much more success with gene discovery in ASD when focusing on rare variation. This increased success is due partly to the fact that there is a significant amount of de novo mutation in ASD: with de novo mutation being quite rare, even a few cases with de novo deleterious variation in a given gene are sufficient to provide statistically significant support for that gene in ASD (see (3) and citations therein). Although the effect size for discoverable rare variation is higher than that of common variation, the total variance explained by rare variation is quite low. Gaugler et al. (2) showed that within a given family with ASD, a rare de novo copy number variant (CNV) or single nucleotide variant can often be the difference between an ASD diagnosis or no ASD diagnosis; however, there must be a “genetic background” in the family, defined by multiple inherited SNPs and other genetic variation, that is a critical part of the architecture in that family. In other words, there appears to be risk in the family in most cases in the form of a multiplicity of common variation, and higher risk rare variation pushes an individual in that family over a liability threshold to manifest with an NDD. This model can explain why risk of family recurrence is high in ASD, while, at the same time, affected sibling or relative pairs within a family may have different rare risk variants, as first shown with CNV and, more recently, with single nucleotide variant (4,5). Chaste et al. study a large (N = 2576 families) and behaviorally well-phenotyped ASD sample (the Simons Simplex Collection) to ask whether subgrouping to enhance phenotypic homogeneity increases the ability to make SNP
findings in ASD. The results provide a quite clear “no.” The authors show that subphenotyping by many reasonable criteria, including IQ, extent of repetitive behaviors, insistence on sameness, and more severe ASD, failed to increase power substantially, and the subgroups showed very similar heritability estimates (.4). Most importantly, allele scores from the entire sample predicted case status equally well regardless of subgroup. In short, reducing phenotypic heterogeneity did nothing to increase genetic homogeneity. Although Chaste et al. focus on common variation, similar findings are being made with rare variation. From the very first studies of recurrent CNV, there was already abundant evidence that many were associated with a multiplicity of phenotypes. For example, DiGeorge/velocardiofacial syndrome (also known as 22q11 deletion syndrome) is associated with intellectual disability (ID), epilepsy, autism, schizophrenia, and congenital heart disease. Most genomic and genetic disorders show both variable expressivity and pleiotropy (6). The recent completion of two very large whole-exome sequencing studies in ASD, comprising .20,000 samples with 4000 ASD cases, has identified .30 rare ASD risk genes with a false discovery rate , .05 and .50 with a false discovery rate , .1 (7,8). Many of the top genes have already been associated with diverse phenotypes, including all those noted earlier for DiGeorge syndrome (Figure 1). The associated phenotypes extend beyond NDDs: for example, the findings supporting a major role for chromatin remodeling genes in ASD (7) overlap to a stunning degree with more recent findings in congenital heart disease (9). One area that is of specific interest in ASD is the question of ASD with or without ID. There is a persistent view that ASD without ID is genetically distinct from ASD with ID and that findings made in this latter group may be related to the ID phenotype. The data from Chaste et al. challenge this view, as do analyses in the Autism Genome Project cohort (2), which includes a broader representation of IQ. Although it has not been looked at as extensively with rare variation, it is clear that many rare risk genes and CNV associated with ID are frequently also associated with ASD and vice versa (Figure 1) (6). However, it may be that diagnosing ASD in the context of severe ID may reflect a different disorder than what has sometimes been called “Kanner autism” because social impairments can arise either because of a lack of social drive (Kanner autism) or because of other factors that inhibit effective social interactions. There is little doubt that severely affected individuals experience multiple barriers to social communication, even in the presence of intact social drive. How do we understand this failure of well-defined psychiatric phenotypes to map unambiguously to defined genetic loci and vice versa (Figure 1)? Also, how can we optimally pursue
SEE CORRESPONDING ARTICLE ON PAGE 775 & 2015 Society of Biological Psychiatry Biological Psychiatry May 1, 2015; 77:766–768 www.sobp.org/journal
766
ISSN: 0006-3223
Biological Psychiatry
Commentary
CHD8
ASD
SCN2A SYNGAP1 ARID1B
ID
DYRK1A ANK2 KDM5B
SCZ
ADNP POGZ
EPI
SUV420H1 DSCAM GRIN2B
CHD
TBR1 CHD2 PTEN
MCA
Figure 1. Many-to-many relationships in the genetics of neurodevelopmental disorders. Many ( 1000) genes can contribute to risk of autism spectrum disorder, and many phenotypes are already shown to be associated with known autism spectrum disorder genes. The top-ranked autism spectrum disorder genes, identified in recent whole-exome sequencing analyses, are shown together with various phenotypes associated with each. ASD, autism spectrum disorder; CHD, congenital heart disease; EPI, epilepsy; ID, intellectual disability; MCA, multiple congenital anomalies (which can include congenital heart disease); SCZ, schizophrenia.
enhanced treatment of neuropsychiatric disorders in light of this issue? First, it is possible that, when we know much more about common and rare risk loci, we may be able to begin to make better predictions regarding expected diagnosis. In this scenario, the “genetic background” in a family may be specifically increasing risk for ASD, and a high-risk recent variant (which can result in any of several phenotypes) occurring within that family leads to the manifestation of ASD. In addition, although certain genetically defined syndromes may be associated with specific features, the results provided by Chaste et al. and others suggest that most NDD risk genes are not associated with such features. One caveat to this, which applies to the Simons Simplex Collection and most other ASD samples, is that the phenotyping is almost exclusively behavioral, whereas distinct features in genetic syndromes are typically not behavioral. Recontacting individuals carrying rare variants of major effect for detailed phenotyping may identify unique features associated with some genes.
From both a research and a clinical perspective, we should consider a broad array of neurodevelopmental disorders as possible outcomes from specific genetic and genomic abnormalities. Moreno-De-Luca et al. (6) addressed the diverse phenotypes associated with various high-risk genetic and genomic findings by proposing to classify a large group of disorders under a broad category of “developmental brain dysfunction.” Similarly, Gillberg (10) used the acronym ESSENCE (Early Symptomatic Syndromes Eliciting Neurodevelopmental Clinical Examination) to capture a similar idea, which is meant to encourage clinical interventions at the earliest, nonspecific manifestations of NDDs. An immediate outcome from such a perspective is that penetrance estimates for certain genetic variants increase when one considers developmental brain dysfunction/ESSENCE as the outcome, rather than any specific diagnosis (6). It is a mistake to look for very specific behavioral phenotypes in rodent models of NDDs: because there is little evidence that the known NDD genes map singularly to specific phenotypes, an exclusive emphasis on such phenotypes in rodent models is not evidence based. However, rodent models have frequently been challenged if they do not show specific deficits. Similarly, it appears unlikely that investments in deep phenotyping for gene discovery in NDDs will lead to much increased yields. Neurobiologically, understanding how specific insults produce diverse phenotypes and how specific phenotypes can map to diverse insults is an exciting challenge. One potentially relevant concept, developed by Waddington during the 1940s–1960s, is that of canalization in brain development. In this scheme, brain development occurs along well-defined, robust “canals,” but the absence or inappropriate presence of critical factors at key junctures leads to a switch to alternate “canals.” The degree to which this construct will prove useful in clinical neuroscience remains to be seen. In conclusion, Chaste et al. and others show that genetic and genomic findings do not fit neatly into categorical psychiatric diagnoses. Developmental brain dysfunction and ESSENCE are useful concepts in advancing translational research on NDDs: with shared genes across multiple NDDs, jointly leveraging ongoing findings in, for example, ASD, ID, and epilepsy is certain to lead to more rapid identification of genes and pathways (molecular and neurodevelopmental) in these disorders. Ultimately, the sharing of pathways across diverse disorders may simplify the identification of novel therapeutics with the broadest impact.
Acknowledgments and Disclosures This work was supported by the National Institute of Mental Health Grant Nos. MH097849 and MH100233 and the Seaver Foundation. I thank Drs. Catalina Betancur and Silvia De Rubeis for comments on the manuscript and Dr. De Rubeis for creating the figure. The author reports no biomedical financial interests or potential conflicts of interest.
Article Information From the Seaver Autism Center for Research and Treatment, Departments of Psychiatry, Neuroscience, and Genetics and Genomic Sciences, Friedman Brain Institute and Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, New York.
Biological Psychiatry May 1, 2015; 77:766–768 www.sobp.org/journal
767
Biological Psychiatry
Commentary
Address correspondence to Joseph D. Buxbaum, Ph.D., Department of Psychiatry, Icahn School of Medicine at Mount Sinai, One Gustave Levy Place, Box 1668, New York, NY 10029; E-mail: [email protected]. Received Feb 25, 2015; accepted Feb 25, 2015.
5.
6.
References 1.
2.
3.
4.
768
Chaste P, Klei L, Sanders SJ, Hus V, Murtha MT, Lowe JK, et al. (2015): A genomewide association study of autism using the Simons Simplex Collection: Does reducing phenotypic heterogeneity in autism increase genetic homogeneity? Biol Psychiatry 77:775–784. Gaugler T, Klei L, Sanders SJ, Bodea CA, Goldberg AP, Lee AB, et al. (2014): Most genetic risk for autism resides with common variation. Nat Genet 46:881–885. Buxbaum JD, Daly MJ, Devlin B, Lehner T, Roeder K, State MW (2012): The autism sequencing consortium: Large-scale, high-throughput sequencing in autism spectrum disorders. Neuron 76:1052–1056. Consortium AGP, Szatmari P, Paterson AD, Zwaigenbaum L, Roberts W, Brian J, et al. (2007): Mapping autism risk loci using genetic linkage and chromosomal rearrangements. Nat Genet 39:319–328.
7.
8.
9.
10.
Biological Psychiatry May 1, 2015; 77:766–768 www.sobp.org/journal
Yuen RK, Thiruvahindrapuram B, Merico D, Walker S, Tammimies K, Hoang N, et al. (2015): Whole-genome sequencing of quartet families with autism spectrum disorder. Nat Med 21:185–191. Moreno-De-Luca A, Myers SM, Challman TD, Moreno-De-Luca D, Evans DW, Ledbetter DH (2013): Developmental brain dysfunction: Revival and expansion of old concepts based on new genetic evidence. Lancet Neurol 12:406–414. De Rubeis S, He X, Goldberg AP, Poultney CS, Samocha K, Ercument Cicek A, et al. (2014): Synaptic, transcriptional and chromatin genes disrupted in autism. Nature 515:209–215. Iossifov I, OʼRoak BJ, Sanders SJ, Ronemus M, Krumm N, Levy D, et al. (2014): The contribution of de novo coding mutations to autism spectrum disorder. Nature 515:216–221. Zaidi S, Choi M, Wakimoto H, Ma L, Jiang J, Overton JD, et al. (2013): De novo mutations in histone-modifying genes in congenital heart disease. Nature 498:220–223. Gillberg C (2010): The ESSENCE in child psychiatry: Early Symptomatic Syndromes Eliciting Neurodevelopmental Clinical Examinations. Res Dev Disabil 31:1543–1551.
