- Email: [email protected]
The Sacred Disease: The Puzzling Genetics of Epileptic Disorders
Neuron
Previews REFERENCES Daw, N.D., O’Doherty, J.P., Dayan, P., Seymour, B., and Dolan, R.J. (2006). Nature 441, 876–879. Doll, B.B., Simon, D.A., and Daw, N.D. (2012). Curr. Opin. Neurobiol. 22, 1075–1081. Donahue, C.H., Seo, H., and Lee, D. (2013). Neuron 80, this issue, 223–234. Gehring, W.J., Gross, B., Coles, M.G.H., Meyer, D.E., and Donchin, E. (1993). Psychol. Sci. 4, 385–390.
Glimcher, P.W. (2003). Annu. Rev. Neurosci. 26, 133–179. Ito, S., Stuphorn, V., Brown, J.W., and Schall, J.D. (2003). Science 302, 120–122. Lee, D., Seo, H., and Jung, M.W. (2012). Annu. Rev. Neurosci. 35, 287–308. Schall, J.D., Stuphorn, V., and Brown, J.W. (2002). Neuron 36, 309–322. Schultz, W., Dayan, P., and Montague, P.R. (1997). Science 275, 1593–1599.
Shadlen, M.N., and Newsome, W.T. (2001). J. Neurophysiol. 86, 1916–1936. Stuphorn, V., and Schall, J.D. (2006). Nat. Neurosci. 9, 925–931. Stuphorn, V., Brown, J.W., and Schall, J.D. (2010). J. Neurophysiol. 103, 801–816. Sutton, R.S., and Barto, A.G. (1998). Reinforcement Learning: An Introduction. (Cambridge: MIT Press). Usher, M., Cohen, J.D., Servan-Schreiber, D., Rajkowski, J., and Aston-Jones, G. (1999). Science 283, 549–554.
The Sacred Disease: The Puzzling Genetics of Epileptic Disorders Gaia Novarino,1 Seung Tae Baek,1 and Joseph G. Gleeson1,* 1Howard Hughes Medical Institute, Department of Neurosciences, University of California, San Diego, La Jolla, CA 92093, USA *Correspondence: [email protected] http://dx.doi.org/10.1016/j.neuron.2013.09.019
In the September 12, 2013 issue of Nature, the Epi4K Consortium (Allen et al., 2013) reported sequencing 264 patient trios with epileptic encephalopathies. The Consortium focused on genes exceptionally intolerant to sequence variations and found substantial interconnections with autism and intellectual disability gene networks.
This is a particularly exciting time for the genetics of the epilepsies, especially considering its sordid history. For centuries, epilepsy was considered a sign of a supernatural presence, often of a demonic variety. More than 2,000 years ago, however, Hippocrates warned that epilepsy was no more sacred than other diseases and that its origins may lie in heredity. Yet, for many years, the epilepsies were not considered of genetic origin and up until the 20th century epileptics were socially isolated. The strong genetic component of epilepsy was suggested by observation of familial aggregation, confirmed by several monozygotic-dizygotic twin studies, and in the 1990s, the first epilepsy-specific genes were cloned, encoding ion channels and neurotransmitter receptors. Dominant and recessive Mendelian inheritance patterns have been verified for several of these, but it became clear that these mutations are subject to partial penetrance (i.e., not every person
with the mutation will develop epilepsy) and that even if epilepsy develops in a carrier, not everyone with the mutation will display the same form of epilepsy. Other types of genetic alterations, such as de novo (sporadic) and somatic (limited to specific brain areas) mutations were long suspected to contribute to epilepsy but not validated until recently. Wholeexome sequencing (WES) provided a new tool to understand this multifactorial disorder, allowing a window into the genetic architecture that for the first time did not require pedigree and linkage analysis. Over the course of several years and with generous support from the NINDS, the Epilepsy Genome Phenome Program (http://www.epgp.org) recruited hundreds of patients and families from an international network of 27 clinical centers in the U.S., Europe, and Australia. The goal of the EPGP program was to enroll 1,500 families in which two or more
affecteds displayed epilepsy and 750 individuals with epileptic encephalopathies (EEs). EEs are a group of progressive partially overlapping neurological syndromes in which patients, usually young children, present with psychomotor dysfunctions and concurrent severe clinical epilepsy, often with infantile spasms, Lennox-Gastaut syndrome, polymicrogyria, or periventricular heterotopias. The EPGP cohorts were recruited, meticulously phenotyped, and subsequently underwent DNA exome sequencing through the Epi4K consortium, again funded by the NINDS. The results of the first sequencing effort of the EEs were recently published in the journal Nature (Allen et al., 2013). Following the idea that clinical homogeneity corresponds to genetic homogeneity, Allen et al. (2013) focused on two well-described EEs: infantile spasms and Lennox-Gastaut syndrome. Collectively, they sequenced 264 patients and their
Neuron 80, October 2, 2013 ª2013 Elsevier Inc. 9
Neuron
Previews parents (trios), as well as 600 control trios, and analyzed the exome sequences, hunting for de novo mutations. De novo mutations are common, occurring on average about once per generation in the exome even in healthy individuals, and mutation rates rise with increasing parental age (Kong et al., 2012). De novo mutations have been shown to contribute to many neurodevelopmental disorders such as intellectual disability and autism spectrum disorders (Sanders et al., 2012; Rauch et al., 2012), as well as other diseases with reduced reproductive rates. Allen et al. (2013) identified approximately one de novo mutation per patient or control, in agreement with previously published rates (O’Roak et al., 2012), and were left with the difficult task of interpreting the results. The power of large genetic studies is that they can draw statistically significant conclusions, but one of the biggest challenges in analyzing whole-exome sequencing data is to confidently prioritize potentially deleterious genetic variants. Here Allen et al. (2013) confirmed that patients with EEs are more likely to carry predicted loss-of-function mutations than the controls, and then, using available single-nucleotide polymorphism data, calculated that de novo mutations in EE patients are several times more likely to occur in genes intolerant to genomic sequence variations than in controls. Indeed, based on their analysis, the most commonly mutated epilepsy genes appear to be extremely intolerant to genomic changes. Both observations may guide variant prioritization in future human whole-exome sequencing studies. Importantly, Allen et al. (2013) identified seven recurrently mutated genes, hence probably disease-causing, in 26 patients, which accounted for 10% of total cases. Among these genes, five were previously linked to EEs and two were novel. Interestingly, the two novel EE-causative genes have been previously linked to other neurological disorders. This was not unexpected, because it is not rare that genes in which mutations have been first associated with a specific phenotype have been subsequently shown to associate with a broader phenotypic spectrum. Although not surprising, the observation endorses the necessity of a modification in the genetic diagnostic
approach when dealing with neurodevelopmental disorders. While, until a few years ago, genetic analysis was concentrated on a restricted gene panel for each specific syndromic disorder, it is clear now that the future will need to focus on the genome as a whole. As an example, Yu and colleagues reported earlier this year that exome analysis of a cohort of families with autism uncovered loss-of-function mutations in genes already linked to highly syndromic presentations (Yu et al., 2013). The results speak to the weakness in differentiating clinical brain disorders but the strengths of exome sequencing to distinguish these same or genome approaches disorders genetically. This observation may have relevance to neurodevelopmental and neuropsychiatric diseases in general. In line with these previous observations, Allen et al. (2013) report that patients with de novo exonic mutations in the GABRB3 gene—deletion of GABRB3 was already linked to epilepsy in Angelman syndrome—may present with nonAngelman EE. These findings indicate that in the absence of a deep understanding of the molecular mechanisms underlying neurodevelopmental disorders and of the precise function and operational mode of the protein encoded by disorder-causative genes, it will be difficult to make clinical diagnostic predictions based solely on the patient genotype and vice versa. Although Allen et al. (2013) identified several recurrently mutated genes, it was perhaps not surprising that there was also tremendous locus heterogeneity suggested by the analysis, even in clinically well-defined epilepsy syndromes. Indeed, in the great majority of the cases, Allen et al. (2013) identified private genes (i.e., mutated in a single patient), leaving to future genetic studies and functional work to determine whether these contribute to epilepsy. At the same time, Allen et al. (2013) suggest that genes seemingly diverse in function may in fact be functionally interconnected. Following this principle, Allen et al. (2013) determined interconnectivity between these private genes and previously known epilepsy genes and were successful in constructing a unique network containing 71 nodes (genes). This type of analysis, already
10 Neuron 80, October 2, 2013 ª2013 Elsevier Inc.
demonstrated to be useful for other neurological disorders (O’Roak et al., 2012), is a profound advance as it provides evidence that many rare genetic alterations may converge on a few key biological processes. Identifying these processes will be extremely important in guiding future gene discoveries as well as pathway-specific treatments. Allen et al. (2013) also found significant overlap between their gene network and the autism spectrum disorder (ASD) and intellectual disability gene networks previously published (Sanders et al., 2012; O’Roak et al., 2012; Iossifov et al., 2012; Neale et al., 2012; de Ligt et al., 2012). This observation reinforces the idea that epilepsy, autism, and intellectual disability may share, at least in part, common pathophysiological mechanisms (Figure 1). Some of the nodes of this network have been previously identified as mutated in forms of epilepsy with other genetic architecture. Thus, genes mutated in either de novo or inherited epilepsies help to fill in the epilepsy network and contribute separately or in conjunction to the overall genetic risk of seizures. For example, mutations in TNK2, encoding a brain-expressed tyrosine kinase, have been recently identified as a very rare cause of autosomal recessive early onset epilepsy (Hitomi et al., 2013), and it directly interacts with the product of NEDD4L, known to promote ubiquitination and internalization of various plasma channels, including sodium channels mutated in epilepsy. The Epi4K paper (Allen et al., 2013) found a de novo mutation in MTOR, a serine/threonine kinase that plays roles in cell proliferation, cell size, survival, and metabolism. Altered mTOR signaling has been found in patients with focal cortical malformations associated with epilepsy (Aronica et al., 2012), and MTOR, together with other components of the PI3K-AKTmTOR pathway, have been recently found to harbor de novo somatic variants in focal brain dysplasia and epilepsy (Lee et al., 2012). The fact that somatic gainof-function mutations in components of the PI3K-AKT-mTOR pathway have been implicated in tumorigenesis might suggest that similar etiologies are shared by even a broader range of diseases. Despite the presentation of mutations in MTOR, DCX, or FLNA in the Epi4K
Neuron
Previews Epileptic Encephalopathies
Autism Spectrum Disorders
Consortium; Epilepsy Phenome/Genome Project. (2013). Nature 501, 217–221.
De Novo / Dominant mutations / genes
Aronica, E., Becker, A.J., and Spreafico, R. (2012). Brain Pathol. 22, 380–401.
Autosmal recessive mutations / genes
de Ligt, J., Willemsen, M.H., van Bon, B.W., Kleefstra, T., Yntema, H.G., Kroes, T., Vulto-van Silfhout, A.T., Koolen, D.A., de Vries, P., Gilissen, C., et al. (2012). N. Engl. J. Med. 367, 1921–1929.
Somatic mutations / genes
Hitomi, Y., Heinzen, E.L., Donatello, S., Dahl, H.H., Damiano, J.A., McMahon, J.M., Berkovic, S.F., Scheffer, I.E., Legros, B., Rai, M., et al. (2013). Ann. Neurol. Published online May 20, 2013. http://dx.doi.org/10.1002/ana.23934. Iossifov, I., Ronemus, M., Levy, D., Wang, Z., Hakker, I., Rosenbaum, J., Yamrom, B., Lee, Y.H., Narzisi, G., Leotta, A., et al. (2012). Neuron 74, 285–299.
Intellectual Disability Figure 1. Depiction of Overlapping Nodes of Genetic Mutation among EEs, ASD, and Intellectual Disability
paper, Allen et al. (2013) found no associated structural brain malformations, even though these genes are well known to cause cortical dysplasias, possibly explained by functional diversity of human mutations. Different alleles in the same gene might affect protein function differently, which in turn might directly relate to the severity of the disease (i.e., gain-of-function versus loss-of-function or hypermorphic versus hypomorphic). The functional impact of such mutations needs to be further validated in order to understand the mechanisms of diseases. In conclusion, this and other recent publications reinforce the concept that whole-exome sequencing is unquestionably a powerful tool for dissecting the genetic basis of diseases and traits not
completely tractable to previous genediscovery strategies. Due to the extreme locus heterogeneity and the hitherto mentioned phenotypical variability, future studies will undoubtedly coalesce patients with a range of clinical presentations rather than analyze very selective collections. In addition, functional work will be essential to prove the causative role of private genes, to identify and understand the implicated biological pathways, to comprehend the structurefunction-phenotype relationship, and, finally, to develop better treatments.
REFERENCES Allen, A.S., Berkovic, S.F., Cossette, P., Delanty, N., Dlugos, D., Eichler, E.E., Epstein, M.P., Glauser, T., Goldstein, D.B., Han, Y., et al.; Epi4K
Kong, A., Frigge, M.L., Masson, G., Besenbacher, S., Sulem, P., Magnusson, G., Gudjonsson, S.A., Sigurdsson, A., Jonasdottir, A., Jonasdottir, A., et al. (2012). Nature 488, 471–475. Lee, J.H., Huynh, M., Silhavy, J.L., Kim, S., DixonSalazar, T., Heiberg, A., Scott, E., Bafna, V., Hill, K.J., Collazo, A., et al. (2012). Nat. Genet. 44, 941–945. Neale, B.M., Kou, Y., Liu, L., Ma’ayan, A., Samocha, K.E., Sabo, A., Lin, C.F., Stevens, C., Wang, L.S., Makarov, V., et al. (2012). Nature 485, 242–245. O’Roak, B.J., Vives, L., Girirajan, S., Karakoc, E., Krumm, N., Coe, B.P., Levy, R., Ko, A., Lee, C., Smith, J.D., et al. (2012). Nature 485, 246–250. Rauch, A., Wieczorek, D., Graf, E., Wieland, T., Endele, S., Schwarzmayr, T., Albrecht, B., Bartholdi, D., Beygo, J., Di Donato, N., et al. (2012). Lancet 380, 1674–1682. Sanders, S.J., Murtha, M.T., Gupta, A.R., Murdoch, J.D., Raubeson, M.J., Willsey, A.J., Ercan-Sencicek, A.G., DiLullo, N.M., Parikshak, N.N., Stein, J.L., et al. (2012). Nature 485, 237–241. Yu, T.W., Chahrour, M.H., Coulter, M.E., Jiralerspong, S., Okamura-Ikeda, K., Ataman, B., Schmitz-Abe, K., Harmin, D.A., Adli, M., Malik, A.N., et al. (2013). Neuron 77, 259–273.
Neuron 80, October 2, 2013 ª2013 Elsevier Inc. 11
Previews REFERENCES Daw, N.D., O’Doherty, J.P., Dayan, P., Seymour, B., and Dolan, R.J. (2006). Nature 441, 876–879. Doll, B.B., Simon, D.A., and Daw, N.D. (2012). Curr. Opin. Neurobiol. 22, 1075–1081. Donahue, C.H., Seo, H., and Lee, D. (2013). Neuron 80, this issue, 223–234. Gehring, W.J., Gross, B., Coles, M.G.H., Meyer, D.E., and Donchin, E. (1993). Psychol. Sci. 4, 385–390.
Glimcher, P.W. (2003). Annu. Rev. Neurosci. 26, 133–179. Ito, S., Stuphorn, V., Brown, J.W., and Schall, J.D. (2003). Science 302, 120–122. Lee, D., Seo, H., and Jung, M.W. (2012). Annu. Rev. Neurosci. 35, 287–308. Schall, J.D., Stuphorn, V., and Brown, J.W. (2002). Neuron 36, 309–322. Schultz, W., Dayan, P., and Montague, P.R. (1997). Science 275, 1593–1599.
Shadlen, M.N., and Newsome, W.T. (2001). J. Neurophysiol. 86, 1916–1936. Stuphorn, V., and Schall, J.D. (2006). Nat. Neurosci. 9, 925–931. Stuphorn, V., Brown, J.W., and Schall, J.D. (2010). J. Neurophysiol. 103, 801–816. Sutton, R.S., and Barto, A.G. (1998). Reinforcement Learning: An Introduction. (Cambridge: MIT Press). Usher, M., Cohen, J.D., Servan-Schreiber, D., Rajkowski, J., and Aston-Jones, G. (1999). Science 283, 549–554.
The Sacred Disease: The Puzzling Genetics of Epileptic Disorders Gaia Novarino,1 Seung Tae Baek,1 and Joseph G. Gleeson1,* 1Howard Hughes Medical Institute, Department of Neurosciences, University of California, San Diego, La Jolla, CA 92093, USA *Correspondence: [email protected] http://dx.doi.org/10.1016/j.neuron.2013.09.019
In the September 12, 2013 issue of Nature, the Epi4K Consortium (Allen et al., 2013) reported sequencing 264 patient trios with epileptic encephalopathies. The Consortium focused on genes exceptionally intolerant to sequence variations and found substantial interconnections with autism and intellectual disability gene networks.
This is a particularly exciting time for the genetics of the epilepsies, especially considering its sordid history. For centuries, epilepsy was considered a sign of a supernatural presence, often of a demonic variety. More than 2,000 years ago, however, Hippocrates warned that epilepsy was no more sacred than other diseases and that its origins may lie in heredity. Yet, for many years, the epilepsies were not considered of genetic origin and up until the 20th century epileptics were socially isolated. The strong genetic component of epilepsy was suggested by observation of familial aggregation, confirmed by several monozygotic-dizygotic twin studies, and in the 1990s, the first epilepsy-specific genes were cloned, encoding ion channels and neurotransmitter receptors. Dominant and recessive Mendelian inheritance patterns have been verified for several of these, but it became clear that these mutations are subject to partial penetrance (i.e., not every person
with the mutation will develop epilepsy) and that even if epilepsy develops in a carrier, not everyone with the mutation will display the same form of epilepsy. Other types of genetic alterations, such as de novo (sporadic) and somatic (limited to specific brain areas) mutations were long suspected to contribute to epilepsy but not validated until recently. Wholeexome sequencing (WES) provided a new tool to understand this multifactorial disorder, allowing a window into the genetic architecture that for the first time did not require pedigree and linkage analysis. Over the course of several years and with generous support from the NINDS, the Epilepsy Genome Phenome Program (http://www.epgp.org) recruited hundreds of patients and families from an international network of 27 clinical centers in the U.S., Europe, and Australia. The goal of the EPGP program was to enroll 1,500 families in which two or more
affecteds displayed epilepsy and 750 individuals with epileptic encephalopathies (EEs). EEs are a group of progressive partially overlapping neurological syndromes in which patients, usually young children, present with psychomotor dysfunctions and concurrent severe clinical epilepsy, often with infantile spasms, Lennox-Gastaut syndrome, polymicrogyria, or periventricular heterotopias. The EPGP cohorts were recruited, meticulously phenotyped, and subsequently underwent DNA exome sequencing through the Epi4K consortium, again funded by the NINDS. The results of the first sequencing effort of the EEs were recently published in the journal Nature (Allen et al., 2013). Following the idea that clinical homogeneity corresponds to genetic homogeneity, Allen et al. (2013) focused on two well-described EEs: infantile spasms and Lennox-Gastaut syndrome. Collectively, they sequenced 264 patients and their
Neuron 80, October 2, 2013 ª2013 Elsevier Inc. 9
Neuron
Previews parents (trios), as well as 600 control trios, and analyzed the exome sequences, hunting for de novo mutations. De novo mutations are common, occurring on average about once per generation in the exome even in healthy individuals, and mutation rates rise with increasing parental age (Kong et al., 2012). De novo mutations have been shown to contribute to many neurodevelopmental disorders such as intellectual disability and autism spectrum disorders (Sanders et al., 2012; Rauch et al., 2012), as well as other diseases with reduced reproductive rates. Allen et al. (2013) identified approximately one de novo mutation per patient or control, in agreement with previously published rates (O’Roak et al., 2012), and were left with the difficult task of interpreting the results. The power of large genetic studies is that they can draw statistically significant conclusions, but one of the biggest challenges in analyzing whole-exome sequencing data is to confidently prioritize potentially deleterious genetic variants. Here Allen et al. (2013) confirmed that patients with EEs are more likely to carry predicted loss-of-function mutations than the controls, and then, using available single-nucleotide polymorphism data, calculated that de novo mutations in EE patients are several times more likely to occur in genes intolerant to genomic sequence variations than in controls. Indeed, based on their analysis, the most commonly mutated epilepsy genes appear to be extremely intolerant to genomic changes. Both observations may guide variant prioritization in future human whole-exome sequencing studies. Importantly, Allen et al. (2013) identified seven recurrently mutated genes, hence probably disease-causing, in 26 patients, which accounted for 10% of total cases. Among these genes, five were previously linked to EEs and two were novel. Interestingly, the two novel EE-causative genes have been previously linked to other neurological disorders. This was not unexpected, because it is not rare that genes in which mutations have been first associated with a specific phenotype have been subsequently shown to associate with a broader phenotypic spectrum. Although not surprising, the observation endorses the necessity of a modification in the genetic diagnostic
approach when dealing with neurodevelopmental disorders. While, until a few years ago, genetic analysis was concentrated on a restricted gene panel for each specific syndromic disorder, it is clear now that the future will need to focus on the genome as a whole. As an example, Yu and colleagues reported earlier this year that exome analysis of a cohort of families with autism uncovered loss-of-function mutations in genes already linked to highly syndromic presentations (Yu et al., 2013). The results speak to the weakness in differentiating clinical brain disorders but the strengths of exome sequencing to distinguish these same or genome approaches disorders genetically. This observation may have relevance to neurodevelopmental and neuropsychiatric diseases in general. In line with these previous observations, Allen et al. (2013) report that patients with de novo exonic mutations in the GABRB3 gene—deletion of GABRB3 was already linked to epilepsy in Angelman syndrome—may present with nonAngelman EE. These findings indicate that in the absence of a deep understanding of the molecular mechanisms underlying neurodevelopmental disorders and of the precise function and operational mode of the protein encoded by disorder-causative genes, it will be difficult to make clinical diagnostic predictions based solely on the patient genotype and vice versa. Although Allen et al. (2013) identified several recurrently mutated genes, it was perhaps not surprising that there was also tremendous locus heterogeneity suggested by the analysis, even in clinically well-defined epilepsy syndromes. Indeed, in the great majority of the cases, Allen et al. (2013) identified private genes (i.e., mutated in a single patient), leaving to future genetic studies and functional work to determine whether these contribute to epilepsy. At the same time, Allen et al. (2013) suggest that genes seemingly diverse in function may in fact be functionally interconnected. Following this principle, Allen et al. (2013) determined interconnectivity between these private genes and previously known epilepsy genes and were successful in constructing a unique network containing 71 nodes (genes). This type of analysis, already
10 Neuron 80, October 2, 2013 ª2013 Elsevier Inc.
demonstrated to be useful for other neurological disorders (O’Roak et al., 2012), is a profound advance as it provides evidence that many rare genetic alterations may converge on a few key biological processes. Identifying these processes will be extremely important in guiding future gene discoveries as well as pathway-specific treatments. Allen et al. (2013) also found significant overlap between their gene network and the autism spectrum disorder (ASD) and intellectual disability gene networks previously published (Sanders et al., 2012; O’Roak et al., 2012; Iossifov et al., 2012; Neale et al., 2012; de Ligt et al., 2012). This observation reinforces the idea that epilepsy, autism, and intellectual disability may share, at least in part, common pathophysiological mechanisms (Figure 1). Some of the nodes of this network have been previously identified as mutated in forms of epilepsy with other genetic architecture. Thus, genes mutated in either de novo or inherited epilepsies help to fill in the epilepsy network and contribute separately or in conjunction to the overall genetic risk of seizures. For example, mutations in TNK2, encoding a brain-expressed tyrosine kinase, have been recently identified as a very rare cause of autosomal recessive early onset epilepsy (Hitomi et al., 2013), and it directly interacts with the product of NEDD4L, known to promote ubiquitination and internalization of various plasma channels, including sodium channels mutated in epilepsy. The Epi4K paper (Allen et al., 2013) found a de novo mutation in MTOR, a serine/threonine kinase that plays roles in cell proliferation, cell size, survival, and metabolism. Altered mTOR signaling has been found in patients with focal cortical malformations associated with epilepsy (Aronica et al., 2012), and MTOR, together with other components of the PI3K-AKTmTOR pathway, have been recently found to harbor de novo somatic variants in focal brain dysplasia and epilepsy (Lee et al., 2012). The fact that somatic gainof-function mutations in components of the PI3K-AKT-mTOR pathway have been implicated in tumorigenesis might suggest that similar etiologies are shared by even a broader range of diseases. Despite the presentation of mutations in MTOR, DCX, or FLNA in the Epi4K
Neuron
Previews Epileptic Encephalopathies
Autism Spectrum Disorders
Consortium; Epilepsy Phenome/Genome Project. (2013). Nature 501, 217–221.
De Novo / Dominant mutations / genes
Aronica, E., Becker, A.J., and Spreafico, R. (2012). Brain Pathol. 22, 380–401.
Autosmal recessive mutations / genes
de Ligt, J., Willemsen, M.H., van Bon, B.W., Kleefstra, T., Yntema, H.G., Kroes, T., Vulto-van Silfhout, A.T., Koolen, D.A., de Vries, P., Gilissen, C., et al. (2012). N. Engl. J. Med. 367, 1921–1929.
Somatic mutations / genes
Hitomi, Y., Heinzen, E.L., Donatello, S., Dahl, H.H., Damiano, J.A., McMahon, J.M., Berkovic, S.F., Scheffer, I.E., Legros, B., Rai, M., et al. (2013). Ann. Neurol. Published online May 20, 2013. http://dx.doi.org/10.1002/ana.23934. Iossifov, I., Ronemus, M., Levy, D., Wang, Z., Hakker, I., Rosenbaum, J., Yamrom, B., Lee, Y.H., Narzisi, G., Leotta, A., et al. (2012). Neuron 74, 285–299.
Intellectual Disability Figure 1. Depiction of Overlapping Nodes of Genetic Mutation among EEs, ASD, and Intellectual Disability
paper, Allen et al. (2013) found no associated structural brain malformations, even though these genes are well known to cause cortical dysplasias, possibly explained by functional diversity of human mutations. Different alleles in the same gene might affect protein function differently, which in turn might directly relate to the severity of the disease (i.e., gain-of-function versus loss-of-function or hypermorphic versus hypomorphic). The functional impact of such mutations needs to be further validated in order to understand the mechanisms of diseases. In conclusion, this and other recent publications reinforce the concept that whole-exome sequencing is unquestionably a powerful tool for dissecting the genetic basis of diseases and traits not
completely tractable to previous genediscovery strategies. Due to the extreme locus heterogeneity and the hitherto mentioned phenotypical variability, future studies will undoubtedly coalesce patients with a range of clinical presentations rather than analyze very selective collections. In addition, functional work will be essential to prove the causative role of private genes, to identify and understand the implicated biological pathways, to comprehend the structurefunction-phenotype relationship, and, finally, to develop better treatments.
REFERENCES Allen, A.S., Berkovic, S.F., Cossette, P., Delanty, N., Dlugos, D., Eichler, E.E., Epstein, M.P., Glauser, T., Goldstein, D.B., Han, Y., et al.; Epi4K
Kong, A., Frigge, M.L., Masson, G., Besenbacher, S., Sulem, P., Magnusson, G., Gudjonsson, S.A., Sigurdsson, A., Jonasdottir, A., Jonasdottir, A., et al. (2012). Nature 488, 471–475. Lee, J.H., Huynh, M., Silhavy, J.L., Kim, S., DixonSalazar, T., Heiberg, A., Scott, E., Bafna, V., Hill, K.J., Collazo, A., et al. (2012). Nat. Genet. 44, 941–945. Neale, B.M., Kou, Y., Liu, L., Ma’ayan, A., Samocha, K.E., Sabo, A., Lin, C.F., Stevens, C., Wang, L.S., Makarov, V., et al. (2012). Nature 485, 242–245. O’Roak, B.J., Vives, L., Girirajan, S., Karakoc, E., Krumm, N., Coe, B.P., Levy, R., Ko, A., Lee, C., Smith, J.D., et al. (2012). Nature 485, 246–250. Rauch, A., Wieczorek, D., Graf, E., Wieland, T., Endele, S., Schwarzmayr, T., Albrecht, B., Bartholdi, D., Beygo, J., Di Donato, N., et al. (2012). Lancet 380, 1674–1682. Sanders, S.J., Murtha, M.T., Gupta, A.R., Murdoch, J.D., Raubeson, M.J., Willsey, A.J., Ercan-Sencicek, A.G., DiLullo, N.M., Parikshak, N.N., Stein, J.L., et al. (2012). Nature 485, 237–241. Yu, T.W., Chahrour, M.H., Coulter, M.E., Jiralerspong, S., Okamura-Ikeda, K., Ataman, B., Schmitz-Abe, K., Harmin, D.A., Adli, M., Malik, A.N., et al. (2013). Neuron 77, 259–273.
Neuron 80, October 2, 2013 ª2013 Elsevier Inc. 11