Genomics of autism spectrum disorders

Chapter 14

Genomics of autism spectrum disorders Margarita Raygadaa,b,d, Paul Grantc and Owen M. Rennerta,d a

Division of Intramural Research, National Institute of Child Health and Human Development, National Institutes of Health (NIH), Bethesda, MD,

United States; b Pediatric Oncology Branch, National Cancer Institute, NIH, Bethesda, MD, United States; c Section on Behavioral Pediatrics, National Institute of Mental Health, NIH, Bethesda, MD, United States; d Department of Pediatrics, Georgetown University Medical School, Washington, DC, United States

1 Definition of the clinical phenotype 1.1 Background and emerging diagnosis In 1943, Leo Kanner described 11 children he had evaluated who seemed to him “remote from affective and communicative contact with people, [though] they develop a remarkable and not unskillful relationship to the inanimate environment”(Kanner, 1943). Since then, this constellation of symptoms and deficits has been recognized as having its onset in childhood, but persisting through the lifespan. Over the years, the diagnostic term for the disorder has changed from Kanner’s “inborn autistic disturbances of affective contact,” while the fundamental symptoms have been agreed upon by the consensus of clinicians who evaluate, treat, and investigate the disorder. Most recently, it has been called autism spectrum disorder (ASD) (DSM-5 [APA] and ICD-10). Criteria include the following: 1. Persistent deficits in social communication and social interaction across multiple contexts. 2. Restricted, repetitive patterns of behavior, interest, or activities, as manifested by at least two of the following: a. stereotyped or repetitive motor movements, use of objects, or speech; b. insistence on sameness, inflexible adherence to routines, or ritualized patterns of verbal or nonverbal behavior; c. highly restricted, fixated interests that are abnormal in intensity or focus; d. hyper- or hyporeactivity to sensory input, or unusual interest in sensory aspects of the environment. 3. Symptoms must be present in the early developmental period. 4. Symptoms cause clinically significant impairment in social, occupational, or other important areas of current functioning. 5. The disturbances are not better explained by intellectual disability or global developmental delay. ASD is now well characterized by the DSM-5 as a neurobehavioral single entity. Yet, it has defied the traditional concept of a single disease because it is neither a homogenous phenotype nor a compilation of signs and symptoms that can be classified as one syndrome (Spence, Grant, Thurm, & Swedo, 2009). Rather, it seems to be a collection of different diseases that have in common a neurobehavioral trait called autism.

1.2 Incidence Perhaps because of changes in diagnostic criteria over time, perhaps because of increasing recognition, or perhaps even because of changing incidence of the disorders, ASD has moved from a rare diagnosis to a current (2012) overall prevalence estimate of 1 in 68 children at 8 years of age (14.6 cases per 1000) in the most recent CDC (Centers for Disease Control and Prevention) report (Christensen et al., 2016). According to this report, its prevalence is higher among boys (23.6 per 1000) than among girls (5.3 per 1000), and ASD is estimated to be significantly higher among non-Hispanic white children at 8 years of age than among non-Hispanic black children. Notably, estimated prevalence in the CDC studies varies widely among regions, depending on a number of factors, including whether health records only, or education records as well, were reviewed. Even in this relatively consistent survey system, prevalence of ASD has increased from 1 in 150 in the year 1992 to the current estimate of more than twice this frequency. Personalized Psychiatry. https://doi.org/10.1016/B978-0-12-813176-3.00014-6 2020 Published by Elsevier Inc.

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But this prevalence currently includes a phenotypically highly heterogeneous group, ranging in severity from only “significantly impaired” in one area of functioning to severely limited and requiring almost continuous supervision and assistance. Thus, the marked increase in prevalence may be a consequence of a broader and a continuously expanding spectrum of “autism spectrum disorders.”

1.3 Cost of autism spectrum disorders Leaving aside the issue of homogeneity of the diagnosis, the costs of supporting individuals identified with ASD over the lifespan are considerable. A recent review suggests that the direct and indirect economic effects of a diagnosis of ASD and intellectual disability are $2.4 million in the United States and US $2.2 million in the United Kingdom. For an individual with ASD without intellectual disability, the costs of support are $1.4 million and the US equivalent of $1.4 million in the United Kingdom. During childhood, the largest components of the cost are special education services and productivity loss for the parents. In adulthood, the costs include supportive accommodations and loss of individual productivity (Buescher, Cidav, Knapp, & Mandell, 2014).

1.4 Current state of personalized care l

There is no pharmacological intervention with an indication for any of the core symptoms of ASD. Indicated pharmacologic treatments are presently directed at ancillary symptoms, such as aggressive behaviors.

l

ASD is phenotypically heterogeneous so that it is not presently possible to use behaviors beyond the core symptoms to subcategorize ASD variants. It seems unlikely, based on present knowledge, that there will be a genetic manipulation in the near future that would affect the core symptoms of ASD. Such manipulations have been attempted with single gene disorders. This reality may be revisited in the future if the genetic bases of ASD can be resolved by assessing gene networks; yet the heterogeneous nature of the disease makes it complex. If a single (or a few) gene(s) of large effect could be found to account even for a small number of nonsyndromic ASD cases, these might be instructive in pointing to the perturbed systems. Though no pharmacologic treatments affect core symptoms, it is possible to consider that counteracting the effects of genes might be feasible in the future.

l

l

How can we conceptualize these observations into a unifying approach that allows us to define the etiology for an individual patient, develop a therapeutic/interventional plan that capitalizes on the “-omic” technology available, and addresses the specific needs, capabilities, and weaknesses of the patient (personalized psychiatry)? The answer lies in the study of its genetic pathophysiology.

2

Genetic causes of autism spectrum disorders

Autism spectrum can be classified into two large categories, which we will call syndromic and nonsyndromic. The syndromic category includes well-defined syndromes with known genetic etiology and inheritance patterns. Even within these disorders, the understanding of the genetic “defect” is evolving, as evidenced, for example, by the “transferase defect” in Rett syndrome, and the more recent definition atypical mutations, which produce the same phenotype. The nonsyndromic category includes an equally variable spectrum of genetic changes, such as monogenic defects and copy number variants, including deletions and duplications, and some with presently undetectable defects.

2.1 Syndromic ASD Today, ASD is defined as a disease with a prevalence of 1:68 children, and associated with more than 800 genes, and more than 2300 copy number variation (CNV) loci. A consideration of the genetic causes of ASD needs to include a precise definition of the syndromes that have “autism” as part of their phenotype. Prototypes of syndromes within ASD include Fragile X, Rett, Phelan-McDermid syndromes, and Tuberous Sclerosis. The elements that unite them in the autism spectrum are deficits in socialization and communication, and restricted and repetitive patterns of behavior, though the remainder of their phenotypes are quite distinctive. The fundamental molecular defect in each appears to be different. For example, Fragile-X is the consequence of a trinucleotide repeat, Rett syndrome has a methyl transferase deficiency, Phelan-McDermid has a contiguous gene deletion syndrome, and tuberous sclerosis has a single gene mutation. Yet common functional networks (ontogeny) identify a pathophysiological synergy among them (see “Pathway Analysis and Molecular Networks,” which follows).

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2.2 Nonsyndromic What defines “nonsyndromic autism” (Sztainberg & Zoghbi, 2016) is simply that it identifies those patients in whom the physician fails to make a diagnosis of an entity with a definable, recognizable disease. This classification varies over time as numbers of cases reach a threshold that allows the clinical features of patients to be clustered so that criteria are established that lead to recognition of its etiology as being associated with a recognizable genetic mutation (or etiology). The identification of more than 800 gene mutations and 2000 CNVs in patients with social communication/language deficits and repetitive/restricted behavior has provided the basis for future genotype-phenotype correlations (Chaste, Roeder, & Devlin, 2017; Finucane & Myers, 2016). It provides a rationale for an additional approach, specifically to accumulate many affected individuals’ DNA variants, and to subsequently characterize these individuals in order to define the phenotype and its variable expression. The study of the CHD8 mutation in ASD patients (Bernier et al., 2014) confirms the validity of such endeavors, as do subsequent studies of patients with 22q13.3 deletion (Phelan-McDermid syndrome), which led to the recognition of the extreme variability of the phenotype (Zwanberg, Ruiter, van den Heuvel, Flapper, & Van Ravenswaaij-Arts, 2016). Numerous studies have taken this approach, and thousands of ASD patients and nonrelated controls have been analyzed. These studies have demonstrated that ASD patients are 10–20 times more likely to have CNVs ( Jaquemont et al., 2006; Sebat et al., 2007), and CNVs are more prevalent in the population of cognitively impaired individuals (Autism Genome Project Consortium, 2007; Christian et al., 2008; Gilman et al., 2011; Glessner et al., 2009; Itsara et al., 2010; Marshall et al., 2008; Pinto et al., 2010; Sanders et al., 2011). Each of these “associations” occurs in approximately 1% of patients (Finucane & Myers, 2016). These facts highlight the importance of focusing research efforts into the characterization of CNVs. The challenge is now to understand how so many variations across the genome lead to one phenotype. Several attempts have been made to identify common molecular and functional pathways that unify the numerous CNVs involved in ASD. These studies have defined three common cellular networks; specifically, neurotransmission, synapse formation, and ubiquitination (Ben-David & Shifman, 2012, 2013; Parikshak et al., 2013; Ziats & Rennert, 2016) (Fig. 1). Similar research efforts have focused on whole exome sequencing (WES) and whole genome sequencing (WGS). These studies have proven to be equally informative in identifying specific single gene defects associated with ASD. So far, approximately 800 such defects have been described in the literature ( Jiang et al., 2013). These mutations occur throughout the genome, in both coding and noncoding genes (Wu, Parikshak, Belgard, & Geschwind, 2016; Ziats & Rennert, 2014). Thus, again, the challenge remains to understand how such diverse and numerous defects are responsible for one common phenotype. As with CNVs, attempts have been made to identify common denominators that may shed light on this matter. These investigations (genome sequencing plus CNV) have identified three broad underlying mechanisms (Fig. 1): synaptic and neuronal adhesion components, RNA processing, and transcriptional regulation (Bernier et al., 2014; Codina-Sola` et al., 2015; de la torre-Ubieta, Won, Stein, & Geschwind, 2016; Liu et al., 2013). Can one define distinct anatomical or functional abnormalities of the brain in ASD? A literature survey yields inconclusive data on anatomic, regional, or embryological deficits. However, recently Ecker, Schmeisser, Loth, and Murphy (2017) and Varghese et al. (2017) proposed that growth patterns characteristic of ASD may be identified in patients, as well as in certain animal models. General studies in patients with ASD suggest that 20% of these children exhibit early brain overgrowth (2–4 years of age), as evidenced both by imaging studies of brain volume, as well as by fronto-occipital FIG. 1 Cellular networks of neural transmission. Genome - sequence + CNV • Synaptic & Neural Adhesion components • RNA processing • Transcriptional regulation

CNVs

Exome sequencing

• Neurotransmission

• Ion transport

• Synapse formation/function

• Cell synaptic function

• Ubiquitination

• Transcriptional regulation

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circumference measurement (Carper & Courchesne, 2005; Lainhart et al., 2006; Redcay & Courchesne, 2005). Between 5 and 6 years of age, growth velocity declines or plateaus in these children, and beyond 6 years of age, no increase is noted. This phenomenon may be indicative of an “atypical brain maturation trajectory.” These data suggest that aberrant brain developmental patterns may be most prominent during the period from mid-gestation through the second postnatal years of life. However, it should be stressed that other investigations identify that 80% of children with ASD do not display this feature. Additionally, the finding is highly variable, relatively small populations have been studied (predominantly retrospective studies), and correlation with somatic “overgrowth” (particularly in males) would suggest this finding may not be specific for brain development only. Assuming the brain maturation trajectory is deranged in autism, it would suggest altered brain connectivity is a pathophysiological characteristic of this disorder, and that this phenomenon occurs during the period between mid-gestation and 2–3 years of life.

3

Diagnostic work-up for nonsyndromic autism spectrum disorders

Nevertheless, based on the preceding considerations, there are no established guidelines for conducting a diagnostic work up for nonsyndromic ASD. The set of evaluations and tests to initiate assessment should be tailored to each individual based on medical and family history. However, there are some tests that are used as a part of the standard work-up that have been shown to have good diagnostic yields for the population of ASD patients. These include the following (Fig. 2 depicts the algorithm for the diagnostic work-up for nonsydromic ASD): 1. DNA-based chromosome microarrays (CMAs): This technique has a 14%–18% positive yield for detecting CNVs in the ASD patient population. Some studies report an increase in positive findings as high as 30% (Schaefer et al., 2010). CMAs are now established as first-tier tests for patients with developmental delays, ASD, and other neurobehavioral disorders. The advent of CMA use in the ASD population has flooded the field with more than 2000 CNVs that cover the entire genome, and contain genes involved in diverse pathways. These data have confirmed the profound diversity at the genotypic level of the already recognized heterogeneity of the clinical phenotype characteristic of the autism spectrum

Autism: An approach Birth history

Developmental Hx

Family history

Pregnancy hx—fever, medications... Gestational age Birth weight, height, FOC...

Motor development Speech/communication Social, behavior Intellectual

Development, intellect Seizures, epilepsy Neurological disease Behavioral disorders Cancer

Phenotype/physical exam Height, weight, FOC Dysmorphisms (malformations) Neurological exam Neuropsychological assessment

Diagnostic testing General MRI/EEG FRAXA CGH Metabolic screen FIG. 2 Diagnostic algorithm for autism.

More directed Autism/ID panel Macrocephaly panel Microcephalic panel Rhett panel Seizure/epilepsy panel

Across the board Whole exome sequencing Whole genome sequencing

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TABLE 1 Newer CNVs associated with ASD. 

1q21.1

del



5q35

del



7q11.23

del



15q11–13

del (Maternal—Angelman/Paternal—PWS)



16p13.11

del



16p11.2

del



17p11.2

del



17q12



22q11.2

del/dup



22q13.3

del (PMS)

Reference for preceding CNV, Leppa; V. M., Kravitz, S. N., Martin, C, L., Andrieux, J., Le Caignec, C., Martin-Coignard, D., et al. (2016). Rare inherited and de novo CNVs reveal complex contributions to ASD risk in multiplex families. American Journal of Human Genetics, 99(3), 540–554.

disorders. At the same time, it has resulted in the identification of new CNV syndromes, elucidated the function of new genes important in brain development, and has allowed the recognition of at-risk individuals. (Table 1 is a list of newly identified CNV syndromes in ASD.) 2. Fragile X testing: The phenotype of Fragile X syndrome has evolved significantly in the past several years. There is no longer a specific constellation of physical findings that define a Fragile X phenotype. Due to the dynamic nature of these mutations, there is extreme variability in its phenotypes. For these reason, a Fragile X (FRAXA) test is recommended as part of the standard diagnostic work-up of ASD patients. 3. Next generation sequencing (NGS) panels: The vast number of gene mutations and CNVs associated with ASD (800 + genes, 2100 + CNVs) have increased the complexity of defining molecular events that give rise to autism. Indeed, at times, it seems that the individual mutations appear to be unique for each family. Additionally, associated features of intellectual disability, seizures, and other co-morbid features lead to difficulties in assessing prognosis and potential therapeutic interventions. Advances in the technology available for assessing individual patients and their families have been greatly enhanced by the development of NGS panels that specifically identify genes that have been identified based on the concurrence of clinical features, such as intellectual disability and “autism,” or seizures and “autism.” This approach has allowed the recognition of genetic etiologies in a significantly greater number of patients (5%–7%) (Poultney et al., 2013) at less cost than whole exome/genome sequencing, and it more readily allows identification of significant mutations that are, as a rule, missed by CMAs. There are numerous examples of relevant NGS panels that highlight the coexistence of autism with easily recognizable clinical features such seizures, microcephaly, intellectual disability, and motor impairments. These targeted panels, because of their increased specificity, result in a higher yield in diagnosis than would occur with WES. The use of these panels has identified both de novo and heritable variants that are now well defined as part of the autism spectrum. The genes included in these panels are involved in diverse products and functions, including post-synaptic adhesion proteins, chromatin modifiers, ubiquitin proteins, cellular proliferation, and methylation. 4. Brain imaging and neuroanatomical studies: Regions thought to be associated with autism include the amygdala, hypothalamus, the anterior cingulate cortex, the prefrontal cortex, the cerebellum, the cingulate, and the limbic system. Individuals with autism lack central coherence, defined as the cognitive ability to bind together a jumble of separate features into a single coherent object or concept. Reduced information transfer is thought to be a consequence of local “over-connectivity” or long range “under-connectivity.” Over-connectivity occurs when sensory input yields abnormally larger deviations with a reduction in selectivity of regions activated. Therefore, brain regions subserving integrative functions should manifest reductions in activation, and functional correlation with sensory regions (Belmonte et al., 2004). Changes in connectivity have been noted as early as 6 months of age in ASD patients and high-risk infants (Lewis et al., 2017). In addition, the corpus callosum facilitates long-distance integration within large brains, and it is postulated that brain size reflects the brain’s microstructure and the relationship between structural versus functional connectivity (Kennedy, Paul, & Adolphs, 2015). Thus, the use of brain imaging in the diagnostic work-up for

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nonsyndromic ASD patients should be individually tailored to each patient, based on clinical and family history. Overall, brain MRIs are the technique most commonly used due to the noninvasive nature of this diagnostic modality. The potential for a dramatic application of diagnostic imaging to study the pathophysiology of autism is a consequence of employing this modality to study brain connectivity during fetal development. Earlier work established the methodology (Seshamani et al., 2016). And the study published recently (Thomason et al., 2017) documented its applicability, presenting data on 32 pregnancies studied during weeks 22–36 of pregnancy. These latter investigators provided data suggesting that altered brain connectivity in the “pre-language region” was demonstrable prior to birth, and that it related to prematurity and an altered prenatal milieu. These observations establish the potential both to study and identify the underlying prenatal pathophysiology of autism, as well as opening new vistas for intervention. 5. Additional tests may be useful to define the particular clinical phenotype in each patient. Such tests could include metabolic panels, organic acids, amino acids, and mitochondrial studies. “We haven’t been able to determine, in terms of genes, what makes a human being a human, and not another mammal” (to quote Walter Gilbert, PhD, Nobel prize in chemistry). “Consider the human brain, a collection of 86  109 neurons, with trillions of connections—yet it orchestrates everything from understanding, and memory, to movement and sleep.”

4

Pathway analysis and molecular networks

The typical psychological features of autism (dysfunctions in social perception/cognition/communication, and the restricted, repetitive patterns of behavior, interest and activities) appear to be independent from the “causes” of autism, yet they seem to be part of a common pathophysiological pathway, that is, a specific altered global network or altered connectivity. Potential genetic mechanisms possibly accounting for this phenomenon during brain neurodevelopment include: ○ ○ ○ ○ ○ ○ ○ ○

Gene haploinsufficiency Gene mutation leading to truncation and loss of alternative transcripts Misregulation of activity-dependent splicing networks Loss of transcription binding sites Altered regulation by DNA methylation Imbalanced transcriptional regulation involving mRNAs LncRNAs’ roles in epigenetic regulation, gene expression regulation Dysregulation of development secondary to nutritional, toxic stimuli.

The majority of the data supports the hypothesis that genes involved in synaptic elimination are enriched in de novo autism mutations, a potential mechanism for autism. Examples include SHANK, PARK2, CASP6, MAPK3, NLGN, NRXN, CTNNB1, NGFR, and PTEN (Provenzano, Chelini, & Bozzi, 2017). In addition, transcriptome analysis by several investigators (Ram Venkataraman, O’Connell, Egawa, Katherine-haghighi, & Wall, 2016) have documented altered expression of gene networks involved in synapse development, neuronal activity, and immune functions, while others have documented changes in glutamatergic and GABAergic brain receptors in ASD (Ram Venkataraman et al., 2016). Likewise, the role of chromatin modifiers, postsynaptic proteins, epilepsy, dysregulation of protein synthesis, and protein degradation have all been associated with the pathogenesis of ASD. The following is a summary of the most common subcategories identified to date (Chahrour et al., 2016; O’Roak et al., 2014): ○ ○ ○ ○ ○

High confidence ASD genes: chromatin modifiers, including CHD8, CHD2, ARID1B Embryonic expression genes: TBR1, DYRK1A, PTEN Postsynaptic proteins: GRIN2B, GABRB3, SHANK3 Recessive loss of function genes: CNTNAP2, SLC9A9, NHE9 Epilepsy, ASD, intellectual disability associated genes: BCKDA, CC2D1A, hypomorphic missense variants: AMT, PEX7, VPS13B, gene clusters, dysregulation of protein synth.: FMR1, TSC1/2, PTEN, protein degradation (ubiquitination): UBE3A, HUWE1, UBE3C, USP7)

Some of the pathways incriminated in such synaptic events include Akt/mTOR (rapamycin) signaling, the downstream effects of this network on molecular nodes including FMR1, PTEN, TSC1, and TSC2, as well as the Wnt signaling pathway. The consequences of these effects impact cellular growth, proliferation, and cytokine production. mTOR, decreased glycogen synthase kinase 3alpha activity, decreased tuberin, and increased activity of the Akt/mTOR pathway have all been reported (Mellios et al., 2017; Onore, Yang, van dear Water, & Ashwood, 2017).

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Additional studies have identified changes in various networks, as well as changes in miRNA, lncRNA, and differences in the cellular composition of various brain regions (Edmonson, Ziats, and Rennert, 2014; Mahfouz, Ziats, Rennert, Lelieveldt, & Reinders, 2015; Ziats & Rennert, 2011; Ziats & Rennert, 2013; Ziats & Rennert, 2014). Newer approaches utilized neurons differentiated from induced pluripotent stem (iPS) cells to study differences in neural electrophysiology and transcription in ASD (Kathuria, Sala, Verpelli, & Price, 2017; Lin et al., 2016; Liu et al., 2017; Nagy et al., 2017; Tamburini & Li, 2017; Yazawa & Dolmetsch, 2013). Prenatal and postnatal studies have elucidated the role of essential genes (EGs) in ASD. Ji, Kember, Brown, and Bucan (2016) postulated that genes essential for completion of prenatal and postnatal development are enriched, and when mutated, give rise to human disease. Data reveal that approximately 30% of protein coding genes (6000) are essential for pre- and postnatal survival. One hundred of these essential genes (EGs) are associated with autism; their function is central to the processes of transcriptional regulation, chromatin modeling, and synaptic functions. Transcriptional regulation genes predominantly act as chromatin modifiers during brain development, whereas synapse protein/genes tend to code for post-synaptic adhesion molecules. These play a role in protein-protein interaction networks. Their impact is reflected in the consequence of their haploinsufficiency. Last, haploinsufficiency of the neuronal splice regulator nsR100/SRRM4 gene (microexon splicing program misregulated) has been identified in 30% of analyzed individuals with ASD (Irinia et al., 2016). Additional pathways continue to be proposed as possibly functional in the pathophysiology of ASD. For example, Konopka and Roberts (Konopka & Roberts, 2016) have identified central genes in networks that are fundamental in the processes of speech and social communication, such as FOXP2,1, CNTNAP2, GNPTG, NAGPA, GNPTAB, and TSC1 involving the basal ganglia, cortex, and cerebellar pathways (Dempsey & Sawtell, 2016). Another example includes the mutations in human accelerated regions (HARs) that disrupt cognition and social behavior (Doan et al., 2016); these contain conserved genomic loci and display elevated divergence in humans. Mutations in these genes are likely to impact cognitive and social disorders. Doan et al. (2016) identified a significant excess in individuals with ASD in cases whose parents share common ancestry, compared with familial controls, suggesting a contribution of consanguinity in 5% of ASD cases. Another evolving pathway is MIR137, which has also been implicated in the pathophysiology of ASD (Doan et al., 2016; Quesnet-Vallieres et al., 2016). The correlation of MIR137 SNPs (brain) and synapse function has been documented; it targets the expression of CPLX1, NSF, SYN3, and SYT1, SYN3 (22q12-13), cohesin neuronal phosphoprotein in Rett syndrome. More recently, Mellios and Sur (2012) demonstrated that miRNAs regulate early neurogenesis by modulating the ERK/AKT signaling pathways. These investigations, if replicated, have important future applications both to define the pathophysiology of these disorders, and to suggest new approaches for diagnosis, as well as potential models to develop “precision” therapy. The current classification system of neurodevelopmental disorders is based on clinical criteria; however, this approach fails to incorporate what is known about genomic similarities and differences among closely related clinical neurodevelopmental disorders. Ziats, Rennert, and Ziats (2019) proposed an alternative clinical molecular classification that contrasts (ASD), and syndromes with autistic features, based upon molecular pathways that define fundamental pathophysiologic mechanisms. This schema (Fig. 1) of known genetic mutations identifies molecular pathways that characterize autism syndromes into three clinical molecular nodes: ion transport disorders, cellular synaptic function disorders, and transcriptional regulation disorders. It further identifies salient clinical characteristics that may guide diagnosis, prognosis, and treatment. Approaches such as these may lead to a shift from the current clinical classification toward one that identifies molecularly-driven pathways. This may result in more precisely guided clinical decision making, and may herald more informative future clinical trials and drug development.

5 Genetic counseling of autism spectrum disorders The phenotype of the autism spectrum has evolved from a rare condition into a complex heterogeneous neurobehavioral disorder (Chaste et al., 2017) with a heritability of approximately 60%–95% (Finucane & Myers, 2016), and an incidence of 1 in 68. Assistance of these families requires a comprehensive plan of interventions and consults that are best managed by genetic counselors (GCs). The diagnosis of ASD sets forth a chain of reactions that begins the long process of resetting goals and expectations. GCs play an important role in helping families during this process by educating the family, and managing the patient’s care. There are two main areas of focus for the GC.

5.1 Navigating the diagnostic work-up Performing diagnostic tests (genetic and nongenetic) in patients newly diagnosed with ASD is a standard of care (see the preceding section describing the diagnostic work-up). The current guidelines for genetic testing of ASD patients include

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deletion/duplication analysis, next generation sequencing panels, and/or whole exome sequencing (WES). Usually, these tests are done in tiers to minimize cost. This, however, increases the waiting time, and prolongs the uncertainty. There are three possible outcomes in this battery of tests. The first one is that a causative link is established, be it a mutation or a deletion, or a duplication that has been seen before in patients who are on the spectrum. The second one is that there are no genetic defects identified. And the last one is that a variant of unknown significance (VUS) has been identified, or a deletion/duplication not previously identified has been found. The pre-test counseling session should include assessment of the family’s understanding of the purpose of the tests, explanation of these three possible outcomes and implications of results, a review of possible inheritance patterns of ASD, and a discussion of the psychosocial impact of genetic testing. The majority of ASD patients will fall in the second category where no genetic defects are found. Thus, the uncertainty about the cause of the disease and lack of clinical framework remains with the family. In this case scenario, the parents need to be reassured that all available tools for finding a diagnosis have been used, and that yearly follow-ups will address retesting with newer technology, if available. The group of patients that fall in the VUS category will be left with a degree of uncertainty. The genetic counselor should work with the testing laboratory to request additional testing of family members that may help shed light into the VUS in question. In addition, most laboratories that perform WES and/or NGS offer free re-analysis after one year. This will include the newest data on previously unclassified variants, and may prove to be informative. Those in the third category who are found to have a genetic defect that has been previously associated with ASD will have a clearer understanding of the etiology of ASD in their family. The GC needs to make sure that genetic and phenotypic heterogeneity are discussed in detail when counseling this group.

5.2 Managing the patient and helping the family cope Every family has the expectation of having a “normal” child, and when the child is diagnosed with ASD, the process of resetting goals and expectations begins. The GC assists the family in this process by helping them manage the symptoms, and by resetting specific goals. Examples of this process would be from having a child that speaks to having a child that communicates, from having a normal child to having a happy child, reducing spasticity or hypotonia, and so forth. A patient with ASD requires a comprehensive plan of interventions that includes many specialists and multiple evaluations and resources. Parents often feel overwhelmed, and they frequently doubt themselves in their role as caretakers of a child with ASD. It is thus important to reassure them and assist them in this process. 1. Discussing recurrence risk: One can characterize recurrence risk in autism into three broad categories, specifically, sporadic, low risk families, and those in whom there is a relatively high risk of recurrence. In sporadic autism (low-risk), spontaneous mutation with high penetrance in males and relatively poor penetrance in females is generally the case. In high-risk families, the mother often carries a new causative mutation, but she is unaffected, and transmits the mutation in dominant fashion to her offspring (Lin et al., 2016). Most cases of syndromic autism have an established pattern of inheritance (autosomal dominant, autosomal recessive, or X linked), with an associated recurrence risk. Thus, discussion of recurrence in future offspring is simple. In contrast, the recurrence risk in cases of idiopathic ASD is complex, and may involve genetic, epigenetic, and environmental factors. Thus, the GC will rely on empirical recurrence risk data, and available information from previously reported cases. Again, genetic and phenotypic heterogeneity needs to be included in this discussion. 2. Discussion of prognosis: The clinical phenotype of ASD is highly variable, even in patients who share the same genetic defects. Thus, predicting clinical outcomes in this patient population is filled with uncertainty. ASD patients share core clinical findings that define them as autistic (some degree of social and communication impairment, and repetitive/ restricted behaviors). However, the spectrum of clinical presentations ranges from mild to severe in every aspect of the disease. When a child is newly diagnosed with autism, parents are haunted by fears of what the future holds. It is thus difficult to engage the family in focusing on immediate goals, and on celebrating small victories. With time, most families learn to appreciate the progress the child is making, and to avoid comparing their child with typical children.

6

Summary

Autism spectrum disorders are a group of syndromes that share the behavior pattern identified by Kanner (1943) as “inborn autistic disturbances of affective contact.” The application of molecular diagnostics/technology has resulted in the identification of hundreds of gene variants and mutations that give rise to its genetic heterogeneity—the challenge for the

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clinician/investigator is to define the molecular networks (pathways) that reveal the pathogenesis of the social communication deficit, and its overlap with the pathways that give rise to the syndromic phenotype. The development and use of therapeutics to treat patients is the objective of precision medicine/psychiatry. Such an approach will lead to therapeutics that will alter disease manifestations based on the molecular homologies that define the syndrome. To establish “precision psychiatry” for a complex array of diseases known as ASD, future research is vital. “Big data,” the establishment of databases that contain carefully acquired phenotypic information (including historical, medical/clinical evaluations, neuropsychological characterization), and genetic assessment, including whole exome/genome sequencing, will be essential to define the molecular basis of the expression of ASD’s behavioral and phenotypic elements. Biological computation delineating elements of neural molecular pathways and their correlation to behavioral traits highlighting the nodes and edges of these networks will be required. A shift in our traditional mode of analysis of diseases is requisite—we now focus on disease manifestations and correlate them to identified molecular (genomic and proteomic) pathways. Because “autism” is an exaggerated expression of behavioral traits, it is essential to explore the genetic correlates by recognizing genes, and their polymorphisms, to understand the neural networks and their intersections (nodes) with pathways operative in diseases such as FRAXA, NF1, tuberous sclerosis, Rett syndrome, and others. Targeted therapeutics can be developed for those molecular nodes. Similarly, research efforts to better define psychiatry syndromes on the basis of their molecular pathways (computational analysis) will lead to development of more effective and new therapies. Exciting new horizons will result from the increasing use of transgenic animal models of ASD, as well as the increased development of iPS-derived neuronal cultures and organoid cultures for the study of the pathophysiology of ASD, and as model systems for development and assessment of pharmaceuticals for therapy.

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Further reading Han, J., Sarkar, A., & Gage, F. H. (2015). MIR137: Big impacts from small changes. Nature Neuroscience, 18, 931–932. Leppa, V. M., Kravitz, S. N., Martin, C. L., Andrieux, J., Le Caignec, C., Martin-Coignard, D., et al. (2016). Rare inherited and de novo CNVs reveal complex contributions to ASD risk in multiplex families. American Journal of Human Genetics, 99(3), 540–554.