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Special Article

Phenotyping, Etiological Factors, and Biomarkers: Toward Precision Medicine in Autism Spectrum Disorders


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Journal of Developmental & Behavioral Pediatrics: October 2016 - Volume 37 - Issue 8 - p 659-673
doi: 10.1097/DBP.0000000000000351
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Significant progress has been made in understanding the biology of autism spectrum disorder (ASD) over the past decade. However, effective biological interventions for the core symptoms remain elusive. Instead of a single or even a small set of causes, a consensus has emerged that genetic and environmental causes of ASD are likely multifactorial. The genetic architecture of ASD has become increasingly clear and increasingly complex with estimates of at least 1000 genetic alterations associated with the risk for ASD.1 These findings hint at starting points for patient stratification and precision medicine for ASD, and indeed, gene targeting has spawned efforts at clinical trials. For example, research exploring the synaptic mechanisms impacted by the fragile X gene in multiple preclinical animal models has led to trials in fragile X and ASD with negative modulators of metabotropic glutamate 5 receptors.2 Evidence from other case series3,4 has fostered clinical trials that aim to modulate glutamatergic and GABAergic functions. Despite the promise of targeted therapies based on a biological rationale, much heralded trials with agents such as the GABA B receptor agonist arbaclofen failed to reveal significant effects for the selected primary outcome measures in Phase II clinical trials.5 This perceived “failure” is likely due to the etiological heterogeneity of the subjects with ASD who received the specific treatment. A review of the data for the arbaclofen study suggests a strong positive response for at least a subset of fragile X and patients with ASD. Positive responses in some individuals, but otherwise statistically nonsignificant beneficial group effects, are characteristic of most of these early pharmacological treatment trials of ASD. Thus, a critical challenge is to identify those individuals (or a subset of individuals) who may benefit from a particular treatment in a clinical trial. A meeting was convened on October 18, 2014, at the University of Missouri and the Thompson Center to foster discussions on strategies for stratifying patients with ASD for the purpose of translating this information to targeted and individualized experimental therapies, a core principle of precision medicine. Attendees agreed that the ultimate development of biomarkers would allow for patient stratification in treatment trials and could translate into safer and more effective individualized treatments. The “white paper” presented here articulates the challenges involved in developing better diagnostics and treatments based on individual biomarkers, and provides some suggestions for future solutions.


Autism spectrum disorder (ASD) encompasses a wide range of clinical presentations.6,7 Heterogeneity can even be observed in the former nomenclature for autism spectrum disorder, consisting of autistic disorder (impaired communication and socialization, repetitive behaviors, and onset before age 3), Asperger disorder (without delays in language or cognitive development), and pervasive developmental disorder—not otherwise specified (features of ASD but not meeting criteria for either autistic disorder or Asperger disorder).8 As a result, multiple studies have attempted to suitably cluster symptoms in large populations. A number of studies have explored factor analysis to determine the structure of symptoms, focusing on “core features” of ASD, revealing a variety of sets of clusters, but overall suggesting that social/communication issues may be distinct from restricted and repetitive behaviors and interests.9 Another recent study identified 4 phenotypic clusters and found that they varied in short-term prognosis regarding diagnostic stability.10 To understand how ASD-related characteristics are manifested in the general population, one recent study clustered 2343 cases based on the autism spectrum quotient (AQ), revealing 2- and 3-factor solutions varying in combinations of severity of impairments in socialization, mentalizing, and orientation to detail.11

Autism spectrum disorder can also be associated with a range of co-occurring medical and/or psychiatric conditions, including seizures, gastrointestinal conditions, sleep disturbances, aggressive behaviors, anxiety symptoms, and attentional deficits. These conditions may or may not be associated with cognitive impairment. One recent study also incorporated co-occurring medical and biological variables in the generation of data-driven phenotypic clusters, revealing clusters for (1) circadian and sensory dysfunction, (2) immune abnormalities, (3) neurodevelopmental delay, and (4) stereotypic behaviors in one analysis of ASD-associated features.12 Although the best course of treatment is clear for some of these conditions (i.e., treat seizures with antiepileptic drugs), it is not known how these various co-occurring phenotypic aspects might relate to potential targeted treatment of the core features.

Several studies have assembled a rich phenotypic database in cases in which genetic information is available, yielding a set of genotype–phenotype clusters.13–15 Additionally, “complex autism,” characterized by the presence of prominent dysmorphic features suggesting altered early morphogenesis, has been found to be associated with greater impairment and a markedly higher rate of chromosomal disorders or broader syndromic conditions in which ASD is a common manifestation.16,17 Other studies have also examined how clusters derived based on diagnostic scores relate to detected genomic variations.18,19 Distinguishable subphenotypes of ASD for transcriptomic and genetic analyses have been found based on multivariate cluster analyses of severity scores queried by the Autism Diagnostic Interview-Revised diagnostic instrument,20,21 revealing differential gene expression (relative to nonautistic controls) by the ASD subtype22 as well as subtype-dependent single-nucleotide polymorphisms23 and linkage regions,24 with class prediction analyses suggesting the potential for developing biomarker screens.25 One group more recently subtyped ASD into 2 networks of highly connected genes,26 while others have examined genetic factors associated with ASD in 6 genetic syndromes that increase the risk for ASD, finding that the pattern of genetic factors could be applied to detect a similar signature in idiopathic ASD.27 Another approach looked at genetic profiles of specific symptoms, such as impaired social communication,28 and yet others have found evidence that the core deficits are genetically heterogeneous.29

Clearly, subtyping of ASD according to the cause or pathobiology could be highly relevant to individualized treatment. With at least several hundred to 1000 different genes1,30 estimated to play a role in ASD risk, development of a “one-size-fits-all” pharmacological intervention would be tremendously challenging. Some of these genes contain rare variants with high penetrance and are directly involved in the cause of ASD, whereas other genes serve as risk factors for ASD that may act in concert with other genetic or environmental risk factors.1,31–34 Some individuals who harbor such variable penetrance variants develop ASD, whereas others harboring the same variants do not. In most cases, no genetic factor is identified to contribute to the diagnosis. Increasing recognition of environmental factors that seem to contribute to ASD may impact a cause–phenotype map. Therefore, a detailed assessment of factors in ASD that would be potentially meaningful in guiding a precision medicine approach must also explore a range of factors beyond genotype and clinical phenotype.35 Finally, it is not yet known whether the heterogeneity of ASD in this context is represented by continuous variability on multiple dimensions or is represented by clusters, which may also have implications for treatment.


Biological markers, or “biomarkers,” are broadly defined as a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacological responses to a therapeutic intervention.36 For autism spectrum disorder (ASD), a broad range of candidates could be considered relevant biomarkers. Biomarkers can be markers of brain activity or anatomy (electroencephalogram [EEG], imaging), genetic, epigenetic, or proteomic, and metabolomic markers (which can range from indicators of immune function, to oxidative activity, to neurotransmitter function). Broadly speaking, clinical outcomes, that are based on direct assessment of the individual, could be considered as markers as well. Certainly, medical (seizures, sleep disturbances, gastrointestinal conditions) and psychiatric comorbidities (aggression, anxiety, attentional deficits) can direct a pharmacological treatment approach, as described in the previous section. For other markers more specific to ASD, the relationship to treatment is less clear. Specific core features such as social communication and reciprocity deficits, repetitive behaviors/hyperfocused interests, and associated features such as sensory hypersensitivity may represent relevant treatment targets. Additionally, the developmental trajectory must be considered in any biomarker approach, as mechanisms of actions that impact the developmental trajectory of neural systems at one stage may have an entirely different relevance at a later stage.37 Thus, a developmental systems approach is also necessary in consideration of relevant biomarkers.

The Research Domain Criteria (RDoC) initiative at the National Institute for Mental Health38 targets specific feature domains as an approach to research across a range of mental disorders, an important consideration in light of the marked heterogeneity of ASD. It proposes the use of targeting symptom domains rather than focusing on targeting a diagnosis that represents a constellation of symptoms. In ASD, it will be important to determine how the severity of specific symptoms cosegregates with the presence of genetic or nongenetic biomarkers, including anatomical and functional indicators of alterations of neural systems, while accounting for the relevant impact of the developmental trajectory, to best facilitate symptom-specific individualized treatment approaches. Although previous work, described in the previous section, has examined the association between genetics and clinical markers,17,19,28,29 incorporation of other molecular data (e.g., transcriptomes39) will also need to be considered.

In addition to protein-coding genes, noncoding RNAs may also play a role in the cause of ASD, as shown by dysregulation of microRNAs in ASD.40–44 A recent genome-wide association study (GWAS) identified a significant association with an single nucleotide polymorphism that is not located on a coding region but rather resides in a noncoding RNA that is an antisense inhibitor of the gene for moesin, a protein that regulates neuronal architecture.45 This discovery demonstrates the potential contribution of noncoding RNA in ASD risk. Furthermore, evidence of convergence of the molecular pathways has been reported at the alternative splicing46,47 and transcriptome level,48 and the importance of mRNA expression has been increasingly emphasized in recent years.32,33,49 Other epigenetic markers have also been identified in association with ASD.50,51

In general, the roles of DNA methylation, histone acetylation, and microRNA markers in ASD are less well understood at this time. However, these issues have become increasingly important in other fields of medicine, such as in the treatment of cancer. With many different types of cancer, recent research has revealed the importance that biomarkers play in optimizing treatment approaches. DNA methylation patterns have predicted who is most likely to respond to certain glioblastoma therapies,52 and it is widely known that hormonal markers predict the response to hormone therapy in breast cancer.53 Therefore, related approaches may become increasingly important in ASD.

Particular attention should also be paid to biomarkers that have a relevant function or relationship to neural systems responsible for the expression of particular phenotype(s) during specific developmental epochs. For example, certain synapses or regional circuits may be excitatory during one phase of development but inhibitory during another epoch. This synaptic physiology may differentially affect expression of a given phenotype or biomarker during specific developmental periods. Abnormalities in the glutamatergic and GABAergic systems have been observed with some consistency in ASD in postmortem brain studies,54–56 and in vivo with regional findings from magnetic resonance spectroscopy (MRS),57,58 or when expressed as a ratio of GABA to glutamate with MRS.59 There may also be the potential for peripheral measurements.60 Mutations affecting the GABAergic system have also been associated with ASD.61,62 As recent large clinical trials have attempted to target glutamatergic (memantine) and GABAergic (arbaclofen) systems,5 markers representing activity in these systems, as assessed by MRS or other proxy markers such as EEG gamma band activity,63 would seem highly relevant.

Other markers that may be relevant for treatment might include immune markers that are often atypical in ASD,64,65 whole-blood serotonin,66,67 genetic polymorphisms that impact the serotonergic system,68 or serotonin ligand markers on positron emission tomography.69–73 Oxidative reactivity,74 and psychophysical reactivity indicative of sympathetic/parasympathetic tone,75 may identify subjects who may be more responsive to metabolic or adrenergic treatments.

Aside from the role of MRS described above, the plethora of neuroimaging findings (especially from anatomical and functional MRI and from more recently diffusion-weighted imaging) raises the question whether any of these may reliably relate to etiological and other biomarkers discussed above, or whether some imaging findings themselves may be considered biomarkers of ASD. A serious challenge is the frequent lack of replication of functional and anatomical imaging findings, which can be in part attributed to methodological issues,76,77 and the complexity of maturational trajectories.78 Functional and anatomical information about the brain is being made available through the large neuroimaging initiative, the Autism Phenome Project (APP), exploring brain size and structural changes across development in a systematic manner.79 Additionally, the NIH-funded Autism Centers for Excellence Program include some projects related to this issue, including brain imaging studies looking at neurodevelopmental patterns associated with genetic variants, studies examining predictors of social disability and language development, and studies aimed at better understanding the role of gender in ASD.80 The grass-roots datashare consortium ABIDE81 provides large multiscale samples of resting-state functional magnetic resonance imaging (MRI) and anatomical MRI data that may be leveraged for identification of imaging-based ASD subtypes.

Common sample size limitations highlight an additional deeper problem: the presence of expected,82,83 but currently unknown etiological subtypes of ASD. In typically sized samples of 20 to 50 participants with ASD, different (unknown) subtype compositions may bias imaging findings one way or the other, resulting in divergent findings across groups and studies. However, a recent study by Ellegood et al.84 found that 26 different ASD-associated mouse models converged onto 3 clusters of brain anatomical features from MRI, which suggests that outcome neuroimaging may be a powerful tool in the detection of ASD subtypes with specific treatment response, despite genetic heterogeneity. Among the well-replicated imaging findings in ASD is anatomical overgrowth in the first postnatal years,85–89 with some concordant evidence from diffusion-weighted imaging.90–92 Because early overgrowth is found not only in gray but also in white matter, it may be a causal contributor to network connectivity abnormalities that have been detected in numerous functional connectivity MRI studies of older children and adults with ASD,93 despite residual uncertainty as to the implications of underconnectivity94 versus overconnectivity95,96 findings. Functional connectivity has also been proposed as a biomarker with relevance to treatment.97 These factors also need to be framed in terms of the neural systems impacted during specific developmental time windows. Because several factors may have critical temporally specific effects on neural systems, resulting in the phenotypic expression of certain behaviors, cognitive dysfunction, or other comorbidities, numerous biomarkers should be considered to best facilitate symptom-specific individualized treatment approaches, and move toward personalized medicine in ASD.

What Are Examples of Biomarkers That Have Guided Clinical Treatment?

There are a number of examples of in which biomarkers have facilitated treatment trials and individualized medicine approaches. Application of individualized medicine has been particularly helpful in the field of oncology. Although most aspects, including the pathobiology and the treatment goals, in oncology do not relate to autism spectrum disorder (ASD), lessons can be learned from this field due to the fact that it is characterized by considerable etiological heterogeneity.98 Recent research has revealed that biomarkers in breast cancer not only provide valuable prognostic information, but can also guide therapy.99 For example, amplification of the oncogene human epidermal growth factor receptor 2 (HER2) predicts good response to anti-HER2 targeted treatment in breast cancer.100,101 Similarly, presence of estrogen receptors in breast cancer predicts a significantly better response to tamoxifen.53 Other areas of cancer treatment have been impacted as well. The presence of the KRAS mutation predicts response to anti-epidermal growth factor receptor (anti-EGFR) monoclonal antibody (MoAb) therapy in the form of cetuximab or panitumumab for metastatic colorectal cancer.102 The value of molecular subtyping has also been recognized outside the field of oncology, such as in cystic fibrosis. In studies attempting to improve lung function by increasing the activity of the cystic fibrosis transmembrane conductance regulator (CFTR) protein, the presence of at least 1 copy of the G551D-CFTR mutation enhances response to ivacaftor, a potentiator designed to increase the time that activated CFTR channels stay open.103 Thus far, ASD has not yet benefited from this approach.


Hu and Lai25 and Pramparo et al.104 have reported panels of differentially expressed genes that may potentially be used for diagnostic screening. To explore genetic markers for autism spectrum disorder (ASD), several large genetic collaborations have been undertaken. The Autism Genetic Resource Exchange (AGRE) is a research repository that has collected genetic information, clinical information, and biomaterials from over 2000 families, focusing on families with 2 or more children with ASD.105 The Simons Simplex Collection (SSC) has collected genetic samples and detailed clinical information from 2600 families with 1 affected child with ASD,106 and The Autism Simplex Collection (TASC) has over 1700 families with 1 affected child with rich phenotypical information.107 Other much larger efforts (>10,000 participants) being developed include MSSNG (, a collaboration between Autism Speaks and Google to create the largest genomic database on autism, and Simons Foundation Powering Autism Research (SPARK) (, funded by the Simons Foundation, which will establish a genotyped research cohort of 50,000 individuals with ASD and their families across the United States. Although the baseline phenotypic information required from all participants in SPARK will be relatively low, much effort will be directed at engaging participants, to increase their interest in deeper phenotyping efforts. Participants in this cohort will donate saliva biospecimens by mail, and further genetic analyses will deepen the field's growing knowledge of the major genetic factors that play a role in ASD. The large cohort will be open to recontacting from the research community to ultimately enable more genotype-driven clinical research in ASD, which may translate into genotype-driven therapeutics and treatment of ASD. Also, The Autism Treatment Network (ATN) has phenotypic information incorporating data with a particular emphasis on medical comorbidities on nearly 7000 patients ranging from ages 2 to 18,108,109 and biomarker sample collection has occurred in a subset of these patients.


Although biomarkers alone may provide critical information regarding an individual's underlying autism spectrum disorder (ASD)-associated biology, phenotypic information would provide additional critical data, that is also more readily available in the clinical setting, which would allow the researcher or clinician to select the optimal individualized treatment for each patient. Additional clinical information may further interact with the relationship between a biomarker and a treatment response. Such incorporation of rich behavioral and phenotypical information alongside the biological information allows the clinician to identify characteristics that might be associated with these biomarkers for the prediction of a best treatment plan. As described with the NIMH RDoC,38 this approach allows a more nuanced understanding of potential outcomes that a treatment might target. Previous biomarker development efforts have varied in the degree to which phenotypic information is incorporated. For subtyping patients with potential relevance for individualized treatment, greater phenotypic information will be necessary.


To date, strategies for conducting phenotyping have largely been either data-driven or outcome-based. Most of these previous efforts at phenotyping have been data-driven, as detailed in earlier sections, targeting symptom clustering,11,12 gene clustering,17,19,28,29 and genetics first approaches,110 and most other efforts have focused on deriving phenotypical clusters within autism spectrum disorder (ASD) based on clinical aspects that cosegregate cases within this group. However, we submit that outcomes should become a key aspect of phenotyping ASD for the optimization of treatment approaches. To understand which group of patients responds best to a particular treatment, the phenotypic subtyping of characteristics will be driven by the outcomes. A large data set will ultimately be necessary for this purpose to overcome apparent variability in treatment response due to other factors or confounders (e.g., the effects of day-to-day variability on assessments and placebo effects). However, data-driven and outcome-based phenotypic groupings may have significant overlap. For example, a clinical group associated with similar markers of GABAergic function may respond similarly to administration of GABAergic drugs. Such hypothesis-driven subtyping would allow critical information to be derived from clinical interventions in a considerably smaller population. However, it is also possible that more than 1 data-driven phenotypic group may cosegregate with a similar response to treatment, thus resulting in multiple data-driven phenotypic clusters mapping to 1 outcome-based cluster. Alternatively, there may be 2 different treatment response groups, with opposite response to treatment, within 1 data-driven phenotypic group, resulting from 2 different pathophysiologies leading to 1 common clinical phenotype, but a different response to treatment. In this manner, one data-driven phenotypic cluster could map to multiple outcome-based clusters. To this point, we have very little information on how data-based and outcome-based phenotypes are interrelated. And, as mentioned in previous sections, the development of these clusters must also take into account the interactions with the developmental stage regarding the impact on the effects on the neural systems in moving toward precision medicine in ASD.


Although earlier reports based on twin studies suggested that autism spectrum disorder (ASD) has a heritability as high as 0.9,111–115 recent evidence has suggested that the purely genetic component in the cause of ASD is somewhat less than previously believed.116–118 Although our understanding of environmental causes is far less than that of genetic causes, their impact on the underlying neural systems associated with the expression of ASD must be considered as well. This area of research has increased in recent years, with several environmental factors gaining prominence. These lines of investigations suggest the hypothesis that the ASD “envirome” interacts with specific underlying neural systems (genetically determined) in the developing human brain to contribute to the expression of ASD.

One such nongenetic contributing factor for ASD is immune system dysregulation, which has been frequently described in individuals with ASD and their family members.65 Most notably, mothers of children with ASD have been reported to harbor antibodies reactive to fetal brain proteins, which are absent in mothers of children who are typically developing or of children with non-ASD developmental delays.119–121 The protein target antigens of these ASD-specific maternal antibodies were recently identified; it is the recognition of various combinations of these proteins by maternal antibodies that confers the specificity of maternal antibody-related (MAR) ASD.122 Antibody reactivity to these proteins was noted in 23% of mothers of children with ASD, versus less than 1% in women with typically developing children, which represents a much higher proportion of ASD than any single gene. The etiological relevance of these antibodies is further supported by numerous rodent and nonhuman primate studies in which injection of these ASD-specific maternal antibodies into pregnant animals resulted in MAR autism-relevant behaviors in the offspring.123–127

Exposure to psychosocial stressors128 or tropical storms129 in the late second to early third trimester is also associated with an increased incidence of ASD. Increased risk has also been found with various other stress exposures in epidemiology studies from the Danish and Swedish cohorts.130,131 In one study, this association was specifically present when maternal psychiatric history was incorporated in the data analysis.132 The risk of ASD associated with prenatal psychosocial stress seems to be linked to maternal genetic susceptibility to greater stress reactivity.133 Furthermore, in a rodent model, prenatal stress exposure in offspring of genetically stress susceptible mothers has been shown to result in aberrant social behavior,134 which was also associated with delayed migration of GABAergic neurons during development.135 Maternal exposure to stress before pregnancy and even early life stress is associated with increased risk for development of ASD in subsequent pregnancies according to recent findings in data from the Nurses' Health Study.136,137

Data are also mounting regarding maternal exposure to pollutants resulting in increased risk of ASD. In particular, there is a growing body of literature implicating air pollutants,138–147 with evidence of an interaction with polymorphisms of the MET receptor tyrosine kinase gene.144 There also seems to be a modestly increased risk of ASD with exposure to drugs, including certain serotonin selective reuptake inhibitors (SSRIs) and valproic acid.148,149 Earlier research suggested that the risk of ASD in association with exposure to β2-adrenergic agonists, commonly used to arrest premature labor, is affected by maternal polymorphisms in the β2-adrenergic receptor.150 More such targeted approaches to gene/environment interactions of this type may be helpful in the future, exploring a targeted set of genes most likely to interact with the environmental factor under investigation, in addition to big data approaches to search for the interactions that would not be predicted in this manner. Other factors are also being explored and identified, including pesticides, endocrine-disrupting chemicals, and a host of maternal dietary factors, including a lack of folate supplementation during early pregnancy,151–153 or even excessive supplementation of folate during pregnancy.154 Aside from nutritional factors, the intrinsic hormonal status of individuals (especially elevated fetal testosterone levels) may also increase the risk for ASD, as suggested by the “extreme male brain hypothesis.”155 The nuclear hormone receptor RORA, a regulator of transcription of genes linked to ASD,156 may contribute to elevated testosterone levels by reducing the expression of aromatase.157 Increased parental age and short intervals between pregnancies have also been observed as risk factors.158,159 Other factors have been explored that have not revealed an association with ASD, such as heavy metals.160

These factors are not really biomarkers in and of themselves, but rather potential risk (or protective) factors for ASD on their own or possibly in conjunction with certain genetic profiles. More typical biomarkers are being explored that could be used to indicate past exposures to these environmental factors, such as blood DNA methylation patterns that have emerged as an indicator of smoking history.161 However, this exemplar may not necessarily be salient as a biomarker for ASD. Instead, as this literature evolves, it will be important to include potential environmental causes in the ASD biomarker development process—that is, include environmental factors in outcome-based clustering of patient populations or analyses of genetic contributors. Inclusion of environmental factors has proven valuable in other conditions. For example, genetic effect sizes have been found to vary and even new diabetes-associated specific loci have been identified when body mass index (BMI) and possible interactions with BMI are considered in the genetic analyses.162–165 Thus, the identification of biomarkers, and ultimately perhaps the effectiveness of a treatment for any individual, may be improved by incorporating important ASD “envirome” elements in the process. Therefore, the ASD “envirome” as a risk factor must be considered in future biomarker research as we expand our understanding of these aspects and move toward precision medicine in ASD.


A. Biomarker-Rich Setting for Early Stage Trials to Inform Larger Trials

The incorporation of data on a rich set of these aforementioned factors into large clinical trials would provide important answers regarding which patients are most likely to respond to a given treatment, and which clinical aspect is most responsive to that treatment. However, clearly, this approach is neither practical nor cost efficient during the earlier stages of drug development. A strategy therefore must exist earlier in the stage of identification of distinct compounds, for identifying which biomarkers are worthy of exploration. Major investments in effort should be limited to subsequent larger trials. In light of both the failures of recent large autism spectrum disorder (ASD) trials,5 and the current research funding climate, novel paradigms must be first developed to explore newer agents in future pilot trials. Smaller pilot clinical trials that assess these newer agents should be conducted looking for effects in the overall ASD population. However, each pilot trial should also investigate selective effects in a hypothesis-driven subset of patients that is based on biomarkers expected to be closely related to the response to that particular treatment. Exploratory analyses could also assess whether other potential markers might also be related to treatment response. For example, biomarkers of GABAergic activity might reasonably be expected to predict response to GABAergic agents in ASD, and could therefore serve as hypothesis-driven biomarkers for treatment response in this case. Results from studies at this level could then be incorporated in the planning of larger trials.

This process would exemplify a reasonable model in which neural systems are being targeted, while sensitive to the developmental timing involved. One critical question is whether earlier intervention could lead to improvement not only in symptoms at the time of the trial but also an improved developmental trajectory. Also, it is possible that treatments that seem to benefit early, such as decreased behavior in the setting of administration of medications causing sedation, may not have optimal long-term outcomes. Thus, age of participation and long-term monitoring may be other crucial components to consider for incorporation in future clinical trials.

These aspects could also be considered to mine existing data in the recent “failed” larger trials to systematically identify subgroups that are or are not most likely to be best responders, or to identify subgroups in studies of other agents still currently under investigation, such as glutamatergic markers for memantine,166,167 measures of oxytocin activity for treatment with oxytocin,168–171 or serotonergic markers for buspirone,172 among many possible examples. The response of biomarkers to treatment would also provide critical information that allows for both better understanding of the mechanism of action, and for future research to further refine treatments, through biomarker-targeted trials. Furthermore, conversations would need to occur with the Food and Drug Administration to provide guidance and establish pathways for approval with new drugs discovered in this manner. In recent years, novel drug development has focused heavily on compounds derived from exploration of the mechanism of action of ASD-associated mutations.173 However, the current arsenal of psychopharmacological agents in clinical use today contains no significant direct contribution from drugs from this pipeline.174 Although we believe that further exploration using this mechanistic-driven approach is vitally important in the long term, we acknowledge that an exclusive mechanism-focused approach will very likely miss opportunities for the development of new and impactful treatment options in the near future. Agents showing promise for ASD should thus be explored regardless of whether or not they were derived from a molecular mechanistic approach (Figure 1).

Figure 1.
Figure 1.:
Outline of suggestions for research progress toward precision medicine. A, One critical initial step is generation of establishment of a map of how etiological factors relate to phenotypes and clinical biomarkers in the clinical setting. With this established, one can identify the biomarkers that are associated with specific causes. Furthermore, one can identify a set of other patients with no known cause that may have common biomarkers with a group with a specific cause, which then allows the possibility of determining whether they have a common pattern of treatment response in subsequent trials, either for domain-specific responses or for more global responses. Numerous efforts at exploring biomarkers are underway across a variety of selected settings. B, To allow animal model translation to the clinical setting, these markers should also be explored across a range of animal models. This will allow future testing of new agents across causes and biomarkers. Ellegood et al.84 have done this for brain imaging markers across animal models. C, For drugs that have shown promise in a broad range of patients with autism spectrum disorder (ASD), whether derived from a molecular drug development approach or not, large trials to further explore efficacy should be taken as an opportunity for biomarker discovery, an exploratory examination for particular causes or biomarkers as they relate to treatment response. This will allow future targeted trails to confirm these associations. The effects of treatment on the biomarkers will help with understanding of the mechanism, and will also contribute to subsequent animal model studies to further refine the understanding of the mechanism and develop more targeted therapeutics. D, For drugs with a highly plausible link to particular causes or biomarkers (e.g., GABAergic-related biomarkers for drugs targeting the GABAergic system), or drugs with causes or biomarkers associated with treatment response discovered in the above pathway (in C), targeted treatment trails can be explored, with targeted biomarkers, for which the study should have sufficient power to determine salience of the biomarker for the trial outcome. With monitoring of the effect of treatment on biomarkers, this will also allow subsequent animal models to further refine the understanding of the mechanism and develop more targeted therapeutics. E, Similarly, new drugs derived from induced pluripotent stem cells and genetic animal models would be assessed with targeted biomarkers in the trial setting. If the patients without the predicted markers also show a positive treatment response, though, the drug could then be reexplored in the exploratory biomarker setting, described above (in C), and in other etiological animal models, to identify other biomarkers salient to treatment response and to move toward a better understanding of its impact.

B. Cause/Biomarker Mapping, Allowing Development of Novel Compounds with Animal Models, Translating to the Clinical Setting in a Targeted Manner

The information derived from research in a biomarker-rich environment would inform the development and assessment of novel compounds in animal models. To optimize this approach, a cause/biomarker map should be established from large clinical populations. The knowledge from ASD envirome studies as well as developmental and cognitive neuroscience should modify the cause/biomarker map at appropriate nodes. Genes associated with ASD have been used extensively in the development of preclinical animal models, including mice, rats, drosophila, and zebrafish. These models have subsequently been used for the assessment of ASD risk genes and responses to novel treatments, with the advantage that ASD-relevant behavioral outcomes can be explored.175 Additionally, animal models allow for the assessment of resulting behavioral and physiological effects produced by the genetic mutations and/or experimental treatments that is not possible in humans.

Although therapeutic treatments based on animal studies that use genetically modified animals in this manner can subsequently be explored in a clinical population, one can only be confident of a response in patients with ASD caused by this particular mutation (representing only a small fraction of the overall population), and potentially others with a very similar mechanism. For this reason, the Preclinical Autism Consortium for Therapeutics (PACT) was developed. The PACT aims to use a selection of genetically modified rodent models in a standardized parallel method to assess the effects of potential new pharmacological interventions.176

With an increased understanding of cause/biomarker maps in ASD, the investigator can make even broader predictions regarding who is most likely to respond to a drug beyond specific genetic mutations. By recognizing how different etiological factors modify developing neural systems related to biomarkers and the ASD “envirome,” one can identify clusters of biomarkers that may cosegregate with specific causes. With this information, patients who do not have ASD resulting from a defined cause studied in a mouse model, but rather have similar biomarkers to the particular cause studied, might be expected to respond similarly to the drug tested. For example, patients who do not have a known ASD-associated GABAergic mutation but who have a similar level of GABAergic activity, as potentially assessed by magnetic resonance spectroscopy or electroencephalography markers,57,63 as patients with these mutations might also be expected to respond similarly to drugs developed based on the GABAergic mutation-based ASD mouse model. Furthermore, animal models have also been developed for environmental factors, including prenatal stress models,134 maternal immune models,125,126,177 and drug effect models.148 Therefore, these principles can also be extended to incorporate environmental cause/risk factors onto the cause/biomarker map of developing neural systems. Exploration of the effects of treatment response is now beginning to be explored in the environmental animal model setting as well.178 With this approach, an expanded cause/biomarker map can result in much broader and richer translational impact with new drug development efforts through animal models. Additionally, this approach would also be valuable for refining the understanding of which individuals are most likely to be a best responder to drugs that are not derived from the new drug development pipeline, and may help reveal the mechanism of action for future, more targeted treatment (Figure 1).

C. How Can Induced Pluripotent Stem Cells Support Biomarker Development?

Another important tool in new drug development is induced pluripotent stem cells (iPSCs) in which, for example, skin or hair cell cultures can be generated by inducing adult cells to behave as stem cells, which are then differentiated into neurons or other central nervous system cell types. Studies of iPSC-derived neurons in other diseases show some potential for identifying disease-specific, cellular phenotypes that can lead to potential therapeutic candidates. For example, studies on amyotrophic lateral sclerosis patient-derived motor neurons found that such neurons are hyperexcitable when compared with control neurons and that gene targeting of the disease mutation corrected the hyperexcitable phenotype.179 Furthermore, the drug retigabine was also shown to correct the hyperexcitable phenotype and improves the in vitro survival of patient-derived motor neurons. A clinical trial is now underway to evaluate the effect of retigabine in motor neuron activity in patients affected with amyotrophic lateral scleroisis ( Identifier: NCT02450552). Culturing these cells from individuals with ASD is a direct path to assessing the effects of mutations that are known to cause ASD, using patient-derived iPS cells, for their impact on a broad range of aspects of cell physiology. This allows the efficient assessment of the nonbehavioral impacts of a broad range of gene mutations that would require a large number of animals and a significantly longer amount of time if attempted in vivo. Although there is tremendous potential in this approach, success depends on studying specific cellular phenotypes in specific neural subtypes that are relevant to ASD pathophysiology.

A recent study using 3-D neural cultures (organoids) derived from patient-derived iPSCs with idiopathic ASD found evidence of overproduction of inhibitory neurons, influenced by FOXG1 overexpression.180 Knockdown of FOXG1 by RNAi in ASD-derived organoids restored the balance between inhibitory/excitatory production of neurons, identifying FOXG1 as a potential drug target. Future translational work is needed to test the therapeutic impact of targeting FOXG1 in idiopathic ASD.

Induced pluripotent stem cells offer the advantage of scalability over animal models. Effects of novel therapeutics can be assessed in a very efficient manner with iPSC models.39,181–183 Furthermore, the fact that this is based on human mutations in human tissue provides further advantages in potential translational impact as compared with animal models. However, iPSCs are limited in their ability to explore both nongenetic causes and in cases in which ASD pathology results from effects requiring the interactions of multiple neural systems during specific developmental epochs. There are also significant technical challenges inherent in iPSC research, including challenges in the specification and maintenance of cerebral cortex neuronal cell types. Initial costs and access to technology limit the groups that can undertake large-scale studies across multiple genetic causes of ASD at this point for iPSCs. This limitation may become less problematic as these technologies become less expensive and more widely available.


Given the complexity of the disorder and the heterogeneity of the etiologic and interacting factors, this will be a daunting task. We propose a multipronged approach, which will gather data from multiple sources that can be integrated to tackle the problem. Large studies in clinical populations at specific developmental stages will need to be performed to generate a clearer understanding of cause/biomarker maps of autism spectrum disorder (ASD) (Figure 1). Genetic, epigenetic, environmental, and other aforementioned factors would all need to contribute. A large consortium of ASD clinics/centers will be required to generate this rich data set, acquired according to identical protocols, possibly enhanced by incorporating Web-based or remote data and media capturing. Current clinical databases may have collected similar information regarding ASD features, but are each in different formats, creating a bioinformatics challenge for consolidation into 1 large data set to address issues such as subtyping. The new larger initiatives such as SPARK, described above, may help with efforts to collect such large data sets. With the ability to recontact participants, large size of this cohort will lend itself to genotype-driven clinical research in ASD and studies of environmental risk factors as well.

Valuable information has been gained from larger epidemiology studies regarding environmental risk factors for ASD, such as the Nurses Health Study and the Danish and Swedish cohorts discussed in earlier sections. The Childhood Autism Risks from Genetics and Environment (CHARGE) study is tracking large samples of ASD and unaffected children in a case–control manner to determine etiological contributions from environmental exposures, inflammatory markers, and genetic factors, and their interactions in ASD (, which has already revealed a number of the potential environmental contributors described in earlier sections. Furthermore, studies such as the Markers of Autism Risk in Babies-Learning Early Signs (MARBLES) ( and Early Autism Risk Longitudinal Investigation (EARLI) ( will provide valuable information on a range of factors by closely tracking the subsequent pregnancies of families with children with autism.

With all of this information, the genetic and environmental causes of ASD can then be explored in specific neural systems during distinct developmental epochs in animal models to assess the impact of novel treatments with particular unique etiologies. Subsequent clinical trials can be performed to examine the effect of the novel treatment in the general ASD population, and the treatment's impact on a relevant subset of individuals at specific ages predicted by the animal model findings and the cause/biomarker map. By monitoring the impact of potential treatments on ASD biomarkers, this will facilitate the understanding of the ASD-specific mechanisms. Subsequent animal models will further refine the understanding of the mechanisms and lead to the development of more targeted therapeutics. Such approaches have been initiated by large collaborations, such as the European Autism Interventions Multicenter Study (EU-AIMS),184–186 funded the Innovative Medicines Initiative, a public–private partnership between the European Union and the European Federation of Pharmaceutical Industries and Associations, with an investment totaling €2 billion. Another such collaboration is the Province of Ontario Neurodevelopmental Disorders (POND) Network,187 funded by the Ontario Brain Institute, with support from the Government of Ontario, with an investment totaling $40 million (Canadian) across the neurodevelopmental disorders. In the United States, in 2015, $28 million was awarded for an NIH initiative to explore biomarkers for social and communicative function in ASD in a 5-site study led by investigators based at Yale University.188

It is hoped that there will be support for the United States to contribute in a more significant manner in this coordinated effort. For treatments currently under exploration in the clinical setting, investigation of the relationships between response and causes/biomarkers should be supported more broadly to better understand individualized effects for development of subsequent larger trials. The resulting goal would be to optimize targeting treatments within the ASD population. Furthermore, the existing collaborative approaches of this type have not yet targeted nongenetic etiologic risk factors. As our understanding of environmental factors expands, it will be critical to incorporate these factors into future research of this nature. Expanding beyond exclusive exploration of mechanism-driven treatment options will further enhance the impact of this effort, particularly in the near future when the benefits of existing agents can be examined. The heterogeneous condition of ASD can be systematically assessed using a data-driven approach to sort ASD into empirically supported subtypes that can each be separately explored for the ultimate development of individualized treatment approaches, whereas monitoring the impact on salient biomarkers to guide future exploration. This line of investigation would likely be far more efficacious than approaches applied broadly to all patients with ASD and represents the most powerful approach moving forward in optimization of ASD treatment. Furthermore, intervention with an individualized approach at earlier ages may have a particularly profound effect on developmental trajectories. In combination with impactful behavioral therapies,189–193 this approach should have a significant impact on the overall burden of ASD over a lifetime in the future. Additionally, optimization of the environment at home, providing family supports and continued behavioral and educational intervention beyond the end of the typical early behavioral intervention time frame will be critical to consider for optimization of impact on outcomes with or without pharmacotherapeutic intervention.


Paul Wang, MD, Department of Medical Research, Autism Speaks, Boston, MA. Greg Barnes, MD, Departments of Neurology, Pediatrics, and Biochemistry, Director, University of Louisville Autism Center, Spafford Ackerly Chair in Child and Adolescent Psychiatry, University of Louisville School of Medicine, Louisville, KY. Mark Weisskopf, PhD, ScD, Departments of Environmental Health and Epidemiology, Harvard T.H. Chan School of Public Health, Cambridge, MA. Antonio Hardan, MD, Department of Psychiatry and Behavioral Sciences, Director, Autism and Developmental Disorders Clinic, Stanford University, Stanford, CA. Valerie W. Hu, PhD, Department of Biochemistry and Molecular Medicine, The George WA University School of Medicine and Health Sciences, Washington, DC. Micah O. Mazurek, PhD, Department of Health Psychology, Thompson Center for Autism & Neurodevelopmental Disorders, University of Missouri, Columbia, MO. Zohreh Talebizadeh, PhD, Departmnt of Pediatrics, Children's Mercy Hospital and University of Missouri-Kansas City School of Medicine, Kansas City, MO. Wendy Goldberg, PhD, Professor, Department of Psychology and Social Behavior, School of Social Ecology. Courtesy Appointment, School of Education, University of California, Irvine, Irvine, CA. Karen L. Jones, PhD, Division of Rheumatalogy, Allergy and Clinical Immunology, M.I.N.D. Institute, University of California—Davis, Davis, CA. Daniel B. Campbell, PhD, Zilkha Neurogenetic Institute and Department of Psychiatry and the Behavioral Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA. Pamela Feliciano, PhD, The Simons Foundation, New York, NY. Sarah Spence, MD, Codirector Autism Spectrum Center, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA. Ralph-Axel Müller, PhD, Professor, Department of Psychology, San Diego State University. Rachel M A Brown, MBBS, MRCPsych, MPhil, Department of Psychiatry, University of Missouri School of Medicine, Columbia, MO. Stephen M. Kanne, PhD, ABPP, Thompson Center for Autism & Neurodevelopmental Disorders, University of Missouri, Columbia, MO. Kristin Sohl, MD, Department of Child Health, University of Missouri, Thompson Center for Autism and Neurodevelopmental Disorders, Columbia, MO. Daniel G. Smith, PhD, Department of Innovative Technologies, Autism Speaks, New York, NY. Eric London, MD, New York State Institute for Basic Research in Developmental Disabilities, Staten Island, NY. Margaret L. Bauman, MD, Departments of Anatomy and Laboratory Medicine, Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA. David Amaral, PhD, The M.I.N.D. Institute, Department of Psychiatry and Behavioral Sciences, University of California—Davis, Davis, CA.


1. De Rubeis S, He X, Goldberg AP, et al. Synaptic, transcriptional and chromatin genes disrupted in autism. Nature. 2014;515:209–215.
2. Bear MF, Huber KM, Warren ST. The mGluR theory of fragile X mental Retardation. Trends Neurosci. 2004;27:370–377.
3. Chez MG, Burton Q, Dowling T, et al. Memantine as adjunctive therapy in children diagnosed with autistic spectrum disorders: an observation of initial clinical response and maintenance tolerability. J Child Neurol. 2007;22:574–579.
4. Erickson CA, Posey DJ, Stigler KA, et al. A retrospective study of memantine in children and adolescents with pervasive developmental disorders. Psychopharmacology (Berl). 2007;191:141–147.
5. Veenstra-VanderWeele J, Sikich L, Melmed R, et al. Randomized, controlled, phase 2 trial of STX209 for social function in ASD. Int Meet Autism Res (IMFAR). 2013;13:102.001.
6. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders (DSM-V). 5th ed. Washington, DC: American Psychiatric Association; 2013.
7. Constantino JN, Charman T. Diagnosis of autism spectrum disorder: reconciling the syndrome, its diverse origins, and variation in expression. Lancet Neurol. 2016;15:279–291.
8. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders (DSM-IV). 4th ed. Washington, DC: American Psychiatric Association; 1995.
9. Shuster J, Perry A, Bebko J, et al. Review of factor analytic studies examining symptoms of autism spectrum disorders. J Autism Dev Disord. 2014;44:90–110.
10. Kim SH, Macari S, Koller J, et al. Examining the phenotypic heterogeneity of early autism spectrum disorder: subtypes and short-term outcomes. J Child Psychol Psychiatry. 2015. doi: 10.1111/jcpp.12448.
11. Palmer CJ, Paton B, Enticott PG, et al. “Subtypes” in the presentation of autistic traits in the general adult population. J Autism Dev Disord. 2015;45:1291–1301.
12. Sacco R, Lenti C, Saccani M, et al. Cluster analysis of autistic patients based on principal pathogenic components. Autism Res. 2012;5:137–147.
13. Geschwind DH, Levitt P. Autism spectrum disorders: developmental disconnection syndromes. Curr Opin Neurobiol. 2007;17:103–111.
14. VIP Consortium Simons. Simons Variation in Individuals Project (Simons VIP): a genetics-first approach to studying autism spectrum and related neurodevelopmental disorders. Neuron. 2012;73:1063–1067.
15. Jeste SS, Geschwind DH. Disentangling the heterogeneity of autism spectrum disorder through genetic findings. Nat Rev Neurol. 2014;10:74–81.
16. Miles JH. Autism spectrum disorder-a genetics review. Genet Med. 2011;13:278–294.
17. Miles JH, Takahashi TN, Bagby S, et al. Essential versus complex autism: definition of fundamental prognostic subtypes. Am J Med Genet A. 2005;135:171–180.
18. Shen JJ, Lee PH, Holden JJA, et al. Using cluster ensemble and validation to identify subtypes of pervasive developmental disorders. AMIA Annu Symp Proc. 2007;2007:666–670.
19. Qiao Y, Riendeau N, Koochek M, et al. Phenomic determinants of genomic variation in autism spectrum disorders. J Med Genet. 2009;46:680–688.
20. Lord C, Rutter N, LeCouteur A. Autism diagnostic interview-revised: a revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders. J Autism Dev Disord. 1994;24:659–685.
21. Hu VW, Steinberg ME. Novel clustering of items from the autism diagnostic interview-revised to define phenotypes within autism spectrum disorders. Autism Res. 2009;2:67–77.
22. Hu VW, Sarachana T, Kim KS, et al. Gene expression profiling differentiates autism case-controls and phenotypic variants of autism spectrum disorders: evidence for circadian rhythm dysfunction in sever autism. Autism Res. 2009;2:78–97.
23. Hu VW, Addington A, Hyman A. Novel autism subtype-dependent genetic variants are revealed by quantitative trait and subhpenotype association analysis of published GWAS data. PLoS One. 2011;6:e19067.
24. Talebizadeh Z, Arking DE, Hu VW. A novel stratification method in linkage studies to address inter- and intra-family heterogeneity in autism. PLoS One. 2013;8:e67569.
25. Hu VW, Lai Y. Developing a predictive gene classifier for autism spectrum disorders based on differential gene expression profiles of phenotypic subgroups. N Am J Med Sci (Boston). 2013;6:107–116.
26. Ben-David E, Shifman S. Networks of neuronal genes affected by common and rare variants in autism spectrum disorder. PLoS Genet. 2012;8:e1002556.
27. Bruining H, Eijkemans MJC, Kas MJH, et al. Behavioral signatures related to genetic disorders in autism. Mol Autism. 2014;5:11. Available at: Accessed August 4, 2016.
28. Piggot J, Shrinyan D, Shemmassian S, et al. Neural systems approaches to the neurogenetics of autism spectrum disorders. Neuroscience. 2009;164:247–256.
29. Ronald A, Happé F, Bolton P, et al. Genetic heterogeneity between the three components of the autism spectrum: a twin study. J Am Acad Child Adolesc Psychiatry. 2006;45:691–699.
30. Sanders SJ, He X, Willsey AJ, et al. Insights into autism spectrum disorder genomic architecture and biology from 71 risk loci. Neuron. 2015;87:1215–1233.
31. Iossifov I, O'Roak BJ, Sanders SJ, et al. The contribution of de novo coding mutations to autism spectrum disorder. Nature. 2014;515:216–221.
32. Liu L, Lei J, Sanders SJ, et al. DAWN: a framework to identify autism genes and subnetworks using gene expression and genetics. Mol Autism. 2014;5:22.
33. Tebbenkamp AT, Willsey AJ, State MW, et al. The developmental transcriptome of the human brain: implications for neurodevelopmental disorders. Curr Opin Neurol. 2014;27:149–156.
34. Uddin M, Tammimies K, Pellecchia G, et al. Brain-expressed exons under purifying selection are enriched for de novo mutations in autism spectrum disorder. Nat Genet. 2014;46:742–747.
35. Hu VW. From genes to environment: using integrative genomics to build a “systems-level” understanding of autism spectrum disorder. Child Dev. 2013;84:89–103.
36. Atkinson AJ, Colburn WA, DeGruttola VG, et al. Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin Pharmacol Ther. 2001;69:89–95.
37. Thompson BL, Levitt P. The clinical-basic interface in defining pathogenesis in disorders of neurodevelopmental origin. Neuron. 2010;67:702–712.
38. Cuthbert BN, Insel TR. Toward the future of psychiatric diagnosis: the seven pillars of RDoC. BMC Med. 2013;11:126.
39. Prilutsky D, Palmer NP, Smedemark-Margulies N, et al. iPSC-derived neurons as a higher-throughput readout for autism: promised and pitfalls. Trends Mol Med. 2014;20:91–104.
40. Talebizadeh Z, Butler MG, Theodoro MF. Feasibility and relevance of examining lymphoblastoid cell lines to study role of microRNAs in autism. Autism Res. 2008;1:240–250.
41. Sarachana T, Zhou R, Chen G, et al. Investigation of post-transcriptional gene regulatory networks associated with autism spectrum disorders by microRNA expression profiling of lymphoblastoid cell lines. Genome Med. 2010;2:23.
42. Ghahramani Seno MM, Hu P, Gwadry FG, et al. Gene and miRNA expression profiles in autism spectrum disorders. Brain Res. 2011;1380:85–97.
43. Mundalil Vasu M, Anitha A, Thanseem I, et al. Serum microRNA profiles in children with autism. Mol Autism. 2014;5:40. Available at: Accessed August 4, 2016.
44. Ander BP, Barger N, Stamova B, et al. Atypical miRNA expression in temporal cortex associated with dysregulation of immune, cell cycle, and other pathways in autism spectrum disorders. Mol Autism. 2015;6:37.
45. Kerin T, Ramanathan A, Rivas K, et al. A noncoding RNA antisense to moesin at 5p14.1 in autism. Sci Transl Med. 2012;4:128ra40.
46. Talebizadeh Z, Lam YD, Theodoro MF, et al. Novel splice isoforms for NLGN3 and NLGN4 with possible implications in autism. J Med Genet. 2006;43:e21.
47. Talebizadeh Z, Aldenderfer R, Chen XW. Exon-level expression profiling in autism: a proof of concept study. Psychiatr Genet. 2014;24:1–9.
48. Voineagu I, Wang X, Johnston P, et al. Transcriptomic analysis of autistic brains reveals convergent molecular pathology. Nature. 2011;474:380–386.
49. Gupta S, Ellis SE, Ashar FN, et al. Transcriptome analysis reveals dysregulation of innate immune response genes and neuronal activity-dependent genes in autism. Nat Commun. 2014;5:5748.
50. Hu VW, Frank BC, Heine S, et al. Gene expression profiling of lymphoblastoid cell lines from monozygotic twins discordant in severity of autism reveals differential regulation of neurologically relevant genes. BMC Genomics. 2006;7:118.
51. Nguyen AT, Rauch RA, Pfeifer GP, et al. Global methylation profiling of lymphoblastoid cell lines reveals epigenetic contributions to autism spectrum disorders and a novel autism candidate gene, RORA, whose protein product is reduced in autistic brain. FASEB J. 2010;24:3036–3051.
52. Hegi ME, Diserens A-C, Godard S, et al. Clinical trial substantiates the predictive value of )-6-methylguanine-DNA methyltransferase promoter methylation in glioblastoma patients treated with temozolomide. Clin Cancer Res. 2004;10:1871–1874.
53. Early Breast Cancer Trialists' Collaborative Group (EBCTCG). Relevance of breast cancer hormone receptors and other factors to the efficacy of adjuvant tamoxifen: patient-level meta-analysis of ramdomized trials. Lancet. 2011;378:771–784.
54. Blatt GJ, Fitzgerald CM, Guptill JT, et al. Density and distribution of hippocampal neurotransmitter receptors in autism: an autoradiographic study. J Autism Devel Disord. 2001;31:537–543.
55. Yip J, Soghomonian JJ, Blatt GJ. Decreased GAD67 mRNA levels in cerebellar Purkinje cells in autism: pathophysiological implications. Acta Neuropathol. 2007;113:559–568.
56. Blatt GJ, Fatemi SH. Alterations in GABAergic biomarkers in the autism brain: research findings and clinical implications. Anat Rec (Hoboken). 2011;294:1646–1652.
57. Rojas DC, SIngel D, Steinmetz S, et al. Decreased left perisylvian GABA concentration in children with autism and unaffected siblings. NeuroImage. 2014;86:28–34.
58. Gaetz W, Bloy L, Wang DJ, et al. GABA estimation in the brains of children on the autism spectrum: measurement precision and regional variation. NeuroImage. 2014;86:1–9.
59. Harada M, Taki MM, Nose A, et al. Non-invasive evaluation of the GABAergic/glutamatergic system in autistic patients observed by MEGA-editing proton MR spectroscopy using a clinical 3 Tesla instrument. J Autism Dev Disord. 2011;41:447–454.
60. Khan S, Fatima-Shad K, Ghouse HP. Prospects of using platelets as peripheral marker to study the role of GABA in autism. World J Neurosci. 2014;4:437–442.
61. Kang J-Q, Barnes G. A common susceptibility factor of both autism and epilepsy: functional deficiency of GABA A receptors. J Autism Dev Disord. 2013;43:68–79.
62. Chen C-H, Huang C-C, Cheng M-C, et al. Genetic analysis of GABRB3 as a candidate gene of autism spectrum disorders. Mol Autism. 2014;5:36.
63. Rojas DC, Steinmetz S, Hepburn SL, et al. Auditory gamma-band power is related to GABA concentration in autism. Int Meet Autism Res. 2014;14:166.001.
64. Ashwood P, Wills S, Van de Water J. The immune response in autism: a new frontier for autism research. J Leukoc Biol. 2006;80:1–15.
65. Onore C, Careaga M, Ashwood P. The role of immune dysfunction in the pathophysiology of autism. Brain Behav Immun. 2012;26:383–392.
66. Anderson GM, Horne WC, Chatterjee D, et al. The hyperserotonemia of autism. Ann N Y Acad Sci. 1990;600:331–342.
67. Cook EH. Autism: review of neurochemical investigation. Synapse. 1990;6:292–308.
68. Sutcliffe JS, Delahanty RJ, Prasad HC, et al. Allelic heterogeneity at the serotonin transporter locus (SLC6A4) confers susceptibility to autism and rigid-compulsive behaviors. Am J Hum Genet. 2005;77:265–279.
69. Chugani DA, Muzic O, Behen M, et al. Developmental changed in brain serotonin synthesis capacity in autistic and nonautistic children. Ann Neurol. 1999;45:287–295.
70. Murphy DGM, Daly E, Schmitz N, et al. Cortical serotonin 5-HT2A receptor binding and social communication in adults with Asperger's syndrome: an in vivo SPECT study. Am J Psychiatry. 2006;163:934–936.
71. Makkonen I, Riikonen R, Kokki H, et al. Serotonin and dopamine transporter binding in children with autism determined by SPECT. Dev Med Child Neurol. 2008;50:593–597.
72. Goldberg J, Anderson GM, Zwaigenbaum L, et al. Cortical serotonin type-2 receptor density in parents of children with autism spectrum disorders. J Autism Dev Disord. 2009;39:97–104.
73. Beversdorf DQ, Nordgren RE, Bonab AA, et al. 5-HT2 receptor distribution shown by [18F] setoperone PET in high-functioning autistic adults. J Neuropsychiatry Clin Neurosci. 2012;24:191–197.
74. Rose S, Frye RE, Slattery J, et al. Oxidative stress induces mitochondrial dysfunction in a subset of autistic lymphoblastoid cell lines. Transl Psychiatry. 2014;4:e377.
75. Toichi M, Kamio Y. Paradoxical autonomic response to mental tasks in autism. J Autism Dev Disord. 2003;33:417–426.
76. Nair A, Keown CL, Datko M, et al. Impact of methodological variables on functional connectivity findings in autism spectrum disorders. Hum Brain Mapp. 2014;35:4035–4048.
77. Koldewyn K, Whitney D, Rivera SM. The psychophysics of visual motion and global form processing in autism. Brain. 2010;133:599–610.
78. Uddin LQ, Supekar K, Menon V. Reconceptualizing functional brain connectivity in autism from a developmental perspective. Front Hum Neurosci. 2013;7:458.
79. Autism Phenotype Project. 2015. Available at: Accessed August 4, 2016.
80. National Institutes of Health. NIH awards $100 million for Autism Centers of Excellence Program. 2012. Available at: Accessed August 4, 2016.
81. Di Martino A, Yan CG, Li Q, et al. The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Mol Psychiatry. 2014;19:659–667.
82. Geschwind DH, State MW. Gene hunting in autism spectrum disorder: on the path to precision medicine. Lancet Neurol. 2015;14:1109–1120.
83. Happé F, Ronald A, Plomin R. Time to give up on a single explanation for autism. Nat Neurosci. 2006;9:1218–1220.
84. Ellegood J, Anagnostou E, Babineau BA, et al. Clustering autism: using neuroanatomical differences in 26 mouse models to gain insight into their heterogeneity. Mol Psychiatry. 2015;20:118–125.
85. Courchesne E, Karns CM, Davis HR, et al. Unusual brain growth patterns in early life in patients with autistic disorder: an MRI study. Neurology. 2001;57:245–254.
86. Hazlett HC, Poe MD, Gerig G, et al. Early brain overgrowth in autism associated with an increase in cortical surface area before age 2 years. Arch Gen Psychiatry. 2011;68:467–476.
87. Nordahl CW, Lange N, Li DD, et al. Brain enlargement is associated with regression in preschool-age boys with autism spectrum disorders. Proc Natl Acad Sci U S A. 2011;108:20195–20200.
88. Shen MD, Nordahl CW, Young GS, et al. Early brain enlargement and elevated extra-axial fluid in infants who develop autism spectrum disorder. Brain. 2013;136:2825–2835.
89. Sparks BF, Friedman SD, Shaw DW, et al. Brain structural abnormalities in young children with autism spectrum disorder. Neurology. 2002;59:184–192.
90. Solso S, Xu R, Proudfoot J, et al. Diffusion tensor imaging provides evidence of possible axonal overconnectivity in frontal lobes in autism spectrum disorder toddlers. Biol Psychiatry. 2016;79:676–684.
91. Weinstein M, Ben-Sira L, Levy Y, et al. Abnormal white matter integrity in young children with autism. Hum Brain Mapp. 2011;32:534–543.
92. Wolff JJ, Gu H, Gerig G, et al. Differences in white matter fiber tract development present from 6 to 24 months in infants with autism. Am J Psychiatry. 2012;169:589–600.
93. Vissers ME, Cohen MX, Geurts HM. Brain connectivity and high functioning autism: a promising path of research that needs refined models, methodological convergence, and stronger behavioral links. Neurosci Biobehav Rev. 2012;36:604–625.
94. Just MA, Cherkassky VL, Keller TA, et al. Cortical activation and synchronization during sentence comprehension in high-functioning autism: evidence of underconnectivity. Brain. 2004;127:1811–1821.
95. Fishman I, Keown CL, Lincoln AJ, et al. Atypical cross talk between mentalizing and mirror neuron networks in autism spectrum disorder. JAMA Psychiatry. 2014;71:751–760.
96. Supekar K, Musen M, Menon V. Development of large-scale functional brain networks in children. PLoS Biol. 2009;7:e1000157.
97. Narayanan A, White CA, Saklayen S, et al. Effect of propranolol on functional connectivity in autism spectrum disorder-a pilot study. Brain Imag Behav. 2010;4:189–197.
98. Henry NL, Hayes DF. Cancer biomarkers. Mol Oncol. 2012;6:140–146.
99. Weigel MR, Dowsett M. Current and emerging biomarkers in breast cancer: prognosis and prediction. Endoc Relat Cancer. 2010;17:R245–R262.
100. Romond EH, Perez EA, Bryant J, et al. Trastuzumab plus adjuvant chemotherapy for operable HER2-positive breast cancer. N Engl J Med. 2005;353:1673–1684.
101. Piccart-Gebhart MJ, Procter M, Leyland-Jones B, et al. Trastuzumab after adjuvant chemotherapy in HER2-positive breast cancer. N Engl J Med. 2005;353:1659–1672.
102. Allegra CJ, Jessup JM, Somerfield MR, et al. American Society of Clinical Oncology provisional clinical opinion: testing for KRAS gene mutations in patients with metastatic colorectal carcinoma to predict response to anti-epidermal growth factor receptor monoclonal antibody therapy. J Clin Oncol. 2009;27:2091–2096.
103. Ramsey BW, Davies J, McElvaney NG, et al. A CFTR potentiator in patients with cystic fibrosis and the G551D mutation. N Engl J Med. 2011;365:1663–1672.
104. Pramparo T, Pierce K, Lombardo MV, et al. Prediction of autism by translation and immune/inflammation coesxpressed genes in toddlers from pediatric community practices. JAMA Psychiatry. 2015;72:386–394.
105. Lajonchere CM. Changing the landscape of autism research: the autism genetic resource exchange. Neuron. 2010;68:187–191.
106. Fischbach GD, Lord C. The simons simplex collection: a resource for identification of autism genetic risk factors. Neuron. 2010;68:192–195.
107. Buxbaum JD, Bolshakova N, Brownfeld JM, et al. The autism simplex collection: an international, expertly phenotyped autism sample for genetic and phenotypic analysis. Mol Autism. 2014;5:35.
108. Perrin JM, Coury DL, Jones N, et al. The autism treatment network and autism intervention research network on physical health: future directions. Pediatrics. 2012;130(suppl 2):S198–S201.
109. Lajonchere CM, Jones N, Coury DL, et al. Leadership in health care, research, and quality improvement for children and adolescents with autism spectrum disorders: autism treatment network and autism intervention research network on physical health. Pediatrics. 2012;130(suppl 2):S63–S68.
110. Bernier R, Golzio C, Xiong B, et al. Disruptive CHD8 mutations define a subtype of autism in early development. Cell. 2014;150:263–276.
111. Folstein S, Rutter M. Infantile autism: a genetic study of 21 twin pairs. J Child Psychol Psychiatry. 1977;18:297–321.
112. Ritvo ER, Freeman BJ, Mason-Brothers A, et al. Concordance for the syndrome of autism in 40 pairs of afflicted twins. Am J Psychiatry. 1985;142:74–77.
113. Trottier G, Srivastava L, Walker CD. Etiology of infantile autism: a review of recent advances in genetic and neurobiological research. J Psychiatry Neurosci. 1999;24:103–115.
114. Maestrini E, Paul A, Monaco AP, et al. Identifying autism susceptibility genes. Neuron. 2000;28:19–24.
115. The Autism Genome Project Consortium. Mapping autism risk loci using genetic linkage and chromosomal rearrangements. Nat Genet. 2007;39:319–328.
116. Hallmayer J, Cleveland S, Torres A, et al. Genetic heritability and shared environmental factors among twin pairs with Autism. Arch Gen Psychaitry. 2011;68:1095–1102.
117. Frazier TW, Thompson L, Youngstrom EA, et al. A twin study of heritable and shared environmental contributions to autism. J Autism Dev Disord. 2014;44:2013–2025.
118. Colvert E, Tick B, McEwen F, et al. Heritability of autism spectrum disorder in a UK population-based twin sample. JAMA Psychiatry. 2015;72:415–423.
119. Zimmerman AW, Connors SL, Matteson KJ, et al. Maternal antibrain antibodies in autism. Brain Behav Immun. 2007;21:351–357.
120. Braunschweig D, Ashwood P, Krakowiak P, et al. Autism: maternally derived antibodies specific for fetal brain proteins. Neurotoxicology. 2007;29:226–231. PMCID: 2305723.
121. Croen LA, Braunschweig D, Haapanen L, et al. Maternal mid-pregnancy autoantibodies to fetal brain protein: the early markers for autism study. Biol Psychiatry. 2008;64:583–588.
122. Braunschweig D, Krakowiak P, Duncanson P, et al. Autism-specific maternal autoantibodies recognize critical proteins in developing brain. Transl Psychiatry. 2013;3:e277.
123. Martin LA, Ashwood P, Braunschweig D, et al. Stereotypies and hyperactivity in rhesus monkeys exposed to IgG from mothers of children with autism. Brain Behav Immun. 2008;22:806–816.
124. Singer HS, Morris C, Gause C, et al. Prenatal exposure to antibodies from mothers of children with autism produces neurobehavioral alterations: a pregnant dam mouse model. J Neuroimmunol. 2009;211:39–48.
125. Braunschweig D, Golub MS, Koenig CM, et al. Maternal autism-associated IgG antibodies delay development and produce anxiety in a mouse gestational transfer model. J Neuroimmunol. 2012;252:56–65.
126. Bauman MD, Iosif A-M, Ashwood P, et al. Maternal antibodies from mothers of children with autism alter brain growth and social behavior development in the rhesus monkey. Transl Psychiatry. 2013;3:e278.
127. Camacho J, Jones KL, Miller E, et al. Embryonic intraventricular exposure to autism-specific maternal autoantibodies produces alterations in autistic-like stereotypical behaviors in offspring mice. Behav Brain Res. 2014;266:46–51.
128. Beversdorf DQ, Manning SE, Hillier A, et al. Timing of prenatal stressors and autism. J Autism Dev Disord. 2005;35:471–478.
129. Kinney DK, Miller AM, Crowley DJ, et al. Autism prevalence following prenatal exposure to hurricanes and tropical storms in Louisiana. J Autism Devel Disord. 2008;28:481–488.
130. Larsson JH, Eaton WW, Madsen KM, et al. Risk factors for autism: perinatal factors, parental psychiatric history, and socioeconomic status. Am J Epidemiol. 2005;161:916–925.
131. Class QA, Abel KM, Khashan AS, et al. Offspring psychopathology following preconception, prenatal and postnatal maternal bereavement stress. Psychol Med. 2014;44:71–84.
132. Li J, Vestergaard M, Obel C, et al. A nationwide study on the risk of autism after prenatal stress exposure to maternal bereavement. Pediatrics. 2009;123:1102–1107.
133. Hecht P, Hudson M, Connors S, et al. Maternal serotonin transporter genotype affects risk for ASD with exposure to prenatal stress. Autism Res. [published online ahead of print April 19, 2016]. doi: 10.1002/aur.1629.
134. Jones KL, Smith RM, Edwards KS, et al. Combined effect of maternal serotonin transporter genotype and prenatal stress in modulating offspring social interaction. Int J Dev Neurosci. 2010;28:529–536.
135. Hecht P, Jasarevic E, Matsui F, et al. Combined effects of prenatal stress and maternal genotype on interneuron development. Soc Neurosci Abstr. 2014;44:518.30.
136. Roberts AL, Lyall K, Rich-Edwards JW, et al. Association of maternal exposure to childhood abuse with elevated risk of autism in offspring. JAMA Psychiatry. 2013;70:508–515.
137. Roberts AL, Lyall K, Rich-Edwards JW, et al. Maternal exposure to intimate partner abuse before birth is associated with autism spectrum disorder in offspring. Autism. 2016;20:26–36.
138. Windham GC, Zhang L, Gunier R, et al. Autism spectrum disorders in relation to distribution of hazardous air pollutants in the San Francisco bay area. Environ Health Perspect. 2006;114:1438–1444.
139. Kalkbrenner AE, Daniels JL, Chen JC, et al. Perinatal exposure to hazardous air pollutants and autism spectrum disorders at age 8. Epidemiology. 2010;21:631–641.
140. Becerra TA, Wilhelm M, Olsen J, et al. Ambient air pollution and autism in Los Angeles county, California. Environ Health Perspect. 2013;121:380–386.
141. Volk HE, Lurmann F, Penfold B, et al. Traffic-related air pollution, particulate matter, and Autism. JAMA Psychiatry. 2013;70:71–77.
142. Roberts AL, Lyall K, Hart JE, et al. Perinatal air pollutant exposures and autism spectrum disorder in the children of Nurses' Health Study II participants. Environ Health Perspect. 2013;121:978–984.
143. Jung CR, Lin YT, Hwang BF. Air pollution and newly diagnostic autism spectrum disorders: a population-based cohort study in Taiwan. PLoS One. 2013;8:e75510.
144. Volk HE, Kerin T, Lurmann F, et al. Autism spectrum disorder: interaction of air pollution with the MET receptor tyrosine kinase gene. Epidemiology. 2014;25:44–47.
145. von Ehrenstein OS, Aralis H, Cockburn M, et al. In utero exposure to toxic air pollutants and risk of childhood autism. Epidemiology. 2014;25:851–858.
146. Raz R, Roberts AL, Lyall K, et al. Autism spectrum disorder and particulate matter air pollution before, during, and after pregnancy: a nested case-control analysis within the Nurses' Health Study II Cohort. Environ Health Perspect. 2015;123:264–270.
147. Kalkbrenner AE, Windham GC, Serre ML, et al. Particulate matter exposure, prenatal and postnatal windows of susceptibility, and autism spectrum disorders. Epidemiology. 2015;26:30–42.
148. Ornoy A. Valproic acid in pregnancy: how much are we endangering the embryo and fetus? Reprod Toxicol. 2009;28:1–10.
149. Harrington RA, Lee L-C, Crum RM, et al. Prenatal SSRI use and offspring with autism spectrum disorder or developmental delay. Pediatrics. 2014;133:e1241–e1248.
150. Connors SL, Crowell DE, Eberhart CG, et al. β2-adrenergic receptor activation and genetic polymorphisms in autism: data from dizygotic twins. J Child Neurol. 2005;20:876–884.
151. Lyall K, Schmidt RJ, Hertz-Picciotto I. Maternal lifestyle and environmental risk factors for autism spectrum disorders. Int J Epidemiol. 2014;43:443–464.
152. Rossignol DA, Genuis SJ, Frye RE. Environmental toxicants and autism spectrum disorders: a systematic review. Transl Psychiatry. 2014;11:e360.
153. Surén P, Roth C, Bresnahan M, et al. Association between maternal use of folic acid supplements and risk of autism spectrum disorders in children. JAMA. 2013;309:570–577.
154. Raghavan R, Riley A, Caruso DM, et al. Maternal plasma folate, vitamin B12 levels, and multivitamin supplement during pregnancy and risk for autism spectrum disorders in the Boston Birth Cohort. Int Meet Autism Res Abstr. 2016;16:149.004.
155. Baron-Cohen S, Knickmeyer RC, Belmonte MK. Sex differences in the brain: implications for explaining autism. Science. 2005;310:819–823.
156. Sarachana T, Hu VW. Genome-wide identification of transcriptional targets of RORA reveals direct regulation of multiple genes associated with autism spectrum disorder. Mol Autism. 2013;4:14.
157. Sarachana T, Xu M, Wu R-C, et al. Sex hormones in autism: androgens and estrogens differentially and reciprocally regulate RORA, a novel candidate gene for autism. PLoS One. 2011;6:e17116.
158. D'Onofrio BM, Rickert ME, Frans E, et al. Paternal age at childbearing and offspring psychiatric and academic morbidity. JAMA Psychiatry. 2014;71:432–438.
159. Cheslack-Postava K, Suominen A, Jokiranta E, et al. Increased risk of autism spectrum disorders at short and long interpregnancy intervals. J Am Acad Child Adolesc Psychiatry. 2014;53:1074–1081.
160. Abdullah MM, Ly AR, Goldberg WA, et al. Heavy metal in children's tooth enamel: related to autism and disruptive behaviors? J Autism Dev Disord. 2012;42:929–936.
161. Ladd-Acosta C. Epigenetic signatures as biomarkers of exposure. Curr Envir Health Rep. 2015;2:117–125.
162. Florez JC, Jablonski KA, Sun MW, et al. for the Diabetes Prevention Program Research Group. Effects of the type 2 diabetes-associated PPARG P12A polymorphism on progression to diabetes and response to troglitazone. J Clin Endocrinol Metab. 2007;92:1502–1509.
163. Ludovico O, Pellegrini F, Di Paola R, et al. Heterogeneous effect of peroxisome proliferator-activated receptor gamma2 Ala12 variant on type 2 diabetes risk. Obesity (Silver Spring). 2007;15:1076–1081.
164. Cauchi S, Nead KT, Choquet H, et al. The genetic susceptibility to type 2 diabetes may be modulated by obesity status: implications for association studies. BMC Med Genet. 2008;9:45.
165. Manning AK, Hivert MF, Scott RA, et al. A genome-wide approach accounting for body mass index identifies genetic variants influencing fasting glycemic traits and insulin resistance. Nat Genet. 2012;44:659–669.
166. Soorya L, Youngkin S, Lee SJ, et al. A double-blind, placebo-controlled trial of memantine vs. placebo in children with autism targeting neurocognitive outcomes. Int Meet Autism Res Abstr. 2016;16:190.003.
167. Joshi G, Wozniak J, Faraone SV, et al. A prospective open-label trial of memantine hydrochloride for the treatment of social deficits in intellectually capable adults with autism spectrum disorder. J Clin Psychopharmacol. 2016;36:262–271.
168. Hollander E, Novotny S, Hanratty M, et al. Oxytocin infusion reduces repetitive behaviors in adults with autistic and Asperger's disorders. Neuropsychopharmacology. 2003;28:193–198.
169. Hollander E, Bartz J, Chaplin W, et al. Oxytocin increases retention of social recognition in autism. Biol Psychiatry. 2007;61:498–503.
170. Andari E, Duhamel J-R, Zalla T, et al. Promoting social behavior with oxytocin in high-functioning autism spectrum disorders. Proc Natl Acad Sci U S A. 2010;107:4389–4394.
171. Watanabe T, Abe O, Kuwabara H, et al. Mitigation of sociocommunicational deficits of autism through oxytocin-induced recovery of medial prefrontal activity. a randomized trial. JAMA Psychiatry. 2014;71:166–175.
172. Chugani DC, Chugani HT, Wiznitzer M, et al. on behalf of the Autism Center of Excellence Network. Efficacy of low-dose buspirone for restricted and repetitive behavior in young children with autism spectrum disorder: a randomized trial. J Pediatr. 2016;170:45–53.
173. Vorstman JAS, Spooren W, Persico AM, et al. Using genetic findings in autism for the development of new pharmaceutical compounds. Psychopharmacology (Berl). 2014;231:1063–1078.
174. Veenstra-VanderWeele J. Pathways to new treatments for autism spectrum disorder (keynote address). Int Meet Autism Res. 2015;15:164.
175. Silverman JL, Yang M, Lord C, et al. Behavioural phenotyping assays for mouse models of autism. Nat Rev Neurosci. 2010;11:490–502.
176. Silverman JL, Crawley JN. The promising trajectory of autism therapeutics discovery. Drug Discov Today. 2014;19:838–844.
177. Patterson PH. Maternal infection and immune involvement in autism. Trends Mol Med. 2011;17:389–394.
178. Matsui F, Hecht E, Jasarevic E, et al. Effect of docosahexaenoic acid (DHA) on a gene/prenatal stress autistic mouse model. Soc Neurosci Abstr. 2014;44:518.15.
179. Wainger BJ, Kiskinis E, Mellin C, et al. Intrinsic membrane hyperexcitability of amyotrophic lateral sclerosis patient-derived motor neurons. Cell Rep. 2014;7:1–11.
180. Mariani J, Coppola G, Zhang P, et al. FOXG1-dependent dysregulation of GABA/glutamate neuron differentiation in autism spectrum disorders. Cell. 2015;162:375–390.
181. Paşca SP, Portman T, Voineagu I, et al. Using iPS cell-derived neurons to uncover cellular phenotypes associated with Timothy syndrome. Nat Med. 2011;17:1657–1662.
182. Aigner S, Heckel T, Zhang JD, et al. Human pluripotent stem cell models of autism spectrum disorder: emerging frontiers, opportunities, and challenges towards neuronal networks in a dish. Psychopharmacology (Berl). 2014;231:1089–1104.
183. Tian Y, Voineagu I, Paşca SP, et al. Alteration in basal and depolarization induced transcriptional network in iPSC derived neurons from Timothy syndrome. Genome Med. 2014;6:75.
184. Murphy D, Spooren W. EU-AIMS: a boost to autism research. Nat Rev Drug Discov. 2012;11:815–816.
185. Murphy D. Why are there so few effective treatments of autism—and can translational neuroscience help? (keynote address). Int Meet Autism Res. 2014;14:100.
186. Ghosh A, Michalon A, Lindemann L, et al. Drug discovery for autism spectrum disorder: challenges and opportunities. Nat Rev Drug Discov. 2016;12:777–780.
187. Anagnostou E, Lerch JP, Scherer SW, et al. Province of Ontario neurodevelopmental disorders network: integrated discovery from genes to treatment. Int Meet Autism Res. 2015;15:125.199.
188. National Institutes of Health. NIH Joins Public-Private Partnership to Fund Research on Autism Biomarkers. 2015. Available at: Accessed August 4, 2016.
189. Lovaas OI. Behavioral treatment and normal educational and intellectual functioning in young autistic children. J Consult Clin Psychol. 1987;55:3–9.
190. Eikeseth S, Smith T, Jahr E, et al. Intensive behavioral treatment at School for 4- to 7-Year-Old children with autism. Behav Modif. 2002;26:49–68.
191. Sallows GO, Graupner TD. Intensive behavioral treatment for children with autism: four-year outcome and predictors. Am J Ment Retard. 2005;110:417–438.
192. Cohen H, Amerine-Dickens M, Smith T. Early intensive behavioral treatment: replication of the UCLA model in a community setting. J Dev Behav Pediatr. 2006;27:145–155.
193. Eldevik S, Hastings RP, Hughes JC, et al. Using participant data to extend the evidence base for intensive behavioral intervention for children with autism. Am J Intellect Dev Disabil. 2010;115:381–405.

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