**5. Conclusions**

conclusion that paternal age (and possibly maternal age) is a significant ASD risk factor, but the frequency and size of *de novo* mutations *per se* is not. Evidence for three candidate genes —*CHD8*, *KATNAL2*, and *SCN2A*—would seem quite strong, though further functional stud‐ ies are needed to help define pathogenesis. Perhaps most exciting is the association between *GRM5* and existing/novel candidates. As we have learned from GWAS, larger sample sets are clearly needed to fully harness the power of NGS in relation to such a complex pheno‐ type. While these studies have been important in proposing novel candidates and confirm‐ ing existing hypotheses of ASD, we await with anticipation results from the sequencing of

Ultimately, the primary goal of genome research should be to propose targets for interven‐ tion. As mentioned above, a number of translational studies have begun to probe the metab‐ otropic glutamate receptor, mGluR5, as a potential target for fragile X syndrome treatment. These studies have a theoretical basis in the hypothesis that protein-synthesis-dependent functions of metabotropic receptors are exaggerated in fragile X syndrome (Bear, Huber & Warren, 2004) [87]. Thus, the fragile X protein, FMRP, is thought to work in functional oppo‐ sition to mGluR5 (and mGluR1). Where FMRP is absent, mGluR-dependent protein synthe‐

Dölen *et al*. (2007) [89] crossed *Fmr1* mutant with Grm5 mutant to produce *Fmr1* knockouts who also had a selective reduction in mGluR5 expression. They found that a 50% reduction in mGluR5 gene dosage rescued a range of deficits in *Fmr1* mutants. Relevant measures in‐ cluded protein synthesis in hippocampus, density of dendritic spines (layer 3 pyramidal neurons), visual responsiveness, and cognitive performance (inhibitory avoidance – a hippo‐ campus-dependent memory). This provides confirmation that mGluR5 and FMRP are func‐ tionally oppositional. Moreover, it suggests possible pharmacological avenues by which this

A range of translational studies have begun to target this pathway. These include efforts to inhibit the activity of individual mGluR5 (Jacquemont *et al*., 2011; Berry-Kravis *et al*., 2009) [88,90], and FMRP-regulated proteins (Paribello *et al*., 2010) [91], NMDA (Wei *et al*., 2012) [92], and GSK3β (lithium, Berry-Kravis *et al*., 2008) [93], which have shown promise in open label and (in some instances) clinical trials (see Berry-Kravis *et al*., 2011 for review) [94]. Moreover, these compounds may have clinical application to the broader ASD phenotype. Silverman *et al*. (2012) [95] recently reported that the mGluR5 antagonist, GRN-529, de‐ creased ASD-related symptoms of autism in two different mouse models of the disease (re‐ petitive grooming/repetitive jumping). In addition to the Iossifov *et al*. (2012) [85] sequencing study discussed above, Kelleher *et al*. (2012) [96] recently showed that idiopathic autism cases may have higher burden of mGluR5 variants. The group found that in 209 idio‐ pathic cases, there was significant enrichment for rare functional variants in the mGluR5 pathway—namely the genes *TSC1*, *TSC2* and *SHANK3*, and *HOMER1*—relative to controls

sis becomes over-activated, resulting in neurological and behavioral abnormalities.

all 2,648 families from the Simon Simplex Collection.

288 Recent Advances in Autism Spectrum Disorders - Volume I

**4. Toward a treatment?**

genetic disease may be treated.

ASD are clearly highly heritable disorders and advances in gene-finding technology in the past decade have rapidly accelerated gene discovery. As is typically the case, successive de‐ velopments have made the problem more complex such that there are huge numbers of can‐ didate genes, most of which remain to be replicated. In spite of this complexity, we can observe a number of patterns beginning to unfold 1) the relative scarcity of causal common variants, 2) the growing list of causal rare variants, and 3) the emergence of monogenic dis‐ orders with primary and secondary ASD phenotypes.

The monogenic autisms are particularly interesting from a treatment perspective, as they provide a mechanism for studying ASD phenotypes in model systems and are an obvious target for drug intervention. They are also amenable to clinical testing and the decreasing cost of research technologies means that this capacity is more widely available to clinicians. In fact, as the resolution of clinical instruments becomes more sophisticated, it is likely that the clinic will become a primary workplace for syndromic discovery.

A key requirement in driving gene discovery is the necessity of high-quality phenotype da‐ ta. ASDs are notoriously heterogeneous, and are fractionated in terms of symptoms and tra‐ jectory. Mandy & Skuse (2008) [97] reviewed seven factor analysis studies of ASD symptoms, and found that all but one dissociated social and non-social factors. In a nonclinical sample of 3,000 twin pairs, Happé *et al.* (2006) [98] examined autistic-like traits and found consistently low correlations (r = 0.1-0.4) between each of the core deficits on the au‐ tism spectrum. Endophenotypes, sub-components or sub-processes of the broader pheno‐ type, may provide a productive avenue to disentangling some of this complexity. By filtering out all but a few discrete measures, we can theoretically increase the signal-to-noise ratio in genotype-phenotype associations. A number of endophenotypes for ASD have been associated with disease genes, including head circumference (associated with the *HOXA1* A218G polymorphism, Conciatori *et al.*, 2004) [99]; age at first word (associated with a quan‐ titative trait locus on 7q35, Alarcón *et al.* 2005) [100]; delayed magnetoencephalography evoked responses to auditory stimuli (Roberts *et al.*, 2010) [101]; and enhanced perception (Mottron *et al.*, 2006) [102]. The endophenotype approach is arguably more consistent with rare-/mono-genic discovery, where a mutated network may not yield a diagnosis of autism *per se*, but nevertheless cause associated abnormalities. Note, this approach does not dimin‐ ish the pleiotropic effects of genes involved in neurodevelopment, and only serves to make the point that the relevant genotype may associate with some but not all ASD features.

The converse, of course, is also true, as a large number of candidate genes contribute to the majority of known ASD. With ~80% of genes expressed in the brain it is likely that this num‐ ber will continue to grow, and here again careful phenotyping is critical to identifying func‐ tional consequences. Ultimately, the primary goal is not to determine the frequency of variation/mutation in cases versus controls, but to determine the pathway(s) and gene net‐ works that lead to pathology. We will also need to identify other major biological players such as epigenetic factors, RNA regulatory elements, and environmental exposures, which are critical components of the ASD equation. While daunting, the elucidation of these ele‐ ments will doubtlessly take us closer to developing effective treatments for ASD. Given the current rate of progress, we have cause for cautious optimism in this regard.

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