, 1995; Rosenberg et al , 2009; Hallmayer et al , 2011) Yet, due

, 1995; Rosenberg et al., 2009; Hallmayer et al., 2011). Yet, due to the complexities of both ASD genetic architecture and brain-behavior relationships, great challenges remain in delineating how ASD risk genes shape the circuits underlying social behavior. Brain imaging studies have demonstrated that individual variation in task-based fMRI activation patterns, resting state functional connectivity (rs-fcMRI), and structural connectivity measures has a strong genetic component (Chiang et al., 2011; Kochunov et al., 2010; Fornito et al., 2011; Glahn et al., 2010;

Koten et al., 2009) and is altered in ASD (see Di Martino et al., 2009 and Vissers check details et al., 2012 for review). Thus, neuroimaging endophenotypes Talazoparib cost are ideal for bridging the gap in our understanding of how genetic risk impacts brain circuitry. Yet, both behavioral and imaging phenotypes in ASD present significant heterogeneity and substantial overlap with typical populations, often leading to discrepant findings (e.g., Cheng et al., 2010). A critical question then is how genetic variability underlies phenotypic heterogeneity and, consequently,

whether stratifying by genetic risk factors can improve our understanding of the neurobiology of ASD. Although recent estimates suggest that hundreds of genes are likely to contribute to ASD risk (Buxbaum et al., 2012), the vast majority of evidence comes from rare mutations, such as the recently described copy number variants (CNVs) (Marshall et al., 2008; Pinto et al., 2010) and de novo single-nucleotide variants (SNVs) (Sanders et al., 2012; O’Roak et al., 2012; Neale et al., 2012; Iossifov et al., 2012). These mutations are rare (occurring in less than 1% of the population), may be unique to the individual, and are estimated to collectively impact 10%–20% of the ASD-diagnosed population. Therefore, while de novo events are conceptually important for understanding the many see more potential biological routes to ASD etiology,

their utility for understanding phenotypic heterogeneity across the ASD population remains to be determined. Perhaps due to clinical heterogeneity, small estimated effect sizes, and limited statistical power, genome-wide association (GWA) studies focusing on common variants (>5% allele frequency) have failed to yield conclusive evidence for any specific common variants influencing ASD risk when pooling data across studies (Wang et al., 2009; Weiss et al., 2009; Anney et al., 2010). However, a few notable ASD candidate genes with common variants—namely, contactin-associated protein-like 2 (CNTNAP2) and Met Receptor Tyrosine Kinase (MET)—have been identified using large samples.

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