The considerable clinical heterogeneity observed in ASDs has created a significant challenge to understanding the genetic inheritance underlying the ASDs. The knowledge learned from syndromic ASDs indicated that autistic disorders can be caused by a defect in a single gene. The inheritance of syndromic ASDs becomes clear once the gene implicated in the genetic syndrome is identified. The classic examples in this category are fragile X syndrome and Rett syndrome (Amir et al. 1999; Pieretti et al. 1991), which are also the most common causes for intellectual disability in males and females respectively. With more reports of autism being a clinical feature of different genetic syndromes, the list of syndromic ASDs is growing (Table 4.1). However, because systematic natural history studies have not been conducted for most of these genetic syndromes, these claims may be overstated in some cases or under-appreciated in others. Regardless, knowledge of syndromic ASDs provides a framework for a differential diagnosis in the clinical evaluation of ASDs. The studies of the genes implicated in these syndromic ASDs will also provide insight for the pathophysiology of idiopathic ASD.

Discussion of the genetic basis in idiopathic ASD cases is more complicated. Mutations in single genes probably account for 5–10 % of ASD cases in all studies in the literature (Buxbaum et al. 2012; Huguet et al. 2013; Schaaf et al. 2011; State and Levitt 2011). Two generation pedigrees with typical ASDs have been rarely reported in the literature. The reduced reproduction fitness in individuals with ASD is often cited as the reason. Although the families with more than one affected child, i.e. multiplex families, are frequently encountered, the clinical presentations among affected siblings usually have more differences than similarities posing an intriguing question as to whether recessive inheritance is a good fit in general. De novo genetic defects including copy number variants and single nucleotide variant s have been the main focus for autism genetic studies conducted over the last decade. As described below, the results from these studies strongly support an important role of de novo and rare genetic mutations in the etiology of ASDs (Devlin and Scherer 2012; Ronemus et al. 2014; State and Levitt 2011).

Although the term copy number variant is relatively new, the implication of chromosome dosage, i.e. chromosomal deletion and duplication, in genetic diseases have long been recognized for more than two decades. Conceptually, the chromosomal aneuploidy could be considered as the largest CNV in genome that is always pathogenic. Since the discovery of CNVs in the early 2000, the analyses of CNVs have been conducted in a long list of human diseases. The studies of CNVs associated with ASDs and other neurodevelopmental disorders are probably the most productive (Stankiewicz et al. 2003). Autistic behaviors have been described in several well characterized microdeletion and duplication syndromes including Angelman and Prader-Willi , Smith-Magenis, Potoki-Lupski, Velocadiofacial syndrome (VCFS), and Phelan-McDermid syndromes (PMS) (Laje et al. 2010; Phelan 2008; Potocki et al. 2007; Williams et al. 2001). In the case of idiopathic ASD, one of the first and still best examples of an associated chromosomal rearrangement is the maternally inherited duplication of the chromosomal 15q11-q13 Angelman and Prader-Willi syndrome region observed using traditional and high resolution chromosome analysis (Cook et al. 1997). Although a variable phenotype has been reported with this particular duplication, autism, developmental delay, seizures and hypotonia are common features (Ingason et al. 2011; Roberts et al. 2002; Thomas et al. 2006). This rearrangement has been validated in numerous subsequent case reports and large scale genome-wide CNV studies using chromosome microarray analysis (CMA) (Boyar et al. 2001; Bucan et al. 2009; Depienne et al. 2009; Gurrieri et al. 1999; Wang et al. 2004).

In an extensive review of 33 studies including 22,698 patients overall, the International Standard Cytogenomic Array Consortium found that CMA offered a diagnostic yield of 15–20 % as compared to 3 % for G-banded chromosome analysis (Miller et al. 2010) in patients with idiopathic ASDs or intellectual disability (Miller et al. 2010). A set of CNVs highly penetrant for ASDs that were identified through research studies using genome-wide CNV analyses in a large set of ASD cases are listed in Table 4.2 (Bucan et al. 2009; Celestino-Soper et al. 2011; Pinto et al. 2010; Sanders et al. 2011). These studies clearly support an important role of CNVs in the genetic etiology of ASDs.

It has been suggested that rare CNVs containing genes involved in neural plasticity or synapse formation and maintenance as well as chromatin modification are associated with ASDs and intellectual disability and that disruption of these shared biological pathways can result in neurodevelopmental disorders (Huguet et al. 2013; Pinto et al. 2014; Toro et al. 2010). In a study comparing 996 ASD individuals of European ancestry to 1287 matched controls, ASD cases were found to carry a higher global burden of rare CNVs, especially for loci previously implicated in either ASD and/or intellectual disability (Pinto et al. 2010). Pinto et al. (2010) analyzed 2446 ASD-affected families and confirmed an excess of gene deletions and duplications in affected versus control groups (Pinto et al. 2014). Genes affected by de novo CNVs and/or loss-of-function single-nucleotide variants converged on networks related to neuronal signaling and development, synapse function, and chromatin regulation. Among the CNVs identified, there were numerous de novo and inherited events, sometimes in combination in a given family, implicating many novel ASD genes including CHD2, HDAC4, GDI1, SETD5, MIR137,and HDAC9.

Recently, an exon-targeted oligo array was used to detect intragenic copy number variants in a cohort of 10,362 patients including those with a clinical indication of ASDs (Boone et al. 2010). The more commonly observed CNVs detected in this cohort include those affecting known and candidate genes for ASDs such as NRXN1, CNTNAP2, NLGN4X, A2BP1(RBOX1), CNTN4, CDH18, and TMEM195. A more detailed clinical and molecular characterization of 24 patients who have intragenic deletions of NRXN1 revealed that the seventeen patients with deletions involving exons manifested developmental delay/intellectual disability (93 %), infantile hypotonia (59 %) and ASDs (56 %) (Schaaf et al. 2012). These results indicate that a small intragenic deletion is significant enough to contribute to neurodevelopmental disorders, including ASDs, but the detection of these defects could be easily missed by array designs that lack exonic coverage.

Detection of recurring intragenic CNVs in this patient population will support reclassifying candidate genes implicated in neurodevelopmental disorders as disease-causing genes. For example, two patients with developmental delay and ASDs were identified with small duplication CNVs involving single exons that disrupt AUTS2 (Nagamani et al. 2013). This finding was further verified by another study comparing 17 well-characterized individuals with microdeletions affecting at least one exon of AUTS2 which enabled the identification of a variable syndromic phenotype including intellectual disability (ID), autism, short stature, microcephaly, cerebral palsy, and facial dysmorphisms (Beunders et al. 2013). These results illustrate the different sensitivities for detecting genetic defects based on the resolution of different arrays. However, comparisons among different array designs to detect disease-causing defects in a clinical setting have not been fully evaluated.

The results from research studies and clinical tests not only have confirmed the previous observations from individual case reports but also consolidated several major findings related to the role of CNVs in ASDs as follows: (1) A number of new CNVs are strongly implicated in ASDs but also show both variable expressivity and pleiotropic effects; (2) between 5–10 % of previous idiopathic ASD cases will carry rare CNVs; (3) both de novo and inherited CNVs confer a risk in ASD; and (4) there is a 3 fold increase in large and rare de novo CNVs in probands compared to their siblings (Buxbaum et al. 2012; Devlin and Scherer 2012). These findings, in general, support the clinical application of copy number analysis in ASDs as the recommended first tier molecular test in the clinical genetics evaluation of ASDs (Miller et al. 2010; Schaefer et al. 2013). However, the translation of these findings into clinical practice remains a challenge because the causal role of these CNVs cannot always be firmly established as discussed below.

The identification of genes implicated in syndromic ASDs indicated that a sequence change of a single nucleotide in a single gene can confer a significant genetic risk for ASDs. This principle has been validated in idiopathic ASDs using a candidate gene sequencing approach prior to the emergence of next generation sequencing . One of the best examples is the identification of mutations in the SHANK3 gene (Durand et al. 2007). SHANK3 was mapped to the critical region of chromosome 22q13.3 in Phelan-McDermid syndrome (PMS) in which autism is a prominent feature (Bonaglia et al. 2006; Durand et al. 2007; Wilson et al. 2003). Subsequently, pathogenic point mutations in SHANK3 have been identified in idiopathic ASD in many independent reports (Boccuto et al. 2013; Moessner et al. 2007). Similar scenarios have also contributed to the discovery of other genes such as MBD5, CNTNAP2, Neuroligins and Neurexins , all of which are implicated in ASDs (Arking et al. 2008; Laumonnier et al. 2004; Talkowski et al. 2011; Zweier et al. 2009). The low frequency of these sequence variants posed a challenge in experimental design for discovering new candidate genes until the recent emergence of the new generation sequencing (NGS) technique. Whole exome (WES) and whole genome sequencing (WGS) by NGS have provided an unprecedented opportunity to discover rare variants throughout the genome in ASDs. To date, there are multiple large scale ASD WES and several small scale WGS studies that have been carried out in trios, namely the proband plus the unaffected biological parents (Bi et al. 2012; Chahrour et al. 2012; Iossifov et al. 2012; Jiang et al. 2013; Michaelson et al. 2012; Neale et al. 2012; O’Roak et al. 2012; Sanders et al. 2012; Shi et al. 2013). The majority of these studies were focused on the analysis of de novo mutations. Several studies analyzed both rare inherited and de novo mutations (Chahrour et al. 2012; Lim et al. 2013; Yu et al. 2013). Among more than 1000 families assessed by these studies, the presence of de novo loss-of-function (LOF) mutations was consistently significantly higher in probands compared to controls. These studies have led to confirmation of many known genes and also the discovery of more than a dozen new genes with a high confidence of a causal role in ASDs (Table 4.3). A large number of de novo missense variants (10 folder high than LOF allele) have also been identified in ASDs from WES studies. Some of these missense changes are certain to contribute to the risk of ASD susceptibility. However, only 5–10 % excess of such mutations was found in ASD cohorts compared to controls, which did not reach statistical significance collectively. It is not possible to assign risk for these missense variants confidently without functional studies or through validation in an even larger number of cases. Only three ASD WGS studies have been reported so far (Arking et al. 2008; Chahrour et al. 2012; Sanders et al. 2012). In contrast to WES, the sample sizes of those evaluated by WGS are relatively small. In one study, WGS of 32 trios detected clinical relevant variants in 16 probands (Jiang et al. 2013). This study supports the feasibility of using WGS as a clinical test and validated the improved sensitivity of WGS compared to WES in detecting pathogenic sequence variants. In addition, the WGS has detected an average of 50–70 de novo sequence variants in non-coding regions. It is even more challenging to determine the clinical relevance of these findings to ASDs.

In contrast to the convincing evidence supporting the pathogenicity of rare CNVs and SNVs in ASDs, the implication of common variants remains tenuous (Devlin et al. 2011; Devlin and Scherer 2012). Several large and independent genome-wide association studies have been conducted to identify common variants that exert significant risk for ASDs (Anney et al. 2010; Szatmari et al. 2007; Wang et al. 2009; Weiss 2009). Two earlier large studies (> 3000 subjects) reported a significant association of two different loci at 5p14.1 and 5p15.2, respectively (Wang et al. 2009; Weiss et al. 2009). However, these loci have not been replicated in other studies including WES analysis. Although further investigation may be necessary to clarify the reason underlying the differences among the studies, it is probably fair to conclude that common variants may not have a significant impact on ASDs (Devlin et al. 2011; Zecavati and Spence 2009).

Numerous metabolic abnormalities have been reported in the context of an ASD phenotype but these are usually not specific for a certain diagnosis (Schaefer et al. 2013; Zecavati and Spence 2009). Individual metabolic disorders associated with an ASD phenotype are relatively rare (Table 4.4). However, all together, these disorders may contribute to ~ 3 % of all ASD causes evaluated in clinical genetic clinic. For example, severe ASD is well recognized in individuals with untreated or partially treated phenylketonuria (PKU). Several studies have suggested a link between mitochondrial dysfunction and ASDs (Giulivi et al. 2010; Guevara-Campos et al. 2013; Legido et al. 2013; Rossignol and Frye 2012). However, it remains to be determined whether the mitochondrial dysfunction is primary or secondary to an unidentified defect. Most metabolic disorders in a classical form including mitochondrial diseases are associated with other clinical symptomatology such as seizures and extrapyramidal signs as well as biochemical disturbances. However, it is not known whether the atypical or mild presentations of metabolic disorders may present more commonly as an ASD phenotype and are under-detected.

The current practice in the US is that a clinical genetics evaluation is recommended for all children with a confirmed diagnosis of ASD (Johnson and Myers 2007; Schaefer et al. 2013; Zwaigenbaum et al. 2009). It is estimated that a specific genetic etiology can be determined in about 15–20 % of individuals with an ASD (Schaefer et al. 2013). However, high quality or unbiased clinical data to validate the real yield of a clinical genetics evaluation in ASD is lacking (Herman et al. 2007; Shen et al. 2010). The variable practice models among clinics and different diagnostic platforms used in molecular diagnostic laboratories complicates the assessment of validity. A flowchart for the proposed clinical genetics evaluation of ASDs is presented in Fig. 4.2, which highlights the incorporation of new testing methodologies for determining the molecular defect.

Detection of copy number variants is usually achieved through array technology and is now the recommended first tier genetic test for the evaluation of ASDs (Miller et al. 2010; Schaefer et al. 2013). Initially, CMA via array comparative hybridization (aCGH) was used for CNV analysis. Single nucleotide polymorphim (SNP) arrays were then introduced and enabled detection of copy number neutral regions of absence of heterozygosity (AOH) in addition to copy number analysis. The level of genomic resolution achieved by an array analysis depends on the number, size and distance between the interrogating probes. Array CGH utilizes oligonucleotides (60–85 mers) while SNPs (25–50 mers) are the probes for SNP arrays, of which millions of both probe types can be synthesized on one glass slide (Ou et al. 2008). There are also customized arrays that can detect single exon CNVs involving only a few hundred base pairs (Boone et al. 2010). However, because SNPs predominate in non-coding regions, it is possible that certain regions of the genome such as single exons may not be represented on SNP arrays. In addition, SNP arrays demonstrate a lower signal-to-noise ratio per probe than CMA, which can result in less sensitivity for detecting CNVs. Therefore, to maximize clinical sensitivity, platforms that are a combination of aCGH and SNP genotyping are becoming the predominant type of array used clinically (Papenhausen et al. 2011; Peiffer et al. 2006; Schwartz 2011; Wiszniewska et al. 2014).

An advantage of SNP arrays is that they can reveal regions within the genome that lack heterozygosity, i.e. absence of heterozygosity. Identification of these regions is useful clinically in detecting polyploidy, uniparental disomy, consanguinity and recessive diseases although these are thought to be infrequent causes of ASDs (Alkan et al. 2011). However, a recent study reported that ASD individuals with intellectual disability have more regions of AOH compared to their unaffected siblings (Gamsiz et al. 2013). Furthermore, analysis of the specific ASD AOH regions aided in the discovery of autism candidate genes by either identifying single genes within an AOH interval that are recurrent in ASD or harbor a homozygous, rare deleterious variant upon analysis of exome-sequencing data (Gamsiz et al. 2013).

While the CNVs listed in Table 4.2 represent large-scale, recurrent CNVs that were detected from the first generation of clinical arrays, the increased resolution of arrays currently being used has promise for discovering more ASD-associated CNVs or individual candidate genes. For example, the combined analyses of CNVs in exonic regions and sequencing of coding exons led to a diagnosis of an autosomal recessive disorder by unmasking a recessive allele in a patient with autism. In this case, a deletion of exons 22–25 in the VPS13B gene, which is associated with Cohen syndrome, was first detected by CNV analysis using an exon-targeted array (BCM V8) and subsequent sequence analysis of the VPS13B gene identified a missense mutation in the other allele (Cohen et al. 2005; Wiszniewska et al. 2014).

These examples highlight that, to discover the precise molecular etiology , it may be necessary to combine different technologies within one test (array CGH + SNP) or use a combination of molecular diagnostic tests (CNV analysis plus medical re-sequencing of candidate genes or whole exome sequencing ).