**3. Genetic variation in ASD**

With the advent of NGS techniques and increasing usage of CMA screening, the number of SNVs and CNVs that have been identified in ASD individuals has grown significantly. Based on a survey of recently published exome sequencing studies of ASD cohorts, it was estimated that the number of dosage-sensitive ASD susceptibility genes is approximately 370, with roughly a third of these genes having been identified [10]. However, even this number might be a conservative projection. As shown in Figure 2, the number of ASD susceptibility genes in the Human Gene Module of the autism genetic database AutDB [27] has increased from 284 genes in September 2011 to 369 genes in June 2012. A large number of newer susceptibility genes have been annotated from reports employing whole exome sequencing of ASD cases [2, 4, 7-10], illustrating the increasing usage of NGS techniques in the study of genetic variation in ASD. In addition to the identification of novel ASD susceptibility genes, NGS techniques have identified novel rare variants in previously identified ASD susceptibility genes. The number of ASD-associated CNV loci has also increased significantly, with the CNV module of AutDB expanding from 1034 CNV loci in September 2011 to 1173 loci in June 2012 (Figure 2). In this section we describe the genetic categories into which ASD susceptibility genes have been classified, as well as describe recent studies that have yielded invaluable insight on the functional profiles of ASD-associated genes and CNV loci.

**Figure 2.** The number of genes and CNV loci associated with ASD in the genetic database AutDB has increased over the last four quarterly release dates.

#### **3.1. Genetic categories of ASD susceptibility genes**

without compromising genomic coverage. The test and reference DNA samples hybridize with the probes on the slide, and the fluorescence intensities of the test and reference DNA can then be measured. Following analysis with software that is typically specific for the platform being used, one or more algorithms are used to call the CNV. The ratio between the two fluorescence intensities is used to identify copy number changes. For example, if the test-to-reference ratio is 1 (yellow in the example below), then there is no change in copy number at the chromosomal region corresponding to a given probe, If the test-to-reference fluorescence ratio is > 1 for a particular probe (green in the example below), then the ASD patient carries a duplication in the chromosomal region corresponding to that probe. If the test-to-reference ratio is < 1 (red

in the example below), then the patient carries a deletion at that site of the genome.

**Figure 1.** Chromosomal microarray (CMA) analysis involves hybridization of differently labeled test and reference DNA samples with oligonucleotide probes, followed by computerized analysis and identification of copy number variants

Despite the recommended use of CMA as a first-tier genetic evaluation tool in place of conven‐ tional cytogenetic techniques, it should be noted that aCGH is unable to detect balanced chro‐ mosomal rearrangments and other chromosomal abnormalities that have traditionally been detected by karyotype analysis [25]. In addition to their traditional utilization in the detection of risk-conferring common polymorphisms, SNP arrays have the added advantage of being able to detect copy number neutral genetic variation such as uniparental disomy and long con‐

With the advent of NGS techniques and increasing usage of CMA screening, the number of SNVs and CNVs that have been identified in ASD individuals has grown significantly. Based on a survey of recently published exome sequencing studies of ASD cohorts, it was estimated that the number of dosage-sensitive ASD susceptibility genes is approximately 370, with roughly a third of these genes having been identified [10]. However, even this number might be a conservative projection. As shown in Figure 2, the number of ASD susceptibility genes in

tiguous streteches of homozygosity (LCSH) that cannot be detected by aCGH [25, 26].

(CNVs) based on changes in fluorescence intensity ratio.

196 Recent Advances in Autism Spectrum Disorders - Volume I

**3. Genetic variation in ASD**

The earliest ASD susceptibility genes were rare single gene variants in genes associated with syndromes such as Fragile X syndrome and Rett syndrome. The discovery of single gene mutations/disruptions in two neuroligin genes, NLGN3 and NLGN4, in ASD siblings [28] initiated the search for additional ASD susceptibility genes in non-syndromic ASD cases. The continued identification of rare genetic variants associated with both syndromic and nonsyndromic ASD, as well as of risk-conferring polymorphisms enriched in ASD populations compared to unaffected controls in genetic association studies, has led to significant increases in the number of ASD-linked genes. While the majority of ASD-associated genes have been linked to disease on the basis of genetic studies in human populations, a number of additional ASD-linked genes have been identified by alternate methodologies, such as gene expression studies in post-mortem brain tissue of ASD individuals.

The classification of ASD-linked genes into genetic categories is a useful tool in assessing the strength of the evidence for the connection of a given gene with ASD. Genes within the rare and syndromic categories are generally considered to have the strongest link to ASD [34]. Due to the frequent lack of replication in their association with ASD from one study to the next, genes within the association category are considered to have a weaker link to ASD than genes within the rare and syndromic categories. Genes within the functional category have no direct documented connection to ASD and are therefore considered to be among the weakest ASD

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**Figure 3.** The number of rare and common ASD-associated variants in the Human Gene module of AutDB has in‐

The increasing number of ASD-associated genetic factors, as shown in Figure 2, has only added to the well-established genetic heterogeneity of ASD. In spite of the complexity caused by this genetic heterogeneity, bioinformatic analysis of ASD-linked genes and CNV loci has yielded valuable insight into the molecular interactions and cellular pathways preferentially targeted by genetic lesions in individuals with ASD. Not only can this information be potentially used to design therapeutic approaches targeting disrupted pathways, but it can also aid in assessing the clinical importance of newly-discovered ASD candidate genes and CNV loci in which pathogenic variants are identified by NGS and CMA, respectively. For example, a gene whose encoded gene product resides within a known ASD-associated cellular process or interacts with a known ASD-associated gene is a stronger candidate than a gene that fails to reside within known ASD-associated cellular processes or interact with known ASD-associated

candidate genes.

creased over the last four quarterly release dates.

genes.

**3.2. Functional profiles of ASD-associated genes and CNV loci**

ASD susceptibilty genes in the Human Gene Module of AutDB are defined into four distinct categories:

**1. Rare.** This category features genes implicated in rare monogenic forms of non-syndromic ASD. Rare allelic variants within this category include single nucleotide variants, small insertions and deletions, chromosomal rearrangements such as translocations and inversions, and monogenic submicroscopic deletions and duplications. Among the genes within this category are *CACNA1H* and *SHANK1.*

**2. Syndromic.** Syndromic genes were among the first genes for which rare genetic variants linked to autism were identifed. In addition to well-characterized syndromic genes such as *FMR1* (Fragile X syndrome), *MECP2* (Rett syndrome), and *CACNA1C* (Timothy syndrome), genes such as *CHD7* and *SLC9A6* fall into the syndromic category.

**3. Association.** This category includes genes in which small risk-conferring common poly‐ morphisms have been identified from genetic association studies in idiopathic ASD popula‐ tions. Among the genes within this category are *MET* and *MTHFR*.

**4. Functional.** This category includes functional candidate genes that have not yet been experimentally linked to ASD by genetic studies. Among the genes in this category are *BCL2* and *PDE4B*, whose inclusion is based on changes in gene expression in post-mortem brain tissue of ASD subjects.

As shown in Figure 3, while the number of both rare and common ASD-associated variants in the Human Gene module of AutDB has increased over the last four quarterly release dates, the number of rare variants has increased at a much greater rate than the number of common variants. The number of rare variants increased from 1141 in September 2011 to 1675 in June 2012, an increase of ~146%. In contrast, the number of common variants rose form 508 to 575 over the same span of time, an increase of only ~113%. This disparity between the addition of rare and common variants to AutDB is in part due to the increased usage of NGS and CMA and subsequent identification of rare ASD-associated variants in large ASD cohorts.

It should be noted that a given gene can fall under multiple genetic categories, depending on the affected population under investigation and the type of study. For example, both rare variants and risk-conferring common polymorphisms have been identified in the *CNTNAP2* gene in ASD individuals across multiple studies [2, 29-31] However, in addition to its role as an ASD susceptibility factor, recent studies suggest that rare variants in *CNTNAP2* are responsible for two additional syndromes: cortical dysplasia-focal epilepsy syndrome [32] and Pitt-Hopkins-like syndrome 1 [33]. Therefore, based on the combined evidence from all of these aforementioned studies, *CNTNAP2* is classified in AutDB as a syndromic gene, a rare gene, and an association gene.

The classification of ASD-linked genes into genetic categories is a useful tool in assessing the strength of the evidence for the connection of a given gene with ASD. Genes within the rare and syndromic categories are generally considered to have the strongest link to ASD [34]. Due to the frequent lack of replication in their association with ASD from one study to the next, genes within the association category are considered to have a weaker link to ASD than genes within the rare and syndromic categories. Genes within the functional category have no direct documented connection to ASD and are therefore considered to be among the weakest ASD candidate genes.

compared to unaffected controls in genetic association studies, has led to significant increases in the number of ASD-linked genes. While the majority of ASD-associated genes have been linked to disease on the basis of genetic studies in human populations, a number of additional ASD-linked genes have been identified by alternate methodologies, such as gene expression

ASD susceptibilty genes in the Human Gene Module of AutDB are defined into four distinct

**1. Rare.** This category features genes implicated in rare monogenic forms of non-syndromic ASD. Rare allelic variants within this category include single nucleotide variants, small insertions and deletions, chromosomal rearrangements such as translocations and inversions, and monogenic submicroscopic deletions and duplications. Among the genes within this

**2. Syndromic.** Syndromic genes were among the first genes for which rare genetic variants linked to autism were identifed. In addition to well-characterized syndromic genes such as *FMR1* (Fragile X syndrome), *MECP2* (Rett syndrome), and *CACNA1C* (Timothy syndrome),

**3. Association.** This category includes genes in which small risk-conferring common poly‐ morphisms have been identified from genetic association studies in idiopathic ASD popula‐

**4. Functional.** This category includes functional candidate genes that have not yet been experimentally linked to ASD by genetic studies. Among the genes in this category are *BCL2* and *PDE4B*, whose inclusion is based on changes in gene expression in post-mortem brain

As shown in Figure 3, while the number of both rare and common ASD-associated variants in the Human Gene module of AutDB has increased over the last four quarterly release dates, the number of rare variants has increased at a much greater rate than the number of common variants. The number of rare variants increased from 1141 in September 2011 to 1675 in June 2012, an increase of ~146%. In contrast, the number of common variants rose form 508 to 575 over the same span of time, an increase of only ~113%. This disparity between the addition of rare and common variants to AutDB is in part due to the increased usage of NGS and CMA

It should be noted that a given gene can fall under multiple genetic categories, depending on the affected population under investigation and the type of study. For example, both rare variants and risk-conferring common polymorphisms have been identified in the *CNTNAP2* gene in ASD individuals across multiple studies [2, 29-31] However, in addition to its role as an ASD susceptibility factor, recent studies suggest that rare variants in *CNTNAP2* are responsible for two additional syndromes: cortical dysplasia-focal epilepsy syndrome [32] and Pitt-Hopkins-like syndrome 1 [33]. Therefore, based on the combined evidence from all of these aforementioned studies, *CNTNAP2* is classified in AutDB as a syndromic gene, a rare gene,

and subsequent identification of rare ASD-associated variants in large ASD cohorts.

studies in post-mortem brain tissue of ASD individuals.

genes such as *CHD7* and *SLC9A6* fall into the syndromic category.

tions. Among the genes within this category are *MET* and *MTHFR*.

category are *CACNA1H* and *SHANK1.*

198 Recent Advances in Autism Spectrum Disorders - Volume I

tissue of ASD subjects.

and an association gene.

categories:

**Figure 3.** The number of rare and common ASD-associated variants in the Human Gene module of AutDB has in‐ creased over the last four quarterly release dates.

#### **3.2. Functional profiles of ASD-associated genes and CNV loci**

The increasing number of ASD-associated genetic factors, as shown in Figure 2, has only added to the well-established genetic heterogeneity of ASD. In spite of the complexity caused by this genetic heterogeneity, bioinformatic analysis of ASD-linked genes and CNV loci has yielded valuable insight into the molecular interactions and cellular pathways preferentially targeted by genetic lesions in individuals with ASD. Not only can this information be potentially used to design therapeutic approaches targeting disrupted pathways, but it can also aid in assessing the clinical importance of newly-discovered ASD candidate genes and CNV loci in which pathogenic variants are identified by NGS and CMA, respectively. For example, a gene whose encoded gene product resides within a known ASD-associated cellular process or interacts with a known ASD-associated gene is a stronger candidate than a gene that fails to reside within known ASD-associated cellular processes or interact with known ASD-associated genes.

Recent large-scale ASD genetic studies have used a systems biology approach to translate ge‐ netic information into functional profiles that shed light on how genetic variation in ASD may lead to disease onset and pathogenesis. Rare CNVs identified in large ASD cohorts have been shown to be enriched for genes involved in cellular processes of relevance for ASD, including cellular proliferation, projection, and motility, and GTPase/Ras signaling [1], neuronal cell ad‐ hesion and ubiquitin-mediated degradation [22], glycobiology [35], axon growth and path‐ finding [36], and synapse development, axon targeting, and neuron motility [37]. Gene datasets from genome-wide association studies in ASD populations were demonstrated to be enriched for Gene Ontology (GO) classifications for cellular processes including pyruvate me‐ tabolism, transcription factor activation, cell signaling and cell-cycle regulation [38]. A recent report describing gene pathway analysis using single nucleotide polymorphism (SNP) data from the Autism Genetics Research Exchange (AGRE) identified cellular pathways such as cal‐ cium signaling, long-term depression and potentiation, and phosphotidylinositol signaling that reached statistical significance in both Central European and Han Chinese populations [39]. More recently, whole exome sequencing studies in large ASD cohorts have demonstrated that proteins encoded by genes in which potentially disruptive *de novo* mutations were identi‐ fied showed a higher degree of connectivity among themselves and to previously identified ASD genes based on protein-protein interaction network analysis [4, 8]. Another exome se‐ quencing study in ASD individuals found that many of the genes in which potentially disrup‐ tive variants were identified associated with the Fragile X Mental Retardation Protein (FMRP), the encoded product of the syndromic ASD gene *FMR1* [10]. Taken together, these functional maps suggest that specific cellular pathways and processes are preferentially targeted by ge‐ netic variation in ASD cases, and that association with the encoded products of well-character‐ ized ASD-linked genes offers evidence for pathogenic relevance.

**4.1. AutDB**

which licenses it as SFARI Gene.

thereof, with known ASD-linked genes.

**4.2. Gene scoring module of SFARI gene**

Our autism database AutDB (http://autism.mindspec.org/autdb/Welcome.do) is a webbased, searchable database of ASD candidate genes identified in genetic association stud‐ ies, genes linked to syndromic autism, and rare single gene mutations [27]. Evidence regarding ASD candidate genes is systematically extracted from peer-reviewed, primary scientific literature and manually curated by our researchers for inclusion in AutDB. To provide high-resolution view of various components linked to ASD, we developed de‐ tailed annotation rules based on the biology of each data type and generated controlled vocabulary for data representation. AutDB is widely used by individual laboratories in the ASD research community, as well as by consortiums such as the Simons Foundation,

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AutDB is designed with a systems biology approach, integrating genetic information with‐ in the original Human Gene module to corresponding data in subsequent Animal Model, Protein Interaction (PIN) and Copy Number Variant (CNV) modules. The Animal Model module contains a comprehensive collection of mouse models linked to ASD [41]. While the Animal Model module initially contained only genetic mouse models of ASD, it has since been expanded to include induced mouse models of ASD in which a chemical or bi‐ ological agent linked to ASD has been administered. As core behavioral features of ASD such as social interactions and communications can only be approximated in animal mod‐ els, the annotation strategy for this module includes four broad areas: 1) core behavioral features of ASD, 2) ASD-related traits such as seizures and circadian rhythms that are her‐ itable and more easily quantified in animal models; 3) neuroanatomical features, and 4) molecular profiles. To this end, we developed PhenoBase, a classification table for system‐ atically annotating models with controlled vocabulary containing 16 major categories and >100 standardized phenotype terms. The PIN module of AutDB serves as a repository for all known protein interactions of ASD candidate genes, documenting six major types of direct interactions: 1) protein binding, 2) promoter binding, 3), RNA binding, 4) protein modification, 5) direct regulation, and 6) autoregulation. Its content is envisioned to have immediate application for network biology analysis of molecular pathways involved in ASD pathogenesis. For the purposes of genetic evaluation of individuals with ASD, knowledge of the protein interactions of ASD-associated genes can potentially aid in the clinical assessment of novel ASD candidate genes based on their interactions, or lack

As previously mentioned, AutDB is licensed to the Simons Foundation as SFARI Gene. However, unlike AutDB, SFARI Gene includes a unique feature initiated by the Simons Foundation called the Gene Scoring module (https://gene.sfari.org/autdb/GS\_Home.do). The Gene Scoring module is a web-based platform detailing the rank of ASD-associated genes in the SFARI Gene Human Gene module [42]. With the increase in the number of genes linked to ASD, a Gene Scoring initiative was launched to assess the ASD candidate genes based on a set of standardized annotation rules. Following evaluation by an expert panel of advisors, the

Knowledge of ASD-associated genes can also be used to identify novel ASD candidate genes. Following the construction of functional and expression profiles from a reference set of 84 rare and syndromic ASD-linked genes, we generated a predictive map of novel ASD candidate genes [40]. In total, 460 potential candidate genes were identified that overlapped both the functional profile and the brain expression profile of the initial reference set. The power of this predictive gene map was demonstrated by the capture of 18 pre-existing ASD-associated genes that were not included in the reference gene dataset, with the remaining 442 genes serving as novel ASD candidate genes. Since the publication of our predictive gene map, 12 of the novel ASD candidate genes identified in [40] have been added to AutDB, demonstrating the continued power of this analysis (manuscript in preparation).

#### **4. Bioinformatics of ASD**

With the rapid growth of genetic data obtained from ASD individuals, there has become a critical need for databases specializing in the storage and assessment of this data. Here we highlight several of the ASD-related genetics databases that are available to researchers.

#### **4.1. AutDB**

Recent large-scale ASD genetic studies have used a systems biology approach to translate ge‐ netic information into functional profiles that shed light on how genetic variation in ASD may lead to disease onset and pathogenesis. Rare CNVs identified in large ASD cohorts have been shown to be enriched for genes involved in cellular processes of relevance for ASD, including cellular proliferation, projection, and motility, and GTPase/Ras signaling [1], neuronal cell ad‐ hesion and ubiquitin-mediated degradation [22], glycobiology [35], axon growth and path‐ finding [36], and synapse development, axon targeting, and neuron motility [37]. Gene datasets from genome-wide association studies in ASD populations were demonstrated to be enriched for Gene Ontology (GO) classifications for cellular processes including pyruvate me‐ tabolism, transcription factor activation, cell signaling and cell-cycle regulation [38]. A recent report describing gene pathway analysis using single nucleotide polymorphism (SNP) data from the Autism Genetics Research Exchange (AGRE) identified cellular pathways such as cal‐ cium signaling, long-term depression and potentiation, and phosphotidylinositol signaling that reached statistical significance in both Central European and Han Chinese populations [39]. More recently, whole exome sequencing studies in large ASD cohorts have demonstrated that proteins encoded by genes in which potentially disruptive *de novo* mutations were identi‐ fied showed a higher degree of connectivity among themselves and to previously identified ASD genes based on protein-protein interaction network analysis [4, 8]. Another exome se‐ quencing study in ASD individuals found that many of the genes in which potentially disrup‐ tive variants were identified associated with the Fragile X Mental Retardation Protein (FMRP), the encoded product of the syndromic ASD gene *FMR1* [10]. Taken together, these functional maps suggest that specific cellular pathways and processes are preferentially targeted by ge‐ netic variation in ASD cases, and that association with the encoded products of well-character‐

Knowledge of ASD-associated genes can also be used to identify novel ASD candidate genes. Following the construction of functional and expression profiles from a reference set of 84 rare and syndromic ASD-linked genes, we generated a predictive map of novel ASD candidate genes [40]. In total, 460 potential candidate genes were identified that overlapped both the functional profile and the brain expression profile of the initial reference set. The power of this predictive gene map was demonstrated by the capture of 18 pre-existing ASD-associated genes that were not included in the reference gene dataset, with the remaining 442 genes serving as novel ASD candidate genes. Since the publication of our predictive gene map, 12 of the novel ASD candidate genes identified in [40] have been added to AutDB, demonstrating the

With the rapid growth of genetic data obtained from ASD individuals, there has become a critical need for databases specializing in the storage and assessment of this data. Here we highlight several of the ASD-related genetics databases that are available to researchers.

ized ASD-linked genes offers evidence for pathogenic relevance.

200 Recent Advances in Autism Spectrum Disorders - Volume I

continued power of this analysis (manuscript in preparation).

**4. Bioinformatics of ASD**

Our autism database AutDB (http://autism.mindspec.org/autdb/Welcome.do) is a webbased, searchable database of ASD candidate genes identified in genetic association stud‐ ies, genes linked to syndromic autism, and rare single gene mutations [27]. Evidence regarding ASD candidate genes is systematically extracted from peer-reviewed, primary scientific literature and manually curated by our researchers for inclusion in AutDB. To provide high-resolution view of various components linked to ASD, we developed de‐ tailed annotation rules based on the biology of each data type and generated controlled vocabulary for data representation. AutDB is widely used by individual laboratories in the ASD research community, as well as by consortiums such as the Simons Foundation, which licenses it as SFARI Gene.

AutDB is designed with a systems biology approach, integrating genetic information with‐ in the original Human Gene module to corresponding data in subsequent Animal Model, Protein Interaction (PIN) and Copy Number Variant (CNV) modules. The Animal Model module contains a comprehensive collection of mouse models linked to ASD [41]. While the Animal Model module initially contained only genetic mouse models of ASD, it has since been expanded to include induced mouse models of ASD in which a chemical or bi‐ ological agent linked to ASD has been administered. As core behavioral features of ASD such as social interactions and communications can only be approximated in animal mod‐ els, the annotation strategy for this module includes four broad areas: 1) core behavioral features of ASD, 2) ASD-related traits such as seizures and circadian rhythms that are her‐ itable and more easily quantified in animal models; 3) neuroanatomical features, and 4) molecular profiles. To this end, we developed PhenoBase, a classification table for system‐ atically annotating models with controlled vocabulary containing 16 major categories and >100 standardized phenotype terms. The PIN module of AutDB serves as a repository for all known protein interactions of ASD candidate genes, documenting six major types of direct interactions: 1) protein binding, 2) promoter binding, 3), RNA binding, 4) protein modification, 5) direct regulation, and 6) autoregulation. Its content is envisioned to have immediate application for network biology analysis of molecular pathways involved in ASD pathogenesis. For the purposes of genetic evaluation of individuals with ASD, knowledge of the protein interactions of ASD-associated genes can potentially aid in the clinical assessment of novel ASD candidate genes based on their interactions, or lack thereof, with known ASD-linked genes.

#### **4.2. Gene scoring module of SFARI gene**

As previously mentioned, AutDB is licensed to the Simons Foundation as SFARI Gene. However, unlike AutDB, SFARI Gene includes a unique feature initiated by the Simons Foundation called the Gene Scoring module (https://gene.sfari.org/autdb/GS\_Home.do). The Gene Scoring module is a web-based platform detailing the rank of ASD-associated genes in the SFARI Gene Human Gene module [42]. With the increase in the number of genes linked to ASD, a Gene Scoring initiative was launched to assess the ASD candidate genes based on a set of standardized annotation rules. Following evaluation by an expert panel of advisors, the gene assessment results are then integrated in the form of Gene Score Cards to display the scores and the evidence in a graphical user interface for the ASD-linked gene. Recently, a community-wide annotation functionality was incorporated into the Gene Scoring module, allowing users to download the Gene Scoring dataset, score genes of their choice, and submit their scores to SFARI for possible inclusion.

MBII52 (the mouse ortholog of the human snoRNA HBII52), which could potentially alter serotonergic signaling and contribute in part to the ASD-associated traits exhibited by these mice [46]. More recently, it was discovered that a non-coding RNA is transcribed from a genepoor region of chromosome 5p14.1 identified in genome-wide association studies of ASD cohorts [47]. Expression of the non-coding RNA, designated MSNP1AS, was shown to be higher both in individuals carrying the ASD-associated T allele and in post-mortem brain

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Spontaneous breakage during DNA replication at rare chromosomal fragile sites may also play a role in the pathogenesis of neuropsychiatric disorders such as ASD. The chromosomal fragile site FRAXA has been implicated in fragile X syndrome, and other fragile sites have been

NGS and CMA have expanded the ability of clinical geneticists and researchers to identify potential genetic causes of ASD. However, there are many challenges still present in the field of genetic evaluation. A recent report in the *American Journal of Medical Genetics* found that many children with ASD fail to get genetic evaluation, and that parents and medical professionals need to be better educated about the potential benefits of genetic evaluation [49]. Educating parents on genetic evaluation is especially critical in light of a recent survey of nine parents regarding their child's participation in genetic research in ASD [50], in which parents valued having had their child enrolled for a variety of rea‐ sons, including the potential use of genetic results in tailoring intervention and in family planning, the establishment of connections with experts in the field of ASD, and network‐

Even with the increased sensitivity of genetic evaluation techniques, an underlying genetic cause of ASD is still only identified in a minority (< 25%) of ASD cases [51]. One of the major challenges in the clinical interpretation of NGS and CMA lies in differentiating between pathogenic and benign genetic variants identified in ASD patients. The pathogenic relevance of the vast majority of ASD-linked genetic variants remains unknown; such variants are frequently classified as variants of unknown significance, or VOUS. While the identification of a genetic lesion in an existing ASD susceptibility gene or CNV locus is suggestive of a possible genetic cause of disease, variants in these genes and CNV loci have also been observed in seemingly unaffected individuals. Furthermore, it is important to note that, while techno‐ logical advances have expanded the ability of clinical geneticists and researchers to identify these potential genetic causes of ASD, there is no genetic test available for the diagnosis of ASD. A recent report proposed a means of predicting a diagnosis of ASD based on the identification of candidate SNPs [39]. The accuracy of the predictive classifier was found to be 71.7% in individuals of Central European descent from validation datasets. However, the accuracy of the predictive classifier fell when tested in a Han Chinese cohort, a finding that stresses how genetic heterogeneity across populations complicates the use of such an ap‐

identified that associate with ASD, such as FRA2B, FRA6A, and FRA13A [48].

**5. Challenges of genetic evaluation in ASD**

ing with other families, among others.

tissue of individuals with ASD.

#### **4.3. DECIPHER**

DECIPHER (Database of Chromosomal Imbalance and Phenotype in Humans Using Ensembl Resources) (http://decipher.sanger.ac.uk/) is an interactive web-based database that incorpo‐ rates a suite of tools designed to aid in the interpretation of submicroscopic chromosomal deletions and duplications [43]. Genetic and phenotypic information is publically available not only for individuals diagnosed with idiopathic ASD, but also for individuals diagnosed with a recognized microdeletion or microduplication syndrome in which a subset of affected individuals also develop ASD.

#### **4.4. AutismKB**

AutismKB (http://autismkb.cbi.pku.edu.cn/) is a web-based, searchable database hosted by the Center for Bioinformatics, Peking University [44]. AutismKB is an evidence-based knowledge resource for ASD genetics containing information on genes, copy number variants, and linkage regions associated with ASD. Analysis of the gene content in AutismKB is available for users in the form of GO term enrichment analysis using the DAVID functional annotation tool and pathway enrichment analysis. Much like the Gene Scoring Module of SFARI Gene (see section 4.2), the genes within AutismKB are scored.

#### **4.5. Autism Chromosome Rearrangement Database**

The Autism Chromosome Rearrangement Database (http://projects.tcag.ca/autism/) is a webbased, searchable genetic database of chromosomal structural variation in ASD that is hosted by The Centre for Applied Genomics at the Hospital for Sick Children in Toronto, Canada [21]. The content of this database, which is derived both from published research articles and inhouse experimental results, includes cytogenetic and microarray data from individuals with ASD.

#### **4.6. Autism Genetic Database**

The Autism Genetic Database (http://wren.bcf.ku.edu/) is a web-based, searchable genetic database developed by researchers at the University of Kansas [45]. In addition to ASDassociated genes and CNVs, this database also includes information on known non-coding RNAs and chemically-induced fragile sites in the human genome.

Recent lines of evidence have placed non-coding RNAs under increased scrutiny with regards to their potential pathogenic role in ASD. A number of small nucleolar RNAs (snoRNAs) reside within the ASD-associated 15q11-q13 region. A mouse model engineered to mimic duplication of the 15q11-q13 region observed in ~1% of ASD cases exhibited overexpression of the snoRNA MBII52 (the mouse ortholog of the human snoRNA HBII52), which could potentially alter serotonergic signaling and contribute in part to the ASD-associated traits exhibited by these mice [46]. More recently, it was discovered that a non-coding RNA is transcribed from a genepoor region of chromosome 5p14.1 identified in genome-wide association studies of ASD cohorts [47]. Expression of the non-coding RNA, designated MSNP1AS, was shown to be higher both in individuals carrying the ASD-associated T allele and in post-mortem brain tissue of individuals with ASD.

Spontaneous breakage during DNA replication at rare chromosomal fragile sites may also play a role in the pathogenesis of neuropsychiatric disorders such as ASD. The chromosomal fragile site FRAXA has been implicated in fragile X syndrome, and other fragile sites have been identified that associate with ASD, such as FRA2B, FRA6A, and FRA13A [48].
