**1. Introduction**

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The field of genetics has made considerable scientific progress in the past several years and continues to evolve at a rapid pace. This progress parallels developments in genom‐ ic technology, where instrumentation and methodology are becoming increasingly so‐ phisticated and cost-effective. Here, we review recent developments in understanding autism spectrum disorder (ASD) from a genomics perspective. A large catalog of com‐ mon and rare variants has now been associated with ASD, and we are beginning to see some of these discoveries translate into pharmacogenomic intervention. This review pro‐ vides an overview of genome-wide association studies (GWAS) and common genetic var‐ iants, followed by an overview of the status of rare variant research, which have risen to prominence with the proliferation of next-generation sequencing and techniques for iden‐ tifying copy number variants. While these approaches need not be mutually exclusive, they provide a useful structure for organizing relevant genetic factors. Although there is much work to be done before these discoveries will enter the clinic, the past decade has seen us make major inroads in elucidating the causes of ASD and making tentative steps towards developing treatments.

#### **1.1. Defining the autism phenotype**

Autism is known to be highly heterogeneous, and this phenomenon has made definitions of the phenotype somewhat problematic. The American Psychiatric Association recently pro‐ posed revisions to its Diagnostic and Statistical Manual of Mental Disorders V (DSM-5) cri‐ teria for ASD (see Wing *et al*., 2011) [1], acknowledging the long-observed overlap between social and communication dimensions (previously separate). Thus, ASD will be defined by 1) persistent deficits in social communication and social interaction across contexts, and 2) restricted, repetitive patterns of behavior, interests, or activities. These should impair every‐

© 2013 Connolly and Hakonarson; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2013 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

day functioning, not be accounted for by general developmental delays, and be present from early childhood.

teria for autism (Rogers *et al.*, 2001; Harris *et al.*, 2008) [15,16]. Similarly, linkage studies have been important to identifying *MECP2* as the major cause of Rett syndrome (e.g. Curtis *et al.*,

Autism Spectrum Disorders: Insights from Genomics

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Association studies take a different approach. Rather than track transmission of specific ge‐ nomic regions through generations, association studies scan the breadth of the genome. Here, the goal is to determine *post-hoc* whether identified variants are more or less common in affected individuals. Early association studies (i.e. pre HapMap) were complementary with the linkage approach, and in many designs, linkage primed target loci for this more

These early insights have played an important role in shaping our current understanding of ASD. Functional studies of *FMR1* and *MECP2* have highlighted the importance of synaptic dysfunction (Ramocki & Zoghbi, 2008) [18] as a unifying factor that could extend into the more common forms of autism and, as discussed below, remain highly relevant to our un‐

Aside from notable successes with fragile X and Rett syndrome, early linkage and associa‐ tion studies have been inconsistent in resolving more complex genetic correlates of ASD, and candidate genes have often not being replicated between studies. These challenges may in part be accounted-for by their relatively low resolution/coverage, making it difficult to detect candidate loci other than those of major effect. A shift in technology was required to get beyond such challenges, which was engendered by the introduction of high-resolution single nucleotide polymorphism (SNP) arrays. SNP arrays provided coverage of many thou‐ sand (now several million) common SNPs, which could be examined at a relatively low cost

Genome-wide association studies (GWAS) examine the frequency of SNPs in case *versus* control populations, and can adopt either a case-control or family-based design. The former allows researchers to avoid the often complex process of acquiring diagnostic/phenotype da‐ ta from a patient's family, and can incorporate very large numbers of control datasets that may be more readily available. The latter controls for the often confounding phenomenon of population stratification, where variants more common to specific racial groups may either be erroneously identified as causal, or obscure actual causal variants. A major caveat with family-based designs is the often unfounded assumption that unaffected family members do

GWAS test for common variants (>1% population frequency), with the assumption that ASD are at least in part caused by the coinheritance of multiple risk variants, each of small indi‐ vidual effect (odds ratios typically between ~1:1 and ~1:5). This assumption is known as the

common disease-common variant (CDCV) model (Risch & Merikangas, 1996) [20].

1993) [17].

fine-grained analysis.

across large sample sets.

not share causal variants.

derstanding of the broader ASD phenotype.

**2. Genome wide association and common variants**

For large-scale genome analyses, DSM criteria have been considered insufficiently precise, and cases are often selected using scores from the Autism Diagnostic Interview (ADI-R) [2], Autism Diagnostic Observation Schedule (ADOS) [3], and/or Social Responsiveness Scale (SRS) [4]. These instruments offer a more robust psychometric platform, and cases defined as "autism" are required to meet strict threshold criteria (e.g. all sub-dimensions of the ADI-R and ADOS). Individuals not quite meeting these criteria may be subsumed under the "broader" autism phenotype, which also typically includes Asperger syndrome, childhood disintegrative disorder and pervasive developmental disorder not otherwise specified. A di‐ agnosis of Rett syndrome—which has a reportedly distinct pathophysiology, clinical course, and diagnostic strategy (Levy, Mandell & Schultz, 2009) [5] and will likely be removed in the impending publication of DSM-V—is typically exclusionary. Intellectual impairment, which is often co-morbid with ASD (Dawson *et al*., 2007; Bölte *et al*., 2009) [6,7] is not an ex‐ clusionary criterion, but is co-varied in statistical analyses. Given the broad range of IQ tests and their associated psychometric properties, this requires considerable finesse.

Standardization of diagnostic criteria has facilitated the accumulation of large ASD samplesets, where institutions can share (de-identified) data. In this vein, initiatives such as the Au‐ tism Genome Project include data from several thousand ASD individuals, greatly increasing statistical power of relevant analyses.

#### **1.2. Heritability of ASD**

Although Skuse (2007) [8] cautions that heritability estimates of ASD may have been skewed by the co-inheritance of (low) intelligence or other variables, there is little doubt that genetic factors play a key role in autism. In the most widely-cited twin study, Bailey *et al.* (1995) [9] report that monozygotic twins are 92% concordant on a broad spectrum of cognitive or so‐ cial abnormalities, compared with only 10% for dizygotic twins. Parents and siblings of indi‐ viduals with ASD often exhibit subsyndromal levels of impairment (Piven *et al.*, 1997) [10], and having an affected sibling is the single biggest risk factor for developing an ASD. In an analysis of 943,664 Danish children (Lauritsen *et al.*, 2005) [11], the strongest predictors of autism were siblings with ASD, who conferred a 22-fold increased risk, while Fombonne (2005) [12] suggested that this risk may be even greater.

#### **1.3. Early genetic studies – insights from Rett syndrome and Fragile X**

Early efforts to identify the genetic causes of ASD utilized linkage and association ap‐ proaches. Linkage studies, more prominent in the 1980s and 1990s, typically focus on fami‐ lies or larger pedigrees and are well powered to identify rarer genetic variants. The most common linkage approach is the affected sib-pair design (see O'Roak & State, 2008) [13], which examines the transmission of genomic segments through generations. Linkage stud‐ ies helped define the locus containing *FMR1*, which is mutated in fragile X syndrome (e.g. Richards *et al.*, 1991) [14], Approximately 30% of children with fragile X syndrome meet cri‐ teria for autism (Rogers *et al.*, 2001; Harris *et al.*, 2008) [15,16]. Similarly, linkage studies have been important to identifying *MECP2* as the major cause of Rett syndrome (e.g. Curtis *et al.*, 1993) [17].

Association studies take a different approach. Rather than track transmission of specific ge‐ nomic regions through generations, association studies scan the breadth of the genome. Here, the goal is to determine *post-hoc* whether identified variants are more or less common in affected individuals. Early association studies (i.e. pre HapMap) were complementary with the linkage approach, and in many designs, linkage primed target loci for this more fine-grained analysis.

These early insights have played an important role in shaping our current understanding of ASD. Functional studies of *FMR1* and *MECP2* have highlighted the importance of synaptic dysfunction (Ramocki & Zoghbi, 2008) [18] as a unifying factor that could extend into the more common forms of autism and, as discussed below, remain highly relevant to our un‐ derstanding of the broader ASD phenotype.
