**1. Introduction**

Autism is a complex neurodevelopmental disorder. It is characterized by social isolation, language deficits and repetitive or stereotyped behaviors. Autism spectrum disorder (ASD) has received a great deal of attention in the recent years not only due to the increasing rate of affected children but also because of the social and economical impact of the disorder on their families. Various studies and researches have been proposed to deal with and tackle the ASD. They can be divided into three categories as follows.


© 2013 Alqallaf et al.; 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.

teaching, speech and language therapy and social skills therapy. When behavioral treat‐ ment fails, many medications are used to treat ASD symptoms.

tion custom-tiled samples improve the accuracy of our proposed approach in determining

Discovering the Genetics of Autism http://dx.doi.org/10.5772/ 53797 343

This chapter aims at utilizing research to bring benefits to individuals and families affected by autism spectrum disorders and to improve the quality of their life. And this can be done by clear mapping and identifying the biomarkers associated with ASD at the early child‐ hood stages which are essential to provide better treatments and therapies. Finally, the pro‐ posed approach presented in this chapter is broadly applicable to case-control studies of

The chapter is organized as follows. In section 2, we demonstrate the genetic data generat‐ ing techniques, data modeling and chromosomal variations that are associated with the tar‐ geted disorder, ASD. Section 3 is devoted for the methods used to analyze the genetic data trying to discover the variant regions along the genome and to identify the tested samples. In section 4, we apply molecular test to evaluate the predictive power of the proposed ap‐ proach. Finally, discussion and conclusion based on the results are presented in section 5.

Genetic alterations in the form of chromosomal rearrangements are genomic structural var‐ iations that lead to changes in the DNA copy number such as duplications and deletions of the DNA copies. However, copy number changes do not include other genomic structural variations such as inversions, insertions and reciprocal translocations. Figure 2 demonstrates

previously reported and new genetic contributors that warrant investigation.

**2.1. Genomic structural variations and ASD susceptibility**

**Figure 2.** a schematic representation of types of chromosomal rearrangements [67].

different types of chromosomal rearrangements.

genetic diseases beyond the ASD.

**2. Genetic data**

Figure 1 demonstrates the interaction of the autism spectrum disorders researches and studies.

**Figure 1.** A puzzle-like representation of the interaction process of the researches and studies for autism spectrum disorders.

The advancements of the technologies in the field of genetics provide the opportunities for researchers and scientists to explore in depth the biological information and to convert it in‐ to meaningful biological knowledge through computational-based models.

In this chapter, we will investigate the genetics origins of autism and demonstrate the latest techniques and technologies available for diagnosing the complex disorder. We will also propose a robust approach for detecting and identifying the targeted disorder based upon the advantages and strengths of the publically available and commercial approaches while avoid‐ ing their weaknesses. The proposed approach is divided into two steps. The preprocessing step is a feature-extraction method used to clearly map and detect the genetic variations and structural rearrangements followed by a statistical-based model as feature-selection to evalu‐ ate and measure the statistical and biological significance of the predicted variations. The classification step is to discover the relationship among the tested samples into groups and/or subgroups, and to provide insight into the complex pattern of the genome.

The results suggest that autism is associated with an increased amount of alterations in un‐ stable segments of the genome. The experimental results also show that using high-resolu‐ tion custom-tiled samples improve the accuracy of our proposed approach in determining previously reported and new genetic contributors that warrant investigation.

This chapter aims at utilizing research to bring benefits to individuals and families affected by autism spectrum disorders and to improve the quality of their life. And this can be done by clear mapping and identifying the biomarkers associated with ASD at the early child‐ hood stages which are essential to provide better treatments and therapies. Finally, the pro‐ posed approach presented in this chapter is broadly applicable to case-control studies of genetic diseases beyond the ASD.

The chapter is organized as follows. In section 2, we demonstrate the genetic data generat‐ ing techniques, data modeling and chromosomal variations that are associated with the tar‐ geted disorder, ASD. Section 3 is devoted for the methods used to analyze the genetic data trying to discover the variant regions along the genome and to identify the tested samples. In section 4, we apply molecular test to evaluate the predictive power of the proposed ap‐ proach. Finally, discussion and conclusion based on the results are presented in section 5.
