Mitra will develop a novel methodology to analyze the transcriptional networks of genes involved in ASD and assign these genes into shared molecular pathways.

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition that is the result of interplay amongst hundreds of genes. Even though researchers have implicated dozens of genes in ASD to date, there is still much more to be understood. A few network-based ASD gene discovery algorithms have been developed with the following goals: (1) speeding up the gene discovery process by using the guilt-by-association principle, and (2) understanding affected functionalities by analyzing genes and links in predicted clusters. Fundamentally, these algorithms rely on the assumption that ASD risk genes are part of a functional gene network. However, the definition of a gene network in these analyses is a single, flat and static network. This approach disregards the temporal dimension in the development and differentiation of neurons and brain tissue. The assumption of ASD genes being part of a functional network is reasonable. However, the functional clustering of genes is bound to evolve over time and more than likely to have a cascading effect on future associations.

The diagnostic dyad defining autism spectrum disorder (ASD) belies a tremendous phenotypic heterogeneity that remains a key challenge to diagnosis and treatment. The strong genetic component of ASD suggests that heterogeneity in underlying genetic mutations may contribute to phenotypic heterogeneity. However, the germline genetics of ASD has thus far proven insufficient to explain the clinical heterogeneity of the disorder.

In contrast to rare copy number variants (CNVs) causing classical syndromes such as Smith-Magenis syndrome and Williams syndrome, recent studies have identified a class of rare CNVs associated with the risk of developing a wide variety of neurodevelopmental and neuropsychiatric features. Individuals affected by these variants often have carrier parents who are apparently unaffected or manifest only subclinical neuropsychiatric features. This makes genetic diagnosis, counseling and management of individuals affected by such CNVs difficult. Several identified CNVs of this category, including 16p11.2 deletion, 1q21.1 deletion, 15q13.3 deletion and 16p12.1 deletion, collectively account for about 20 percent of individuals with neurodevelopmental disorders. Although these CNVs confer higher risk for a disorder, alone they are not sufficient for the manifestation of the disorder. It is therefore essential to consider additional genetic factors that may account for the observed variability in manifestation of these disorders.

Over the past few years, the number of genes associated with autism spectrum disorder (ASD) has increased substantially. More than 65 genes are strongly associated with autism, and current estimates suggest that hundreds of genes may play a role.

SPARK (Simons Foundation Powering Autism Research for Knowledge) is an autism research initiative that aims to recruit, engage and retain a community of 50,000 individuals with autism and their family members living in the U.S. Participation in this cohort will involve contribution of medical and behavioral information, mailing in saliva for genetic analysis, the potential option to have genetic findings related to autism returned, and consenting to be invited to participate in future research studies.

Autism spectrum disorders (ASDs) are highly heritable but have a complex genetic architecture. There is significant locus heterogeneity for ASD and distinct subgroups, including syndromic and nonsyndromic cases. Whole-exome sequencing (WES) has emerged as an important tool in understanding ASD. WES studies have revealed an increased burden of de novo variants in people with ASD compared with unaffected individuals. However, due to variants in thousands of different genes — many of which are poorly characterized — interpreting the disease relevance of individual variants has been difficult.

Recent advances in genome-wide approaches for gene discovery in autism spectrum disorders (ASDs) have identified a large list of strongly associated ASD risk genes, as well as an even larger list of potential ASD risk genes. In total, these comprise approximately 250 genes. In order to further distinguish the true ASD risk genes from false-positive associations, additional sequencing data is required. Molecular inversion probe (MIP) sequencing is an efficient approach because of the low cost, potential for parallelization and high-throughput capacity.

Gene discovery in autism spectrum disorders (ASDs) has accelerated in the past several years. However, current efforts have mainly focused on the coding portion of the genome, which reflects approximately 1 percent of the total genome. This focus is partly due to the expense of characterizing the entire genome, as well as difficulties in interpreting the significance of variations in noncoding DNA information.