Abstract
Several genomic studies are being done in Autism Spectrum Disease (ASD) aiming to understand the genetic architecture of the disorder and to identify genes and causative genetic alterations. This increase in the use of next generation sequencing techniques is demanding support of powerful computers to storage and process big data, besides specialized bioinformaticists to run tools and design scripts. In addition, the filtering strategy to determine which variants are indeed pathogenic, particularly for the missense variants, remains challenging and requires specialized biological knowledge and pipelines to accelerate multiple analysis considering several hypotheses simultaneously. In this context, some important steps in the process of filtering variants are essential, as populational databases to check variant frequency, the variant type to predict its impact to protein, functional studies and clinical evidence of previous cases with variants in the same gene presenting consistent phenotype data. More than 1000 genes were already described in patients with ASD, but many of these genes remain with an unclear role in the pathogenesis of the disease, mainly due to lack of evidence of other patients harboring pathogenic variants in these genes that could validate them. Our research group identified more than 20 new candidate genes in ASD patients and certainly will identify more genes in the analysis of the septets, which is the one of main aims of my PhD project. However, the role of these genes it is not clear and validation of these candidates in another cohort is essential to confirm these loci as novel ASD candidates. Then, our aim is to improve our exome data analysis process, from the bioinformatics tools to the filtering variants strategy, as well as to evaluate new candidate genes, that were already found by our group and that will be identified in next analysis steps, using the largest cohort of ASD patients available. Considering that one of our difficulties is also to process large amounts of big data, we expect to analyze them with an expert and specialized bioinformatics group. Additionally, we aim to implement whole genome sequencing analysis in our center and we expect to be trained in this type of data analysis, not only for point variants but also for structural variants, which may generate more robust information about genomic alterations in ASD.
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