AnÃlise in silico de novos potenciais polimorfismos genÃticos de risco na DoenÃa de Alzheimer em bancos de dados de Microarrays

AUTOR(ES)

Roberta Rodrigues de Lemos

DATA DE PUBLICAÇÃO

2009

RESUMO

Genomic and Proteomic studies of neurodegenerative disorders require complementary approaches to integrate the massive amount of data generated in high throughput experimental procedures. We propose a Bioinformatics pipeline in which expression studies guide the selection of candidate genes that should be screened for potential new genetic variations from a public Expressed site tags (ESTs) database. Motivated by the former interest of our group in genetic polymorphisms involved with the immune system, we selected nine genes from a previous expression microarrays study of hippocampal Cornu Ammonis (CA1) area and Inferior Parietal Lobe (IPL) of Alzheimerâs Disease subjects (AD). The CLCbio Workbench Combined version 3.6.2. was initially used to build ESTs and mRNA files retrieved respectively from the Goldenpath of University of California Santa Cruz (UCSC) and National Center for Biotechnology Information (NCBI) databases and latter to perform multiple batches of Smith-Waterman alignments. A total of 479 ESTs sequences were selected after proper stringent parameters were applied to the first set of mismatches. The annotation revealed various classes of variations, most of them deletions (415), but also transitions (253), transversions (52), synonymous (48), non synonymous (400) and Single Nucleotide Polymorphisms (SNPs) in Untranslated Regions (UTRs) (50). Deletions are often associated to major genetics syndromes with dysmorphic features. However, various recent studies show that common microdeletions might be highly associated with common neuropsychiatric disorders such as schizophrenia, autism, mental retardation or even in various ethnicities detected in whole genome sequencing experiments. A virtual validation confirmed that some of the variations identified were previously reported and confirmed in DNA samples, showing that this method is a feasible way to detect genetic variations that merit further exploration in AD genetic risk factor association studies

ASSUNTO(S)

microarrays in silico alzheimerÂs disease ests in silico ests bioinformatics ciencias biologicas bioinformÃtica doenÃa de alzheimer microarrays

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