In silico identification of new genetic variations as potential risk factors for Alzheimer's disease in a microarray-oriented simulation

Searching for new genetic variations in expression databases for the GABAergic and glutamatergic systems

Changes in gene expression and genetic variations in coding regions have likely functional impact, potentially associated with complex diseases, such as neuropsychiatric conditions. A current need for high throughput analysis of genomic data is leading to the development and improvement of sophisticated bioinformatics approaches, which allows the processing of large amounts of sequence and gene expression data. In this study, we identified new potential genetic variations prioritizing genes related to glutamatergic and GABAergic systems, using different bioinformatics resources. The CLCbio Workbench Combined platform was initially used to build expressed sequence tags and mRNA files retrieved, respectively, from the Goldenpath and National Center for Biotechnology Information databases and latter to perform multiple batches of Smith-Waterman alignments. The PMUT software was used to increase an accurate association between potential variations and pathogenic predictions. The annotation revealed various classes of variations and most of them are deletions ranging from 1 to 7 bp. Bioinformatic pipelines seem to be useful approaches to help screening for genetic variations with potential impact in gene expression. Further analysis will foster this aim to provide celerity at the massive analysis of data currently generated in large scale high throughput experiments.

Exploring the implications of INDELs in neuropsychiatric genetics: challenges and perspectives

The decade passed after publishing the Human Genome first draft faced an enormous growth at the understanding of the genomic variation among different subjects, populations, and groups of patients. Single nucleotide polymorphisms (SNPs) and insertion or deletions (INDELs) have been increasingly recognized as a major type of genetic variations, with potential impact in protein activities and gene expression changes observed in complex genetic traits, like neuropsychiatric diseases. INDELs represent the second most common class of variations after SNPs, but there is still an important gap between the number of INDELs reported and the actual knowledge about the functional implications of such variations. There are approximately 10 million SNPs already reported, and the human populations are expected to collectively harbor at least 1.6-2.5 million INDELs. One of the major challenges is to find better platforms to screen for INDELs in a high throughput manner. The discordance in between the data from different studies might be explained by the diverse approaches employed to sequence the genomes with variable platforms. Short INDEL variations increased the scope of genetic markers in human genetic diseases, and various studies showed that common microdeletions and smaller INDELs might be highly associated with neuropsychiatric diseases such as schizophrenia, autism, mental retardation, and Alzheimer disease. The rapidly increasing amount of resequencing, genotyping, and personal genome data generated by large-scale genetic human projects require the development of integrated bioinformatics tools able to efficiently manage and analyze these genetic data. Our group is currently dealing with different approaches that might optimize sequencing and bioinformatics analyses of short INDELs to broaden our research capabilities of identifying those intriguing genetic variations. Hopefully, INDELs might become a new trend in association studies in neuropsychiatric genetics since so far the level of significant and positive associations with the standard SNPs reported presents limited predictive application.

Searching for new genetic risk factors for neuropsychiatric disorders in expression databases

Genetic variations might contribute to differences in protein activities and gene expression levels observed in complex genetic traits, like neuropsychiatric disease. This finding motivated the development of original approaches using expression studies to guide the finding of new genetic variations. We extended this approach to new genes selected from microarrays studies of brain samples of patients with Alzheimer's disease, major depressive disorder, bipolar affective disorder, and sporadic Creutzfeldt-Jakob disease. The CLCbio Workbench Combined version 3.6.2 was initially used to build expression sites tags (ESTs) and mRNA files retrieved, respectively, from the Goldenpath (UCSC) and NCBI databases and latter to perform multiple batches of Smith-Waterman alignments. The total of 438 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 ranging from 1 to 10 pb. These deletions were present in coding regions, 5' and 3' UTR regions. Deletions are often associated to major genetic syndromes with dysmorphic features; however, recent studies show that common microdeletions might be highly associated with common neuropsychiatric disorders.