Bioinformatic analysis of autism positional candidate genes using biological databases and computational gene network prediction

Genes, Brain and Behavior - Tập 2 Số 5 - Trang 303-320 - 2003
Amanda L Yonan1,2, Abraham A. Palmer1, Kenneth Smith1, Igor Feldman1,3, H. K. Lee1, J. Yonan4, Stuart G. Fischer1, Paul Pavlidis1,3, T. Conrad Gilliam1,2,5
1Columbia Genome Center, Columbia University, New York
2Department of Genetics and Development, Columbia University, New York
3Department of Biomedical Informatics, Columbia University, New York, USA
4Division of Molecular Genetics, Departments of Pediatrics and Medicine, Columbia University, New York,
5Department of Psychiatry, Columbia University and New York State Psychiatric Institute, New York

Tóm tắt

Common genetic disorders are believed to arise from the combined effects of multiple inherited genetic variants acting in concert with environmental factors, such that any given DNA sequence variant may have only a marginal effect on disease outcome. As a consequence, the correlation between disease status and any given DNA marker allele in a genomewide linkage study tends to be relatively weak and the implicated regions typically encompass hundreds of positional candidate genes. Therefore, new strategies are needed to parse relatively large sets of ‘positional’ candidate genes in search of actual disease‐related gene variants. Here we use biological databases to identify 383 positional candidate genes predicted by genomewide genetic linkage analysis of a large set of families, each with two or more members diagnosed with autism, or autism spectrum disorder (ASD). Next, we seek to identify a subset of biologically meaningful, high priority candidates. The strategy is to select autism candidate genes based on prior genetic evidence from the allelic association literature to query the known transcripts within the 1‐LOD (logarithm of the odds) support interval for each region. We use recently developed bioinformatic programs that automatically search the biological literature to predict pathways of interacting genes (pathwayassist and geneways). To identify gene regulatory networks, we search for coexpression between candidate genes and positional candidates. The studies are intended both to inform studies of autism, and to illustrate and explore the increasing potential of bioinformatic approaches as a compliment to linkage analysis.

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Genes, Brain and Behavior

Tập 2 Số 5

303-320

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