Characteristics and Predictive Value of Blood Transcriptome Signature in Males with Autism Spectrum Disorders
| Autor: | Adrianna Saada, Ally Eran, Jessica Egan, Christin D. Collins, Heather K. Harris, Michal Galdzicki, Rachel J. Hundley, Stephanie J. Brewster, Ellen Hanson, Ingrid A. Holm, Kimberly Madison, Sek Won Kong, Louis M. Kunkel, Yuko Shimizu-Motohashi, Leonard Rappaport, Kathryn R. Lowe, Isaac S. Kohane, In-Hee Lee, Andrea Mora, Jillian McCarthy, Malcolm G. Campbell |
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| Jazyk: | angličtina |
| Rok vydání: | 2012 |
| Předmět: |
Oncology
Male Genetic Screens lcsh:Medicine Gene Expression Bioinformatics Cohort Studies 0302 clinical medicine lcsh:Science Child Oligonucleotide Array Sequence Analysis Psychiatry Child Psychiatry 0303 health sciences Multidisciplinary Mental Health Autism spectrum disorder Cohort Medicine Cohort study Research Article medicine.medical_specialty Cognitive Neuroscience Biology Molecular Genetics 03 medical and health sciences Internal medicine Molecular genetics medicine Genetics Humans Gene Networks 030304 developmental biology Computational Neuroscience Receiver operating characteristic Models Genetic Gene Expression Profiling lcsh:R Computational Biology medicine.disease Confidence interval Gene expression profiling Child Development Disorders Pervasive Genetics of Disease Autism lcsh:Q Gene Function Transcriptome 030217 neurology & neurosurgery Neuroscience |
| Zdroj: | PLoS ONE PLoS ONE, Vol 7, Iss 12, p e49475 (2012) |
| ISSN: | 1932-6203 |
| Popis: | Autism Spectrum Disorders (ASD) is a spectrum of highly heritable neurodevelopmental disorders in which known mutations contribute to disease risk in 20% of cases. Here, we report the results of the largest blood transcriptome study to date that aims to identify differences in 170 ASD cases and 115 age/sex-matched controls and to evaluate the utility of gene expression profiling as a tool to aid in the diagnosis of ASD. The differentially expressed genes were enriched for the neurotrophin signaling, long-term potentiation/depression, and notch signaling pathways. We developed a 55-gene prediction model, using a cross-validation strategy, on a sample cohort of 66 male ASD cases and 33 age-matched male controls (P1). Subsequently, 104 ASD cases and 82 controls were recruited and used as a validation set (P2). This 55-gene expression signature achieved 68% classification accuracy with the validation cohort (area under the receiver operating characteristic curve (AUC): 0.70 [95% confidence interval [CI]: 0.62–0.77]). Not surprisingly, our prediction model that was built and trained with male samples performed well for males (AUC 0.73, 95% CI 0.65–0.82), but not for female samples (AUC 0.51, 95% CI 0.36–0.67). The 55-gene signature also performed robustly when the prediction model was trained with P2 male samples to classify P1 samples (AUC 0.69, 95% CI 0.58–0.80). Our result suggests that the use of blood expression profiling for ASD detection may be feasible. Further study is required to determine the age at which such a test should be deployed, and what genetic characteristics of ASD can be identified. |
| Databáze: | OpenAIRE |
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