Details

Autor(en) / Beteiligte

• Kong, Sek Won
• Collins, Christin D
• Shimizu-Motohashi, Yuko
• Holm, Ingrid A
• Campbell, Malcolm G
• Lee, In-Hee
• Brewster, Stephanie J
• Hanson, Ellen
• Harris, Heather K
• Lowe, Kathryn R
• Saada, Adrianna
• Mora, Andrea
• Madison, Kimberly
• Hundley, Rachel
• Egan, Jessica
• McCarthy, Jillian
• Eran, Ally
• Galdzicki, Michal
• Rappaport, Leonard
• Kunkel, Louis M
• Kohane, Isaac S
• Esteban, Francisco José

Titel

Characteristics and predictive value of blood transcriptome signature in males with autism spectrum disorders

Ist Teil von

• PloS one, 2012-12, Vol.7 (12), p.e49475-e49475

Ort / Verlag

United States: Public Library of Science

Erscheinungsjahr

2012

Quelle

MEDLINE

Beschreibungen/Notizen

• 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.