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. 2012;7(12):e49475.
doi: 10.1371/journal.pone.0049475. Epub 2012 Dec 5.

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

Sek Won Kong  1 , Christin D Collins, Yuko Shimizu-Motohashi, Ingrid A Holm, Malcolm G Campbell, In-Hee Lee, Stephanie J Brewster, Ellen Hanson, Heather K Harris, Kathryn R Lowe, Adrianna Saada, Andrea Mora, Kimberly Madison, Rachel Hundley, Jessica Egan, Jillian McCarthy, Ally Eran, Michal Galdzicki, Leonard Rappaport, Louis M Kunkel, Isaac S Kohane
Affiliations

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

Sek Won Kong et al. PLoS One. 2012.

Abstract

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.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. Heterogeneous subgroups in dysregulated pathways.
For immune response and synaptic gene sets, robust Mahalanobis distances (RDs) were calculated for all P1 samples. The outlier cutoff was set at the 97.5% quantile of the chi-squared distribution for each gene set (dotted green lines). When all samples were plotted in the 2-dimensional plane of Pathway Cluster 1 (x axis) by RDs in the Pathway Cluster 2 (y axis) (Table 4), four subgroups of samples were distinct. Both gene sets were perturbed for the samples in quadrant I; however, the samples in quadrants II and IV were significant for one gene set but not the other. A majority of samples were in quadrant III where no significant perturbation was found. The marginal density plots show the RD distributions for each gene set. Twenty-three out of 66 ASD samples (34.8%) were outliers for the synaptic gene set compared to 4 of 33 for controls (12.1%) (Fisher's exact test P = 0.017). For the immune response gene set, outliers were not biased towards case or control (Fisher's exact test P = 0.36).
Figure 2
Figure 2. Performance of the ASD55 prediction model.
Receiver operating characteristic (ROC) curve analysis was performed to evaluate the prediction accuracy. The dotted diagonal line represents random classification accuracy (AUC 0.5). A. The accuracy of ASD55 within P1 was unsurprisingly high (AUC 0.98, 95% confidence interval (CI), 0.965–1.000, black ROC curve). The ASD55 model was trained with P1 to predict the diagnosis of each sample in an independently collected dataset P2 (dark blue ROC curve). The performance measured by AUC was 0.70 (95% CI, 0.62–0.77). ASD55 genes showed similar performance when the training and testing datasets were switched (AUC 0.69, 95% CI 0. 58–0.80, brown ROC curve). B. P2 male samples were accurately predicted (dark green) while female samples (red) were not (AUC 0.73 and 0.51 respectively) when the ASD55 model was trained with P1.
Figure 3
Figure 3. Cluster analysis of the 55 genes used in the prediction model (ASD55).
The dendrogram and heatmap on top show hierarchical clustering (average linkage) of the 99 samples in the training set (P1) and the 55 genes used in our prediction model. The first 2 lines in the graph on bottom indicate whether each sample is from the patient group or the control group. Finally, the bottom line shows the distribution of Fisher's linear discriminant scores (dots) based on ASD55 with moving average (line). The distributions of linear discriminant scores are shown on the right (blue solid line for controls and black broken line for patients). ASD cases and controls are well separated using linear discriminant analysis on the ASD55 genes.
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References

    1. American Psychiatric Association (2000) Diagnostic and statistical manual of mental disorders: DSM-IV-TR. Washington, DC: American Psychiatric Association.
    1. Autism, Developmental Disabilities Monitoring Network Surveillance Year Principal I (2012) Prevalence of autism spectrum disorders - autism and developmental disabilities monitoring network, 14 sites, United States, 2008. MMWR Surveill Summ 61: 1–19. - PubMed
    1. Lord C, Risi S, DiLavore PS, Shulman C, Thurm A, et al. (2006) Autism from 2 to 9 years of age. Arch Gen Psychiatry 63: 694–701. - PubMed
    1. Howland A, Rasbury W, Heilman KM, Hammer L (1975) The development of auditory figure-ground discrimination and ear asymmetry under nonaural stimulus presentation. Dev Med Child Neurol 17: 325–332. - PubMed
    1. Shattuck PT, Durkin M, Maenner M, Newschaffer C, Mandell DS, et al. (2009) Timing of identification among children with an autism spectrum disorder: findings from a population-based surveillance study. J Am Acad Child Adolesc Psychiatry 48: 474–483. - PMC - PubMed

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