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Human Genetics

, Volume 135, Issue 7, pp 797–811 | Cite as

Exploiting aberrant mRNA expression in autism for gene discovery and diagnosis

  • Jinting Guan
  • Ence Yang
  • Jizhou Yang
  • Yong Zeng
  • Guoli Ji
  • James J. Cai
Original Investigation

Abstract

Autism spectrum disorder (ASD) is characterized by substantial phenotypic and genetic heterogeneity, which greatly complicates the identification of genetic factors that contribute to the disease. Study designs have mainly focused on group differences between cases and controls. The problem is that, by their nature, group difference-based methods (e.g., differential expression analysis) blur or collapse the heterogeneity within groups. By ignoring genes with variable within-group expression, an important axis of genetic heterogeneity contributing to expression variability among affected individuals has been overlooked. To this end, we develop a new gene expression analysis method—aberrant gene expression analysis, based on the multivariate distance commonly used for outlier detection. Our method detects the discrepancies in gene expression dispersion between groups and identifies genes with significantly different expression variability. Using this new method, we re-visited RNA sequencing data generated from post-mortem brain tissues of 47 ASD and 57 control samples. We identified 54 functional gene sets whose expression dispersion in ASD samples is more pronounced than that in controls, as well as 76 co-expression modules present in controls but absent in ASD samples due to ASD-specific aberrant gene expression. We also exploited aberrantly expressed genes as biomarkers for ASD diagnosis. With a whole blood expression data set, we identified three aberrantly expressed gene sets whose expression levels serve as discriminating variables achieving >70 % classification accuracy. In summary, our method represents a novel discovery and diagnostic strategy for ASD. Our findings may help open an expression variability-centered research avenue for other genetically heterogeneous disorders.

Keywords

Autism Spectrum Disorder Mahalanobis Distance Autism Spectrum Disorder Group Autism Spectrum Disorder Patient Autism Spectrum Disorder Sample 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

We thank Shannon Ellis and Dan Arking for sharing the data, Oliver Stegle and Tuuli Lappalainen for helping with data normalization, and Steve Horvath for the co-expression network analysis. We thank Rae L. Russell for proofreading and editing this paper. We acknowledge the Texas A&M Institute for Genome Sciences and Society (TIGSS) for providing computing resources and system administration support. This work was supported by the fund of China Scholarship Council to JG, and the National Natural Science Foundation of China (No. 61573296), the Specialized Research Fund for the Doctoral Program of Higher Education of China (No. 20130121130004), the Fundamental Research Funds for the Central Universities in China (Xiamen University: Nos. 201412G009, 201510384106) to GJ.

Compliance with ethical standards

Competing interests

The authors declare that they have no competing interests.

Supplementary material

439_2016_1673_MOESM1_ESM.docx (94 kb)
Supplementary Fig. 1. Results of principal component analysis (PCA) showing the first four principal components (from PC1 to PC4). The distributions of 104 samples (57 controls and 47 ASD samples) on PCA spaces defined by PC1 and 2, PC2 and 3, and PC3 and 4 are shown
439_2016_1673_MOESM2_ESM.docx (609 kb)
Supplementary Fig. 2. Reproducibility of co-expression modules in the non-ASD control group and the breakdown of modules in ASD. Ten example modules are shown with two independent data sets from controls, as well as one data set from ASD samples. Edge width is proportional to the Pearson’s correlation coefficients (ranging 0.5 and 1). Node size is proportional to ΔSSMD for each gene
439_2016_1673_MOESM3_ESM.docx (16 kb)
Supplementary Fig. 3. Distribution of correlation coefficients between t statistics of DE test and ΔSSMD values of genes in 76 significant modules. The kernel density estimate of the distribution is shown with the gray line; values of Spearman correlation coefficient (rho) of modules are shown with orange triangles; rho=0 is shown with the dotted vertical line
439_2016_1673_MOESM4_ESM.docx (16 kb)
Supplementary Fig. 4. Box plot of AUC (area under ROC curve) value against the size of classifier gene set. For each size of the gene set (from 3 to 15), 100 different random gene sets were constructed and tested on the training set and test set for obtaining AUCs. The black and red boxplots denote AUC values tested on the training set (AUC1) and test set (AUC2) varying with the size of classifier gene set, respectively
439_2016_1673_MOESM5_ESM.docx (25 kb)
Supplementary Fig. 5. Scatter plot of AUC values tested on the test set (AUC2) against AUC values tested on the training set (AUC1) for 100 different random classifier 5-gene sets. Red line denotes the least-squares line of the scatter plot. The Spearman correlation coefficient between AUC1 and AUC2 is 0.32 (P = 1.1 × 10−3). The inset shows the distribution of the Spearman rank correlation coefficients between AUC1 and AUC2 calculated with 1,000 replicates of such 100 random classifier 5-gene sets
439_2016_1673_MOESM6_ESM.docx (23 kb)
Supplementary Table 1. GO term-defined gene sets that tend to be aberrantly expressed in brain tissues of ASD-affected individuals. Gene sets contain genes annotated with GO terms of three sub-ontologies: biological process (BP), molecular function (MF), and cellular component (CC)
439_2016_1673_MOESM7_ESM.docx (34 kb)
Supplementary Table 2. WGCNA co-expression network modules containing genes that tend to be aberrantly expressed in the brains of ASD-affected individuals. Modules are annotated with the DAVID-defined gene function keyword clusters. Representative genes with the corresponding function are shown in bold. Statistics of the preservation between modules built for cases and controls, medianRank and Zsummary, calculated using function modulePreservation of WGCNA are given
439_2016_1673_MOESM8_ESM.docx (27 kb)
Supplementary Table 3. Genes in the three classifier gene sets obtained from blood data set (GEO accession: GSE18123) and corresponding SSMD and ΔSSMD values
439_2016_1673_MOESM9_ESM.docx (19 kb)
Supplementary Table 4. Genes in the three classifier gene sets obtained from brain data set (57 controls and 47 ASD cases) and corresponding SSMD and ΔSSMD values. The performances of classifiers based on gene set I, II, III tested on the training and test sets are also reported including sensitivity (SN), specificity (SP) and accuracy (ACC) values

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Department of AutomationXiamen UniversityXiamenChina
  2. 2.Department of Veterinary Integrative BiosciencesTexas A&M UniversityCollege StationUSA
  3. 3.Institute for Systems Biomedicine, School of Basic Medical Sciences, Peking University Health Science CenterBeijingChina
  4. 4.Innovation Center for Cell Signaling Network, Xiamen UniversityXiamenChina
  5. 5.Interdisciplinary Program of GeneticsTexas A&M UniversityCollege StationUSA

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