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Decision Support for Health Professionals (1185) Autism Spectrum Disorder (ASD) (109) Artificial Intelligence (1371) Machine Learning (1465) Clinical Information and Decision Making (1407) Clinical Informatics (1022) Diagnostic Tools in Mental Health (242) Precision Medicine and Genetics (48)

Published on in Vol 9, No 4 (2021): April

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/24754, first published .
Diagnostic Classification and Prognostic Prediction Using Common Genetic Variants in Autism Spectrum Disorder: Genotype-Based Deep Learning

Diagnostic Classification and Prognostic Prediction Using Common Genetic Variants in Autism Spectrum Disorder: Genotype-Based Deep Learning

Diagnostic Classification and Prognostic Prediction Using Common Genetic Variants in Autism Spectrum Disorder: Genotype-Based Deep Learning

Authors of this article:

Haishuai Wang1, 2 Author Orcid Image ;   Paul Avillach1 Author Orcid Image
Article Authors Cited by (36) Tweetations (3) Metrics

    Original Paper

    1Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States

    2Department of Computer Science and Engineering, Fairfield University, Fairfield, CT, United States

    Corresponding Author:

    Paul Avillach, MD, PhD

    Department of Biomedical Informatics

    Harvard Medical School

    10 Shattuck Street

    Boston, MA, 02115

    United States

    Phone: 1 617 432 2144

    Email: Paul_Avillach@hms.harvard.edu


    Background: In the United States, about 3 million people have autism spectrum disorder (ASD), and around 1 out of 59 children are diagnosed with ASD. People with ASD have characteristic social communication deficits and repetitive behaviors. The causes of this disorder remain unknown; however, in up to 25% of cases, a genetic cause can be identified. Detecting ASD as early as possible is desirable because early detection of ASD enables timely interventions in children with ASD. Identification of ASD based on objective pathogenic mutation screening is the major first step toward early intervention and effective treatment of affected children.

    Objective: Recent investigation interrogated genomics data for detecting and treating autism disorders, in addition to the conventional clinical interview as a diagnostic test. Since deep neural networks perform better than shallow machine learning models on complex and high-dimensional data, in this study, we sought to apply deep learning to genetic data obtained across thousands of simplex families at risk for ASD to identify contributory mutations and to create an advanced diagnostic classifier for autism screening.

    Methods: After preprocessing the genomics data from the Simons Simplex Collection, we extracted top ranking common variants that may be protective or pathogenic for autism based on a chi-square test. A convolutional neural network–based diagnostic classifier was then designed using the identified significant common variants to predict autism. The performance was then compared with shallow machine learning–based classifiers and randomly selected common variants.

    Results: The selected contributory common variants were significantly enriched in chromosome X while chromosome Y was also discriminatory in determining the identification of autistic individuals from nonautistic individuals. The ARSD, MAGEB16, and MXRA5 genes had the largest effect in the contributory variants. Thus, screening algorithms were adapted to include these common variants. The deep learning model yielded an area under the receiver operating characteristic curve of 0.955 and an accuracy of 88% for identifying autistic individuals from nonautistic individuals. Our classifier demonstrated a considerable improvement of ~13% in terms of classification accuracy compared to standard autism screening tools.

    Conclusions: Common variants are informative for autism identification. Our findings also suggest that the deep learning process is a reliable method for distinguishing the diseased group from the control group based on the common variants of autism.

    JMIR Med Inform 2021;9(4):e24754

    doi:10.2196/24754

    Keywords

    deep learning; autism spectrum disorder; common genetic variants, diagnostic classification 


    Autism spectrum disorder (ASD) is a common neurodevelopmental disorder that begins early in childhood and lasts throughout a person's life. In the United States, around 1 out of 59 children have been diagnosed with ASD. People with ASD have characteristic social communication deficits and repetitive behaviors. Early detection of ASD enables timely interventions for children with ASD. Such interventions could provide the best opportunity to improve outcomes as opposed to treatments started after diagnosis. The epigenetic landscape has revealed that ASD may result from a complex regulatory network, including epigenetic, genetic, and environmental factors [1]. Although the causes of ASD remain unknown, recent studies have found that ASDs are 80% reliant on the inherited genes [1-3]. Twin studies of ASD show heritability as a highly responsible factor causing the disorder [4,5]. Therefore, identifying genomic mutations for autism based upon genotype information for early diagnosis of autism is significantly important. The genetic landscape of ASD is heterogeneous and consists of various types of genetic abnormalities involving almost all genes (eg, SHANK3, SHANK2, CHD8, SEMA5A, DOCK4) with different levels of penetrance [6-8]. Thus, autism studies have been conducted with different types of genetic variants [9-14], including de novo or inherited copy number variants, multiple hits, rare variants, common variants, and genetic pathways associated with ASD.

    Rare variants, both inherited and de novo, are causal in 10%-30% of people with ASDs [15-17]. Although risk-associated genes of autism have been identified from rare variations, recent studies have shown that most genetic risks for ASD reside with common variations [18]. A Population-Based Autism Genetics and Environment Study on a Swedish epidemiological sample shows synthesis of results regarding the genetic architecture of ASD and concludes that inherited rare variations constitute a smaller fraction of the total heritability than common variations [18]. Several genome-wide association studies have also examined that 15%-40% of the genetic risk associated with ASD diagnosis is tagged by common variants [19-21]. Therefore, common variants may be informative with respect to the identification of ASD. Numerous studies have since used genetic information to predict the diagnosis of ASD. A single nucleotide polymorphism–based test has been demonstrated to allow for early identification of ASD [22]. In this study, they applied machine learning to identify single nucleotide polymorphisms to generate a predictive classifier for ASD diagnosis and have proved and concluded that the predictive classifier can be a tool to estimate the probability of at-risk status for ASD. To enable earlier and more accurate diagnoses of ASD, a statistical model has been developed for autism to analyze measurements of metabolite concentrations and it indicated that the metabolites under consideration are highly associated with an autism diagnosis [23]. A gene expression–based study has demonstrated that the accuracy of distinguishing ASD subgroups from nonautistic controls by using a support vector machine can be up to 94% [24]. Combining a brain-specific gene network with a complementary machine learning approach has also been conducted to present a genome-wide prediction of autism risk genes [25]. However, none of the existing works provide adequate accuracy or specificity that can be used for autism diagnosis with common variations. Recently, deep neural networks have achieved record-breaking performance in a variety of real-world applications [26-29]. In this study, we adapt deep learning to the task of predicting ASD and propose a deep learning–based framework, named DeepAutism, to predict autism disorder phenotypes by using common variants.

    This study first identified significant common variants that may be protective or pathogenic for ASD as well as their additive contribution to ASD; therefore, deep learning models are applicable using common variants. Then, this study applied deep learning prediction algorithms to verify the identified common variants and generate a predictive classifier for ASD diagnosis. The results were tested on a hold-out test data set from the Simons Simplex Collection (SSC), and the proposed strategic approach achieved the best performance in distinguishing the diseased group from the control group based on selected significant common variants of ASD.

    The objectives of this study were to (1) discover significant common variants that may be protective or pathogenic for ASD, (2) create an advanced diagnostic classifier for autism screening based on the identified common variants, and (3) verify the developed classifier and significant common variants across thousands of simplex families.


    Data Set

    We used an autism data set from the SSC [30]. The SSC data consist of 2600 simplex families, each of which has 1 child affected with ASD (a proband), unaffected parents, and at least one unaffected sibling. The data consist of 3931 individuals whose exome sequences are available (Figure 1A), and 2249 samples of these individuals are labeled as diseased group (ASD). From the SSC data set, we can query the specific variables for exome variants (Figure 1A), and the variants are in the variant call format (VCF). There are more than 1.5 million variants in the data set, which has the genotype information along with read depth, allele depth, and genotype quality. In the VCF data, VCF_GT represents the genotype quality, encoded as allele values separated by “/,” such as “0/1” and “2/3”, where 0 represents the reference allele, 1 for the first allele listed in the alternate allele, 2 for the second allele listed in the alternate allele, and so on. Thus, VCF_GT can be “0/0”, “0/1”, “2/0”, “1/2”, and so on. The read depth is denoted as VCF_DP, and the conditional genotype quality is denoted as VCF_CQ. We mainly used the information of VCF_GT, VCF_DP, and VCF_CQ in the SSC data for this study. The Harvard Medical School Research Ethics Committee approved this study.

    ‎
    Figure 1. Overall framework for deciphering contributory common variants and predicting autism spectrum disorder diagnosis. A. Data preprocessing. VCF_GT recoding is to encode VCF_GT values as dummy variables. If both alleles are reference alleles, it is encoded as 0; if both alleles are alternate alleles, it is encoded as 2; otherwise, it is 1. B. Data split and significant variant selection. The data set was split into training set and test set. Variants were ranked based on their chi-score and P value, and only top ranked (high chi-score value and low P value) variants were selected as contributory common variants for autism spectrum disorder. C. Convolutional neural network classifier. The selected significant common variants in the training data were fed into a convolutional neural network to train a classifier. Thereafter, the trained model was applied on the test data for autism spectrum disorder diagnosis prediction. ASD: autism spectrum disorder; CNN: convolutional neural network; SSC: Simons Simplex Collection; VCF: variant call format; VCF_CQ: variant call format-conditional genotype quality; VCF_DP: variant call format-read depth; VCF_GT: variant call format-genotype quality.
    View this figure

    Data Preprocessing and Genotype Quality Filters

    For all the variants, we have their unphased genotype information using the format of VCF_GT. To make the data processable for deep learning models, we encoded the VCF_GT data by creating categorical values to represent different types of genotype [27]. Specifically, 0 denotes that both allele values are reference alleles, 1 represents one allele value is a reference allele and the other one is the alternate allele, and 2 represents both are alternate alleles. For example, 0/0 is made as 0, 1/0 is made as 1, 1/1 is made as 2, and so on. Therefore, the variants consist of 3 categories: 0, 1, and 2. We used VCF_DP (read depth at a position for a sample) and VCF_CQ (conditional genotype quality) as a filter to control the genotype information quality (Figure 1A). We extracted the genotype information for each variant that has a read depth no less than 10 and genotype quality no less than 90 [31]. Therefore, the genotypes of read depth less than 10 (VCF_DP < 10) or genotype quality less than 90 (VCF_CQ < 90) were excluded. Since we only explored common variants in our study, we removed all the variants with occurrence frequency less than 1% over the whole data set, resulting in 153,347 variants selected as common variants after the genotype quality filters (Figure 1A). We used these common variants for our study. After selecting the common variants, the SSC samples were partitioned into 2 sets based on random sampling of individuals into a training set (80%) and a hold-out test set (20%). There was no overlap of individuals across the 2 partitions. The test set was only used after model fitting to assess performance.

    Identifying Contributory Common Genetic Variants

    As the number of variants was too large to apply deep learning models directly, to construct the features for the deep learning models, we used feature selection to reduce variant dimension (Figure 1B). Feature selection is one of the core concepts in machine learning that hugely impacts the performance of a model [32-35]. The data features that are used to train machine learning models have a huge influence on the performance that we can achieve. Therefore, our hypothesis is that not all variables contribute to the predictive performance of the models we built. Variant selection is the process wherein we automatically select those features that contribute most to our prediction accuracy and are considered as contributory variants to ASD diagnosis. Therefore, significant common variation selection was applied because variant selection is the process of removing redundant or irrelevant features from the original data set to reduce overfitting. To this end, we analyzed the importance scores of the common variants that are related with ASD development mechanisms in the training data set. For each individual, a 153,347-dimensional vector was constructed, corresponding to 153,347 common variants identified from the data preprocessing. Chi-square test was applied to evaluate the importance of each variant to distinguish the class in order to select the most significant common variants. Given the training data D, we estimated the following quantity for each variant and ranked them by their scores:

    where N is the observed frequency in D and E is the expected frequency, et takes the value 1 if the training data contains term t and 0; otherwise, ec takes the value 1 if the training data is in class c and 0 otherwise. For each variant, a corresponding high score indicates that the null hypothesis H0 of independence (meaning the individual’s category has no influence over the term’s frequency) should be rejected and the occurrence of the variant and class are dependent. In this case, we select the variant for the ASD diagnosis prediction. We used the implementation from scikit-learn [36] for “Chi-Square Feature Selection” with default settings.

    By calculating the chi-square scores for all the variants, we can rank the variants by the chi-square scores and then choose the top ranked variants as significant variants for model training. Figure 2A lists the variant importance of the high scoring 20 features (variants) that are selected via the chi-square test. In Figure 2A, while the Y-axis corresponds to variant IDs of the variants, the X-axis corresponds to relative importance, which is calculated using the chi-square score. We selected the top 100 most significant variants as inputs to train a deep learning classifier. Therefore, the number of input contributory variants to our classifier after selection was 100 for ASD prediction. In order to analyze whether the variation data can be divided into 2 clusters representing control and ASD cases, the first 2 groups of data were obtained using t-distributed stochastic neighbor embedding (t-SNE) as an unsupervised learning approach. The visualization of clusters for the top 100 variants using t-SNE in both case group and control group is shown in Figure 2B. From the visualization, accurate genetic classification of control group versus ASD is possible using 100 common variants determined to be highly significant. Therefore, for each individual in the training set, a 100-dimensional input vector was constructed corresponding to 100 selected significant common variants for training a deep learning model.

    ‎
    Figure 2. A. Variants with high relative importance scores in chi-square test. The Y-axis corresponds to variant IDs of these variants, and the X-axis corresponds to the relative importance values of the corresponding variants. B. Visualization of the top 100 selected significantly common variants using t-distributed stochastic neighbor embedding. Different colors represent different classes (ie, case and control). This visualization indicates that the 2 groups are differentiable using the selected top common variants. t-SNE: t-distributed stochastic neighbor embedding.
    View this figure

    DeepAutism Architecture

    The overall framework of the proposed DeepAutism (Figure 1) consists of 3 components, namely, data preprocessing, variant selection, and neural network classifier. Figure 1C illustrates the convolutional neural network (CNN) architecture. We used Keras and TensorFlow version 2.0 for constructing and training the CNN model. We used a block of two 1D convolutional layers, followed by a max-pooling layer to generate feature maps that contain only the most important features. The max-pooling layer is followed by a dropout layer to avoid overfitting the data. Then, the learned feature maps are combined using a fully connected layer. The final layer contains a sigmoid function to produce probabilities of output from 0 to 1, with the diseased group belonging to class 1 and the control set belonging to class 0. All the parameters, including the weights and biases of hidden layers, are learned through backpropagation [37]. The detailed network topology used in our CNN architecture is shown in Multimedia Appendix 1.

    DeepAutism Training and Evaluation

    For training, DeepAutism uses a set of selected common variants (top 100 significant common variants) to estimate the probabilities of an individual belonging to control case or autism. For a set of variants v from a testing individual, DeepAutism computes a probability p(v) using 4 states:

    p(v) = Sigmoid(netW(pool(ReLUb(convf(v))))

    The sigmoid function is used for computing probabilities of a set of variants v belonging to either control group or autism group, and the produced probabilities are from 0 to 1, with the control set belonging to class 0 and the ASD group belonging to class 1. The convolution stage (convf) scans a set of filters as feature maps across the variants. Each neuron consists of a rectified linear unit (ReLU) activation function to introduce nonlinearity between 2 neural networks. The pooling layer only picks the maximum values from the convolved feature maps. Since the variant data are categorical values, one-hot encoding is employed to ensure that the DeepAutism model is unbiased and does not favor one genotype over the others. The DeepAutism is then trained using mini-batch gradient descent by backpropagation algorithm [37]. The performance was evaluated using the area under the receiver operating characteristic curve (AUC). We also used the most common procedure for evaluating classifiers for ASD prediction, including accuracy, sensitivity (recall), specificity (precision), F1-score, and false discovery rate, in which the lower value indicates better performance to evaluate the classifiers for ASD diagnosis.

    Baseline Methods to Compare the Effectiveness of DeepAutism

    Apart from CNNs, we also employed conventional machine learning techniques to evaluate the effectiveness of DeepAutism for classifying autism diagnosis. The conventional machine learning models that we compared were random forests, logistic regression, and Naive Bayes. We used the same training and test data (with the selected 100 common variants) for the conventional machine learning models as used for the DeepAutism model, aiming to evaluate whether the CNN model outperforms other machine learning classifiers. To evaluate whether the selected top 100 common variants are significant for ASD diagnosis, we also compared the chi-square–based variant selection method with random variant selection by using the same training and test data sets. We randomly selected 100 common variants as inputs that were fed into both DeepAutism and conventional machine learning models to compare the changes in their performance.


    Identification of Contributory Variants and Genes

    Statistical analyses focused on the selected top 100 common variants, which most significantly contributed to the classifiers of ASD. Of the 100 common variants within our classifier, 66% are exonic mutations and 23% are intronic mutations, while small proportions are splicing mutations or from an untranslated region. Within the 66% exonic mutations, about half are synonymous single nucleotide variants and about half are nonsynonymous single nucleotide variants. It is important to point out that the selected contributory common variants were significantly enriched on chromosome X while chromosome Y is also discriminatory in identifying individuals with ASD from individuals without ASD.

    A number of variants were populated by the same genes. Related to the contributory common variants, the statistically significant genes were ARSD, MAGEB16, and MXRA5. There are 18 common variants in the ARSD gene. ARSD is a protein-coding gene and is located within a cluster of similar arylsulfatase genes on chromosome X, while a related pseudogene has been identified in the pseudoautosomal region of chromosome Y. Variants rs209372, rs2109135, and rs1047248 in 3 genes, namely, NRK, TLR8, and MAGEA4, respectively, have the highest scores in determining an individual’s classification as with ASD or with no ASD.

    Deep Learning Performance Based Upon Contributory Common Variants

    After the training phase was over, we picked the same common variations from the test data for each individual. We used the rest of the 787 samples for testing. Based on the trained DeepAutism model, each test individual was predicted the probabilities of belonging to the control group or the diseased group. The deep learning model was extremely accurate in classification of the holdout test set with an AUC of 0.955 (Figure 3A). Figure 3B describes the performance of the DeepAutism classifier on the test data. DeepAutism predicted ASD in 423 samples out of 456 samples with ASD.

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    Figure 3. A. The area under the receiver operating characteristic curve of DeepAutism, random forest, logistic regression, and Naive Bayes for predicting autism spectrum disorder diagnosis based on the selected top 100 significantly common variants on the test data. B. The visualization table that describes the performance of the DeepAutism classifier on the test data. DeepAutism correctly predicted 697 out of 787 total samples and correctly predicted autism spectrum disorder in 423 samples out of 456 samples with autism spectrum disorders. AUC: area under the receiver operating characteristic curve; ASD: autism spectrum disorder; NB: Naive Bayes; LR: logistic regression; RF: random forest.
    View this figure

    Apart from deep learning, we also employed Naive Bayes, logistic regression, support vector machine, random forest, and deep neural network classifiers to compare the prediction of ASD diagnosis. We applied five-fold cross-validation to evaluate the selected significant common variants. Our classifier performed better than the conventional machine learning techniques in terms of AUC, accuracy, specificity, sensitivity, and F1-score. As shown in Table 1, accuracy was 0.886 in the case of DeepAutism, followed by 0.808 for random forest in the same test data set for ASD diagnosis prediction. DeepAutism also yielded the best sensitivity of 0.881 for prediction of ASD and best specificity of 0.893 for non-ASD prediction. The false positive (discriminatory) rate is minimum for DeepAutism with 7% compared with other machine learning techniques. These results are shown in Table 1.

    Table 1. Performance of the classifiers with respect to accuracy, sensitivity, specificity, F1-score, and false discovery rate on test sets.a
    ModelAccuracySensitivitySpecificityF1-scoreFalse discovery rate
    DeepAutism0.8860.8810.8930.9050.072
    Naive Bayes0.6790.7060.6330.7330.237
    Random forest0.8080.7850.8570.8480.079
    Logistic regression0.7040.7150.6830.7610.186
    Support vector machine0.7890.7730.8210.8310.101
    Deep neural network0.8040.7660.8850.8420.073

    aItalicized data demonstrate the best performance; DeepAutism outperformed other models on all the metrics.

    Performance Using Randomly Selected Common Variants for ASD Diagnosis

    We assessed the classification performance by using randomly picked 100 common variants as inputs to train classifiers. We used the same training and test data as in the above experiment. As shown in Table 2, when the classifiers classify ASD using randomly selected common variants, all the classifiers achieved reduced performance compared to using selected significant common variants. For instance, the AUC and accuracy of DeepAutism dramatically dropped from 0.955 to 0.670 and from 0.885 to 0.689, respectively. The random 100 common variants yielded accuracy of 0.454 and 0.583 using Naive Bayes and logistic regression classifiers, respectively, which is like random guessing. This revealed that the random 100 common variants are not discriminative in distinguishing ASD diagnosis. These results suggest that variant selection is important for identifying significant common variants that are more correlated and significant in improving the classification accuracy.

    Table 2. Performance of the classifiers with respect to area under receiver operating characteristic curve, accuracy, sensitivity, specificity, F1-score, and false discovery rate on test sets with randomly picked 100 common variants.a
    ModelArea under receiver operating characteristic curveAccuracySensitivitySpecificityF1-scoreFalse discovery rate
    DeepAutism0.6700.6890.6850.6970.7550.145
    Naive Bayes0.5560.4540.7170.4320.1660.906
    Random forest0.7010.6290.6120.8550.7540.018
    Logistic regression0.5710.5830.5980.4890.7040.143
    Support vector machine0.6720.6790.6330.5710.6960.139
    Deep neural network0.6560.6770.6810.7020.7330.143

    aItalicized data show the best performance; the performance of all models became worse on all the metrics with randomly selected common variants.


    Predicting ASD based on genetic data is challenging. Using common variant analysis, we generated a genetic diagnostic classifier (DeepAutism) based on a deep learning architecture using 100 significant common variants, and we accurately distinguished ASD from controls within the SSC data set. The diagnostic classifier was able to correctly classify individuals with ASD with an accuracy of 88.6% and an AUC of 0.955. Our findings showed that the sensitivity and specificity of the classifier when applied to identify ASD were 88% and 89%, respectively. It is notable that the sensitivity for identifying cases is highly desirable for screening purposes. We also investigated the classification performance of different approaches and the corresponding proportion of subjects who did not have ASD who could be reliably classified as controls. DeepAutism can be suggested as an alternative to conventional shallow machine learning approaches. In the comparisons among the classifiers, DeepAutism performed the best, followed by random forest. Both these classifiers are nonlinear models. Therefore, the causes of ASD are not a simple linear combination of common variants.

    Interestingly, when we altered the classifier by using randomly selected 100 common variants, the AUC and accuracy of DeepAutism reduced to 0.670 and 0.689, respectively. The performance became worse because irrelevant variants can include noisy data, thereby affecting the classification accuracy negatively. This verifies the significance of selecting common variants and greatly adds strength to our original findings. Our results suggest that common variants may contribute to ASD diagnosis. A study [18] has shown that the genetic architecture of ASD is contributed by inherited common variants, which supports our findings. The common variants contributing most to the diagnosis in our classifier corresponded to genes on chromosome X. This suggests that ASD is associated with gender. As ASD is strongly biased toward males with ratios of 4:1 (male:female) [38] and statistics have also shown that ASD has a higher prevalence in males than in females [39], mutations in the genes on the X chromosome may explain the increased prevalence of autism in boys compared to that in girls. Thus, this supports our finding that gender bias affects individuals with autism.

    In our findings, ARSD, MAGEB16, and MXRA5 genes were found to have a high contributory effect on ASD. ARSD is located within a cluster of similar arylsulfatase genes on chromosome X. ARSD is clinically heterogeneous and is likely to result from mutations in developmental genes or from regulating transcription factors [40]. ARSD has already been reported to be related to ASD or Asperger’s syndrome [41]. The cytogenetic location of ARSD is Xp22.33, and this location significantly contributes to ASD, as shown in the SFARI Gene Database [42]. These regions play a role in neurodevelopment disorders [31,43-48]. Although we used common variants as features in our classifier, we also found that prevalence of X-chromosome copy number variations contribute to ASD. MAGEB16 is also a protein-coding gene, which is located on Xp21.1. MAGEB16 has been implicated in syndromic X-linked intellectual disability and neurodevelopmental disorders. It has also been reported to be associated with autistic disorders [49]. MXRA5 is a protein-coding gene and encodes a protein that forms the extracellular matrix structural constituent. It is involved in the response to transforming growth factor beta and has a pseudogene on chromosome Y. An association has been curated linking MXRA5 and an autistic disorder in Pan paniscus. Although mutations in SHANK3 have been identified in multiple individuals with ASD, most of the mutations are rare variants and not common variants, where the ratio between rare variants and common variants is 230:9 according to the SFARI Gene Database [50].

    ASD is a complex behavioral disorder with a strong genetic influence [51]. Diagnosing ASD can be difficult because there is no medical test (such as a blood test) to diagnose this disorder. Although the majority of studies toward biomarker identification for autism have focused on rare genetic variants, we have proven that common genetic variants are also informative with respect to the identification of ASD. In our study, our genetic classifier obtained a high level of diagnostic accuracy, thereby demonstrating that genetic biomarkers can correctly identify individuals with ASD from individuals without ASD. Common variants can play a very important role in screening ASD at an early stage. We identified a few genes with various common variants that could determine whether an individual fell within the case or control group. Our results demonstrate the value of a data-driven approach for the identification of significant common variants and a deep learning method for ASD diagnosis. Overall, these findings indicate that a common variant–based test may allow for early identification of ASD. A genetic predictive classifier as described here may be a tool for ASD screening at birth to provide probability estimates of ASD.

    Although our approach for identifying autism based on the selected common variants achieves high accuracy, some limitations exist that need improvement in the future work: (1) the experiments were conducted on the SSC dataset; however, more datasets could be used to evaluate the proposed method and the selected common variants and (2) the proposed algorithm, based on CNN, is a straightforward solution for identifying autism from nonautism; however, more state-of-the-art classifiers could be applied to this ASD classification problem.

    While the proposed DeepAutism approach has achieved great success in ASD identification with promising empirical results, we would still like to explore several important directions on DeepAutism in the future. First, we plan to further design an advanced deep learning algorithm that can handle high-dimensional features and output the feature importance for variant selection. By using the designed model, we can select significant variants and classify autistic individuals simultaneously as an end-to-end framework. Second, we will evaluate the proposed method on 2 more distinct ASD cohorts: (1) Simons Foundation Powering Autism Research for Knowledge data and (2) Autism Speaks MMSSNG cohort. We will also validate our algorithms with the UK Biobank clinical and genomic data. Third, we will investigate the full sequences of coding and noncoding regions of the genome between probands and unaffected siblings to explore all of the components in the genetic architecture of ASD.

    Conflicts of Interest

    None declared.

    ‎
    Multimedia Appendix 1

    Architecture of DeepAutism.

    DOCX File , 106 KB
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    ‎
    ASD: autism spectrum disorder
    AUC: area under the receiver operating characteristic curve
    CNN: convolutional neural network
    SSC: Simons Simplex Collection
    t-SNE: t-distributed stochastic neighbor embedding
    VCF: variant call format
    VCF_CQ: variant call format-conditional genotype quality
    VCF_DP: variant call format-read depth
    VCF_GT: variant call format-genotype quality

    Edited by G Eysenbach; submitted 03.10.20; peer-reviewed by S Pang, F Li, M Manzanares; comments to author 23.11.20; revised version received 18.02.21; accepted 14.03.21; published 07.04.21

    Copyright

    ©Haishuai Wang, Paul Avillach. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 07.04.2021.

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