The 15 machine learning algorithms used to analyze the Autism Genetic Resource Exchange ADI-R data.

<p>These algorithms were deployed using the toolkit WEKA <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0043855#pone.0043855-Weka1" target="_blank">[28]</a>. The false positive rate (FPR) and true positive rate (TPR) are provided together with overall accuracy. The Alternating Decision Tree (ADTree) performed with highest accuracy and was used for further analyses.</p

The seven attributes used in the ADTree model.

<p>Listed are the number corresponding to the question in the full ADI-R instrument, the question code used by Autism Genetic Research Exchange (AGRE) and a brief description of the question.</p

Summary of the data used for both construction and validation of the autism classifier.

<p>Full sets of answers to the Autism Diagnostic Instrument-Revised questionnaire were downloaded from the Autism Genetic Research Exchange (AGRE), the Simons Simplex Collection (SSC), and the Boston Autism Consortium (AC). The AGRE data were used for training, testing, and construction of the classifier. The SSC and AC data were used for independent validation of the resulting classifier. The table lists the total numbers of spectrum and non-spectrum individuals represented in each of the three data sets with a breakdown of age by quartiles.</p

Decision tree scores and classification of cases with and without a diagnosis of autism.

<p>The Alternating Decision Tree (ADTree) scores of individuals in the both the AC and AGRE data sets versus their age in years. A majority of the ADTree scores were clustered towards greater magnitudes according to their respective classifications, regardless of age.</p

Performance of 15 machine learning algorithms evaluated for classifying autism cases and non-spectrum controls.

<p>Plot comparing 1-specificity and sensitivity for the 15 different machine learning algorithms used to construct classifiers from the 93-item Autism Diagnostic Interview-Revised (ADI-R) instrument from the Autism Genetic Resource Exchange (AGRE). The best performing algorithm was the alternating decision tree (ADTree), followed by LADTree, PART, and FilteredClassifier. Table 2 summarizes the 15 machine learning algorithms in more detail, and the elements contained in the ADTree classifier are listed in Table 3.</p

Distribution of 1000 resamplings of the mean pN/ mean pS ratio of the genes involved in ASD (yellow), comorbid disorders within the ASD cluster (red) and other comorbid disorders (blue).

The different panels show comparisons between: genes associated with ASD only (yellow) versus all other comorbid disorders (red+blue) (Panel A), genes associated with ASD only (yellow) versus comorbid disorders within the ASD cluster (red) (Panel B), genes associated with ASD and comorbid within the cluster (yellow + red) versus the genes outside the cluster (blue) (Panel C), genes associated with ASD only (yellow) versus genes associated with ASD outside the ASD cluster (blue) (Panel D).</p

Network analysis of the KEGG Orthologs and the pathways with which they are associated in the KEGG database.

<p>Each node in the inner circle represents a KEGG Ortholog and each node in the outer circle indicates the pathway in which each KO is involved. The size of each node is proportional to its connectivity.</p

Table_6_Cross-Disorder Genomics Data Analysis Elucidates a Shared Genetic Basis Between Major Depression and Osteoarthritis Pain.XLSX

Osteoarthritis (OA) and major depression (MD) are two debilitating disorders that frequently co-occur and affect millions of the elderly each year. Despite the greater symptom severity, poorer clinical outcomes, and increased mortality of the comorbid conditions, we have a limited understanding of their etiologic relationships. In this study, we conducted the first cross-disorder investigations of OA and MD, using genome-wide association data representing over 247K cases and 475K controls. Along with significant positive genome-wide genetic correlations (rg = 0.299 ± 0.026, p = 9.10 × 10–31), Mendelian randomization (MR) analysis identified a bidirectional causal effect between OA and MD (βOA→MD = 0.09, SE = 0.02, z-score p-value –5; βMD→OA = 0.19, SE = 0.026, p –13), indicating genetic variants affecting OA risk are, in part, shared with those influencing MD risk. Cross-disorder meta-analysis of OA and MD identified 56 genomic risk loci (Pmeta ≤ 5 × 10–8), which show heightened expression of the associated genes in the brain and pituitary. Gene-set enrichment analysis highlighted “mechanosensory behavior” genes (GO:0007638; Pgene_set = 2.45 × 10–8) as potential biological mechanisms that simultaneously increase susceptibility to these mental and physical health conditions. Taken together, these findings show that OA and MD share common genetic risk mechanisms, one of which centers on the neural response to the sensation of mechanical stimulus. Further investigation is warranted to elaborate the etiologic mechanisms of the pleiotropic risk genes, as well as to develop early intervention and integrative clinical care of these serious conditions that disproportionally affect the aging population.</p

Table_3_Cross-Disorder Genomics Data Analysis Elucidates a Shared Genetic Basis Between Major Depression and Osteoarthritis Pain.XLSX

Osteoarthritis (OA) and major depression (MD) are two debilitating disorders that frequently co-occur and affect millions of the elderly each year. Despite the greater symptom severity, poorer clinical outcomes, and increased mortality of the comorbid conditions, we have a limited understanding of their etiologic relationships. In this study, we conducted the first cross-disorder investigations of OA and MD, using genome-wide association data representing over 247K cases and 475K controls. Along with significant positive genome-wide genetic correlations (rg = 0.299 ± 0.026, p = 9.10 × 10–31), Mendelian randomization (MR) analysis identified a bidirectional causal effect between OA and MD (βOA→MD = 0.09, SE = 0.02, z-score p-value –5; βMD→OA = 0.19, SE = 0.026, p –13), indicating genetic variants affecting OA risk are, in part, shared with those influencing MD risk. Cross-disorder meta-analysis of OA and MD identified 56 genomic risk loci (Pmeta ≤ 5 × 10–8), which show heightened expression of the associated genes in the brain and pituitary. Gene-set enrichment analysis highlighted “mechanosensory behavior” genes (GO:0007638; Pgene_set = 2.45 × 10–8) as potential biological mechanisms that simultaneously increase susceptibility to these mental and physical health conditions. Taken together, these findings show that OA and MD share common genetic risk mechanisms, one of which centers on the neural response to the sensation of mechanical stimulus. Further investigation is warranted to elaborate the etiologic mechanisms of the pleiotropic risk genes, as well as to develop early intervention and integrative clinical care of these serious conditions that disproportionally affect the aging population.</p

Table_4_Cross-Disorder Genomics Data Analysis Elucidates a Shared Genetic Basis Between Major Depression and Osteoarthritis Pain.XLSX

Osteoarthritis (OA) and major depression (MD) are two debilitating disorders that frequently co-occur and affect millions of the elderly each year. Despite the greater symptom severity, poorer clinical outcomes, and increased mortality of the comorbid conditions, we have a limited understanding of their etiologic relationships. In this study, we conducted the first cross-disorder investigations of OA and MD, using genome-wide association data representing over 247K cases and 475K controls. Along with significant positive genome-wide genetic correlations (rg = 0.299 ± 0.026, p = 9.10 × 10–31), Mendelian randomization (MR) analysis identified a bidirectional causal effect between OA and MD (βOA→MD = 0.09, SE = 0.02, z-score p-value –5; βMD→OA = 0.19, SE = 0.026, p –13), indicating genetic variants affecting OA risk are, in part, shared with those influencing MD risk. Cross-disorder meta-analysis of OA and MD identified 56 genomic risk loci (Pmeta ≤ 5 × 10–8), which show heightened expression of the associated genes in the brain and pituitary. Gene-set enrichment analysis highlighted “mechanosensory behavior” genes (GO:0007638; Pgene_set = 2.45 × 10–8) as potential biological mechanisms that simultaneously increase susceptibility to these mental and physical health conditions. Taken together, these findings show that OA and MD share common genetic risk mechanisms, one of which centers on the neural response to the sensation of mechanical stimulus. Further investigation is warranted to elaborate the etiologic mechanisms of the pleiotropic risk genes, as well as to develop early intervention and integrative clinical care of these serious conditions that disproportionally affect the aging population.</p