Polygenic risk associated with post-traumatic stress disorder onset and severity.

Post-traumatic stress disorder (PTSD) is a psychiatric illness with a highly polygenic architecture without large effect-size common single-nucleotide polymorphisms (SNPs). Thus, to capture a substantial portion of the genetic contribution, effects from many variants need to be aggregated. We investigated various aspects of one such approach that has been successfully applied to many traits, polygenic risk score (PRS) for PTSD. Theoretical analyses indicate the potential prediction ability of PRS. We used the latest summary statistics from the largest published genome-wide association study (GWAS) conducted by Psychiatric Genomics Consortium for PTSD (PGC-PTSD). We found that the PRS constructed for a cohort comprising veterans of recent wars (n = 244) explains a considerable proportion of PTSD onset (Nagelkerke R2 = 4.68%, P = 0.003) and severity (R2 = 4.35%, P = 0.0008) variances. However, the performance on an African ancestry sub-cohort was minimal. A PRS constructed with schizophrenia GWAS also explained a significant fraction of PTSD diagnosis variance (Nagelkerke R2 = 2.96%, P = 0.0175), confirming previously reported genetic correlation between the two psychiatric ailments. Overall, these findings demonstrate the important role polygenic analyses of PTSD will play in risk prediction models as well as in elucidating the biology of the disorder

The Genetic Basis for the Increased Prevalence of Metabolic Syndrome among Post-Traumatic Stress Disorder Patients

Post-traumatic stress disorder (PTSD) is a highly debilitating psychiatric disorder that can be triggered by exposure to extreme trauma. Even if PTSD is primarily a psychiatric condition, it is also characterized by adverse somatic comorbidities. One illness commonly co-occurring with PTSD is Metabolic syndrome (MetS), which is defined by a set of health risk/resilience factors including obesity, elevated blood pressure, lower high-density lipoprotein cholesterol, higher low-density lipoprotein cholesterol, higher triglycerides, higher fasting blood glucose and insulin resistance. Here, phenotypic association between PTSD and components of MetS are tested on a military veteran cohort comprising chronic PTSD presentation (n = 310, 47% cases, 83% male). Consistent with previous observations, we found significant phenotypic correlation between the various components of MetS and PTSD severity scores. To examine if this observed symptom correlations stem from a shared genetic background, we conducted genetic correlation analysis using summary statistics data from large-scale genetic studies. Our results show robust positive genetic correlation between PTSD and MetS (rg[SE] = 0.33 [0.056], p = 4.74E-09), and obesity-related components of MetS (rg = 0.25, SE = 0.05, p = 6.4E-08). Prioritizing genomic regions with larger local genetic correlation implicate three significant loci. Overall, these findings show significant genetic overlap between PTSD and MetS, which may in part account for the markedly increased occurrence of MetS among PTSD patients

Polygenic risk associated with post-traumatic stress disorder onset and severity

Post-traumatic stress disorder (PTSD) is a psychiatric illness with a highly polygenic architecture without large effect-size common single-nucleotide polymorphisms (SNPs). Thus, to capture a substantial portion of the genetic contribution, effects from many variants need to be aggregated. We investigated various aspects of one such approach that has been successfully applied to many traits, polygenic risk score (PRS) for PTSD. Theoretical analyses indicate the potential prediction ability of PRS. We used the latest summary statistics from the largest published genome-wide association study (GWAS) conducted by Psychiatric Genomics Consortium for PTSD (PGC-PTSD). We found that the PRS constructed for a cohort comprising veterans of recent wars (n = 244) explains a considerable proportion of PTSD onset (Nagelkerke R2 = 4.68%, P = 0.003) and severity (R2 = 4.35%, P = 0.0008) variances. However, the performance on an African ancestry sub-cohort was minimal. A PRS constructed with schizophrenia GWAS also explained a significant fraction of PTSD diagnosis variance (Nagelkerke R2 = 2.96%, P = 0.0175), confirming previously reported genetic correlation between the two psychiatric ailments. Overall, these findings demonstrate the important role polygenic analyses of PTSD will play in risk prediction models as well as in elucidating the biology of the disorder

Integrated analysis of proteomics, epigenomics and metabolomics data revealed divergent pathway activation patterns in the recent versus chronic post-traumatic stress disorder

Metabolomics, proteomics and DNA methylome assays, when done in tandem from the same blood sample and analyzed together, offer an opportunity to evaluate the molecular basis of post-traumatic stress disorder (PTSD) course and pathogenesis. We performed separate metabolomics, proteomics, and DNA methylome assays on blood samples from two well-characterized cohorts of 159 active duty male participants with relatively recent onset PTSD (\u3c1.5 years) and 300 male veterans with chronic PTSD (\u3e7 years). Analyses of the multi-omics datasets from these two independent cohorts were used to identify convergent and distinct molecular profiles that might constitute potential signatures of severity and progression of PTSD and its comorbid conditions. Molecular signatures indicative of homeostatic processes such as signaling and metabolic pathways involved in cellular remodeling, neurogenesis, molecular safeguards against oxidative stress, metabolism of polyunsaturated fatty acids, regulation of normal immune response, post-transcriptional regulation, cellular maintenance and markers of longevity were significantly activated in the active duty participants with recent PTSD. In contrast, we observed significantly altered multimodal molecular signatures associated with chronic inflammation, neurodegeneration, cardiovascular and metabolic disorders, and cellular attritions in the veterans with chronic PTSD. Activation status of signaling and metabolic pathways at the early and late timepoints of PTSD demonstrated the differential molecular changes related to homeostatic processes at its recent and multi-system syndromes at its chronic phase. Molecular alterations in the recent PTSD seem to indicate some sort of recalibration or compensatory response, possibly directed in mitigating the pathological trajectory of the disorder

The Genetic Basis for the Increased Prevalence of Metabolic Syndrome among Post-Traumatic Stress Disorder Patients

Post-traumatic stress disorder (PTSD) is a highly debilitating psychiatric disorder that can be triggered by exposure to extreme trauma. Even if PTSD is primarily a psychiatric condition, it is also characterized by adverse somatic comorbidities. One illness commonly co-occurring with PTSD is Metabolic syndrome (MetS), which is defined by a set of health risk/resilience factors including obesity, elevated blood pressure, lower high-density lipoprotein cholesterol, higher low-density lipoprotein cholesterol, higher triglycerides, higher fasting blood glucose and insulin resistance. Here, phenotypic association between PTSD and components of MetS are tested on a military veteran cohort comprising chronic PTSD presentation (n = 310, 47% cases, 83% male). Consistent with previous observations, we found significant phenotypic correlation between the various components of MetS and PTSD severity scores. To examine if this observed symptom correlations stem from a shared genetic background, we conducted genetic correlation analysis using summary statistics data from large-scale genetic studies. Our results show robust positive genetic correlation between PTSD and MetS (rg[SE] = 0.33 [0.056], p = 4.74E-09), and obesity-related components of MetS (rg = 0.25, SE = 0.05, p = 6.4E-08). Prioritizing genomic regions with larger local genetic correlation implicate three significant loci. Overall, these findings show significant genetic overlap between PTSD and MetS, which may in part account for the markedly increased occurrence of MetS among PTSD patients

Sparse feature selection for classification and prediction of metastasis in endometrial cancer

Abstract Background Metastasis via pelvic and/or para-aortic lymph nodes is a major risk factor for endometrial cancer. Lymph-node resection ameliorates risk but is associated with significant co-morbidities. Incidence in patients with stage I disease is 4–22% but no mechanism exists to accurately predict it. Therefore, national guidelines for primary staging surgery include pelvic and para-aortic lymph node dissection for all patients whose tumor exceeds 2cm in diameter. We sought to identify a robust molecular signature that can accurately classify risk of lymph node metastasis in endometrial cancer patients. 86 tumors matched for age and race, and evenly distributed between lymph node-positive and lymph node-negative cases, were selected as a training cohort. Genomic micro-RNA expression was profiled for each sample to serve as the predictive feature matrix. An independent set of 28 tumor samples was collected and similarly characterized to serve as a test cohort. Results A feature selection algorithm was designed for applications where the number of samples is far smaller than the number of measured features per sample. A predictive miRNA expression signature was developed using this algorithm, which was then used to predict the metastatic status of the independent test cohort. A weighted classifier, using 18 micro-RNAs, achieved 100% accuracy on the training cohort. When applied to the testing cohort, the classifier correctly predicted 90% of node-positive cases, and 80% of node-negative cases (FDR = 6.25%). Conclusion Results indicate that the evaluation of the quantitative sparse-feature classifier proposed here in clinical trials may lead to significant improvement in the prediction of lymphatic metastases in endometrial cancer patients

Pre-deployment risk factors for PTSD in active-duty personnel deployed to Afghanistan: a machine-learning approach for analyzing multivariate predictors

Active-duty Army personnel can be exposed to traumatic warzone events and are at increased risk for developing post-traumatic stress disorder (PTSD) compared with the general population. PTSD is associated with high individual and societal costs, but identification of predictive markers to determine deployment readiness and risk mitigation strategies is not well understood. This prospective longitudinal naturalistic cohort study-the Fort Campbell Cohort study-examined the value of using a large multidimensional dataset collected from soldiers prior to deployment to Afghanistan for predicting post-deployment PTSD status. The dataset consisted of polygenic, epigenetic, metabolomic, endocrine, inflammatory and routine clinical lab markers, computerized neurocognitive testing, and symptom self-reports. The analysis was computed on active-duty Army personnel (N = 473) of the 101st Airborne at Fort Campbell, Kentucky. Machine-learning models predicted provisional PTSD diagnosis 90-180 days post deployment (random forest: AUC = 0.78, 95% CI = 0.67-0.89, sensitivity = 0.78, specificity = 0.71; SVM: AUC = 0.88, 95% CI = 0.78-0.98, sensitivity = 0.89, specificity = 0.79) and longitudinal PTSD symptom trajectories identified with latent growth mixture modeling (random forest: AUC = 0.85, 95% CI = 0.75-0.96, sensitivity = 0.88, specificity = 0.69; SVM: AUC = 0.87, 95% CI = 0.79-0.96, sensitivity = 0.80, specificity = 0.85). Among the highest-ranked predictive features were pre-deployment sleep quality, anxiety, depression, sustained attention, and cognitive flexibility. Blood-based biomarkers including metabolites, epigenomic, immune, inflammatory, and liver function markers complemented the most important predictors. The clinical prediction of post-deployment symptom trajectories and provisional PTSD diagnosis based on pre-deployment data achieved high discriminatory power. The predictive models may be used to determine deployment readiness and to determine novel pre-deployment interventions to mitigate the risk for deployment-related PTSD

Additional file 1 of Sparse feature selection for classification and prediction of metastasis in endometrial cancer

List and description of supplemental tables. Table S1. This table contains the measurements of 1428 micro-RNAs for 94 Samples. The rows correspond to the features (miRNA) and the columns correspond to the samples. The samples consist of 47 lymph node-positive and 47 lymph node-negative samples. 43.75% of the entries in this sheet are NaN. It contains measurements for 213 miRNAs of 86 samples. Out of those 86 samples, 43 are lymph node-positive, and the remaining 43 are lymph node-negative. A sample whose label has the term IB or IC belongs to a lymph node-negative patient, whereas a sample with a label containing IIIC belong to a lymph node-positive patient. A lymph node-positive or neagtive status was defined empiracally during pimary staging. Table S2. This table contains a subset of the raw data, used for training the classifier. This data was obtained by removing four patients from each class, and 1,215 features. It contains measurements for 213 miRNAs of 86 samples. Out of those 86 samples, 43 are lymph node-positive, and the remaining 43 are lymph node-negative. Table S3. This table contains the normalized version of the training data. The following procedure is used for normalization: 1) From each entry of the i-th row vector (i-th feature vector), we subtract the mean value m i of the i-th row vector computed over all the 86 samples. 2) Multiply each entry of the i-th row vector by a scale factor s i so that the resulting vector has euclidean norm equal to the square root of 86. Table S4. The lone star algorithm selected 18 final features. This sheet contains the 20 best classifiers based on these eightteen features, sorted with respect to accuracy. The sensitivity, specificity and accuracy figures (columns T, U and V) are based on the classification of the 86 samples in the training data by the corresponding classifier.Table S5. This table shows the classifier obtained by taking the average of the classifiers in Sheet 4. In particular, we average the numbers in each column of the 20 classifiers given in Sheet 4 (20 best classifiers) (Columns A-S). Table S6. This sheet contains clinical information about the independent cohort of 28 patients who were used to validate the classifier. Out of these, 9 are lymph-node positive and 19 are lymph node-negative. Table S7. This sheet contains the raw microRNA measurements on the 28 test data samples. Table S8. This is the transformed version of the test data. We apply the same transformation as w did for the training data, as described on Sheet 3. For each of the 18 features (miRNAs), we subtract the original mean value m i from each entry and multiply each entry by the constant s i . The calculation of m i and s i is as in Additional file 1, Table S3. Table S9. This sheet contains the discriminant values of the classifier on the Test Data. In column D an entry of 1 means that the sample is correctly classified. Table 10. This sheet contains the number of overlaps between our 23 gene signature with the pathways in the KEGG database. The q-value is obtained from the Fisher exact test after the Benjamini-Hochberg multiple testing correction and quantifies the statistical significance of the overlap between the gene list and a set of genes in a particular pathway. (1170 KB XLSX