MOTIVATION: Non-coding rare variants (RVs) may contribute to Mendelian disorders but have been challenging to study due to small sample sizes, genetic heterogeneity and uncertainty about relevant non-coding features. Previous studies identified RVs associated with expression outliers, but varying outlier definitions were employed and no comprehensive open-source software was developed. RESULTS: We developed Outlier-RV Enrichment (ORE) to identify biologically-meaningful non-coding RVs. We implemented ORE combining whole-genome sequencing and cardiac RNAseq from congenital heart defect patients from the Pediatric Cardiac Genomics Consortium and deceased adults from Genotype-Tissue Expression. Use of rank-based outliers maximized sensitivity while a most extreme outlier approach maximized specificity. Rarer variants had stronger associations, suggesting they are under negative selective pressure and providing a basis for investigating their contribution to Mendelian disorders. AVAILABILITY AND IMPLEMENTATION: ORE, source code, and documentation are available at https://pypi.python.org/pypi/ore under the MIT license. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Accurate cancer risk prediction from genetic and environment variables is a key problem in medicine. One approach is to use somatic mutations which could potentially be used in early detection and prevention. SNP based studies are the most common ones utilizing this approach, however most studies lack a cross-study validation component across at least two independent studies. Here we explore the cross-validation and cross-study validation of predicting kidney cancer case and controls with SNPs obtained from whole exome sequences at the National Cancer Institute. From the Genomics Data Commons portal we obtained aligned whole exome sequences of two different kidney cancer studies: 110 cases and controls of KIRP for renal papillary cell carcinoma and 34 cases and controls of KICH for kidney chromophobe cell carcinoma. We performed a rigorous quality control procedure to obtain SNPs and rank them with feature selection. On top ranked SNPs we find the support vector machine to obtain a cross-validation accuracy of 71% (with 10 SNPs) and 72% (with 20 SNPs) in KIRP and KICH respectively. We then learn a model on KIRP and with 10 SNPs achieve an accuracy of 66% on the KICH samples. Our work shows that we can predict kidney chromophobe carcinoma from a kidney papillary carcinoma dataset with better than a random classification which would have 50% accuracy. In continuing work we are expanding these sample sizes and extending cross- study to other kidney cancer datasets in the NCI GDC portal.

The era of genomics brings the potential of better DNA based risk prediction and treatment. While genome-wide association studies are extensively studied for risk prediction, the potential of using whole exome data for this purpose is unclear. We explore this problem for chronic lymphocytic leukemia that is one of the largest whole exome dataset of 186 case and 169 controls available from the NIH dbGaP database. We perform a standard next generation sequence procedure to obtain SNP variants on 153 cases and 144 controls after exclusion of samples with missing data. To evaluate their predictive power we first conduct a 50% training and 50% test cross-validation study on the full dataset with the support vector machine as the classifier. There we obtain a mean accuracy of 82% with top 20 ranked SNPs obtained by the Pearson correlation coefficient. We then perform a cross-study validation on case and controls from a lymphoma external study and just controls from head and neck cancer and breast cancer studies (all obtained from NIH dbGaP). On the external dataset we obtain an accuracy of 70% with top ranked SNPs obtained from the original dataset. We also find our top Pearson ranked SNPs to lie on previously implicated genes for this disease. Our study shows that even with a small sample size we can obtain moderate to high accuracy with exome sequences and is thus encouraging for future work.