A study of normal copy number variations in Israeli population.

The population of Israel is ethnically diverse, and individuals from different ethnic groups share specific genetic variations. These variations, which have been passed on from common ancestors, are usually reported in public databases as rare variants. Here, we aimed to identify ethnicity-based benign copy number variants (CNVs) and generate the first Israeli CNV database. We applied a data-mining approach to the results of 10,193 chromosomal microarray tests, of which 2150 tests were from individuals of 13 common ethnic backgrounds (n ≥ 10). We found 165 CNV regions (> 50 kbp) that are unique to specific ethnic groups (uCNVRs). The frequency of more than 19% of these uCNVRs is between 1 and 20% of the common ethnic origin, while their frequency in the overall cohort is between 0.5 and 1.6%. Of these 165 uCNVRs, 98 are reported as variants of unknown significance or as not available in dbVar; we postulate that these uCNVRs should be annotated as either "likely benign" or "benign". The ethnic-specific CNVs extracted in this study will allow geneticists to distinguish between relevant pathogenic genomic aberrations and benign ethnicity-related variations, thus preventing variant misinterpretation that may lead to unnecessary pregnancy terminations.

Optimizing prediction of response to antidepressant medications using machine learning and integrated genetic, clinical, and demographic data.

Major depressive disorder (MDD) is complex and multifactorial, posing a major challenge of tailoring the optimal medication for each patient. Current practice for MDD treatment mainly relies on trial and error, with an estimated 42-53% response rates for antidepressant use. Here, we sought to generate an accurate predictor of response to a panel of antidepressants and optimize treatment selection using a data-driven approach analyzing combinations of genetic, clinical, and demographic factors. We analyzed the response patterns of patients to three antidepressant medications in the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study, and employed state-of-the-art machine learning (ML) tools to generate a predictive algorithm. To validate our results, we assessed the algorithm's capacity to predict individualized antidepressant responses on a separate set of 530 patients in STAR*D, consisting of 271 patients in a validation set and 259 patients in the final test set. This assessment yielded an average balanced accuracy rate of 72.3% (SD 8.1) and 70.1% (SD 6.8) across the different medications in the validation and test set, respectively (p < 0.01 for all models). To further validate our design scheme, we obtained data from the Pharmacogenomic Research Network Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS) of patients treated with citalopram, and applied the algorithm's citalopram model. This external validation yielded highly similar results for STAR*D and PGRN-AMPS test sets, with a balanced accuracy of 60.5% and 61.3%, respectively (both p's < 0.01). These findings support the feasibility of using ML algorithms applied to large datasets with genetic, clinical, and demographic features to improve accuracy in antidepressant prescription.

Characterizing Human Cell Types and Tissue Origin Using the Benford Law.

Processing massive transcriptomic datasets in a meaningful manner requires novel, possibly interdisciplinary, approaches. One principle that can address this challenge is the Benford law (BL), which posits that the occurrence probability of a leading digit in a large numerical dataset decreases as its value increases. Here, we analyzed large single-cell and bulk RNA-seq datasets to test whether cell types and tissue origins can be differentiated based on the adherence of specific genes to the BL. Then, we used the Benford adherence scores of these genes as inputs to machine-learning algorithms and tested their separation accuracy. We found that genes selected based on their first-digit distributions can distinguish between cell types and tissue origins. Moreover, despite the simplicity of this novel feature-selection method, its separation accuracy is higher than that of the mean-expression level approach and is similar to that of the differential expression approach. Thus, the BL can be used to obtain biological insights from massive amounts of numerical genomics data-a capability that could be utilized in various biomedical applications, e.g., to resolve samples of unknown primary origin, identify possible sample contaminations, and provide insights into the molecular basis of cancer subtypes.