Anjali D. Zimmer

Introduction: Genetic screenings can have a large impact on enabling personalized preventative care. However, this can be limited by the primary use of medical history-based screenings in determining care. The purpose of this study was to understand the impact of DNA10K, a population-based genetic screening program mediated by primary care physicians (PCPs) within an integrated health system to emphasize its contribution to preventative healthcare. Methods: Construction of the patient experience as part of DNA10K shaped the context for PCP engagement within the program. A cross-sectional analysis of patient consents, orders, tests, and results of nearly 10,000 patients within the primary care specialties of family medicine, internal medicine or obstetrics/gynecology between April 1, 2019 and January 22, 2020 was conducted. Results: Across all specialties, a median number of 7.5 cancer and cardiovascular disease variants per PCP was found. The average age of the study population was 49.6 years. Over 8% of these patients had at least one actionable genetic risk variant and almost 2% of patients had at least one CDC Tier 1 variant. The median number of patients per PCP with either hereditary breast and ovarian cancer, Lynch Syndrome, or Familial Hypercholesterolemia was 1 (Interquartile Range 0-2). Discussion: The analysis of test results and the engagement of an integrated healthcare system in the implementation of a genetic screening program suggests that it can have a large impact on population health outcomes and minimal referral burden to PCPs if identified risks can lead to preventative care.

Advances in genome sequencing have led to a tremendous increase in the discovery of novel missense variants, but evidence for determining clinical significance can be limited or conflicting. Here, we present LEAP, a machine learning model that utilizes a variety of feature categories to classify variants, and achieves high performance in multiple genes and different health conditions. Feature categories include functional predictions, splice predictions, population frequencies, conservation scores, protein domain data, and clinical observation data such as personal and family history and co‐variant information. L2‐regularized logistic regression and random forest classification models were trained on missense variants detected and classified during the course of routine clinical testing at Color Genomics (14,226 variants from 24 cancer‐related genes, and 5,398 variants from 30 cardiovascular‐related genes). Using 10‐fold cross‐validated predictions, the logistic regression model achieved an AUROC of 97.8% (cancer) and 98.8% (cardiovascular), while the random forest model achieved 98.3% (cancer) and 98.6% (cardiovascular). We demonstrate generalizability to different genes by validating predictions on genes withheld from training (96.8% AUROC). High accuracy and broad applicability makes LEAP effective in the clinical setting as a high‐throughput quality control layer.

Publicly-available genetic databases promote data sharing and fuel scientific discoveries for the prevention, treatment, and management of disease. In 2018, we built Color Data, a user-friendly, open access database containing genotypic and self-reported phenotypic information from 50,000 individuals who were sequenced for 30 genes associated with hereditary cancer. In a continued effort to promote access to these types of data, we launched Color Data v2, an updated version of the Color Data database. This new release includes additional clinical genetic testing results from more than 18,000 individuals who were sequenced for 30 genes associated with hereditary cardiovascular conditions, as well as polygenic risk scores for breast cancer, coronary artery disease, and atrial fibrillation. In addition, we used self-reported phenotypic information to implement the following four clinical risk models: Gail Model for five-year risk of breast cancer, Claus Model for lifetime risk of breast cancer, simple office-based Framingham Coronary Heart Disease Risk Score for ten-year risk of coronary heart disease, and CHARGE-AF simple score for five-year risk of atrial fibrillation. These new features and capabilities are highlighted through two sample queries in the database. We hope that the broad dissemination of this data will help researchers continue to explore genotype-phenotype correlations and identify novel variants for functional analysis, enabling scientific discoveries in the field of population genomics.Database URL: https://data.color.com/

Publicly available genetic databases promote data sharing and fuel scientific discoveries for the prevention, treatment and management of disease. In 2018, we built Color Data, a user-friendly, open access database containing genotypic and self-reported phenotypic information from 50 000 individuals who were sequenced for 30 genes associated with hereditary cancer. In a continued effort to promote access to these types of data, we launched Color Data v2, an updated version of the Color Data database. This new release includes additional clinical genetic testing results from more than 18 000 individuals who were sequenced for 30 genes associated with hereditary cardiovascular conditions as well as polygenic risk scores for breast cancer, coronary artery disease and atrial fibrillation. In addition, we used self-reported phenotypic information to implement the following four clinical risk models: Gail Model for 5-year risk of breast cancer, Claus Model for lifetime risk of breast cancer, simple office-based Framingham Coronary Heart Disease Risk Score for 10-year risk of coronary heart disease and CHARGE-AF simple score for 5-year risk of atrial fibrillation. These new features and capabilities are highlighted through two sample queries in the database. We hope that the broad dissemination of these data will help researchers continue to explore genotype–phenotype correlations and identify novel variants for functional analysis, enabling scientific discoveries in the field of population genomics. Database URL: https://data.color.com/

Next generation sequencing multi-gene panels have greatly improved the diagnostic yield and cost effectiveness of genetic testing and are rapidly being integrated into the clinic for hereditary cancer risk. With this technology comes a dramatic increase in the volume, type and complexity of data. This invaluable data though is too often buried or inaccessible to researchers, especially to those without strong analytical or programming skills. To effectively share comprehensive, integrated genotypic–phenotypic data, we built Color Data, a publicly available, cloud-based database that supports broad access and data literacy. The database is composed of 50 000 individuals who were sequenced for 30 genes associated with hereditary cancer risk and provides useful information on allele frequency and variant classification, as well as associated phenotypic information such as demographics and personal and family history. Our user-friendly interface allows researchers to easily execute their own queries with filtering, and the results of queries can be shared and/or downloaded. The rapid and broad dissemination of these research results will help increase the value of, and reduce the waste in, scientific resources and data. Furthermore, the database is able to quickly scale and support integration of additional genes and human hereditary conditions. We hope that this database will help researchers and scientists explore genotype–phenotype correlations in hereditary cancer, identify novel variants for functional analysis and enable data-driven drug discovery and development.

Next generation sequencing (NGS) has become a common technology for clinical genetic tests. The quality of NGS calls varies widely and is influenced by features like reference sequence characteristics, read depth, and mapping accuracy. With recent advances in NGS technology and software tools, the majority of variants called using NGS alone are in fact accurate and reliable. However, a small subset of difficult-to-call variants that still do require orthogonal confirmation exist. For this reason, many clinical laboratories confirm NGS results using orthogonal technologies such as Sanger sequencing. Here, we report the development of a deterministic machine-learning-based model to differentiate between these two types of variant calls: those that do not require confirmation using an orthogonal technology (high confidence), and those that require additional quality testing (low confidence). This approach allows reliable NGS-based calling in a clinical setting by identifying the few important variant calls that require orthogonal confirmation. We developed and tested the model using a set of 7179 variants identified by a targeted NGS panel and re-tested by Sanger sequencing. The model incorporated several signals of sequence characteristics and call quality to determine if a variant was identified at high or low confidence. The model was tuned to eliminate false positives, defined as variants that were called by NGS but not confirmed by Sanger sequencing. The model achieved very high accuracy: 99.4% (95% confidence interval: +/− 0.03%). It categorized 92.2% (6622/7179) of the variants as high confidence, and 100% of these were confirmed to be present by Sanger sequencing. Among the variants that were categorized as low confidence, defined as NGS calls of low quality that are likely to be artifacts, 92.1% (513/557) were found to be not present by Sanger sequencing. This work shows that NGS data contains sufficient characteristics for a machine-learning-based model to differentiate low from high confidence variants. Additionally, it reveals the importance of incorporating site-specific features as well as variant call features in such a model.