Sounding out solutions: using SONAR to connect participants with relevant healthcare resources.

OBJECTIVE: This article outlines a scalable system developed by the All of Us Research Program's Genetic Counseling Resource to vet a large database of healthcare resources for supporting participants with health-related DNA results. MATERIALS AND METHODS: After a literature review of established evaluation frameworks for health resources, we created SONAR, a 10-item framework and grading scale for health-related participant-facing resources. SONAR was used to review clinical resources that could be shared with participants during genetic counseling. RESULTS: Application of SONAR shortened resource approval time from 7 days to 1 day. About 256 resources were approved and 8 rejected through SONAR review. Most approved resources were relevant to participants nationwide (60.0%). The most common resource types were related to support groups (20%), cancer care (30.6%), and general educational resources (12.4%). All of Us genetic counselors provided 1161 approved resources during 3005 (38.6%) consults, mainly to local genetic counselors (29.9%), support groups (21.9%), and educational resources (21.0%). DISCUSSION: SONAR's systematic method simplifies resource vetting for healthcare providers, easing the burden of identifying and evaluating credible resources. Compiling these resources into a user-friendly database allows providers to share these resources efficiently, better equipping participants to complete follow up actions from health-related DNA results. CONCLUSION: The All of Us Genetic Counseling Resource connects participants receiving health-related DNA results with relevant follow-up resources on a high-volume, national level. This has been made possible by the creation of a novel resource database and validation system.

A Randomized Trial of Two Remote Health Care Delivery Models on the Uptake of Genetic Testing and Impact on Patient-Reported Psychological Outcomes in Families With Pancreatic Cancer: The Genetic Education, Risk Assessment, and Testing (GENERATE) Study.

BACKGROUND & AIMS: Genetic testing uptake for cancer susceptibility in family members of patients with cancer is suboptimal. Among relatives of patients with pancreatic ductal adenocarcinoma (PDAC), The GENetic Education, Risk Assessment, and TEsting (GENERATE) study evaluated 2 online genetic education/testing delivery models and their impact on patient-reported psychological outcomes. METHODS: Eligible participants had ≥1 first-degree relative with PDAC, or ≥1 first-/second-degree relative with PDAC with a known pathogenic germline variant in 1 of 13 PDAC predisposition genes. Participants were randomized by family, between May 8, 2019, and June 1, 2021. Arm 1 participants underwent a remote interactive telemedicine session and online genetic education. Arm 2 participants were offered online genetic education only. All participants were offered germline testing. The primary outcome was genetic testing uptake, compared by permutation tests and mixed-effects logistic regression models. We hypothesized that Arm 1 participants would have a higher genetic testing uptake than Arm 2. Validated surveys were administered to assess patient-reported anxiety, depression, and cancer worry at baseline and 3 months postintervention. RESULTS: A total of 424 families were randomized, including 601 participants (n = 296 Arm 1; n = 305 Arm 2), 90% of whom completed genetic testing (Arm 1 [87%]; Arm 2 [93%], P = .014). Arm 1 participants were significantly less likely to complete genetic testing compared with Arm 2 participants (adjusted ratio [Arm1/Arm2] 0.90, 95% confidence interval 0.78-0.98). Among participants who completed patient-reported psychological outcomes questionnaires (Arm 1 [n = 194]; Arm 2 [n = 206]), the intervention did not affect mean anxiety, depression, or cancer worry scores. CONCLUSIONS: Remote genetic education and testing can be a successful and complementary option for delivering genetics care. (Clinicaltrials.gov, number NCT03762590).

Investigating the prevalence of pathogenic variants in Saudi Arabian patients with familial cancer using a multigene next generation sequencing panel.

Family history is an important factor in determining hereditary cancer risk for many cancer types. The emergence of next-generation sequencing (NGS) has expedited the discovery of many hereditary cancer susceptibility genes and the development of rapid, affordable testing kits. Here, a 30-gene targeted NGS panel for hereditary cancer risk assessment was tested and validated in a Saudi Arabian population. A total of 310 subjects were screened, including 57 non-cancer patients, 110 index patients with cancer and 143 of the cancer patients' family members, 16 of which also had cancer. Of the 310 subjects, 119 (38.4%) were carriers of pathogenic or likely pathogenic variants (PVs) affecting one or more of the following genes: TP53, ATM, CHEK2, CDH1, CDKN2A, BRCA1, BRCA2, PALB2, BRIP1, RAD51D, APC, MLH1, MSH2, MSH6, PMS2, PTEN, NBN/NBS1 and MUTYH. Among 126 patients and relatives with a history of cancer, 49 (38.9%) were carriers of PVs or likely PVs. Two variants in particular were significantly associated with the occurrence of a specific cancer in this population (APC c.3920T>A - colorectal cancer/Lynch syndrome (p = 0.026); TP53 c.868C>T; - multiple colon polyposis (p = 0.048)). Diverse variants in BRCA2, the majority of which have not previously been reported as pathogenic, were found at higher frequency in those with a history of cancer than in the general patient population. There was a higher background prevalence of genetic variants linked to familial cancers in this cohort than expected based on prevalence in other populations.

Clinical Implementation of Combined Monogenic and Polygenic Risk Disclosure for Coronary Artery Disease.

BACKGROUND: State-of-the-art genetic risk interpretation for a common complex disease such as coronary artery disease (CAD) requires assessment for both monogenic variants-such as those related to familial hypercholesterolemia-as well as the cumulative impact of many common variants, as quantified by a polygenic score. OBJECTIVES: The objective of the study was to describe a combined monogenic and polygenic CAD risk assessment program and examine its impact on patient understanding and changes to clinical management. METHODS: Study participants attended an initial visit in a preventive genomics clinic and a disclosure visit to discuss results and recommendations, primarily via telemedicine. Digital postdisclosure surveys and chart review evaluated the impact of disclosure. RESULTS: There were 60 participants (mean age 51 years, 37% women, 72% with no known CAD), including 30 (50%) referred by their cardiologists and 30 (50%) self-referred. Two (3%) participants had a monogenic variant pathogenic for familial hypercholesterolemia, and 19 (32%) had a high polygenic score in the top quintile of the population distribution. In a postdisclosure survey, both the genetic test report (in 80% of participants) and the discussion with the clinician (in 89% of participants) were ranked as very or extremely helpful in understanding the result. Of the 42 participants without CAD, 17 or 40% had a change in management, including statin initiation, statin intensification, or coronary imaging. CONCLUSIONS: Combined monogenic and polygenic assessments for CAD risk provided by preventive genomics clinics are beneficial for patients and result in changes in management in a significant portion of patients.

Digital health-enabled genomics: Opportunities and challenges.

Digital health solutions, with apps, virtual care, and electronic medical records, are gaining momentum across all medical disciplines, and their adoption has been accelerated, in part, by the COVID-19 pandemic. Personal wearables, sensors, and mobile technologies are increasingly being used to identify health risks and assist in diagnosis, treatment, and monitoring of health and disease. Genomics is a vanguard of digital healthcare as we witness a convergence of the fields of genomic and digital medicine. Spurred by the acute need to increase health literacy, empower patients' preference-sensitive decisions, or integrate vast amounts of complex genomic data into the clinical workflow, there has been an emergence of digital support tools in genomics-enabled care. We present three use cases that demonstrate the application of these converging technologies: digital genomics decision support tools, conversational chatbots to scale the genetic counseling process, and the digital delivery of comprehensive genetic services. These digital solutions are important to facilitate patient-centered care delivery, improve patient outcomes, and increase healthcare efficiencies in genomic medicine. Yet the development of these innovative digital genomic technologies also reveals strategic challenges that need to be addressed before genomic digital health can be broadly adopted. Alongside key evidentiary gaps in clinical and cost-effectiveness, there is a paucity of clinical guidelines, policy, and regulatory frameworks that incorporate digital health. We propose a research agenda, guided by learning healthcare systems, to realize the vision of digital health-enabled genomics to ensure its sustainable and equitable deployment in clinical care.

Whole-genome sequencing as an investigational device for return of hereditary disease risk and pharmacogenomic results as part of the All of Us Research Program.

BACKGROUND: The All of Us Research Program (AoURP, "the program") is an initiative, sponsored by the National Institutes of Health (NIH), that aims to enroll one million people (or more) across the USA. Through repeated engagement of participants, a research resource is being created to enable a variety of future observational and interventional studies. The program has also committed to genomic data generation and returning important health-related information to participants. METHODS: Whole-genome sequencing (WGS), variant calling processes, data interpretation, and return-of-results procedures had to be created and receive an Investigational Device Exemption (IDE) from the United States Food and Drug Administration (FDA). The performance of the entire workflow was assessed through the largest known cross-center, WGS-based, validation activity that was refined iteratively through interactions with the FDA over many months. RESULTS: The accuracy and precision of the WGS process as a device for the return of certain health-related genomic results was determined to be sufficient, and an IDE was granted. CONCLUSIONS: We present here both the process of navigating the IDE application process with the FDA and the results of the validation study as a guide to future projects which may need to follow a similar path. Changes to the program in the future will be covered in supplementary submissions to the IDE and will support additional variant classes, sample types, and any expansion to the reportable regions.

Machine learning enables new insights into genetic contributions to liver fat accumulation.

Excess liver fat, called hepatic steatosis, is a leading risk factor for end-stage liver disease and cardiometabolic diseases but often remains undiagnosed in clinical practice because of the need for direct imaging assessments. We developed an abdominal MRI-based machine-learning algorithm to accurately estimate liver fat (correlation coefficients, 0.97-0.99) from a truth dataset of 4,511 middle-aged UK Biobank participants, enabling quantification in 32,192 additional individuals. 17% of participants had predicted liver fat levels indicative of steatosis, and liver fat could not have been reliably estimated based on clinical factors such as BMI. A genome-wide association study of common genetic variants and liver fat replicated three known associations and identified five newly associated variants in or near the MTARC1, ADH1B, TRIB1, GPAM, and MAST3 genes (p < 3 × 10-8). A polygenic score integrating these eight genetic variants was strongly associated with future risk of chronic liver disease (hazard ratio > 1.32 per SD score, p < 9 × 10-17). Rare inactivating variants in the APOB or MTTP genes were identified in 0.8% of individuals with steatosis and conferred more than 6-fold risk (p < 2 × 10-5), highlighting a molecular subtype of hepatic steatosis characterized by defective secretion of apolipoprotein B-containing lipoproteins. We demonstrate that our imaging-based machine-learning model accurately estimates liver fat and may be useful in epidemiological and genetic studies of hepatic steatosis.

Novel Models of Genetic Education and Testing for Pancreatic Cancer Interception: Preliminary Results from the GENERATE Study.

Up to 10% of patients with pancreatic ductal adenocarcinoma (PDAC) carry underlying germline pathogenic variants in cancer susceptibility genes. The GENetic Education Risk Assessment and TEsting (GENERATE) study aimed to evaluate novel methods of genetic education and testing in relatives of patients with PDAC. Eligible individuals had a family history of PDAC and a relative with a germline pathogenic variant in APC, ATM, BRCA1, BRCA2, CDKN2A, EPCAM, MLH1, MSH2, MSH6, PALB2, PMS2, STK11, or TP53 genes. Participants were recruited at six academic cancer centers and through social media campaigns and patient advocacy efforts. Enrollment occurred via the study website (https://GENERATEstudy.org) and all participation, including collecting a saliva sample for genetic testing, could be done from home. Participants were randomized to one of two remote methods that delivered genetic education about the risks of inherited PDAC and strategies for surveillance. The primary outcome of the study was uptake of genetic testing. From 5/8/2019 to 5/6/2020, 49 participants were randomized to each of the intervention arms. Overall, 90 of 98 (92%) of randomized participants completed genetic testing. The most frequently detected pathogenic variants included those in BRCA2 (N = 15, 17%), ATM (N = 11, 12%), and CDKN2A (N = 4, 4%). Participation in the study remained steady throughout the onset of the Coronavirus disease (COVID-19) pandemic. Preliminary data from the GENERATE study indicate success of remote alternatives to traditional cascade testing, with genetic testing rates over 90% and a high rate of identification of germline pathogenic variant carriers who would be ideal candidates for PDAC interception approaches. PREVENTION RELEVANCE: Preliminary data from the GENERATE study indicate success of remote alternatives for pancreatic cancer genetic testing and education, with genetic testing uptake rates over 90% and a high rate of identification of germline pathogenic variant carriers who would be ideal candidates for pancreatic cancer interception.

Model-driven mitigation measures for reopening schools during the COVID-19 pandemic.

Reopening schools is an urgent priority as the COVID-19 pandemic drags on. To explore the risks associated with returning to in-person learning and the value of mitigation measures, we developed stochastic, network-based models of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission in primary and secondary schools. We find that a number of mitigation measures, alone or in concert, may reduce risk to acceptable levels. Student cohorting, in which students are divided into two separate populations that attend in-person classes on alternating schedules, can reduce both the likelihood and the size of outbreaks. Proactive testing of teachers and staff can help catch introductions early, before they spread widely through the school. In secondary schools, where the students are more susceptible to infection and have different patterns of social interaction, control is more difficult. Especially in these settings, planners should also consider testing students once or twice weekly. Vaccinating teachers and staff protects these individuals and may have a protective effect on students as well. Other mitigations, including mask wearing, social distancing, and increased ventilation, remain a crucial component of any reopening plan.

Color Data v2: a user-friendly, open-access database with hereditary cancer and hereditary cardiovascular conditions datasets.

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/.

Polygenic background modifies penetrance of monogenic variants for tier 1 genomic conditions.

Genetic variation can predispose to disease both through (i) monogenic risk variants that disrupt a physiologic pathway with large effect on disease and (ii) polygenic risk that involves many variants of small effect in different pathways. Few studies have explored the interplay between monogenic and polygenic risk. Here, we study 80,928 individuals to examine whether polygenic background can modify penetrance of disease in tier 1 genomic conditions - familial hypercholesterolemia, hereditary breast and ovarian cancer, and Lynch syndrome. Among carriers of a monogenic risk variant, we estimate substantial gradients in disease risk based on polygenic background - the probability of disease by age 75 years ranged from 17% to 78% for coronary artery disease, 13% to 76% for breast cancer, and 11% to 80% for colon cancer. We propose that accounting for polygenic background is likely to increase accuracy of risk estimation for individuals who inherit a monogenic risk variant.

Burden of hereditary cancer susceptibility in unselected patients with pancreatic ductal adenocarcinoma referred for germline screening.

BACKGROUND: Recent guidelines recommend consideration of germline testing for all newly diagnosed pancreatic ductal adenocarcinoma (PDAC). The primary aim of this study was to determine the burden of hereditary cancer susceptibility in PDAC. A secondary aim was to compare genetic testing uptake rates across different modes of genetic counselling. PATIENTS AND METHODS: All patients diagnosed with PDAC in the province of British Columbia, Canada referred to a population-based hereditary cancer program were eligible for multi-gene panel testing, irrespective of cancer family history. Any healthcare provider or patients themselves could refer. RESULTS: A total of 305 patients with PDAC were referred between July 2016 and January 2019. Two hundred thirty-five patients attended a consultation and 177 completed index germline genetic testing. 25/177 (14.1%) of unrelated patients had a pathogenic variant (PV); 19/25 PV were in known PDAC susceptibility genes with cancer screening or risk-reduction implications. PDAC was significantly associated with PV in ATM (OR, 7.73; 95% CI, 3.10 to 19.33, P = 6.14E-05) when comparing age and gender and ethnicity-matched controls tested on the same platform. The overall uptake rate for index testing was 59.2% and was significantly higher with 1-on-1 consultations and group consultations compared to telehealth consultations (88.9% vs 82.9% vs 61.8%, P < .001). CONCLUSION: In a prospective clinic-based cohort of patients with PDAC referred for testing irrespective of family history, germline PV were detected in 14.1%. PV in ATM accounted for half of all PVs and were significantly associated with PDAC. These findings support recent guidelines and will guide future service planning in this population.

Patient acceptance of genetic testing for familial hypercholesterolemia in the CASCADE FH Registry.

BACKGROUND: Barriers to genetic testing and subsequent family cascade screening for familial hypercholesterolemia (FH) include cost, patient and provider awareness, privacy and discrimination concerns, need for a physician order, underutilization of genetic counselors, and family concerns about the implications of genetic testing for care. OBJECTIVES: The objective of the study was to determine the uptake of genetic testing with cost and privacy removed. METHODS: The FH Foundation offered free genetic testing and counseling to patients in the patient portal of the CASCADE FH Registry, who had not previously undergone genetic testing for 3 genes associated with FH (LDLR, APOB, and PCSK9). The free testing offer was extended to first-degree relatives of participants who had a positive genetic test result for cascade screening. RESULTS: Of 435 eligible patients, 147 opted in to participate, 122 consented, and 110 (68.2% female, median age: 52 years) received genetic testing. Of the participants, 64 had a positive genetic test result for a pathogenic variant in LDLR (59) or APOB (5); 11 had a variant of uncertain significance. Only 3 first-degrees relatives underwent genetic testing. CONCLUSIONS: Although there was substantial interest in genetic testing, uptake of family cascade screening was poor. Innovative approaches to increase family cascade screening should be explored.

LEAP: Using machine learning to support variant classification in a clinical setting.

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 Learning from Evidence to Assess Pathogenicity (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 covariant 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 area under the receiver operating characteristic curve (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 make LEAP effective in the clinical setting as a high-throughput quality control layer.

Prevalence of Inherited Mutations in Breast Cancer Predisposition Genes among Women in Uganda and Cameroon.

BACKGROUND: Sub-Saharan Africa (SSA) has a high proportion of premenopausal hormone receptor negative breast cancer. Previous studies reported a strikingly high prevalence of germline mutations in BRCA1 and BRCA2 among Nigerian patients with breast cancer. It is unknown if this exists in other SSA countries. METHODS: Breast cancer cases, unselected for age at diagnosis and family history, were recruited from tertiary hospitals in Kampala, Uganda and Yaoundé, Cameroon. Controls were women without breast cancer recruited from the same hospitals and age-matched to cases. A multigene sequencing panel was used to test for germline mutations. RESULTS: There were 196 cases and 185 controls with a mean age of 46.2 and 46.6 years for cases and controls, respectively. Among cases, 15.8% carried a pathogenic or likely pathogenic mutation in a breast cancer susceptibility gene: 5.6% in BRCA1, 5.6% in BRCA2, 1.5% in ATM, 1% in PALB2, 0.5% in BARD1, 0.5% in CDH1, and 0.5% in TP53. Among controls, 1.6% carried a mutation in one of these genes. Cases were 11-fold more likely to carry a mutation compared with controls (OR = 11.34; 95% confidence interval, 3.44-59.06; P < 0.001). The mean age of cases with BRCA1 mutations was 38.3 years compared with 46.7 years among other cases without such mutations (P = 0.03). CONCLUSIONS: Our findings replicate the earlier report of a high proportion of mutations in BRCA1/2 among patients with symptomatic breast cancer in SSA. IMPACT: Given the high burden of inherited breast cancer in SSA countries, genetic risk assessment could be integrated into national cancer control plans.

Low coverage whole genome sequencing enables accurate assessment of common variants and calculation of genome-wide polygenic scores.

BACKGROUND: Inherited susceptibility to common, complex diseases may be caused by rare, pathogenic variants ("monogenic") or by the cumulative effect of numerous common variants ("polygenic"). Comprehensive genome interpretation should enable assessment for both monogenic and polygenic components of inherited risk. The traditional approach requires two distinct genetic testing technologies-high coverage sequencing of known genes to detect monogenic variants and a genome-wide genotyping array followed by imputation to calculate genome-wide polygenic scores (GPSs). We assessed the feasibility and accuracy of using low coverage whole genome sequencing (lcWGS) as an alternative to genotyping arrays to calculate GPSs. METHODS: First, we performed downsampling and imputation of WGS data from ten individuals to assess concordance with known genotypes. Second, we assessed the correlation between GPSs for 3 common diseases-coronary artery disease (CAD), breast cancer (BC), and atrial fibrillation (AF)-calculated using lcWGS and genotyping array in 184 samples. Third, we assessed concordance of lcWGS-based genotype calls and GPS calculation in 120 individuals with known genotypes, selected to reflect diverse ancestral backgrounds. Fourth, we assessed the relationship between GPSs calculated using lcWGS and disease phenotypes in a cohort of 11,502 individuals of European ancestry. RESULTS: We found imputation accuracy r2 values of greater than 0.90 for all ten samples-including those of African and Ashkenazi Jewish ancestry-with lcWGS data at 0.5×. GPSs calculated using lcWGS and genotyping array followed by imputation in 184 individuals were highly correlated for each of the 3 common diseases (r2 = 0.93-0.97) with similar score distributions. Using lcWGS data from 120 individuals of diverse ancestral backgrounds, we found similar results with respect to imputation accuracy and GPS correlations. Finally, we calculated GPSs for CAD, BC, and AF using lcWGS in 11,502 individuals of European ancestry, confirming odds ratios per standard deviation increment ranging 1.28 to 1.59, consistent with previous studies. CONCLUSIONS: lcWGS is an alternative technology to genotyping arrays for common genetic variant assessment and GPS calculation. lcWGS provides comparable imputation accuracy while also overcoming the ascertainment bias inherent to variant selection in genotyping array design.

Multi-Gene Panel Testing of 23,179 Individuals for Hereditary Cancer Risk Identifies Pathogenic Variant Carriers Missed by Current Genetic Testing Guidelines.

Recent advancements in next-generation sequencing have greatly expanded the use of multi-gene panel testing for hereditary cancer risk. Although genetic testing helps guide clinical diagnosis and management, testing recommendations are based on personal and family history of cancer and ethnicity, and many carriers are being missed. Herein, we report the results from 23,179 individuals who were referred for 30-gene next-generation sequencing panel testing for hereditary cancer risk, independent of current testing guidelines-38.7% of individuals would not have met National Comprehensive Cancer Network criteria for genetic testing. We identified a total of 2811 pathogenic variants in 2698 individuals for an overall pathogenic frequency of 11.6% (9.1%, excluding common low-penetrance alleles). Among individuals of Ashkenazi Jewish descent, three-quarters of pathogenic variants were outside of the three common BRCA1 and BRCA2 founder alleles. Across all ethnic groups, pathogenic variants in BRCA1 and BRCA2 occurred most frequently, but the contribution of pathogenic variants in other genes on the panel varied. Finally, we found that 21.7% of individuals with pathogenic variants in genes with well-established genetic testing recommendations did not meet corresponding National Comprehensive Cancer Network criteria. Taken together, the results indicate that more individuals are at genetic risk for hereditary cancer than are identified by current testing guidelines and/or use of single-gene or single-site testing.

A scalable, aggregated genotypic-phenotypic database for human disease variation.

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.

Cascade Genetic Testing of Relatives for Hereditary Cancer Risk: Results of an Online Initiative.

In cascade testing, genetic testing for an identified familial pathogenic variant extends to disease-free relatives to allow genetically targeted disease prevention. We evaluated the results of an online initiative in which carriers of 1 of 30 cancer-associated genes, or their first-degree relatives, could offer low-cost testing to at-risk first-degree relatives. In the first year, 1101 applicants invited 2280 first-degree relatives to undergo genetic testing. Of invited relatives, 47.5% (95% confidence interval [CI] = 45.5 to 49.6%) underwent genetic testing, and 12.0% (95% CI = 9.2 to 14.8%) who tested positive continued the cascade by inviting additional relatives to test. Of tested relatives, 4.9% (95% CI = 3.8 to 6.1%) had a pathogenic variant in a different gene from the known familial one, and 16.8% (95% CI = 14.7 to 18.8%) had a variant of uncertain significance. These results suggest that an online, low-cost program is an effective approach to implementing cascade testing, and that up to 5% of the general population may carry a pathogenic variant in 1 of 30 cancer-associated genes.