Addressing the routine failure to clinically identify monogenic cases of common disease.

Changes in medical practice are needed to improve the diagnosis of monogenic forms of selected common diseases. This article seeks to focus attention on the need for universal genetic testing in common diseases for which the recommended clinical management of patients with specific monogenic forms of disease diverges from standard management and has evidence for improved outcomes.We review evidence from genomic screening of large patient cohorts, which has confirmed that important monogenic case identification failures are commonplace in routine clinical care. These case identification failures constitute diagnostic misattributions, where the care of individuals with monogenic disease defaults to the treatment plan offered to those with polygenic or non-genetic forms of the disease.The number of identifiable and actionable monogenic forms of common diseases is increasing with time. Here, we provide six examples of common diseases for which universal genetic test implementation would drive improved care. We examine the evidence to support genetic testing for common diseases, and discuss barriers to widespread implementation. Finally, we propose recommendations for changes to genetic testing and care delivery aimed at reducing diagnostic misattributions, to serve as a starting point for further evaluation and development of evidence-based guidelines for implementation.

Enabling Diagnostic Resulting as a New Category of Secondary Genomic Findings.

Over the past decade, the secondary analysis of existing DNA datasets for clinical resulting has become an established practice. However, this established practice is typically limited to only one category of secondary genomic findings, the identification of "disease risk". Diagnostic resulting has been left out of secondary genomic findings. In medical practice, diagnostic resulting is triggered when a test is ordered for a patient based on a recognizable clinical indication for evaluation; most genetic and genomic testing is carried out in support of diagnostic evaluations. The secondary analysis of existing DNA data has the potential to cost less and have more rapid turnaround times for diagnostic results compared to current DNA diagnostic approaches that typically generate a new dataset with every test ordered. Worldwide, innovative health systems could position themselves to deliver valid secondary genomic finding results in both the established category of disease risk results, as well as a new category of diagnostic results. To support the ongoing delivery of both categories of secondary findings, health systems will need comprehensive genomic datasets for patients and secure workflows that allow for repeated access to that data for on-demand secondary analysis.

Improved provider preparedness through an 8-part genetics and genomic education program.

PURPOSE: Large-scale genetics education appropriate for general practice providers is a growing priority. We describe the content and impact of a mandatory system-wide program implemented at Sanford Health. METHODS: The Imagenetics Initiative at Sanford Health developed a 2-year genetics education program with quarterly web-based modules that were mandatory for all physicians and advanced practice providers. Scores of 0 to 5 were calculated for each module on the basis of the number of objectives that the participants reported as fulfilled. In addition, the participants completed surveys before starting and after finishing the education program, which included a 7-item measure scored 7 to 28 on the perceived preparedness to practice genetics. RESULTS: Between 2252 and 2822 Sanford Health employees completed each of the 8 quarterly education modules. The ratings were highest for the module about using genomics to improve patient management (mean score = 4.3) and lowest for the module about different types of genetic tests and specialists. The mean perceived preparedness scores increased from 15.7 at pre-education to 19.1 at post-education (P < .001). CONCLUSION: Web-based genetics education was highly effective in increasing health care providers' confidence about using genetics. Both comfort with personal knowledge and confidence regarding access to the system's genomic medicine experts increased significantly. The results demonstrate how scalable approaches can improve provider preparedness.

A Genome-First Approach to Characterize DICER1 Pathogenic Variant Prevalence, Penetrance, and Phenotype.

Importance: Genetic disorders are historically defined through phenotype-first approaches. However, risk estimates derived from phenotype-linked ascertainment may overestimate severity and penetrance. Pathogenic variants in DICER1 are associated with increased risks of rare and common neoplasms and thyroid disease in adults and children. This study explored how effectively a genome-first approach could characterize the clinical traits associated with germline DICER1 putative loss-of-function (pLOF) variants in an unselected clinical cohort. Objective: To examine the prevalence, penetrance, and phenotypic characteristics of carriers of germline DICER1 pLOF variants via genome-first ascertainment. Design, Setting, and Participants: This cohort study classifies DICER1 variants in germline exome sequence data from 92 296 participants of the Geisinger MyCode Community Health Initiative. Data for each MyCode participant were used from the start of the Geisinger electronic health record to February 1, 2018. Main Outcomes and Measures: Prevalence of germline DICER1 variation; penetrance of malignant tumors and thyroid disease in carriers of germline DICER1 variation; structured, manual review of electronic health records; and DICER1 sequencing of available tumors from an associated cancer registry. Results: A total of 92 296 adults (mean [SD] age, 59 [18] years; 98% white; 60% female) participated in the study. Germline DICER1 pLOF variants were observed in 1 in 3700 to 1 in 4600 participants, more than double the expected prevalence. Malignant tumors (primarily thyroid carcinoma) were observed in 4 of 25 participants (16%) with DICER1 pLOF variants, which is comparable (by 50 years of age) to the frequency of neoplasms in the largest registry- and clinic-based (phenotype-first) DICER1 studies published to date. DICER1 pLOF variants were significantly associated with risks of thyroidectomy (odds ratio [OR], 6.0; 95% CI, 2.2-16.3; P = .007) and thyroid cancer (OR, 9.2; 95% CI, 2.1-34.7; P = .02) compared with controls, but there was not a significant increase in the risk of goiter (OR, 1.8; 95% CI, 0.7-4.9). A female patient in her 80s who was a carrier of a germline DICER1 hotspot variant was apparently healthy on electronic health record review. The term DICER1 did not appear in any of the medical records of the 25 participants with a pLOF DICER1 variant, even in those affected with a known DICER1-associated tumor or thyroid phenotype. Conclusions and Relevance: This cohort study was able to ascertain individuals with germline DICER1 variants based on a genome-first approach rather than through a previously established DICER1-related phenotype. Use of the genome-first approach may complement more traditional approaches to syndrome delineation and may be an efficient approach for risk estimation.

Healthcare Utilization and Costs after Receiving a Positive BRCA1/2 Result from a Genomic Screening Program.

Population genomic screening has been demonstrated to detect at-risk individuals who would not be clinically identified otherwise. However, there are concerns about the increased utilization of unnecessary services and the associated increase in costs. The objectives of this study are twofold: (1) determine whether there is a difference in healthcare utilization and costs following disclosure of a pathogenic/likely pathogenic (P/LP) BRCA1/2 variant via a genomic screening program, and (2) measure the post-disclosure uptake of National Comprehensive Cancer Network (NCCN) guideline-recommended risk management. We retrospectively reviewed electronic health record (EHR) and billing data from a female population of BRCA1/2 P/LP variant carriers without a personal history of breast or ovarian cancer enrolled in Geisinger's MyCode genomic screening program with at least a one-year post-disclosure observation period. We identified 59 women for the study cohort out of 50,726 MyCode participants. We found no statistically significant differences in inpatient and outpatient utilization and average total costs between one-year pre- and one-year post-disclosure periods ($18,821 vs. $19,359, p = 0.76). During the first year post-disclosure, 49.2% of women had a genetic counseling visit, 45.8% had a mammography and 32.2% had an MRI. The uptake of mastectomy and oophorectomy was 3.5% and 11.8%, respectively, and 5% of patients received chemoprevention.

Clinical and Molecular Prevalence of Lipodystrophy in an Unascertained Large Clinical Care Cohort.

Lipodystrophies are a group of disorders characterized by absence or loss of adipose tissue and abnormal fat distribution, commonly accompanied by metabolic dysregulation. Although considered rare disorders, their prevalence in the general population is not well understood. We aimed to evaluate the clinical and genetic prevalence of lipodystrophy disorders in a large clinical care cohort. We interrogated the electronic health record (EHR) information of >1.3 million adults from the Geisinger Health System for lipodystrophy diagnostic codes. We estimate a clinical prevalence of disease of 1 in 20,000 individuals. We performed genetic analyses in individuals with available genomic data to identify variants associated with inherited lipodystrophies and examined their EHR for comorbidities associated with lipodystrophy. We identified 16 individuals carrying the p.R482Q pathogenic variant in LMNA associated with Dunnigan familial partial lipodystrophy. Four had a clinical diagnosis of lipodystrophy, whereas the remaining had no documented clinical diagnosis despite having accompanying metabolic abnormalities. We observed a lipodystrophy-associated variant carrier frequency of 1 in 3,082 individuals in our cohort with substantial burden of metabolic dysregulation. We estimate a genetic prevalence of disease of ∼1 in 7,000 in the general population. Partial lipodystrophy is an underdiagnosed condition. and its prevalence, as defined molecularly, is higher than previously reported. Genetically guided stratification of patients with common metabolic disorders, like diabetes and dyslipidemia, is an important step toward precision medicine.

Finding missed cases of familial hypercholesterolemia in health systems using machine learning.

Familial hypercholesterolemia (FH) is an underdiagnosed dominant genetic condition affecting approximately 0.4% of the population and has up to a 20-fold increased risk of coronary artery disease if untreated. Simple screening strategies have false positive rates greater than 95%. As part of the FH Foundation's FIND FH initiative, we developed a classifier to identify potential FH patients using electronic health record (EHR) data at Stanford Health Care. We trained a random forest classifier using data from known patients (n = 197) and matched non-cases (n = 6590). Our classifier obtained a positive predictive value (PPV) of 0.88 and sensitivity of 0.75 on a held-out test-set. We evaluated the accuracy of the classifier's predictions by chart review of 100 patients at risk of FH not included in the original dataset. The classifier correctly flagged 84% of patients at the highest probability threshold, with decreasing performance as the threshold lowers. In external validation on 466 FH patients (236 with genetically proven FH) and 5000 matched non-cases from the Geisinger Healthcare System our FH classifier achieved a PPV of 0.85. Our EHR-derived FH classifier is effective in finding candidate patients for further FH screening. Such machine learning guided strategies can lead to effective identification of the highest risk patients for enhanced management strategies.

Genomics-First Evaluation of Heart Disease Associated With Titin-Truncating Variants.

BACKGROUND: Truncating variants in the Titin gene (TTNtvs) are common in individuals with idiopathic dilated cardiomyopathy (DCM). However, a comprehensive genomics-first evaluation of the impact of TTNtvs in different clinical contexts, and the evaluation of modifiers such as genetic ancestry, has not been performed. METHODS: We reviewed whole exome sequence data for >71 000 individuals (61 040 from the Geisinger MyCode Community Health Initiative (2007 to present) and 10 273 from the PennMedicine BioBank (2013 to present) to identify anyone with TTNtvs. We further selected individuals with TTNtvs in exons highly expressed in the heart (proportion spliced in [PSI] >0.9). Using linked electronic health records, we evaluated associations of TTNtvs with diagnoses and quantitative echocardiographic measures, including subanalyses for individuals with and without DCM diagnoses. We also reviewed data from the Jackson Heart Study to validate specific analyses for individuals of African ancestry. RESULTS: Identified with a TTNtv in a highly expressed exon (hiPSI) were 1.2% individuals in PennMedicine BioBank and 0.6% at Geisinger. The presence of a hiPSI TTNtv was associated with increased odds of DCM in individuals of European ancestry (odds ratio [95% CI]: 18.7 [9.1-39.4] {PennMedicine BioBank} and 10.8 [7.0-16.0] {Geisinger}). hiPSI TTNtvs were not associated with DCM in individuals of African ancestry, despite a high DCM prevalence (odds ratio, 1.8 [0.2-13.7]; P=0.57). Among 244 individuals of European ancestry with DCM in PennMedicine BioBank, hiPSI TTNtv carriers had lower left ventricular ejection fraction (ß=-12%, P=3×10-7), and increased left ventricular diameter (ß=0.65 cm, P=9×10-3). In the Geisinger cohort, hiPSI TTNtv carriers without a cardiomyopathy diagnosis had more atrial fibrillation (odds ratio, 2.4 [1.6-3.6]) and heart failure (odds ratio, 3.8 [2.4-6.0]), and lower left ventricular ejection fraction (ß=-3.4%, P=1×10-7). CONCLUSIONS: Individuals of European ancestry with hiPSI TTNtv have an abnormal cardiac phenotype characterized by lower left ventricular ejection fraction, irrespective of the clinical manifestation of cardiomyopathy. Associations with arrhythmias, including atrial fibrillation, were observed even when controlling for cardiomyopathy diagnosis. In contrast, no association between hiPSI TTNtvs and DCM was discerned among individuals of African ancestry. Given these findings, clinical identification of hiPSI TTNtv carriers may alter clinical management strategies.

Obtaining a Genetic Family History Using Computer-Based Tools.

Family health history has long been known to be a powerful predictor of individual disease risk. It can be obtained prior to DNA sequencing in order to examine inheritance patterns, to be used as a proxy for genetic information, or as a tool to guide decision-making on the utility of diagnostic genetic testing. Increasingly, it is also being obtained retrospectively from sequenced individuals to examine familial disease penetrance and to identify at-risk relatives for cascade testing. The collection of adequate family history information to screen patients for disease risk and guide decision-making is a time-consuming process that is difficult to accomplish exclusively through discussion between patients and their providers. Engaging individuals and families in data collection and data entry has the potential to improve data accuracy through re-iterative review with family members and health care providers, and to empower patients in their healthcare. In addition, electronic datasets can be shared amongst relatives and stored in electronic health records or personal files, enabling portability of family history information. The U.S. Surgeon General, the Centers for Disease Control and Prevention (CDC), and others have developed tools for electronic family history collection to help families and providers obtain this useful information in an efficient manner. This unit describes the utility of the web-based My Family Health Portrait (https://familyhistory.hhs.gov) as the prototype for patient-entered family history. © 2018 by John Wiley & Sons, Inc.