Publications by authors named "Michael F Murray"

84 Publications

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

JAMA Netw Open 2021 02 1;4(2):e210112. Epub 2021 Feb 1.

Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland.

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.
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http://dx.doi.org/10.1001/jamanetworkopen.2021.0112DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7907958PMC
February 2021

Genetic screening for familial hypercholesterolemia identifies patients not meeting cholesterol treatment guidelines.

Coron Artery Dis 2020 Dec 23;Publish Ahead of Print. Epub 2020 Dec 23.

Department of Population Health Sciences, Phenomic Analytics and Clinical Data Core, Genomic Medicine Institute, Geisinger, Danville, Pennsylvania Department of Genetics, Yale School of Medicine, New Haven, Connecticut, USA.

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http://dx.doi.org/10.1097/MCA.0000000000000998DOI Listing
December 2020

Precision screening for familial hypercholesterolaemia: a machine learning study applied to electronic health encounter data.

Lancet Digit Health 2019 12 21;1(8):e393-e402. Epub 2019 Oct 21.

The Familial Hypercholesterolemia Foundation, Pasadena, CA, USA; Department of Genetics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA; Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA; Department of Pediatrics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.

Background: Cardiovascular outcomes for people with familial hypercholesterolaemia can be improved with diagnosis and medical management. However, 90% of individuals with familial hypercholesterolaemia remain undiagnosed in the USA. We aimed to accelerate early diagnosis and timely intervention for more than 1·3 million undiagnosed individuals with familial hypercholesterolaemia at high risk for early heart attacks and strokes by applying machine learning to large health-care encounter datasets.

Methods: We trained the FIND FH machine learning model using deidentified health-care encounter data, including procedure and diagnostic codes, prescriptions, and laboratory findings, from 939 clinically diagnosed individuals with familial hypercholesterolaemia (395 of whom had a molecular diagnosis) and 83 136 individuals presumed free of familial hypercholesterolaemia, sampled from four US institutions. The model was then applied to a national health-care encounter database (170 million individuals) and an integrated health-care delivery system dataset (174 000 individuals). Individuals used in model training and those evaluated by the model were required to have at least one cardiovascular disease risk factor (eg, hypertension, hypercholesterolaemia, or hyperlipidemia). A Health Insurance Portability and Accountability Act of 1996-compliant programme was developed to allow providers to receive identification of individuals likely to have familial hypercholesterolaemia in their practice.

Findings: Using a model with a measured precision (positive predictive value) of 0·85, recall (sensitivity) of 0·45, area under the precision-recall curve of 0·55, and area under the receiver operating characteristic curve of 0·89, we flagged 1 331 759 of 170 416 201 patients in the national database and 866 of 173 733 individuals in the health-care delivery system dataset as likely to have familial hypercholesterolaemia. Familial hypercholesterolaemia experts reviewed a sample of flagged individuals (45 from the national database and 103 from the health-care delivery system dataset) and applied clinical familial hypercholesterolaemia diagnostic criteria. Of those reviewed, 87% (95% Cl 73-100) in the national database and 77% (68-86) in the health-care delivery system dataset were categorised as having a high enough clinical suspicion of familial hypercholesterolaemia to warrant guideline-based clinical evaluation and treatment.

Interpretation: The FIND FH model successfully scans large, diverse, and disparate health-care encounter databases to identify individuals with familial hypercholesterolaemia.

Funding: The FH Foundation funded this study. Support was received from Amgen, Sanofi, and Regeneron.
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http://dx.doi.org/10.1016/S2589-7500(19)30150-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8086528PMC
December 2019

Bringing monogenic disease screening to the clinic.

Nat Med 2020 08;26(8):1172-1174

Yale-New Haven Health System, New Haven, CT, USA.

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http://dx.doi.org/10.1038/s41591-020-1017-yDOI Listing
August 2020

COVID-19 outcomes and the human genome.

Genet Med 2020 07 12;22(7):1175-1177. Epub 2020 May 12.

Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA.

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http://dx.doi.org/10.1038/s41436-020-0832-3DOI Listing
July 2020

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

J Pers Med 2020 Feb 3;10(1). Epub 2020 Feb 3.

Department of Health Policy and Behavioral Science, School of Public Health, Georgia State University, Atlanta, GA 30302, USA.

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) 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 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, = 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.
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http://dx.doi.org/10.3390/jpm10010007DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7151600PMC
February 2020

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

Diabetes 2020 02 13;69(2):249-258. Epub 2019 Dec 13.

Regeneron Genetics Center, Regeneron Pharmaceuticals, Inc., Tarrytown, NY.

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.
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http://dx.doi.org/10.2337/db19-0447DOI Listing
February 2020

DNA-Based Population Screening: Potential Suitability and Important Knowledge Gaps.

JAMA 2020 Jan;323(4):307-308

Office of Genomics and Precision Public Health, Centers for Disease Control and Prevention, Atlanta, Georgia.

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http://dx.doi.org/10.1001/jama.2019.18640DOI Listing
January 2020

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

NPJ Digit Med 2019 11;2:23. Epub 2019 Apr 11.

3Cardiovascular Medicine and Cardiovascular Institute, Stanford University, Stanford, CA USA.

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 ( = 197) and matched non-cases ( = 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.
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http://dx.doi.org/10.1038/s41746-019-0101-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6550268PMC
April 2019

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

Circulation 2019 07 20;140(1):42-54. Epub 2019 Jun 20.

Perelman School of Medicine, University of Pennsylvania, Philadelphia (S.M.D., M.G.L., D.B., R.J., R.L.K., L.L., C.M.-R., A. Babu, M.M., A.S., H.W., K.B.M., T.P.C., A.V., X.Z., M.D.R., D.J.R., Z.A.).

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 (β=-12%, P=3×10), and increased left ventricular diameter (β=0.65 cm, P=9×10). 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 (β=-3.4%, P=1×10).

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.
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http://dx.doi.org/10.1161/CIRCULATIONAHA.119.039573DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6602806PMC
July 2019

Exome Sequencing-Based Screening for BRCA1/2 Expected Pathogenic Variants Among Adult Biobank Participants.

JAMA Netw Open 2018 09 7;1(5):e182140. Epub 2018 Sep 7.

Genomic Medicine Institute, Geisinger, Danville, Pennsylvania.

Importance: Detection of disease-associated variants in the BRCA1 and BRCA2 (BRCA1/2) genes allows for cancer prevention and early diagnosis in high-risk individuals.

Objectives: To identify pathogenic and likely pathogenic (P/LP) BRCA1/2 variants in an unselected research cohort, and to characterize the features associated with P/LP variants.

Design, Setting, And Participants: This is a cross-sectional study of adult volunteers (n = 50 726) who underwent exome sequencing at a single health care system (Geisinger Health System, Danville, Pennsylvania) from January 1, 2014, to March 1, 2016. Participants are part of the DiscovEHR cohort and were identified through the Geisinger MyCode Community Health Initiative. They consented to a research protocol that included sequencing and return of actionable test results. Clinical data from electronic health records and clinical visits were correlated with variants. Comparisons were made between those with (cases) and those without (controls) P/LP variants in BRCA1/2.

Main Outcomes: Prevalence of P/LP BRCA1/2 variants in cohort, proportion of variant carriers not previously ascertained through clinical testing, and personal and family history of relevant cancers among BRCA1/2 variant carriers and noncarriers.

Results: Of the 50 726 health system patients who underwent exome sequencing, 50 459 (99.5%) had no expected pathogenic BRCA1/2 variants and 267 (0.5%) were BRCA1/2 carriers. Of the 267 cases (148 [55.4%] were women and 119 [44.6%] were men with a mean [range] age of 58.9 [23-90] years), 183 (68.5%) received clinically confirmed results in their electronic health record. Among the 267 participants with P/LP BRCA1/2 variants, 219 (82.0%) had no prior clinical testing, 95 (35.6%) had BRCA1 variants, and 172 (64.4%) had BRCA2 variants. Syndromic cancer diagnoses were present in 11 (47.8%) of the 23 deceased BRCA1/2 carriers and in 56 (20.9%) of all 267 BRCA1/2 carriers. Among women, 31 (20.9%) of 148 variant carriers had a personal history of breast cancer, compared with 1554 (5.2%) of 29 880 noncarriers (odds ratio [OR], 5.95; 95% CI, 3.88-9.13; P < .001). Ovarian cancer history was present in 15 (10.1%) of 148 variant carriers and in 195 (0.6%) of 29 880 variant noncarriers (OR, 18.30; 95% CI, 10.48-31.4; P < .001). Among 89 BRCA1/2 carriers without prior testing but with comprehensive personal and family history data, 44 (49.4%) did not meet published guidelines for clinical testing.

Conclusions And Relevance: This study found that compared with previous clinical care, exome sequencing-based screening identified 5 times as many individuals with P/LP BRCA1/2 variants. These findings suggest that genomic screening may identify BRCA1/2-associated cancer risk that might otherwise remain undetected within health care systems and may provide opportunities to reduce morbidity and mortality in patients.
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http://dx.doi.org/10.1001/jamanetworkopen.2018.2140DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6324494PMC
September 2018

Association Between Titin Loss-of-Function Variants and Early-Onset Atrial Fibrillation.

JAMA 2018 12;320(22):2354-2364

Department of Molecular and Functional Genomics, Geisinger, Danville, Pennsylvania.

Importance: Atrial fibrillation (AF) is the most common arrhythmia affecting 1% of the population. Young individuals with AF have a strong genetic association with the disease, but the mechanisms remain incompletely understood.

Objective: To perform large-scale whole-genome sequencing to identify genetic variants related to AF.

Design, Setting, And Participants: The National Heart, Lung, and Blood Institute's Trans-Omics for Precision Medicine Program includes longitudinal and cohort studies that underwent high-depth whole-genome sequencing between 2014 and 2017 in 18 526 individuals from the United States, Mexico, Puerto Rico, Costa Rica, Barbados, and Samoa. This case-control study included 2781 patients with early-onset AF from 9 studies and identified 4959 controls of European ancestry from the remaining participants. Results were replicated in the UK Biobank (346 546 participants) and the MyCode Study (42 782 participants).

Exposures: Loss-of-function (LOF) variants in genes at AF loci and common genetic variation across the whole genome.

Main Outcomes And Measures: Early-onset AF (defined as AF onset in persons <66 years of age). Due to multiple testing, the significance threshold for the rare variant analysis was P = 4.55 × 10-3.

Results: Among 2781 participants with early-onset AF (the case group), 72.1% were men, and the mean (SD) age of AF onset was 48.7 (10.2) years. Participants underwent whole-genome sequencing at a mean depth of 37.8 fold and mean genome coverage of 99.1%. At least 1 LOF variant in TTN, the gene encoding the sarcomeric protein titin, was present in 2.1% of case participants compared with 1.1% in control participants (odds ratio [OR], 1.76 [95% CI, 1.04-2.97]). The proportion of individuals with early-onset AF who carried a LOF variant in TTN increased with an earlier age of AF onset (P value for trend, 4.92 × 10-4), and 6.5% of individuals with AF onset prior to age 30 carried a TTN LOF variant (OR, 5.94 [95% CI, 2.64-13.35]; P = 1.65 × 10-5). The association between TTN LOF variants and AF was replicated in an independent study of 1582 patients with early-onset AF (cases) and 41 200 control participants (OR, 2.16 [95% CI, 1.19-3.92]; P = .01).

Conclusions And Relevance: In a case-control study, there was a statistically significant association between an LOF variant in the TTN gene and early-onset AF, with the variant present in a small percentage of participants with early-onset AF (the case group). Further research is necessary to understand whether this is a causal relationship.
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http://dx.doi.org/10.1001/jama.2018.18179DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6436530PMC
December 2018

Trajectory of exonic variant discovery in a large clinical population: implications for variant curation.

Genet Med 2019 06 19;21(6):1417-1424. Epub 2018 Nov 19.

Geisinger Clinic, Geisinger Health System, Danville, PA, USA.

Purpose: Precision health initiatives and reduced sequencing costs are driving large-scale human genome analyses. Genetic variant curation is a bottleneck in clinical applications. The burden of variant curation can be high for newly discovered variants because they are less likely to have undergone previous clinical annotation; the rate of discovery of genetic variants in large clinical populations has not been empirically determined.

Methods: We determined the rate of accrual of unique sequence variants in 90,000 exome sequences. Separate analyses were done for 17,267 autosomal genes and a subset of 74 actionable genes; the effect of relatedness in the cohort was also determined.

Results: Variant discovery showed a nonlinear growth pattern. The rate of unique variant accrual decreased as the database size increased; by 90,000 exomes 97% of all projected coding and splicing variants had been observed. Variants in 74 actionable genes showed a similar pattern. Family relatedness slightly reduced the rate of discovery of unique variants.

Conclusion: The heaviest burden of interpretation for genetic variants occurs early and diminishes as the database size increases. Our data provide a framework for scaling pathogenic genetic variant discovery and curation, a critical element of patient care in the era of precision health.
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http://dx.doi.org/10.1038/s41436-018-0353-5DOI Listing
June 2019

Healthcare Utilization and Patients' Perspectives After Receiving a Positive Genetic Test for Familial Hypercholesterolemia.

Circ Genom Precis Med 2018 08;11(8):e002146

Genomic Medicine Institute (A.K.R., K.M., L.B., A.L., T.C., D.K., A.C.S., M.F.M.).

Background: The MyCode Community Health Initiative (MyCode) is returning actionable results from whole exome sequencing. Familial hypercholesterolemia (FH) is an inherited condition characterized by premature cardiovascular disease.

Methods: We used multiple methods to assess care in 28 MyCode participants who received FH results. Chart reviews were conducted on 23 individuals in the sample and 7 individuals participated semistructured interviews.

Results: Chart reviews for 23 individuals with a Geisinger primary care provider found that 4 individuals (17% of 23) were at LDL-C (low-density lipoprotein cholesterol) goal (of either LDL-C <100 mg/dL for primary prevention and LDL-C <70 mg/dL for secondary prevention) and 17 individuals (74% of 23) were prescribed lipid-lowering therapy before genetic result disclosure. After disclosure of the genetic test result, 5 individuals (22% of 23) met their LDL-C goal and 18 individuals (78% of 23) were prescribed lipid-lowering therapy. Follow-up care about this result was not documented for 4 individuals (17% of 23). Changes to intensity of medication management were made for 8 individuals (47% of 17 individuals previously prescribed lipid-lowering therapy). Interviewed individuals (n=7) were not surprised by their result as all knew they had high cholesterol; however, individuals did not seem to discern FH as a separate condition from their high cholesterol.

Conclusions: Among individuals receiving genetic diagnosis of FH, >25% had no changes to lipid-lowering therapy, despite not being at LDL-C goal and learning their high cholesterol is related to a genetic condition requiring more aggressive treatment. Individuals and clinicians may have an inadequate understanding of FH as a distinct condition requiring enhanced medical management.
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http://dx.doi.org/10.1161/CIRCGEN.118.002146DOI Listing
August 2018

Obtaining a Genetic Family History Using Computer-Based Tools.

Curr Protoc Hum Genet 2019 01 18;100(1):e72. Epub 2018 Oct 18.

Genetics Department, Yale University, New Haven, Connecticut.

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.
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http://dx.doi.org/10.1002/cphg.72DOI Listing
January 2019

A Model for Genome-First Care: Returning Secondary Genomic Findings to Participants and Their Healthcare Providers in a Large Research Cohort.

Am J Hum Genet 2018 09 9;103(3):328-337. Epub 2018 Aug 9.

Geisinger, Danville, PA 17822, USA; Yale School of Medicine, New Haven, CT 06510, USA. Electronic address:

There is growing interest in communicating clinically relevant DNA sequence findings to research participants who join projects with a primary research goal other than the clinical return of such results. Since Geisinger's MyCode Community Health Initiative (MyCode) was launched in 2007, more than 200,000 participants have been broadly consented for discovery research. In 2013 the MyCode consent was amended to include a secondary analysis of research genomic sequences that allows for delivery of clinical results. Since May 2015, pathogenic and likely pathogenic variants from a set list of genes associated with monogenic conditions have prompted "genome-first" clinical encounters. The encounters are described as genome-first because they are identified independent of any clinical parameters. This article (1) details our process for generating clinical results from research data, delivering results to participants and providers, facilitating condition-specific clinical evaluations, and promoting cascade testing of relatives, and (2) summarizes early results and participant uptake. We report on 542 participants who had results uploaded to the electronic health record as of February 1, 2018 and 291 unique clinical providers notified with one or more participant results. Of these 542 participants, 515 (95.0%) were reached to disclose their results and 27 (5.0%) were lost to follow-up. We describe an exportable model for delivery of clinical care through secondary use of research data. In addition, subject and provider participation data from the initial phase of these efforts can inform other institutions planning similar programs.
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http://dx.doi.org/10.1016/j.ajhg.2018.07.009DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6128218PMC
September 2018

The Path to Routine Genomic Screening in Health Care.

Authors:
Michael F Murray

Ann Intern Med 2018 09 31;169(6):407-408. Epub 2018 Jul 31.

Yale School of Medicine, New Haven, Connecticut (M.F.M.).

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http://dx.doi.org/10.7326/M18-1722DOI Listing
September 2018

A collaborative translational research framework for evaluating and implementing the appropriate use of human genome sequencing to improve health.

PLoS Med 2018 08 2;15(8):e1002631. Epub 2018 Aug 2.

Center for Translation Research and Implementation Science, National Heart, Lung, and Blood Institute, NIH, Bethesda, Maryland, United States of America.

In a Policy Forum, Muin Khoury and colleagues discuss research on the clinical application of genome sequencing data.
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http://dx.doi.org/10.1371/journal.pmed.1002631DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6071954PMC
August 2018

Whole-Exome Sequencing in Adults With Chronic Kidney Disease.

Ann Intern Med 2018 07;169(2):131-132

Geisinger Health System, Danville, Pennsylvania (A.R.C., J.Z.L., K.H., T.M., M.F.M.).

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http://dx.doi.org/10.7326/L18-0206DOI Listing
July 2018

Genetic inactivation of ANGPTL4 improves glucose homeostasis and is associated with reduced risk of diabetes.

Nat Commun 2018 06 13;9(1):2252. Epub 2018 Jun 13.

Geisinger, Danville, 17822, PA, USA.

Angiopoietin-like 4 (ANGPTL4) is an endogenous inhibitor of lipoprotein lipase that modulates lipid levels, coronary atherosclerosis risk, and nutrient partitioning. We hypothesize that loss of ANGPTL4 function might improve glucose homeostasis and decrease risk of type 2 diabetes (T2D). We investigate protein-altering variants in ANGPTL4 among 58,124 participants in the DiscovEHR human genetics study, with follow-up studies in 82,766 T2D cases and 498,761 controls. Carriers of p.E40K, a variant that abolishes ANGPTL4 ability to inhibit lipoprotein lipase, have lower odds of T2D (odds ratio 0.89, 95% confidence interval 0.85-0.92, p = 6.3 × 10), lower fasting glucose, and greater insulin sensitivity. Predicted loss-of-function variants are associated with lower odds of T2D among 32,015 cases and 84,006 controls (odds ratio 0.71, 95% confidence interval 0.49-0.99, p = 0.041). Functional studies in Angptl4-deficient mice confirm improved insulin sensitivity and glucose homeostasis. In conclusion, genetic inactivation of ANGPTL4 is associated with improved glucose homeostasis and reduced risk of T2D.
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http://dx.doi.org/10.1038/s41467-018-04611-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5997992PMC
June 2018

Genome-first findings require precision phenotyping.

Genet Med 2018 12 8;20(12):1510-1511. Epub 2018 Jun 8.

Department of Genetics, Yale School of Medicine, New Haven, Connecticut, USA.

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http://dx.doi.org/10.1038/s41436-018-0026-4DOI Listing
December 2018

Functional Invalidation of Putative Sudden Infant Death Syndrome-Associated Variants in the -Encoded Kv11.1 Channel.

Circ Arrhythm Electrophysiol 2018 05;11(5):e005859

Department of Physiology, Cardiovascular Research Center, Center for Muscle Biology, University of Kentucky, Lexington (J.L.S., A.R.H., D.E.B., B.P.D.).

Background: Heterologous functional validation studies of putative long-QT syndrome subtype 2-associated variants clarify their pathological potential and identify disease mechanism(s) for most variants studied. The purpose of this study is to clarify the pathological potential for rare nonsynonymous variants seemingly associated with sudden infant death syndrome.

Methods: Genetic testing of 292 sudden infant death syndrome cases identified 9 variants: E90K, R181Q, A190T, G294V, R791W, P967L, R1005W, R1047L, and Q1068R. Previous studies show R181Q-, P967L-, and R1047L-Kv11.1 channels function similar to wild-type Kv11.1 channels, whereas Q1068R-Kv11.1 channels accelerate inactivation gating. We studied the biochemical and biophysical properties for E90K-, G294V-, R791W-, and R1005W-Kv11.1 channels expressed in human embryonic kidney 293 cells; examined the electronic health records of patients who were genotype positive for the sudden infant death syndrome-linked variants; and simulated their functional impact using computational models of the human ventricular action potential.

Results: Western blot and voltage-clamping analyses of cells expressing E90K-, G294V-, R791W-, and R1005W-Kv11.1 channels demonstrated these variants express and generate peak Kv11.1 current levels similar to cells expressing wild-type-Kv11.1 channels, but R791W- and R1005W-Kv11.1 channels accelerated deactivation and activation gating, respectively. Electronic health records of patients with the sudden infant death syndrome-linked variants showed that the patients had median heart rate-corrected QT intervals <480 ms and none had been diagnosed with long-QT syndrome or experienced cardiac arrest. Simulating the impact of dysfunctional gating variants predicted that they have little impact on ventricular action potential duration.

Conclusions: We conclude that these rare Kv11.1 missense variants are not long-QT syndrome subtype 2-causative variants and therefore do not represent the pathogenic substrate for sudden infant death syndrome in the variant-positive infants.
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http://dx.doi.org/10.1161/CIRCEP.117.005859DOI Listing
May 2018

Patient-Centered Precision Health In A Learning Health Care System: Geisinger's Genomic Medicine Experience.

Health Aff (Millwood) 2018 05;37(5):757-764

David H. Ledbetter is executive vice president and chief scientific officer, Geisinger.

Health care delivery is increasingly influenced by the emerging concepts of precision health and the learning health care system. Although not synonymous with precision health, genomics is a key enabler of individualized care. Delivering patient-centered, genomics-informed care based on individual-level data in the current national landscape of health care delivery is a daunting challenge. Problems to overcome include data generation, analysis, storage, and transfer; knowledge management and representation for patients and providers at the point of care; process management; and outcomes definition, collection, and analysis. Development, testing, and implementation of a genomics-informed program requires multidisciplinary collaboration and building the concepts of precision health into a multilevel implementation framework. Using the principles of a learning health care system provides a promising solution. This article describes the implementation of population-based genomic medicine in an integrated learning health care system-a working example of a precision health program.
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http://dx.doi.org/10.1377/hlthaff.2017.1557DOI Listing
May 2018

Profiling and Leveraging Relatedness in a Precision Medicine Cohort of 92,455 Exomes.

Am J Hum Genet 2018 05;102(5):874-889

Regeneron Genetics Center, Regeneron Pharmaceuticals, Tarrytown, NY 10591, USA. Electronic address:

Large-scale human genetics studies are ascertaining increasing proportions of populations as they continue growing in both number and scale. As a result, the amount of cryptic relatedness within these study cohorts is growing rapidly and has significant implications on downstream analyses. We demonstrate this growth empirically among the first 92,455 exomes from the DiscovEHR cohort and, via a custom simulation framework we developed called SimProgeny, show that these measures are in line with expectations given the underlying population and ascertainment approach. For example, within DiscovEHR we identified ∼66,000 close (first- and second-degree) relationships, involving 55.6% of study participants. Our simulation results project that >70% of the cohort will be involved in these close relationships, given that DiscovEHR scales to 250,000 recruited individuals. We reconstructed 12,574 pedigrees by using these relationships (including 2,192 nuclear families) and leveraged them for multiple applications. The pedigrees substantially improved the phasing accuracy of 20,947 rare, deleterious compound heterozygous mutations. Reconstructed nuclear families were critical for identifying 3,415 de novo mutations in ∼1,783 genes. Finally, we demonstrate the segregation of known and suspected disease-causing mutations, including a tandem duplication that occurs in LDLR and causes familial hypercholesterolemia, through reconstructed pedigrees. In summary, this work highlights the prevalence of cryptic relatedness expected among large healthcare population-genomic studies and demonstrates several analyses that are uniquely enabled by large amounts of cryptic relatedness.
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http://dx.doi.org/10.1016/j.ajhg.2018.03.012DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5986700PMC
May 2018

Hereditary cancer genes are highly susceptible to splicing mutations.

PLoS Genet 2018 03 5;14(3):e1007231. Epub 2018 Mar 5.

Molecular and Cellular Biology and Biochemistry, Brown University, Providence, Rhode Island, United States of America.

Substitutions that disrupt pre-mRNA splicing are a common cause of genetic disease. On average, 13.4% of all hereditary disease alleles are classified as splicing mutations mapping to the canonical 5' and 3' splice sites. However, splicing mutations present in exons and deeper intronic positions are vastly underreported. A recent re-analysis of coding mutations in exon 10 of the Lynch Syndrome gene, MLH1, revealed an extremely high rate (77%) of mutations that lead to defective splicing. This finding is confirmed by extending the sampling to five other exons in the MLH1 gene. Further analysis suggests a more general phenomenon of defective splicing driving Lynch Syndrome. Of the 36 mutations tested, 11 disrupted splicing. Furthermore, analyzing past reports suggest that MLH1 mutations in canonical splice sites also occupy a much higher fraction (36%) of total mutations than expected. When performing a comprehensive analysis of splicing mutations in human disease genes, we found that three main causal genes of Lynch Syndrome, MLH1, MSH2, and PMS2, belonged to a class of 86 disease genes which are enriched for splicing mutations. Other cancer genes were also enriched in the 86 susceptible genes. The enrichment of splicing mutations in hereditary cancers strongly argues for additional priority in interpreting clinical sequencing data in relation to cancer and splicing.
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http://dx.doi.org/10.1371/journal.pgen.1007231DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5854443PMC
March 2018