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Science | 2016

Distribution and clinical impact of functional variants in 50,726 whole-exome sequences from the DiscovEHR study

Frederick E. Dewey; Michael F. Murray; John D. Overton; Lukas Habegger; Joseph B. Leader; Samantha N. Fetterolf; Colm O’Dushlaine; Cristopher V. Van Hout; Jeffrey Staples; Claudia Gonzaga-Jauregui; Raghu Metpally; Sarah A. Pendergrass; Monica A. Giovanni; H. Lester Kirchner; Suganthi Balasubramanian; Noura S. Abul-Husn; Dustin N. Hartzel; Daniel R. Lavage; Korey A. Kost; Jonathan S. Packer; Alexander E. Lopez; John Penn; Semanti Mukherjee; Nehal Gosalia; Manoj Kanagaraj; Alexander H. Li; Lyndon J. Mitnaul; Lance J. Adams; Thomas N. Person; Kavita Praveen

Unleashing the power of precision medicine Precision medicine promises the ability to identify risks and treat patients on the basis of pathogenic genetic variation. Two studies combined exome sequencing results for over 50,000 people with their electronic health records. Dewey et al. found that ∼3.5% of individuals in their cohort had clinically actionable genetic variants. Many of these variants affected blood lipid levels that could influence cardiovascular health. Abul-Husn et al. extended these findings to investigate the genetics and treatment of familial hypercholesterolemia, a risk factor for cardiovascular disease, within their patient pool. Genetic screening helped identify at-risk patients who could benefit from increased treatment. Science, this issue p. 10.1126/science.aaf6814, p. 10.1126/science.aaf7000 More than 50,000 exomes, coupled with electronic health records, inform on medically relevant genetic variants. INTRODUCTION Large-scale genetic studies of integrated health care populations, with phenotypic data captured natively in the documentation of clinical care, have the potential to unveil genetic associations that point the way to new biology and therapeutic targets. This setting also represents an ideal test bed for the implementation of genomics in routine clinical care in service of precision medicine. RATIONALE The DiscovEHR collaboration between the Regeneron Genetics Center and Geisinger Health System aims to catalyze genomic discovery and precision medicine by coupling high-throughput exome sequencing to longitudinal electronic health records (EHRs) of participants in Geisinger’s MyCode Community Health Initiative. Here, we describe initial insights from whole-exome sequencing of 50,726 adult participants of predominantly European ancestry using clinical phenotypes derived from EHRs. RESULTS The median duration of EHR data associated with sequenced participants was 14 years, with a median of 87 clinical encounters, 687 laboratory tests, and seven procedures per participant. Forty-eight percent of sequenced individuals had one or more first- or second-degree relatives in the sample, and genome-wide autozygosity was similar to other outbred European populations. We found ~4.2 million single-nucleotide variants and insertion/deletion events, of which ~176,000 are predicted to result in loss of gene function (LoF). The overwhelming majority of these genetic variants occurred at a minor allele frequency of ≤1%, and more than half were singletons. Each participant harbored a median of 21 rare predicted LoFs. At this sample size, ~92% of sequenced genes, including genes that encode existing drug targets or confer risk for highly penetrant genetic diseases, harbor rare heterozygous predicted LoF variants. About 7% of sequenced genes contained rare homozygous predicted LoF variants in at least one individual. Linking these data to EHR-derived laboratory phenotypes revealed consequences of partial or complete LoF in humans. Among these were previously unidentified associations between predicted LoFs in CSF2RB and basophil and eosinophil counts, and EGLN1-associated erythrocytosis segregating in genetically identified family networks. Using predicted LoFs as a model for drug target antagonism, we found associations supporting the majority of therapeutic targets for lipid lowering. To highlight the opportunity for genotype-phenotype association discovery, we performed exome-wide association analyses of EHR-derived lipid values, newly implicating rare predicted LoFs, and deleterious missense variants in G6PC in association with triglyceride levels. In a survey of 76 clinically actionable disease-associated genes, we estimated that 3.5% of individuals harbor pathogenic or likely pathogenic variants that meet criteria for clinical action. Review of the EHR uncovered findings associated with the monogenic condition in ~65% of pathogenic variant carriers’ medical records. CONCLUSION The findings reported here demonstrate the value of large-scale sequencing in an integrated health system population, add to the knowledge base regarding the phenotypic consequences of human genetic variation, and illustrate the challenges and promise of genomic medicine implementation. DiscovEHR provides a blueprint for large-scale precision medicine initiatives and genomics-guided therapeutic target discovery. Therapeutic target validation and genomic medicine in DiscovEHR. (A) Associations between predicted LoF variants in lipid drug target genes and lipid levels. Boxes correspond to effect size, given as the absolute value of effect, in SD units; whiskers denote 95% confidence intervals for effect. The size of the box is proportional to the logarithm (base 10) of predicted LoF carriers. (B and C) Prevalence and expressivity of clinically actionable genetic variants in 76 disease genes, according to EHR data. G76, Geisinger-76. The DiscovEHR collaboration between the Regeneron Genetics Center and Geisinger Health System couples high-throughput sequencing to an integrated health care system using longitudinal electronic health records (EHRs). We sequenced the exomes of 50,726 adult participants in the DiscovEHR study to identify ~4.2 million rare single-nucleotide variants and insertion/deletion events, of which ~176,000 are predicted to result in a loss of gene function. Linking these data to EHR-derived clinical phenotypes, we find clinical associations supporting therapeutic targets, including genes encoding drug targets for lipid lowering, and identify previously unidentified rare alleles associated with lipid levels and other blood level traits. About 3.5% of individuals harbor deleterious variants in 76 clinically actionable genes. The DiscovEHR data set provides a blueprint for large-scale precision medicine initiatives and genomics-guided therapeutic discovery.


Science | 2016

Genetic identification of familial hypercholesterolemia within a single U.S. health care system

Noura S. Abul-Husn; Kandamurugu Manickam; Laney K. Jones; Eric A. Wright; Dustin N. Hartzel; Claudia Gonzaga-Jauregui; Colm O’Dushlaine; Joseph B. Leader; H. Lester Kirchner; D’Andra M. Lindbuchler; Marci L Barr; Monica A. Giovanni; Marylyn D. Ritchie; John D. Overton; Jeffrey G. Reid; Raghu Metpally; Amr H. Wardeh; Ingrid B. Borecki; George D. Yancopoulos; Aris Baras; Alan R. Shuldiner; Omri Gottesman; David H. Ledbetter; David J. Carey; Frederick E. Dewey; Michael F. Murray

Unleashing the power of precision medicine Precision medicine promises the ability to identify risks and treat patients on the basis of pathogenic genetic variation. Two studies combined exome sequencing results for over 50,000 people with their electronic health records. Dewey et al. found that ∼3.5% of individuals in their cohort had clinically actionable genetic variants. Many of these variants affected blood lipid levels that could influence cardiovascular health. Abul-Husn et al. extended these findings to investigate the genetics and treatment of familial hypercholesterolemia, a risk factor for cardiovascular disease, within their patient pool. Genetic screening helped identify at-risk patients who could benefit from increased treatment. Science, this issue p. 10.1126/science.aaf6814, p. 10.1126/science.aaf7000 Genomic screening can prompt the diagnosis of familial hypercholesterolemia patients, the majority of whom are receiving inadequate lipid-lowering therapy. INTRODUCTION Familial hypercholesterolemia (FH) is a public health genomics priority but remains underdiagnosed and undertreated despite widespread cholesterol screening. This represents a missed opportunity to prevent FH-associated cardiovascular morbidity and mortality. Pathogenic variants in three genes (LDLR, APOB, and PCSK9) account for the majority of FH cases. We assessed the prevalence and clinical impact of FH-associated genomic variants in 50,726 individuals from the MyCode Community Health Initiative at Geisinger Health System who underwent exome sequencing as part of the DiscovEHR human genetics collaboration with the Regeneron Genetics Center. RATIONALE Genetic testing for FH is uncommon in clinical practice in the United States, and the prevalence of FH variants in U.S. populations has not been well established. We sought to evaluate FH prevalence in a large integrated U.S. health care system using genomic sequencing and electronic health record (EHR) data. We determined the impact of FH variants on low-density lipoprotein cholesterol (LDL-C) levels and coronary artery disease (CAD) risk. We assessed the likelihood of FH variant carriers achieving a presequencing EHR-based FH diagnosis according to established clinical diagnostic criteria. Finally, we examined the rates of statin medication use and outcomes in FH variant carriers. RESULTS Thirty-five known and predicted pathogenic variants in LDLR, APOB, and PCSK9 were identified in 229 individuals. The estimated FH prevalence was 1:256 in unselected participants and 1:118 in participants ascertained via the cardiac catheterization laboratory. FH variants were found in only 2.5% of individuals with severe hypercholesterolemia (maximum EHR-documented LDL-C ≥ 190 mg/dl) in the cohort, and a maximum LDL-C of ≥190 mg/dl was absent in 45% of FH variant carriers. Overall, FH variant carriers had 69 ± 3 mg/dl greater maximum LDL-C than sequenced noncarriers (P = 1.8 × 10−20) and had significantly increased odds of general and premature CAD [odds ratio (OR), 2.6 (P = 4.3 × 10−11) and 3.7 (P = 5.5 × 10−14), respectively]. The increased odds of general and premature CAD were most pronounced in carriers of LDLR predicted loss-of-function variants [OR, 5.5 (P = 7.7 × 10−13) and 10.3 (P = 9.8 × 10−19), respectively]. Fourteen FH variant carriers were deceased; chart review revealed that none of these individuals had a clinical diagnosis of FH. Before genetic testing, only 15% of FH variant carriers had an ICD-10 (International Classification of Diseases, 10th revision) diagnosis code for pure hypercholesterolemia or had been seen in a lipid clinic, suggesting that few had been previously diagnosed with FH. Retrospectively applying Dutch Lipid Clinic Network diagnostic criteria to EHR data, we found presequencing criteria supporting a probable or definite clinical diagnosis of FH in 24% of FH variant carriers, highlighting the limitations of using existing clinical criteria for EHR-based screening in the absence of genetic testing. Active statin use was identified in 58% and high-intensity statin use in 37% of FH variant carriers. Only 46% of carriers currently on statin therapy had a most recent LDL-C level below 100 mg/dl compared to 77% of noncarriers. CONCLUSION In summary, we show that large-scale genomic screening in patients with longitudinal EHR data has the ability to detect FH, uncover and characterize novel pathogenic variants, determine disease prevalence, and enhance overall knowledge of clinical impact and outcomes. The 1:256 prevalence of FH variants in this predominantly European-American cohort is in line with prevalence estimates from recent work in European cohorts. Our findings highlight the undertreatment of FH variant carriers and demonstrate a potential clinical benefit for large-scale sequencing initiatives in service of precision medicine. Prevalence and clinical impact of FH variants in a large U.S. clinical care cohort. (A) Distribution of 229 heterozygous carriers of an FH variant in the DiscovEHR cohort by FH gene. (B) Prevalence of an FH variant in the DiscovEHR cohort and according to recruitment site


JAMA | 2017

Association of Rare and Common Variation in the Lipoprotein Lipase Gene With Coronary Artery Disease.

Amit Khera; Hong-Hee Won; Gina M. Peloso; Colm O'Dushlaine; Dajiang J. Liu; Nathan O. Stitziel; Pradeep Natarajan; Akihiro Nomura; Connor A. Emdin; Namrata Gupta; Ingrid B. Borecki; Rosanna Asselta; Stefano Duga; Piera Angelica Merlini; Adolfo Correa; Thorsten Kessler; James G. Wilson; Matthew J. Bown; Alistair S. Hall; Peter S. Braund; David J. Carey; Michael F. Murray; H. Lester Kirchner; Joseph B. Leader; Daniel R. Lavage; J. Neil Manus; Dustin N. Hartzel; Nilesh J. Samani; Heribert Schunkert; Jaume Marrugat

Importance The activity of lipoprotein lipase (LPL) is the rate-determining step in clearing triglyceride-rich lipoproteins from the circulation. Mutations that damage the LPL gene (LPL) lead to lifelong deficiency in enzymatic activity and can provide insight into the relationship of LPL to human disease. Objective To determine whether rare and/or common variants in LPL are associated with early-onset coronary artery disease (CAD). Design, Setting, and Participants In a cross-sectional study, LPL was sequenced in 10 CAD case-control cohorts of the multinational Myocardial Infarction Genetics Consortium and a nested CAD case-control cohort of the Geisinger Health System DiscovEHR cohort between 2010 and 2015. Common variants were genotyped in up to 305 699 individuals of the Global Lipids Genetics Consortium and up to 120 600 individuals of the CARDIoGRAM Exome Consortium between 2012 and 2014. Study-specific estimates were pooled via meta-analysis. Exposures Rare damaging mutations in LPL included loss-of-function variants and missense variants annotated as pathogenic in a human genetics database or predicted to be damaging by computer prediction algorithms trained to identify mutations that impair protein function. Common variants in the LPL gene region included those independently associated with circulating triglyceride levels. Main Outcomes and Measures Circulating lipid levels and CAD. Results Among 46 891 individuals with LPL gene sequencing data available, the mean (SD) age was 50 (12.6) years and 51% were female. A total of 188 participants (0.40%; 95% CI, 0.35%-0.46%) carried a damaging mutation in LPL, including 105 of 32 646 control participants (0.32%) and 83 of 14 245 participants with early-onset CAD (0.58%). Compared with 46 703 noncarriers, the 188 heterozygous carriers of an LPL damaging mutation displayed higher plasma triglyceride levels (19.6 mg/dL; 95% CI, 4.6-34.6 mg/dL) and higher odds of CAD (odds ratio = 1.84; 95% CI, 1.35-2.51; P < .001). An analysis of 6 common LPL variants resulted in an odds ratio for CAD of 1.51 (95% CI, 1.39-1.64; P = 1.1 × 10−22) per 1-SD increase in triglycerides. Conclusions and Relevance The presence of rare damaging mutations in LPL was significantly associated with higher triglyceride levels and presence of coronary artery disease. However, further research is needed to assess whether there are causal mechanisms by which heterozygous lipoprotein lipase deficiency could lead to coronary artery disease.


Scientific Reports | 2018

Rare variants in drug target genes contributing to complex diseases, phenome-wide

Shefali Setia Verma; Navya Josyula; Anurag Verma; Xinyuan Zhang; Yogasudha Veturi; Frederick E. Dewey; Dustin N. Hartzel; Daniel R. Lavage; Joe Leader; Marylyn D. Ritchie; Sarah A. Pendergrass

The DrugBank database consists of ~800 genes that are well characterized drug targets. This list of genes is a useful resource for association testing. For example, loss of function (LOF) genetic variation has the potential to mimic the effect of drugs, and high impact variation in these genes can impact downstream traits. Identifying novel associations between genetic variation in these genes and a range of diseases can also uncover new uses for the drugs that target these genes. Phenome Wide Association Studies (PheWAS) have been successful in identifying genetic associations across hundreds of thousands of diseases. We have conducted a novel gene based PheWAS to test the effect of rare variants in DrugBank genes, evaluating associations between these genes and more than 500 quantitative and dichotomous phenotypes. We used whole exome sequencing data from 38,568 samples in Geisinger MyCode Community Health Initiative. We evaluated the results of this study when binning rare variants using various filters based on potential functional impact. We identified multiple novel associations, and the majority of the significant associations were driven by functionally annotated variation. Overall, this study provides a sweeping exploration of rare variant associations within functionally relevant genes across a wide range of diagnoses.


Scientific Reports | 2018

Author Correction: Rare variants in drug target genes contributing to complex diseases, phenome-wide

Shefali Setia Verma; Navya Josyula; Anurag Verma; Xinyuan Zhang; Yogasudha Veturi; Frederick E. Dewey; Dustin N. Hartzel; Daniel R. Lavage; Joe Leader; Marylyn D. Ritchie; Sarah A. Pendergrass

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has not been fixed in the paper.


JAMA Network Open | 2018

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

Kandamurugu Manickam; Adam H. Buchanan; Marci Schwartz; Miranda L. G. Hallquist; Janet Williams; Alanna Kulchak Rahm; Heather Rocha; Juliann M. Savatt; Alyson E. Evans; Loren Butry; Amanda Lazzeri; D’Andra M. Lindbuchler; Carroll N. Flansburg; Rosemary Leeming; Victor G. Vogel; Matthew S. Lebo; Heather Mason-Suares; Derick C. Hoskinson; Noura S. Abul-Husn; Frederick E. Dewey; John D. Overton; Jeffrey G. Reid; Aris Baras; Huntington F. Willard; Cara Z. McCormick; Sarath Krishnamurthy; Dustin N. Hartzel; Korey A. Kost; Daniel R. Lavage; Amy C. Sturm

Key Points Question Can population-level genomic screening identify those at risk for disease? Findings In this cross-sectional study of an unselected population cohort of 50 726 adults who underwent exome sequencing, pathogenic and likely pathogenic BRCA1 and BRCA2 variants were found in a higher proportion of patients than was previously reported. Meaning Current methods to identify BRCA1/2 variant carriers may not be sufficient as a screening tool; population genomic screening for hereditary breast and ovarian cancer may better identify patients at high risk and provide an intervention opportunity to reduce mortality and morbidity.


Circulation-arrhythmia and Electrophysiology | 2018

Functional Invalidation of Putative Sudden Infant Death Syndrome–Associated Variants in the KCNH2-Encoded Kv11.1 Channel

Jennifer L. Smith; David J. Tester; Allison R. Hall; Don E. Burgess; Chun-Chun Hsu; Samy Elayi; Corey L. Anderson; Craig T. January; Jonathan Z. Luo; Dustin N. Hartzel; Uyenlinh L. Mirshahi; Michael F. Murray; Tooraj Mirshahi; Michael J. Ackerman; Brian P. Delisle

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 KCNH2 variants seemingly associated with sudden infant death syndrome. Methods: Genetic testing of 292 sudden infant death syndrome cases identified 9 KCNH2 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 KCNH2 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 KCNH2 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.


Genetics in Medicine | 2017

Electronic health record phenotype in subjects with genetic variants associated with arrhythmogenic right ventricular cardiomyopathy: a study of 30,716 subjects with exome sequencing

Christopher M. Haggerty; Cynthia A. James; Hugh Calkins; Crystal Tichnell; Joseph B. Leader; Dustin N. Hartzel; Christopher D. Nevius; Sarah A. Pendergrass; Thomas N. Person; Marci Schwartz; Marylyn D. Ritchie; David J. Carey; David H. Ledbetter; Marc S. Williams; Frederick E. Dewey; Alexander E. Lopez; John S. Penn; John D. Overton; Jeffrey G. Reid; Matthew S. Lebo; Heather Mason-Suares; Christina Austin-Tse; Heidi L. Rehm; Brian P. Delisle; Daniel J. Makowski; Vishal C. Mehra; Michael F. Murray; Brandon K. Fornwalt


American Journal of Human Genetics | 2018

PheWAS and Beyond: The Landscape of Associations with Medical Diagnoses and Clinical Measures across 38,662 Individuals from Geisinger

Anurag Verma; Anastasia Lucas; Shefali S. Verma; Yu Zhang; Navya Josyula; Anqa Khan; Dustin N. Hartzel; Daniel R. Lavage; Joseph B. Leader; Marylyn D. Ritchie; Sarah A. Pendergrass


american medical informatics association annual symposium | 2015

Contrasting Association Results between Existing PheWAS Phenotype Definition Methods and Five Validated Electronic Phenotypes.

Joseph B. Leader; Sarah A. Pendergrass; Anurag Verma; David J. Carey; Dustin N. Hartzel; Marylyn D. Ritchie; H. Lester Kirchner

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Sarah A. Pendergrass

Pennsylvania State University

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David J. Carey

Geisinger Medical Center

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