Omri Gottesman

Loss-of-Function Mutations in APOC3, Triglycerides, and Coronary Disease

BACKGROUND Plasma triglyceride levels are heritable and are correlated with the risk of coronary heart disease. Sequencing of the protein-coding regions of the human genome (the exome) has the potential to identify rare mutations that have a large effect on phenotype. METHODS We sequenced the protein-coding regions of 18,666 genes in each of 3734 participants of European or African ancestry in the Exome Sequencing Project. We conducted tests to determine whether rare mutations in coding sequence, individually or in aggregate within a gene, were associated with plasma triglyceride levels. For mutations associated with triglyceride levels, we subsequently evaluated their association with the risk of coronary heart disease in 110,970 persons. RESULTS An aggregate of rare mutations in the gene encoding apolipoprotein C3 (APOC3) was associated with lower plasma triglyceride levels. Among the four mutations that drove this result, three were loss-of-function mutations: a nonsense mutation (R19X) and two splice-site mutations (IVS2+1G→A and IVS3+1G→T). The fourth was a missense mutation (A43T). Approximately 1 in 150 persons in the study was a heterozygous carrier of at least one of these four mutations. Triglyceride levels in the carriers were 39% lower than levels in noncarriers (P<1×10(-20)), and circulating levels of APOC3 in carriers were 46% lower than levels in noncarriers (P=8×10(-10)). The risk of coronary heart disease among 498 carriers of any rare APOC3 mutation was 40% lower than the risk among 110,472 noncarriers (odds ratio, 0.60; 95% confidence interval, 0.47 to 0.75; P=4×10(-6)). CONCLUSIONS Rare mutations that disrupt APOC3 function were associated with lower levels of plasma triglycerides and APOC3. Carriers of these mutations were found to have a reduced risk of coronary heart disease. (Funded by the National Heart, Lung, and Blood Institute and others.).

The Electronic Medical Records and Genomics (eMERGE) Network: past, present, and future

The Electronic Medical Records and Genomics Network is a National Human Genome Research Institute–funded consortium engaged in the development of methods and best practices for using the electronic medical record as a tool for genomic research. Now in its sixth year and second funding cycle, and comprising nine research groups and a coordinating center, the network has played a major role in validating the concept that clinical data derived from electronic medical records can be used successfully for genomic research. Current work is advancing knowledge in multiple disciplines at the intersection of genomics and health-care informatics, particularly for electronic phenotyping, genome-wide association studies, genomic medicine implementation, and the ethical and regulatory issues associated with genomics research and returning results to study participants. Here, we describe the evolution, accomplishments, opportunities, and challenges of the network from its inception as a five-group consortium focused on genotype–phenotype associations for genomic discovery to its current form as a nine-group consortium pivoting toward the implementation of genomic medicine.Genet Med 15 10, 761–771.Genetics in Medicine (2013); 15 10, 761–771. doi:10.1038/gim.2013.72

Inactivating Variants in ANGPTL4 and Risk of Coronary Artery Disease

BACKGROUND Higher-than-normal levels of circulating triglycerides are a risk factor for ischemic cardiovascular disease. Activation of lipoprotein lipase, an enzyme that is inhibited by angiopoietin-like 4 (ANGPTL4), has been shown to reduce levels of circulating triglycerides. METHODS We sequenced the exons of ANGPTL4 in samples obtain from 42,930 participants of predominantly European ancestry in the DiscovEHR human genetics study. We performed tests of association between lipid levels and the missense E40K variant (which has been associated with reduced plasma triglyceride levels) and other inactivating mutations. We then tested for associations between coronary artery disease and the E40K variant and other inactivating mutations in 10,552 participants with coronary artery disease and 29,223 controls. We also tested the effect of a human monoclonal antibody against ANGPTL4 on lipid levels in mice and monkeys. RESULTS We identified 1661 heterozygotes and 17 homozygotes for the E40K variant and 75 participants who had 13 other monoallelic inactivating mutations in ANGPTL4. The levels of triglycerides were 13% lower and the levels of high-density lipoprotein (HDL) cholesterol were 7% higher among carriers of the E40K variant than among noncarriers. Carriers of the E40K variant were also significantly less likely than noncarriers to have coronary artery disease (odds ratio, 0.81; 95% confidence interval, 0.70 to 0.92; P=0.002). K40 homozygotes had markedly lower levels of triglycerides and higher levels of HDL cholesterol than did heterozygotes. Carriers of other inactivating mutations also had lower triglyceride levels and higher HDL cholesterol levels and were less likely to have coronary artery disease than were noncarriers. Monoclonal antibody inhibition of Angptl4 in mice and monkeys reduced triglyceride levels. CONCLUSIONS Carriers of E40K and other inactivating mutations in ANGPTL4 had lower levels of triglycerides and a lower risk of coronary artery disease than did noncarriers. The inhibition of Angptl4 in mice and monkeys also resulted in corresponding reductions in these values. (Funded by Regeneron Pharmaceuticals.).

Design and anticipated outcomes of the eMERGE-PGx project: a multicenter pilot for preemptive pharmacogenomics in electronic health record systems.

We describe here the design and initial implementation of the eMERGE‐PGx project. eMERGE‐PGx, a partnership of the Electronic Medical Records and Genomics Network and the Pharmacogenomics Research Network, has three objectives: (i) to deploy PGRNseq, a next‐generation sequencing platform assessing sequence variation in 84 proposed pharmacogenes, in nearly 9,000 patients likely to be prescribed drugs of interest in a 1‐ to 3‐year time frame across several clinical sites; (ii) to integrate well‐established clinically validated pharmacogenetic genotypes into the electronic health record with associated clinical decision support and to assess process and clinical outcomes of implementation; and (iii) to develop a repository of pharmacogenetic variants of unknown significance linked to a repository of electronic health record–based clinical phenotype data for ongoing pharmacogenomics discovery. We describe site‐specific project implementation and anticipated products, including genetic variant and phenotype data repositories, novel variant association studies, clinical decision support modules, clinical and process outcomes, approaches to managing incidental findings, and patient and clinician education methods.

Identification of type 2 diabetes subgroups through topological analysis of patient similarity

Patient networks constructed from genotype data and electronic medical records pinpointed three type 2 diabetes subtypes. Networks work for diabetes Big problems require big solutions, and for complex diseases such as cancer or diabetes, the big solution is big data. One long-term goal of U.S. President Barack Obama’s Precision Medicine Initiative is to assemble medical and genetic data from at least one million volunteers. But how might researchers use all those data? Li et al. provide one answer by using patient electronic medical records (EMRs) and genotype data from Mount Sinai Medical Center in New York to characterize new subtypes of type 2 diabetes (T2D). The group first clustered EMR data to identify T2D patients within the larger group. Topological analysis of the T2D group identified three new T2D subtypes on the basis of distinct patterns of clinical characteristics and disease comorbidities. Genetic association analysis identified more than 300 single nucleotide polymorphisms (SNPs) specific to each subtype. The authors found that classical T2D features such as obesity, high blood sugar, kidney disease, and eye disease, were limited to subtype 1, whereas other comorbidities such as cancer and neurological diseases were specific to subtypes 2 and 3, respectively. These distinctions might call for tailored treatment regimens rather than a one-size-fits-all approach for T2D. Although a larger sample size is needed to determine causal relationships, this study demonstrates the potential of precision medicine. Type 2 diabetes (T2D) is a heterogeneous complex disease affecting more than 29 million Americans alone with a rising prevalence trending toward steady increases in the coming decades. Thus, there is a pressing clinical need to improve early prevention and clinical management of T2D and its complications. Clinicians have understood that patients who carry the T2D diagnosis have a variety of phenotypes and susceptibilities to diabetes-related complications. We used a precision medicine approach to characterize the complexity of T2D patient populations based on high-dimensional electronic medical records (EMRs) and genotype data from 11,210 individuals. We successfully identified three distinct subgroups of T2D from topology-based patient-patient networks. Subtype 1 was characterized by T2D complications diabetic nephropathy and diabetic retinopathy; subtype 2 was enriched for cancer malignancy and cardiovascular diseases; and subtype 3 was associated most strongly with cardiovascular diseases, neurological diseases, allergies, and HIV infections. We performed a genetic association analysis of the emergent T2D subtypes to identify subtype-specific genetic markers and identified 1279, 1227, and 1338 single-nucleotide polymorphisms (SNPs) that mapped to 425, 322, and 437 unique genes specific to subtypes 1, 2, and 3, respectively. By assessing the human disease–SNP association for each subtype, the enriched phenotypes and biological functions at the gene level for each subtype matched with the disease comorbidities and clinical differences that we identified through EMRs. Our approach demonstrates the utility of applying the precision medicine paradigm in T2D and the promise of extending the approach to the study of other complex, multifactorial diseases.

Genetic and Pharmacologic Inactivation of ANGPTL3 and Cardiovascular Disease

BACKGROUND Loss‐of‐function variants in the angiopoietin‐like 3 gene (ANGPTL3) have been associated with decreased plasma levels of triglycerides, low‐density lipoprotein (LDL) cholesterol, and high‐density lipoprotein (HDL) cholesterol. It is not known whether such variants or therapeutic antagonism of ANGPTL3 are associated with a reduced risk of atherosclerotic cardiovascular disease. METHODS We sequenced the exons of ANGPTL3 in 58,335 participants in the DiscovEHR human genetics study. We performed tests of association for loss‐of‐function variants in ANGPTL3 with lipid levels and with coronary artery disease in 13,102 case patients and 40,430 controls from the DiscovEHR study, with follow‐up studies involving 23,317 case patients and 107,166 controls from four population studies. We also tested the effects of a human monoclonal antibody, evinacumab, against Angptl3 in dyslipidemic mice and against ANGPTL3 in healthy human volunteers with elevated levels of triglycerides or LDL cholesterol. RESULTS In the DiscovEHR study, participants with heterozygous loss‐of‐function variants in ANGPTL3 had significantly lower serum levels of triglycerides, HDL cholesterol, and LDL cholesterol than participants without these variants. Loss‐of‐function variants were found in 0.33% of case patients with coronary artery disease and in 0.45% of controls (adjusted odds ratio, 0.59; 95% confidence interval, 0.41 to 0.85; P=0.004). These results were confirmed in the follow‐up studies. In dyslipidemic mice, inhibition of Angptl3 with evinacumab resulted in a greater decrease in atherosclerotic lesion area and necrotic content than a control antibody. In humans, evinacumab caused a dose‐dependent placebo‐adjusted reduction in fasting triglyceride levels of up to 76% and LDL cholesterol levels of up to 23%. CONCLUSIONS Genetic and therapeutic antagonism of ANGPTL3 in humans and of Angptl3 in mice was associated with decreased levels of all three major lipid fractions and decreased odds of atherosclerotic cardiovascular disease. (Funded by Regeneron Pharmaceuticals and others; ClinicalTrials.gov number, NCT01749878.)

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

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.

The CLIPMERGE PGx Program: Clinical Implementation of Personalized Medicine Through Electronic Health Records and Genomics–Pharmacogenomics

The exponential rise in genomics research over the past decade has yielded a growing number of sequence variants associated with medication response that may have clinical utility. Despite existing barriers, attention is turning to strategies that integrate these data into clinical care. The CLIPMERGE PGx Program is establishing a best‐practices infrastructure for the implementation of genome‐informed prescribing using a biobank‐derived clinical cohort, preemptive genetic testing, and real‐time clinical decision support deployed through the electronic health record.

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

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

Meta-analysis identifies common and rare variants influencing blood pressure and overlapping with metabolic trait loci

Meta-analyses of association results for blood pressure using exome-centric single-variant and gene-based tests identified 31 new loci in a discovery stage among 146,562 individuals, with follow-up and meta-analysis in 180,726 additional individuals (total n = 327,288). These blood pressure–associated loci are enriched for known variants for cardiometabolic traits. Associations were also observed for the aggregation of rare and low-frequency missense variants in three genes, NPR1, DBH, and PTPMT1. In addition, blood pressure associations at 39 previously reported loci were confirmed. The identified variants implicate biological pathways related to cardiometabolic traits, vascular function, and development. Several new variants are inferred to have roles in transcription or as hubs in protein–protein interaction networks. Genetic risk scores constructed from the identified variants were strongly associated with coronary disease and myocardial infarction. This large collection of blood pressure–associated loci suggests new therapeutic strategies for hypertension, emphasizing a link with cardiometabolic risk.