ABSTRACT
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 × 10-10), 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.
Subject(s)
Angiopoietin-Like Protein 4/deficiency , Angiopoietin-Like Protein 4/genetics , Diabetes Mellitus, Type 2/genetics , Diabetes Mellitus, Type 2/metabolism , Amino Acid Substitution , Angiopoietin-Like Protein 4/metabolism , Animals , Blood Glucose/metabolism , Case-Control Studies , Diabetes Mellitus, Type 2/etiology , Female , Gene Silencing , Genetic Association Studies , Genetic Variation , Heterozygote , Homeostasis , Humans , Insulin Resistance/genetics , Lipoprotein Lipase/metabolism , Male , Mice , Mice, Inbred C57BL , Mice, Knockout , Risk Factors , Exome SequencingABSTRACT
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.
Subject(s)
Delivery of Health Care, Integrated , Disease/genetics , Electronic Health Records , Exome/genetics , High-Throughput Nucleotide Sequencing , Adult , Drug Design , Gene Frequency , Genomics , Humans , Hypolipidemic Agents/pharmacology , INDEL Mutation , Lipids/blood , Molecular Targeted Therapy , Polymorphism, Single Nucleotide , Sequence Analysis, DNAABSTRACT
PURPOSE: Geisinger Health System (GHS) provides an ideal platform for Precision Medicine. Key elements are the integrated health system, stable patient population, and electronic health record (EHR) infrastructure. In 2007, Geisinger launched MyCode, a system-wide biobanking program to link samples and EHR data for broad research use. METHODS: Patient-centered input into MyCode was obtained using participant focus groups. Participation in MyCode is based on opt-in informed consent and allows recontact, which facilitates collection of data not in the EHR and, since 2013, the return of clinically actionable results to participants. MyCode leverages Geisinger's technology and clinical infrastructure for participant tracking and sample collection. RESULTS: MyCode has a consent rate of >85%, with more than 90,000 participants currently and with ongoing enrollment of ~4,000 per month. MyCode samples have been used to generate molecular data, including high-density genotype and exome sequence data. Genotype and EHR-derived phenotype data replicate previously reported genetic associations. CONCLUSION: The MyCode project has created resources that enable a new model for translational research that is faster, more flexible, and more cost-effective than traditional clinical research approaches. The new model is scalable and will increase in value as these resources grow and are adopted across multiple research platforms.Genet Med 18 9, 906-913.
