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Archive for the ‘Computational Biology/Systems and Bioinformatics’ Category

Personal Genomics for Preventive Cardiology Randomized Trial Design and Challenges

Posted in Biological Networks, Gene Regulation and Evolution, Chemical Genetics, Computational Biology/Systems and Bioinformatics, Frontiers in Cardiology and Cardiovascular Disorders, Genome Biology, Genomic Testing: Methodology for Diagnosis, Molecular Genetics & Pharmaceutical, Origins of Cardiovascular Disease, Personalized and Precision Medicine & Genomic Research, Population Health Management, Genetics & Pharmaceutical, Proteomics, tagged on January 2, 2014| Leave a Comment »

Personal Genomics for Preventive Cardiology Randomized Trial Design and Challenges

Reporter: Aviva Lev-Ari, PhD, RN

 

Methods in Genetics and Clinical Interpretation Randomized Trial of Personal Genomics for Preventive Cardiology Design and Challenges

Joshua W. Knowles, MD, PhD, Themistocles L. Assimes, MD, PhD, Michaela Kiernan, PhD, Aleksandra Pavlovic, BS, Benjamin A. Goldstein, PhD, Veronica Yank, MD, Michael V. McConnell, MD, Devin Absher, PhD, Carlos Bustamante, PhD, Euan A. Ashley, MD, DPhil and John P.A. Ioannidis, MD, DSc

Author Affiliations

From the Division of Cardiovascular Medicine (J.W.K., T.L.A., A.P., M.V.M., E.A.A.), Stanford Prevention Research Center (M.K., V.Y., J.P.A.I.), Division of General Medical Disciplines (V.Y.), Department of Genetics (C.B.), Department of Health Research and Policy (J.P.A.I.), Stanford University School of Medicine, Stanford, CA; Quantitative Sciences Unit, Stanford University School of Medicine, Palo Alto, CA (B.A.G.); HudsonAlpha Institute for Biotechnology, Huntsville, AL (D.A.); Department of Statistics, Stanford University School of Humanities and Sciences, Stanford, CA (J.P.A.I.).

Correspondence to Joshua W. Knowles, MD, PhD, Stanford University School of Medicine, Division of Cardiovascular Medicine, Falk CVRC, 300 Pasteur Dr, Stanford, CA 94305. E-mail knowlej@stanford.edu

Background

Genome-wide association studies (GWAS) have identified more than 1500 disease-associated single nucleotide polymorphisms (SNPs), including many related to atherosclerotic cardiovascular disease (CVD). Associations have been found for most traditional risk factors (TRFs), including

  • lipids,1,2
  • blood pressure/hypertension,3,4
  • weight/body mass index,5,6
  • smoking behavior,7 and
  • diabetes.8–13

GWAS have also identified susceptibility variants for coronary heart disease (CHD). The first and, so far, strongest of these signals was found in the 9p21.3 locus, where common variants in this region increase the relative risk of CVD by 15% to 30% per risk allele in most race/ethnic groups.13–20 Subsequent large-scale GWAS meta-analyses and replication studies in largely white/European populations have led to the reliable identification of an additional 26 loci conferring susceptibility to CHD,2,20–23 all with substantially lower effects sizes compared with the 9p21 locus. Many of these CVD susceptibility loci appear to be conferring risk independent of TRFs and thus cannot currently be assessed by surrogate clinical measures (Table 1). Among the 27 independent loci identified in the most recent large meta-analyses of CVD, 21 were reported not to be associated with any of the TRFs.20,21

 SOURCE

Circulation: Cardiovascular Genetics 2012; 5: 368-376

doi: 10.1161/ CIRCGENETICS.112.962746

 

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GenomeWeb Daily News Index: Future is Better for Some than for Others

Posted in Computational Biology/Systems and Bioinformatics, Genome Biology, Genomic Testing: Methodology for Diagnosis, Medical and Population Genetics, Technology Transfer: Biotech and Pharmaceutical, tagged on January 2, 2014| Leave a Comment »

NanoString, Accelerate, PacBio Shares Sharply up in September; Myriad, Sequenom Down

Reporter: Aviva Lev-Ari, PhD, RN

Related Stories

index_0913.jpg

SOURCE

http://www.genomeweb.com/nanostring-accelerate-pacbio-shares-sharply-september-myriad-sequenom-down

 

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The Cost to Value Conundrum in Cardiovascular Healthcare Provision

Posted in Accountable Care Organizations, Affordable Care Act, Anemia, Atherogenic Processes & Pathology, Best evidence, Bio Instrumentation in Experimental Life Sciences Research, Biomarkers & Medical Diagnostics, Biomedical Measurement Science, Ca2+ triggered activation, Ca2+ triggered activation, Calcium Signaling, Cardiac & Vascular Repair Tools Subsegment, Cardiac and Cardiovascular Surgical Procedures, Cardiac muscle regeneration, Cardiomyopathy, Cardiovascular Pharmacogenomics, Coagulation Therapy and Internal Bleeding, Computational Biology/Systems and Bioinformatics, Curation, Curation methodology, Cytoskeleton, Diabetes Mellitus, Electrophysiology, Evidence-based decision-making, Experimental validation, Federal Budget Appropriations, Frontiers in Cardiology and Cardiovascular Disorders, Health Economics and Outcomes Research, Health Law & Patient Safety, Healthcare costs and reimbursement, Healthcare Reform, Historical relevance, HTN, HTN in Youth, Indigent Nutrition, Interviews with Scientific Leaders, Ionic Transporters Na+, K, Medical Devices R&D and Inventions, Medical Imaging Technology, Image Processing/Computing, MRI, CT, Nuclear Medicine, Ultra Sound, Medicare and Medicaid, Na-K transport, Na-K-ATPase, Nephrology, Nitric Oxide in Health and Disease, Nutrition, Nutritional Supplements: Atherogenesis, lipid metabolism, Origins of Cardiovascular Disease, Pharmaceutical Analytics, Pharmacogenomics, Pharmacologic toxicities, Pharmacotherapy of Cardiovascular Disease, Population Health Management, Genetics & Pharmaceutical, Population Health Management, Nutrition and Phytochemistry, Prescription Drugs Costs, Regenerative Biology and Medicine, Resident-cell-based, Skilled Nursing Facilities, Stem Cells for Regenerative Medicine, Stents & Tools, Synaptic vesicle, Systematic Error (Bias), Systemic Inflammatory Response Related Disorders, Technology Advance Assessment of, Technology Capital Expenses, Technology Transfer: Biotech and Pharmaceutical, Translational Effectiveness, Translational Science, Uninsured and Underinsured, Valves & Tools, tagged , , , , , , , , , , , , , , , , , , , , , , , , , on January 1, 2014| Leave a Comment »

The Cost to Value Conundrum in Cardiovascular Healthcare Provision

Author: Larry H. Bernstein, MD, FCAP

Article ID #98: The Cost to Value Conundrum in Cardiovascular Healthcare Provision. Published on 1/1/2014

WordCloud Image Produced by Adam Tubman

I write this introduction to Volume 2 of the e-series on Cardiovascular Diseases, which curates the basic structure and physiology of the heart, the vasculature, and related structures, e.g., the kidney, with respect to:

1. Pathogenesis
2. Diagnosis
3. Treatment

Curation is an introductory portion to Volume Two, which is necessary to introduce the methodological design used to create the following articles. More needs not to be discussed about the methodology, which will become clear, if only that the content curated is changing based on success or failure of both diagnostic and treatment technology availability, as well as the systems needed to support the ongoing advances.  Curation requires:

  • meaningful selection,
  • enrichment, and
  • sharing combining sources and
  • creation of new synnthesis

Curators have to create a new perspective or idea on top of the existing media which supports the content in the original. The curator has to select from the myriad upon myriad options available, to re-share and critically view the work. A search can be overwhelming in size of the output, but the curator has to successfully pluck the best material straight out of that noise.

Part 1 is a highly important treatment that is not technological, but about the system now outdated to support our healthcare system, the most technolog-ically advanced in the world, with major problems in the availability of care related to economic disparities.  It is not about technology, per se, but about how we allocate healthcare resources, about individuals’ roles in a not full list of lifestyle maintenance options for self-care, and about the important advances emerging out of the Affordable Care Act (ACA), impacting enormously on Medicaid, which depends on state-level acceptance, on community hospital, ambulatory, and home-care or hospice restructuring, which includes the reduction of management overhead by the formation of regional healthcare alliances, the incorporation of physicians into hospital-based practices (with the hospital collecting and distributing the Part B reimbursement to the physician, with “performance-based” targets for privileges and payment – essential to the success of an Accountable Care Organization (AC)).  One problem that ACA has definitively address is the elimination of the exclusion of patients based on preconditions.  One problem that has been left unresolved is the continuing existence of private policies that meet financial capabilities of the contract to provide, but which provide little value to the “purchaser” of care.  This is a holdout that persists in for-profit managed care as an option.  A physician response to the new system of care, largely fostered by a refusal to accept Medicaid, is the formation of direct physician-patient contracted care without an intermediary.

In this respect, the problem is not simple, but is resolvable.  A proposal for improved economic stability has been prepared by Edward Ingram. A concern for American families and businesses is substantially addressed in a macroeconomic design concept, so that financial services like housing, government, and business finance, savings and pensions, boosting confidence at every level giving everyone a better chance of success in planning their personal savings and lifetime and business finances.

http://macro-economic-design.blogspot.com/p/book.html

Part 2 is a collection of scientific articles on the current advances in cardiac care by the best trained physicians the world has known, with mastery of the most advanced vascular instrumentation for medical or surgical interventions, the latest diagnostic ultrasound and imaging tools that are becoming outdated before the useful lifetime of the capital investment has been completed.  If we tie together Part 1 and Part 2, there is ample room for considering  clinical outcomes based on individual and organizational factors for best performance. This can really only be realized with considerable improvement in information infrastructure, which has miles to go.  Why should this be?  Because for generations of IT support systems, they are historically focused on billing and have made insignificant inroads into the front-end needs of the clinical staff.

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BioIT World – TRACK 4: Bioinformatics – April 29 – May 1, 2014, Seaport World Trade Center, Boston, MA

Posted in Computational Biology/Systems and Bioinformatics, tagged , , , , , on December 31, 2013| Leave a Comment »

 

Bioinformatics

Track 4 explores technologies and tools that bring together relevant -omic data from multiple physical locations for analysis. Case studies and results will be presented to illustrate how virtual data integration across multiple research initiatives can be applied to any disease. Other topics covered include collaboration tools, biomarker research, imaging, computational models, clinically actionable variants, and gene mapping and expression.

Final Agenda

 

Download Brochure | Pre-Conference Workshops

 

TUESDAY, APRIL 29

 

7:00 am Workshop Registration and Morning Coffee

8:00 – 11:30 Recommended Morning Pre-Conference Workshops*

Data Visualization in Biology: From the Basics to Big Data
Analyzing NGS Data in Galaxy

12:30 – 4:00 pm Recommended Afternoon Pre-Conference Workshops*

Big Data Analytics
Running a Local Galaxy Instance

*Separate Registration Required. Click here for detailed information.

 

2:00 – 7:00 pm Main Conference Registration

4:00 Event Chairperson’s Opening Remarks

Cindy Crowninshield, RD, LDN, Conference Director, Cambridge Healthtech Institute

 

4:05 PLENARY KEYNOTE SESSION

Click here for detailed information.

 

5:00 – 7:00 Welcome Reception in the Exhibit Hall with Poster Viewing

iPad® mini & Bose® QuietComfort 15 Noise Cancelling Headphones Raffle! Drawing held at 6:30pm!*

 

*Apple® & Bose® are not sponsors or participants in this program.

 

WEDNESDAY, APRIL 30

7:00 am Registration Open and Morning Coffee

8:00 Chairperson’s Opening Remarks

Phillips Kuhl, Co-Founder and President, Cambridge Healthtech Institute

 

8:05 PLENARY KEYNOTE SESSION

Click here for detailed information.

 

9:00 Benjamin Franklin Award & Laureate Presentation

9:30 Best Practices Awards Program

9:45 Coffee Break in the Exhibit Hall with Poster Viewing

 

BIOINFORMATICS FOR BIG DATA

10:50 Chairperson’s Remarks

11:00 Data Management Best Practices for Genomics Service Providers

Vas Vasiliadis, Director, Products, Computation Institute, University of Chicago and Argonne National Laboratory

Genomics research teams in academia and industry are increasingly limited at all stages of their work by large and unwieldy datasets, poor integration between the computing facilities they use for analysis, and difficulty in sharing analysis results with their customers and collaborators. We will discuss issues with current approaches and describe emerging best practices for managing genomics data through its lifecycle.

11:30 NGS Analysis to Drug Discovery: Impact of High-Performance Computing in Life Sciences

Bhanu Rekepalli, Ph.D., Assistant Professor and Research Scientist, Joint Institute for Computational Sciences, The University of Tennessee, Oak Ridge National Laboratory

We are working with small-cluster-based applications most widely used by the scientific community on the world’s premier supercomputers. We incorporated these parallel applications into science gateways with user-friendly, web-based portals. Learn how the research at UTK-ORNL will help to bridge the gap between the rate of big data generation in life sciences and the speed and ease at which biologists and pharmacists can study this data.

RemedyMD

12:00 pm The Future of Biobank Informatics

Bruce Pharr, Vice President, Product Marketing, Laboratory Systems, Remedy Informatics

As biobanks become increasingly essential to basic, translational, and clinical research for genetic studies and personalized medicine, biobank informatics must address areas from biospecimen tracking, privacy protection, and quality management to pre-analytical and clinical collection/identification of study data elements. This presentation will examine specific requirements for third-generation biobanks and how biobank informatics will meet those requirements.

YarcData

12:15 Learn How YarcData’s High Performance Hadoop and Graph Analytics Appliances Make It Easy to Use Big Data in Life Sciences

Ted Slater, Senior Solutions Architect, Life Sciences, YarcData, a division of Cray

YarcData, a division of Cray, offers high performance solutions for big data using Hadoop and graph analytics at scale, finally giving researchers the power to leverage all the data they need to stratify patients, discover new drug targets, accelerate NGS analysis, predict biomarkers, and better understand diseases and their treatments.

12:30 Luncheon Presentations (Sponsorship Opportunities Available) or Lunch on Your Own

1:50 Chairperson’s Remarks

1:55 Integration of Multi-Omic Data Using Linked Data Technologies

Aleksandar Milosavljevic, Ph.D., Professor, Human Genetics; Co-Director, Program in Structural & Computational Biology and Molecular Biophysics; Co-Director, Computational and Integrative Biomedical Research Center, Baylor College of Medicine

By virtue of programmatic interoperability (uniform REST APIs), Genboree servers enable virtual integration of multi-omic data that is distributed across multiple physical locations. Linked Data technologies of the Semantic Web provide an additional “logical” layer of integration by enabling distributed queries across the distributed data and by bringing multi-omic data into the context of pathways and other background knowledge required for data interpretation.

2:25 Building Open Source Semantic Web-Based Biomedical Content Repositories to Facilitate and Speed Up Discovery and Research

Bhanu Bahl, Ph.D., Director, Clinical and Translational Science Centre, Harvard Medical School

Douglas MacFadden, CIO, Harvard Catalyst at Harvard Medical School

Eagle-i open source network at Harvard provides a state-of-the-art informatics platform to support the quality control and annotation of resources establishing a sound foundation for a well-curated resource collection in accordance with Semantic Web and Linked Open Data principles. Learn how this ontology-centric architecture is used to efficiently store, create, and search data.

TessellaNEW

2:55 Data Experts: Improving Translational Drug-Development Efficiency

Jamie MacPherson, Ph.D., Consultant, Tessella

We report on a novel approach to translational informatics support: embedding ‘Data Experts’ within drug-project teams. Data experts combine first-line informatics support and Business Analysis. They help teams exploit data sources that are diverse in type, scale and quality; analyse user-requirements and prototype potential software solutions. We then explore scaling this approach from a specific drug development team to all.

3:25 Refreshment Break in the Exhibit Hall with Poster Viewing

 

BIOLOGICAL NETWORKS

4:00 Network Verification Challenge: A Reputation-Based Crowd-Sourced Peer Review Platform for Network Biology

William Hayes, Ph.D., Senior Vice President, Platform Development, IT/Informatics, Selventa

Anselmo DiFabio, Vice President, Technology, Applied Dynamic Solutions

The Network Verification Challenge proposes a new approach for peer review for Network biology. The use of a reputation-based crowd-sourced platform can make previously overwhelming efforts in capturing large-scale network biology and validating it possible. The same approach for peer review can also be applied inside bioPharma for internal collaboration and validation of network biology in and across therapeutic areas.

4:30 NDEx, the Network Data Exchange: Bridging the Knowledge Gap for Commercial and Academic Collaboration on Biological Networks

Dexter Pratt, Project Director, NDEx, Cytoscape Consortium

NDEx is a public portal for collaboration and publication for scientists and organizations working with biological networks of multiple types and in multiple formats. This talk presents key features of the NDEx portal and the underlying open-source server software. The status of NDEx in collaborations with other organizations and the use (or development) of standards will be summarized.

5:00 Sponsored Presentations (Opportunities Available)

5:30 – 6:30 Best of Show Awards Reception in the Exhibit Hall

THURSDAY, MAY 1

7:15 am Registration Open

7:15 Breakfast Presentation (Sponsorship Opportunity Available) or Morning Coffee

8:30 Chairperson’s Opening Remarks

Kevin Davies, Ph.D., Vice President Business Development & Publisher C&EN, American Chemical Society; Founding Editor, Bio-IT World

 

8:35 PLENARY KEYNOTE SESSION

Click here for detailed information.

 

10:00 Coffee Break in the Exhibit Hall and Poster Competition Winners Announced

 

BIOINFORMATICS ACROSS MULTIPLE RESEARCH INITIATIVES

10:30 Chairperson’s Opening Remarks

10:35 Analysis of Genomics Data in an Internal Cloud Computing Environment

Philip Groth, Ph.D., IT Business Partner Genomics, R&D IT – Research, Bayer HealthCare

This talk presents the technical set-up of vCloud, an in-house cloud solution, maintenance and running an internal cloud-computing environment, and how this set-up enables fast & secure analysis of large-scale genomics data. Results of analyzing genomic data from over 4,000 cancer patients will be presented.

11:05 Genome-Wide Multi-Omics Profiling of Colorectal Cancer Identifies Immune Determinants Strongly Associated with Relapse

Subha Madhavan, Ph.D., Director, Innovation Center for Biomedical Informatics, Oncology, Georgetown University

This presentation demonstrates the use of novel informatics methods and data integration approaches in identifying prognostic markers of cancer. The use and benefit of adjuvant chemotherapy to treat patients with state II colorectal cancer (CRC) is not well understood since the majority of these patients are cured by surgery alone.

11:35 Sponsored Presentations (Opportunities Available)

12:05 pm Luncheon Presentations (Sponsorship Opportunities Available) or Lunch on Your Own

1:15 Dessert Refreshment Break in the Exhibit Hall with Poster Viewing

1:55 Chairperson’s Remarks

2:00 An Algorithm to Access Human Memory Showing Alzheimer Symptoms When Distorted

Simon Berkovich, Ph.D., Professor, Computer Science, The George Washington University

This talk presents a novel theoretical framework for bioinformatics. Access to a holographic model of the brain encounters a particular problem of multiple responses resolution. For the given milieu, we employ a digital-analog adjustment of a streaming algorithm for finding a predominant element. Receptacles deterioration incurs preferential recall of prior life stages akin to Alzheimer’s disease.

 

MACHINE LEARNING MODELS

2:30 Accurate Prediction of Clinical Stroke Scales from Robotic Measurements

Dimitris K. Agrafiotis, Ph.D., FRSC, Vice President and Chief Data Officer, Covance

Here, we describe a novel approach that combines robotic devices and advanced machine learning algorithms to derive predictive models of clinical assessments of motor function following stroke. We show that it is possible to derive sensitive biomarkers of motor impairment using a few easily obtained robotic measurements, which can then be used to improve the efficiency and cost of clinical trials.

3:00 GPS Engineering: Machine Learning Approaches to Biological Engineering

Drew Regitsky, Scientist, Bioengineering, Calysta Energy

This talk presents a potential new approach to computational representations of biological systems and applying multidimensional analysis to predicting the behavior of complex systems. Several case studies will be presented to demonstrate applications of the methods and examples of the output of data analysis.

3:30 A Multiclass Extreme-Learning-Machine Approach to the Discovery of Multiple Cancer Biomarkers: Using Binary Coded Genetic Algorithm and IPA Analysis

Saras Saraswathi, Ph.D., Clinical Instructor, Pediatrics, Ohio State University; Postdoctoral Research Associate, Battelle Center for Mathematical Medicine, Research Institute, Nationwide Children’s Hospital

The neural network-based Extreme Learning Machine is combined with a Binary Coded Genetic Algorithm to select a small set of 92 genes which simultaneously classify 14 different types of cancers simultaneously, to high accuracy. IPA analysis of the selected genes reveals that over 60% of the selected genes are related to many cancers that are being classified.

4:00 Conference Adjourns

SOURCE

http://www.bio-itworldexpo.com/Bioinformatics/

 

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Elastin Arteriopathy: The Genetics of Supravalvular Aortic Stenosis

Posted in Aortic Valve: TAVR, TAVI vs Open Heart Surgery, Cardiac and Cardiovascular Surgical Procedures, Cardiovascular Research, Chemical Genetics, Computational Biology/Systems and Bioinformatics, Congenital Heart Disease, Frontiers in Cardiology and Cardiovascular Disorders, Genome Biology, Genomic Testing: Methodology for Diagnosis, Human Sensation and Cellular Transduction: Physiology and Therapeutics, Medical and Population Genetics, Origins of Cardiovascular Disease, Personalized and Precision Medicine & Genomic Research, Pharmacogenomics, Pharmacotherapy of Cardiovascular Disease, Population Health Management, Genetics & Pharmaceutical, Proteomics, Valves & Tools, tagged , , , on December 30, 2013| Leave a Comment »

Elastin Arteriopathy: The Genetics of Supravalvular Aortic Stenosis

Reporter: Aviva Lev-Ari, PhD, RN

 

Supravalvular Aortic Stenosis Elastin Arteriopathy

Giuseppe Merla, PhD, Nicola Brunetti-Pierri, MD, Pasquale Piccolo, PhD, Lucia Micale, PhD and Maria Nicla Loviglio, PhD, MSc

Author Affiliations

From the Medical Genetics Unit, IRCCS Casa Sollievo Della Sofferenza Hospital, San Giovanni Rotondo, Italy (G.M., L.M., M.N.L.); Telethon Institute of Genetics and Medicine, Napoli, Italy (N.B-P., P.P.); Department of Pediatrics, Federico II University of Naples, Naples, Italy (N.B-P.); and CIG Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland (M.N.L.).

Correspondence to Giuseppe Merla, PhD, Medical Genetics Unit, IRCCS Casa Sollievo della Sofferenza, viale Cappuccini, 71013 San Giovanni Rotondo, Italy. E-mailg.merla@operapadrepio.it

Abstract

Supravalvular aortic stenosis is a systemic elastin (ELN) arteriopathy that disproportionately affects the supravalvular aorta. ELN arteriopathy may be present in a nonsyndromic condition or in syndromic conditions such as Williams–Beuren syndrome. The anatomic findings include congenital narrowing of the lumen of the aorta and other arteries, such as branches of pulmonary or coronary arteries. Given the systemic nature of the disease, accurate evaluation is recommended to establish the degree and extent of vascular involvement and to plan appropriate interventions, which are indicated whenever hemodynamically significant stenoses occur. ELN arteriopathy is genetically heterogeneous and occurs as a consequence of haploinsufficiency of the ELN gene on chromosome 7q11.23, owing to either microdeletion of the entire chromosomal region or ELN point mutations. Interestingly, there is a prevalence of premature termination mutations resulting in null alleles among ELN point mutations. The identification of the genetic defect in patients with supravalvular aortic stenosis is essential for a definitive diagnosis, prognosis, and genetic counseling.

SOURCE:

Circulation: Cardiovascular Genetics.2012; 5: 692-696

doi: 10.1161/ CIRCGENETICS.112.962860

 

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Platelet Endothelial Aggregation Receptor-1 (PEAR1) Gene to be most strongly associated with Dual Antiplatelet Therapy Response: Genetic Determinants of Variable Response to Aspirin (alone and in combination with Clopidogrel)

Posted in Atherogenic Processes & Pathology, Cardiac and Cardiovascular Surgical Procedures, Cardiovascular Pharmacogenomics, Cardiovascular Research, Cell Biology, Signaling & Cell Circuits, Chemical Genetics, Coagulation Therapy and Internal Bleeding, Computational Biology/Systems and Bioinformatics, FDA Regulatory Affairs, Frontiers in Cardiology and Cardiovascular Disorders, Genome Biology, Genomic Testing: Methodology for Diagnosis, HTN, Medical and Population Genetics, Origins of Cardiovascular Disease, PCI, Personalized and Precision Medicine & Genomic Research, Pharmacotherapy of Cardiovascular Disease, Stents & Tools, Valves & Tools, tagged , , , , , , on December 30, 2013| Leave a Comment »

Platelet Endothelial Aggregation Receptor-1 (PEAR1) Gene to be most strongly associated with Dual Antiplatelet Therapy Response: Genetic Determinants of Variable Response to Aspirin (alone and in combination with Clopidogrel)

Reporter: Aviva Lev-Ari, PhD, RN

4 Genetic Variation in PEAR1 is Associated with Platelet Aggregation and Cardiovascular Outcomes

Joshua P. Lewis1, Kathleen Ryan1, Jeffrey R. O’Connell1, Richard B. Horenstein1,Coleen M. Damcott1, Quince Gibson1, Toni I. Pollin1, Braxton D. Mitchell1, Amber L. Beitelshees1, Ruth Pakzy1, Keith Tanner1, Afshin Parsa1, Udaya S. Tantry2, Kevin P. Bliden2, Wendy S. Post3, Nauder Faraday3, William Herzog4, Yan Gong5, Carl J. Pepine6, Julie A. Johnson5, Paul A. Gurbel2 and Alan R. Shuldiner7*

Author Affiliations

1University of Maryland School of Medicine, Baltimore, MD

2Sinai Hospital of Baltimore, Baltimore, MD

3Johns Hopkins University School of Medicine, Baltimore, MD

4Sinai Hospital of Baltimore & Johns Hopkins University School of Medicine, Baltimore, MD

5University of Florida College of Pharmacy, Gainesville, FL

6University of Florida College of Medicine, Gainesville, FL

7University of Maryland School of Medicine & Veterans Administration Medical Center, Baltimore, MD

↵* University of Maryland School of Medicine & Veterans Administration Medical Center, Baltimore, MD ashuldin@medicine.umaryland.edu

Abstract

Background-Aspirin or dual antiplatelet therapy (DAPT) with aspirin and clopidogrel is standard therapy for patients at increased risk for cardiovascular events. However, the genetic determinants of variable response to aspirin (alone and in combination with clopidogrel) are not known.

Methods and Results-We measured ex-vivo platelet aggregation before and after DAPT in individuals (n=565) from the Pharmacogenomics of Antiplatelet Intervention (PAPI) Study and conducted a genome-wide association study (GWAS) of drug response. Significant findings were extended by examining genotype and cardiovascular outcomes in two independent aspirin-treated cohorts: 227 percutaneous coronary intervention (PCI) patients, and 1,000 patients of the International VErapamil SR/trandolapril Study (INVEST) GENEtic Substudy (INVEST-GENES). GWAS revealed a strong association between single nucleotide polymorphisms on chromosome 1q23 and post-DAPT platelet aggregation. Further genotyping revealed rs12041331 in the platelet endothelial aggregation receptor-1 (PEAR1) gene to be most strongly associated with DAPT response (P=7.66×10-9). In Caucasian and African American patients undergoing PCI, A-allele carriers of rs12041331 were more likely to experience a cardiovascular event or death compared to GG homozygotes (hazard ratio = 2.62, 95%CI 0.96-7.10, P=0.059 and hazard ratio = 3.97, 95%CI 1.10-14.31, P=0.035 respectively). In aspirin-treated INVEST-GENES patients, rs12041331 A-allele carriers had significantly increased risk of myocardial infarction compared to GG homozygotes (OR=2.03, 95%CI 1.01-4.09, P=0.048).

Conclusions – Common genetic variation in PEAR1 may be a determinant of platelet response and cardiovascular events in patients on aspirin, alone and in combination with clopidogrel.

Clinical Trial Registration Information-clinicaltrials.gov; Identifiers:NCT00799396 and NCT00370045

SOURCE:

http://www.ncbi.nlm.nih.gov/pubmed/23392654

http://circgenetics.ahajournals.org/content/6/2/184.short?rss=1

Circulation: Cardiovascular Genetics.2013; 6: 184-192 Published online before print February 7, 2013,doi: 10.1161/​CIRCGENETICS.111.964627

 

 

 

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Genomics of Carotid-Femoral Pulse Wave Velocity and Excess Cardiovascular Disease Risk: Common Genetic Variation in the 3′-BCL11B Gene Desert

Posted in Biological Networks, Gene Regulation and Evolution, Biomarkers & Medical Diagnostics, Cardiovascular Research, Carotid Artery, Cerebrovascular and Neurodegenerative Diseases, Chemical Genetics, Computational Biology/Systems and Bioinformatics, Electrophysiology, Frontiers in Cardiology and Cardiovascular Disorders, Genome Biology, Genomic Testing: Methodology for Diagnosis, Origins of Cardiovascular Disease, Peripheral Arterial Disease & Peripheral Vascular Surgery, Personalized and Precision Medicine & Genomic Research, Proteomics, tagged on December 30, 2013| Leave a Comment »

Genomics of Carotid-Femoral Pulse Wave Velocity and Excess Cardiovascular Disease Risk: Common Genetic Variation in the 3′-BCL11B Gene Desert

Reporter: Aviva Lev-Ari, PhD, RN

 

Common Genetic Variation in the 3′-BCL11B Gene Desert Is Associated With Carotid-Femoral Pulse Wave Velocity and Excess Cardiovascular Disease Risk – The AortaGen Consortium

Gary F. Mitchell, MD*, Germaine C. Verwoert, MSc*, Kirill V. Tarasov, MD, PhD*,Aaron Isaacs, PhD, Albert V. Smith, PhD, Yasmin, BSc, MA, PhD, Ernst R. Rietzschel, MD, PhD, Toshiko Tanaka, PhD, Yongmei Liu, MD, PhD, Afshin Parsa, MD, MPH,Samer S. Najjar, MD, Kevin M. O’Shaughnessy, MA, BM, DPhil, FRCP, Sigurdur Sigurdsson, MSc, Marc L. De Buyzere, MSc, Martin G. Larson, ScD, Mark P.S. Sie, MD, PhD, Jeanette S. Andrews, MS, Wendy S. Post, MD, MS, Francesco U.S. Mattace-Raso, MD, PhD, Carmel M. McEniery, BSc, PhD, Gudny Eiriksdottir, MSc, Patrick Segers, PhD, Ramachandran S. Vasan, MD, Marie Josee E. van Rijn, MD, PhD,Timothy D. Howard, PhD, Patrick F. McArdle, PhD, Abbas Dehghan, MD, PhD,Elizabeth S. Jewell, MS, Stephen J. Newhouse, MSc, PhD, Sofie Bekaert, PhD, Naomi M. Hamburg, MD, Anne B. Newman, MD, MPH, Albert Hofman, MD, PhD, Angelo Scuteri, MD, PhD, Dirk De Bacquer, PhD, Mohammad Arfan Ikram, MD, PhD†, Bruce M. Psaty, MD, PhD†, Christian Fuchsberger, PhD‡, Matthias Olden, PhD‡, Louise V. Wain, PhD§, Paul Elliott, MB, PhD§, Nicholas L. Smith, PhD‖, Janine F. Felix, MD, PhD‖, Jeanette Erdmann, PhD¶, Joseph A. Vita, MD, Kim Sutton-Tyrrell, PhD, Eric J.G. Sijbrands, MD, PhD, Serena Sanna, PhD, Lenore J. Launer, MS, PhD, Tim De Meyer, PhD, Andrew D. Johnson, MD, Anna F.C. Schut, MD, PhD, David M. Herrington, MD, MHS, Fernando Rivadeneira, MD, PhD, Manuela Uda, PhD, Ian B. Wilkinson, MA, BM, FRCP, Thor Aspelund, PhD, Thierry C. Gillebert, MD, PhD, Luc Van Bortel, MD, PhD, Emelia J. Benjamin, MD, MSc, Ben A. Oostra, PhD, Jingzhong Ding, MD, PhD, Quince Gibson, MBA, André G. Uitterlinden, PhD, Gonçalo R. Abecasis, PhD,John R. Cockcroft, BSc, MB, ChB, FRCP, Vilmundur Gudnason, MD, PhD, Guy G. De Backer, MD, PhD, Luigi Ferrucci, MD, Tamara B. Harris, MD, MS, Alan R. Shuldiner, MD, Cornelia M. van Duijn, PhD, Daniel Levy, MD*, Edward G. Lakatta, MD* andJacqueline C.M. Witteman, PhD*

Correspondence to Gary F. Mitchell, MD, Cardiovascular Engineering, Inc, 1 Edgewater Dr, Suite 201A, Norwood, MA 02062. E-mailGaryFMitchell@mindspring.com

↵* These authors contributed equally.

Abstract

Background—Carotid-femoral pulse wave velocity (CFPWV) is a heritable measure of aortic stiffness that is strongly associated with increased risk for major cardiovascular disease events.

Methods and Results—We conducted a meta-analysis of genome-wide association data in 9 community-based European ancestry cohorts consisting of 20 634 participants. Results were replicated in 2 additional European ancestry cohorts involving 5306 participants. Based on a preliminary analysis of 6 cohorts, we identified a locus on chromosome 14 in the 3′-BCL11B gene desert that is associated with CFPWV (rs7152623, minor allele frequency=0.42, β=−0.075±0.012 SD/allele,P=2.8×10−10; replication β=−0.086±0.020 SD/allele, P=1.4×10−6). Combined results for rs7152623 from 11 cohorts gave β=−0.076±0.010 SD/allele,P=3.1×10−15. The association persisted when adjusted for mean arterial pressure (β=−0.060±0.009 SD/allele, P=1.0×10−11). Results were consistent in younger (<55 years, 6 cohorts, n=13 914, β=−0.081±0.014 SD/allele, P=2.3×10−9) and older (9 cohorts, n=12 026, β=−0.061±0.014 SD/allele, P=9.4×10−6) participants. In separate meta-analyses, the locus was associated with increased risk for coronary artery disease (hazard ratio=1.05; confidence interval=1.02–1.08; P=0.0013) and heart failure (hazard ratio=1.10, CI=1.03–1.16, P=0.004).

Conclusions—Common genetic variation in a locus in the BCL11B gene desert that is thought to harbor 1 or more gene enhancers is associated with higher CFPWV and increased risk for cardiovascular disease. Elucidation of the role this novel locus plays in aortic stiffness may facilitate development of therapeutic interventions that limit aortic stiffening and related cardiovascular disease events.

SOURCE:

Circulation: Cardiovascular Genetics.2012; 5: 81-90

Published online before print November 8, 2011,

doi: 10.1161/ CIRCGENETICS.111.959817

 

 

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Abdominal Aortic Aneurysm: Matrix Metalloproteinase-9 Genotype as a Potential Genetic Marker

Posted in Abdominal Aorta, Bio Instrumentation in Experimental Life Sciences Research, Biological Networks, Gene Regulation and Evolution, Biomarkers & Medical Diagnostics, Cardiovascular Research, Cell Biology, Signaling & Cell Circuits, Chemical Genetics, Computational Biology/Systems and Bioinformatics, Frontiers in Cardiology and Cardiovascular Disorders, Genome Biology, Genomic Testing: Methodology for Diagnosis, HTN, Medical and Population Genetics, Origins of Cardiovascular Disease, Personalized and Precision Medicine & Genomic Research, Population Health Management, Genetics & Pharmaceutical, Proteomics, tagged , , , , on December 30, 2013| Leave a Comment »

Abdominal Aortic Aneurysm: Matrix Metalloproteinase-9 Genotype as a Potential Genetic Marker

Reporter: Aviva Lev-Ari, PhD, RN

 

Matrix Metalloproteinase-9 Genotype as a Potential Genetic Marker for Abdominal Aortic Aneurysm

Tyler Duellman, BS, Christopher L. Warren, PhD, Peggy Peissig, PhD, Martha Wynn, MD and Jay Yang, MD, PhD

Author Affiliations

From the Molecular and Cellular Pharmacology Graduate Program (T.D., J.Y.) and Department of Anesthesiology (M.W., J.Y.), University of Wisconsin School of Medicine and Public Health, Madison; Illumavista Biosciences LLC, Madison, WI (C.L.W.); and Biomedical Informatics Research Center, Marshfield Clinics Research Foundation, Marshfield, WI (P.P.).

Correspondence to Jay Yang, MD, PhD, Department of Anesthesiology, University of Wisconsin SMPH, SMI 301, 1300 University Ave, Madison, WI 53706. E-mailJyang75@wisc.edu

Abstract

Background—Degradation of extracellular matrix support in the large abdominal arteries contribute to abnormal dilation of aorta, leading to abdominal aortic aneurysms, and matrix metalloproteinase-9 (MMP-9) is the predominant enzyme targeting elastin and collagen present in the walls of the abdominal aorta. Previous studies have suggested a potential association between MMP-9 genotype and abdominal aortic aneurysm, but these studies have been limited only to the p-1562 and (CA) dinucleotide repeat microsatellite polymorphisms in the promoter region of the MMP-9 gene. We determined the functional alterations caused by 15 MMP-9 single-nucleotide polymorphisms (SNPs) reported to be relatively abundant in the human genome through Western blots, gelatinase, and promoter–reporter assays and incorporated this information to perform a logistic-regression analysis of MMP-9 SNPs in 336 human abdominal aortic aneurysm cases and controls.

Methods and Results—Significant functional alterations were observed for 6 exon SNPs and 4 promoter SNPs. Genotype analysis of frequency-matched (age, sex, history of hypertension, hypercholesterolemia, and smoking) cases and controls revealed significant genetic heterogeneity exceeding 20% observed for 6 SNPs in our population of mostly white subjects from Northern Wisconsin. A step-wise logistic-regression analysis with 6 functional SNPs, where weakly contributing confounds were eliminated using Akaike information criteria, gave a final 2 SNP (D165N and p-2502) model with an overall odds ratio of 2.45 (95% confidence interval, 1.06–5.70).

Conclusions—The combined approach of direct experimental confirmation of the functional alterations of MMP-9 SNPs and logistic-regression analysis revealed significant association between MMP-9 genotype and abdominal aortic aneurysm.

SOURCE:

Circulation: Cardiovascular Genetics.2012; 5: 529-537

Published online before print August 31, 2012,

doi: 10.1161/ CIRCGENETICS.112.963082

 

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Genomics of Incident Ischemic Stroke Events, Stroke and Cardiovascular Disease

Posted in Atherogenic Processes & Pathology, Cardiovascular Research, Cerebrovascular and Neurodegenerative Diseases, Chemical Genetics, Coagulation Therapy and Internal Bleeding, Computational Biology/Systems and Bioinformatics, Frontiers in Cardiology and Cardiovascular Disorders, Genome Biology, Genomic Testing: Methodology for Diagnosis, HTN, Origins of Cardiovascular Disease, Personalized and Precision Medicine & Genomic Research, Statistical Methods for Research Evaluation, tagged , , , on December 30, 2013| Leave a Comment »

Genomics of Incident Ischemic Stroke Events, Stroke and Cardiovascular Disease

Reporter: Aviva Lev-Ari, PhD, RN

 

Associations Between Incident Ischemic Stroke Events and Stroke and Cardiovascular Disease-Related Genome-Wide Association Studies Single Nucleotide Polymorphisms in the Population Architecture Using Genomics and Epidemiology Study

Cara L. Carty, PhD, Petra Bůžková, PhD, Myriam Fornage, PhD, Nora Franceschini, MD, Shelley Cole, PhD, Gerardo Heiss, MD, PhD, Lucia A. Hindorff, PhD, MPH, Barbara V. Howard, PhD, Sue Mann, MPH, Lisa W. Martin, MD, Ying Zhang, PhD, Tara C. Matise, PhD, Ross Prentice, PhD, Alexander P. Reiner, MD, MS and Charles Kooperberg, PhD

Author Affiliations

From the Public Health Sciences, Fred Hutchinson Cancer Research Center (C.L.C., S.M., R.P., C.K.); Department of Biostatistics, University of Washington, Seattle, WA (P.B.); Institute of Molecular Medicine, University of Texas Health Sciences Center at Houston, Houston, TX (M.F.); Division of Epidemiology, School of Public Health, University of Texas Health Sciences Center, Houston, TX (M.F.); Department of Epidemiology, University of North Carolina, Chapel Hill, NC (N.F., G.H.); Department of Genetics, Texas Biomedical Research Institute, San Antonio, TX (S.C.); Office of Population Genomics, National Human Genome Research Institute, Bethesda, MD (L.A.H.); Medstar Health Research Institute, Washington, DC (B.V.H.); George Washington University School of Medicine, Washington, DC (B.V.H., L.W.M.); University of Oklahoma Health Sciences Center, Oklahoma City, OK (Y.Z.); Department of Genetics, Rutgers University, Piscataway, NJ (T.C.M.); Department of Epidemiology, University of Washington, Seattle, WA (A.P.R.).

Correspondence to Dr Cara L. Carty, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N./M3-A410, Seattle, WA 98109. E-mail ccarty@fhcrc.org

Abstract

Background—Genome-wide association studies (GWAS) have identified loci associated with ischemic stroke (IS) and cardiovascular disease (CVD) in European-descent individuals, but their replication in different populations has been largely unexplored.

Methods and Results—Nine single nucleotide polymorphisms (SNPs) selected from GWAS and meta-analyses of stroke, and 86 SNPs previously associated with myocardial infarction and CVD risk factors, including blood lipids (high density lipoprotein [HDL], low density lipoprotein [LDL], and triglycerides), type 2 diabetes, and body mass index (BMI), were investigated for associations with incident IS in European Americans (EA) N=26 276, African-Americans (AA) N=8970, and American Indians (AI) N=3570 from the Population Architecture using Genomicsand Epidemiology Study. Ancestry-specific fixed effects meta-analysis with inverse variance weighting was used to combine study-specific log hazard ratios from Cox proportional hazards models. Two of 9 stroke SNPs (rs783396 and rs1804689) were associated with increased IS hazard in AA; none were significant in this large EA cohort. Of 73 CVD risk factor SNPs tested in EA, 2 (HDL and triglycerides SNPs) were associated with IS. In AA, SNPs associated with LDL, HDL, and BMI were significantly associated with IS (3 of 86 SNPs tested). Out of 58 SNPs tested in AI, 1 LDL SNP was significantly associated with IS.

Conclusions—Our analyses showing lack of replication in spite of reasonable power for many stroke SNPs and differing results by ancestry highlight the need to follow up on GWAS findings and conduct genetic association studies in diverse populations. We found modest IS associations with BMI and lipids SNPs, though these findings require confirmation.

SOURCE:

Circulation: Cardiovascular Genetics.2012; 5: 210-216

 

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Strong Lipid Gene Contribution But No Evidence for Common Genetic Basis for Clustering of Metabolic Syndrome Traits: Genome-Wide Screen for Metabolic Syndrome Susceptibility Loci Reveals

Posted in Atherogenic Processes & Pathology, Biological Networks, Gene Regulation and Evolution, Biomarkers & Medical Diagnostics, Biomedical Measurement Science, Cell Biology, Signaling & Cell Circuits, Chemical Biology and its relations to Metabolic Disease, Chemical Genetics, Computational Biology/Systems and Bioinformatics, Genome Biology, Genomic Testing: Methodology for Diagnosis, HTN, Origins of Cardiovascular Disease, Personalized and Precision Medicine & Genomic Research on December 30, 2013| Leave a Comment »

Strong Lipid Gene Contribution But No Evidence for Common Genetic Basis for Clustering of Metabolic Syndrome Traits: Genome-Wide Screen for Metabolic Syndrome Susceptibility Loci Reveals

Reporter: Aviva Lev-Ari, PhD, RN

Genome-Wide Screen for Metabolic Syndrome Susceptibility Loci Reveals Strong Lipid Gene Contribution But No Evidence for Common Genetic Basis for Clustering of Metabolic Syndrome Traits

Kati Kristiansson, PhD, Markus Perola, MD, PhD, Emmi Tikkanen, MSc, Johannes Kettunen, PhD, Ida Surakka, MSc, Aki S. Havulinna, DSc (Tech.), Alena Stančáková, MD, PhD, Chris Barnes, PhD, Elisabeth Widen, MD, PhD, Eero Kajantie, MD, PhD,Johan G. Eriksson, MD, DMSc, Jorma Viikari, MD, PhD, Mika Kähönen, MD, PhD,Terho Lehtimäki, MD, PhD, Olli T. Raitakari, MD, PhD, Anna-Liisa Hartikainen, MD, PhD, Aimo Ruokonen, MD, PhD, Anneli Pouta, MD, PhD, Antti Jula, MD, PhD, Antti J. Kangas, MSc, Pasi Soininen, PhD, Mika Ala-Korpela, PhD, Satu Männistö, PhD, Pekka Jousilahti, MD, PhD, Lori L. Bonnycastle, PhD, Marjo-Riitta Järvelin, MD, PhD,Johanna Kuusisto, MD, PhD, Francis S. Collins, MD, PhD, Markku Laakso, MD, PhD,Matthew E. Hurles, PhD, Aarno Palotie, MD, PhD, Leena Peltonen, MD, PhD*, Samuli Ripatti, PhD and Veikko Salomaa, MD, PhD

Correspondence to Dr Kati Kristiansson, National Institute for Health and Welfare, University of Helsinki, Biomedicum, PL 104, FI-00251 Helsinki, Finland. E-mailkati.kristiansson@thl.fi

Abstract

Background—Genome-wide association (GWA) studies have identified several susceptibility loci for metabolic syndrome (MetS) component traits, but have had variable success in identifying susceptibility loci to the syndrome as an entity. We conducted a GWA study on MetS and its component traits in 4 Finnish cohorts consisting of 2637 MetS cases and 7927 controls, both free of diabetes, and followed the top loci in an independent sample with transcriptome and nuclear magnetic resonance-based metabonomics data. Furthermore, we tested for loci associated with multiple MetS component traits using factor analysis, and built a genetic risk score for MetS.

Methods and Results—A previously known lipid locus, APOA1/C3/A4/A5 gene cluster region (SNP rs964184), was associated with MetS in all 4 study samples (P=7.23×10−9 in meta-analysis). The association was further supported by serum metabolite analysis, where rs964184 was associated with various very low density lipoprotein, triglyceride, and high-density lipoprotein metabolites (P=0.024–1.88×10−5). Twenty-two previously identified susceptibility loci for individual MetS component traits were replicated in our GWA and factor analysis. Most of these were associated with lipid phenotypes, and none with 2 or more uncorrelated MetS components. A genetic risk score, calculated as the number of risk alleles in loci associated with individual MetS traits, was strongly associated with MetS status.

Conclusions—Our findings suggest that genes from lipid metabolism pathways have the key role in the genetic background of MetS. We found little evidence for pleiotropy linking dyslipidemia and obesity to the other MetS component traits, such as hypertension and glucose intolerance.

 SOURCE:

Circulation: Cardiovascular Genetics.2012; 5: 242-249

Published online before print March 7, 2012,

doi: 10.1161/ CIRCGENETICS.111.961482

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