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  1. Article ; Online: Introduction to the Compendium on Atrial Fibrillation, and a Few Thoughts Along The Way….

    Ellinor, Patrick T

    Circulation research

    2020  Volume 127, Issue 1, Page(s) 1–3

    MeSH term(s) Animals ; Atrial Fibrillation/genetics ; Atrial Fibrillation/metabolism ; Atrial Fibrillation/physiopathology ; Atrial Fibrillation/therapy ; Humans
    Language English
    Publishing date 2020-06-18
    Publishing country United States
    Document type Editorial ; Introductory Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 80100-8
    ISSN 1524-4571 ; 0009-7330 ; 0931-6876
    ISSN (online) 1524-4571
    ISSN 0009-7330 ; 0931-6876
    DOI 10.1161/CIRCRESAHA.120.317516
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  2. Article ; Online: Atrial Fibrillation in Patients With Cancer: A Persistent and Increasing Challenge.

    Suero-Abreu, Giselle Alexandra / Ellinor, Patrick T

    JACC. CardioOncology

    2023  Volume 5, Issue 2, Page(s) 230–232

    Language English
    Publishing date 2023-04-18
    Publishing country United States
    Document type Editorial
    ISSN 2666-0873
    ISSN (online) 2666-0873
    DOI 10.1016/j.jaccao.2023.03.007
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  3. Article ; Online: Putting a finer point on it-Resolving QT loci using multiethnic studies.

    Ellinor, Patrick T

    Heart rhythm

    2017  Volume 14, Issue 4, Page(s) 581–582

    MeSH term(s) Electrocardiography ; Genome-Wide Association Study ; Humans ; Long QT Syndrome
    Language English
    Publishing date 2017-01-07
    Publishing country United States
    Document type Editorial ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Comment
    ZDB-ID 2229357-7
    ISSN 1556-3871 ; 1547-5271
    ISSN (online) 1556-3871
    ISSN 1547-5271
    DOI 10.1016/j.hrthm.2017.01.007
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  4. Article ; Online: Using Genomics to Identify Novel Therapeutic Targets for Aortic Disease.

    Raghavan, Avanthi / Pirruccello, James P / Ellinor, Patrick T / Lindsay, Mark E

    Arteriosclerosis, thrombosis, and vascular biology

    2023  Volume 44, Issue 2, Page(s) 334–351

    Abstract: Aortic disease, including dissection, aneurysm, and rupture, carries significant morbidity and mortality and is a notable cause of sudden cardiac death. Much of our knowledge regarding the genetic basis of aortic disease has relied on the study of ... ...

    Abstract Aortic disease, including dissection, aneurysm, and rupture, carries significant morbidity and mortality and is a notable cause of sudden cardiac death. Much of our knowledge regarding the genetic basis of aortic disease has relied on the study of individuals with Mendelian aortopathies and, until recently, the genetic determinants of population-level variance in aortic phenotypes remained unclear. However, the application of machine learning methodologies to large imaging datasets has enabled researchers to rapidly define aortic traits and mine dozens of novel genetic associations for phenotypes such as aortic diameter and distensibility. In this review, we highlight the emerging potential of genomics for identifying causal genes and candidate drug targets for aortic disease. We describe how deep learning technologies have accelerated the pace of genetic discovery in this field. We then provide a blueprint for translating genetic associations to biological insights, reviewing techniques for locus and cell type prioritization, high-throughput functional screening, and disease modeling using cellular and animal models of aortic disease.
    MeSH term(s) Animals ; Humans ; Genomics/methods ; Aortic Diseases/genetics ; Aortic Dissection/genetics ; Phenotype ; Aortic Aneurysm, Thoracic/genetics
    Language English
    Publishing date 2023-12-14
    Publishing country United States
    Document type Journal Article ; Review ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 1221433-4
    ISSN 1524-4636 ; 1079-5642
    ISSN (online) 1524-4636
    ISSN 1079-5642
    DOI 10.1161/ATVBAHA.123.318771
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  5. Article ; Online: Healthcare Resource Utilization Following Single-lead Electrocardiogram Screening for Atrial Fibrillation in Older Individuals at Primary Care Visits.

    Atlas, Steven J / Borowsky, Leila H / Chang, Yuchiao / Ashburner, Jeffrey M / Ellinor, Patrick T / Lubitz, Steven A / Singer, Daniel E

    Journal of general internal medicine

    2024  

    Language English
    Publishing date 2024-04-02
    Publishing country United States
    Document type Letter
    ZDB-ID 639008-0
    ISSN 1525-1497 ; 0884-8734
    ISSN (online) 1525-1497
    ISSN 0884-8734
    DOI 10.1007/s11606-024-08733-7
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  6. Article ; Online: Per-Particle Cardiovascular Risk of Lipoprotein(a) vs Non-Lp(a) Apolipoprotein B-Containing Lipoproteins.

    Marston, Nicholas A / Melloni, Giorgio E M / Murphy, Sabina A / Morze, Jakub / Kamanu, Frederick K / Ellinor, Patrick T / Ruff, Christian T / Sabatine, Marc S

    Journal of the American College of Cardiology

    2024  Volume 83, Issue 3, Page(s) 470–472

    MeSH term(s) Humans ; Lipoprotein(a) ; Cardiovascular Diseases/epidemiology ; Cardiovascular Diseases/etiology ; Risk Factors ; Apolipoproteins B
    Chemical Substances Lipoprotein(a) ; Apolipoproteins B
    Language English
    Publishing date 2024-01-17
    Publishing country United States
    Document type Letter
    ZDB-ID 605507-2
    ISSN 1558-3597 ; 0735-1097
    ISSN (online) 1558-3597
    ISSN 0735-1097
    DOI 10.1016/j.jacc.2023.09.836
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    Zs.MO 196: Show issues

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  7. Article: Identification of a new Corin atrial natriuretic peptide-converting enzyme substrate: Agouti-signaling protein (ASIP).

    MacDonald, Bryan T / Elowe, Nadine H / Garvie, Colin W / Kaushik, Virendar K / Ellinor, Patrick T

    bioRxiv : the preprint server for biology

    2023  

    Abstract: Corin is a transmembrane tethered enzyme best known for processing the hormone atrial natriuretic peptide (ANP) in cardiomyocytes to control electrolyte balance and blood pressure. Loss of function mutations in Corin prevent ANP processing and lead to ... ...

    Abstract Corin is a transmembrane tethered enzyme best known for processing the hormone atrial natriuretic peptide (ANP) in cardiomyocytes to control electrolyte balance and blood pressure. Loss of function mutations in Corin prevent ANP processing and lead to hypertension. Curiously, Corin loss of function variants also result in lighter coat color pigmentation in multiple species. Corin pigmentation effects are dependent on a functional Agouti locus encoding the agouti-signaling protein (ASIP) based on a genetic interaction. However, the nature of this conserved role of Corin has not been defined. Here we report that ASIP is a direct proteolytic substrate of the Corin enzyme.
    Language English
    Publishing date 2023-04-27
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2023.04.26.538495
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  8. Article ; Online: Bacon: a comprehensive computational benchmarking framework for evaluating targeted chromatin conformation capture-specific methodologies.

    Tang, Li / Hill, Matthew C / Ellinor, Patrick T / Li, Min

    Genome biology

    2022  Volume 23, Issue 1, Page(s) 30

    Abstract: Chromatin conformation capture (3C)-based technologies have enabled the accurate detection of topological genomic interactions, and the adoption of ChIP techniques to 3C-based protocols makes it possible to identify long-range interactions. To analyze ... ...

    Abstract Chromatin conformation capture (3C)-based technologies have enabled the accurate detection of topological genomic interactions, and the adoption of ChIP techniques to 3C-based protocols makes it possible to identify long-range interactions. To analyze these large and complex datasets, computational methods are undergoing rapid and expansive evolution. Thus, a thorough evaluation of these analytical pipelines is necessary to identify which commonly used algorithms and processing pipelines need to be improved. Here we present a comprehensive benchmark framework, Bacon, to evaluate the performance of several computational methods. Finally, we provide practical recommendations for users working with HiChIP and/or ChIA-PET analyses.
    MeSH term(s) Benchmarking ; Chromatin/genetics ; Chromatin Immunoprecipitation Sequencing ; Chromosomes ; Pork Meat
    Chemical Substances Chromatin
    Language English
    Publishing date 2022-01-21
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 2040529-7
    ISSN 1474-760X ; 1474-760X
    ISSN (online) 1474-760X
    ISSN 1474-760X
    DOI 10.1186/s13059-021-02597-4
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  9. Article ; Online: MMCT-Loop: a mix model-based pipeline for calling targeted 3D chromatin loops.

    Tang, Li / Liao, Jiaqi / Hill, Matthew C / Hu, Jiaxin / Zhao, Yichao / Ellinor, Patrick T / Li, Min

    Nucleic acids research

    2024  Volume 52, Issue 5, Page(s) e25

    Abstract: Protein-specific Chromatin Conformation Capture (3C)-based technologies have become essential for identifying distal genomic interactions with critical roles in gene regulation. The standard techniques include Chromatin Interaction Analysis by Paired-End ...

    Abstract Protein-specific Chromatin Conformation Capture (3C)-based technologies have become essential for identifying distal genomic interactions with critical roles in gene regulation. The standard techniques include Chromatin Interaction Analysis by Paired-End Tag (ChIA-PET), in situ Hi-C followed by chromatin immunoprecipitation (HiChIP) also known as PLAC-seq. To identify chromatin interactions from these data, a variety of computational methods have emerged. Although these state-of-art methods address many issues with loop calling, only few methods can fit different data types simultaneously, and the accuracy as well as the efficiency these approaches remains limited. Here we have generated a pipeline, MMCT-Loop, which ensures the accurate identification of strong loops as well as dynamic or weak loops through a mixed model. MMCT-Loop outperforms existing methods in accuracy, and the detected loops show higher activation functionality. To highlight the utility of MMCT-Loop, we applied it to conformational data derived from neural stem cell (NSCs) and uncovered several previously unidentified regulatory regions for key master regulators of stem cell identity. MMCT-Loop is an accurate and efficient loop caller for targeted conformation capture data, which supports raw data or pre-processed valid pairs as input, the output interactions are formatted and easily uploaded to a genome browser for visualization.
    MeSH term(s) Chromatin/chemistry ; Chromatin/genetics ; Chromatin Immunoprecipitation/methods ; Chromosomes ; Genome ; Genomics/methods ; Genetic Techniques
    Chemical Substances Chromatin
    Language English
    Publishing date 2024-01-28
    Publishing country England
    Document type Journal Article
    ZDB-ID 186809-3
    ISSN 1362-4962 ; 1362-4954 ; 0301-5610 ; 0305-1048
    ISSN (online) 1362-4962 ; 1362-4954
    ISSN 0301-5610 ; 0305-1048
    DOI 10.1093/nar/gkae029
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    Zs.A 1067: Show issues Location:
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  10. Article ; Online: Genetics and mechanisms of thoracic aortic disease.

    Chou, Elizabeth / Pirruccello, James P / Ellinor, Patrick T / Lindsay, Mark E

    Nature reviews. Cardiology

    2022  Volume 20, Issue 3, Page(s) 168–180

    Abstract: Aortic disease has many forms including aortic aneurysm and dissection, aortic coarctation or abnormalities in aortic function, such as loss of aortic distensibility. Genetic analysis in humans is one of the most important experimental approaches in ... ...

    Abstract Aortic disease has many forms including aortic aneurysm and dissection, aortic coarctation or abnormalities in aortic function, such as loss of aortic distensibility. Genetic analysis in humans is one of the most important experimental approaches in uncovering disease mechanisms, but the relative infrequency of thoracic aortic disease compared with other cardiovascular conditions such as coronary artery disease has hindered large-scale identification of genetic associations. In the past decade, advances in machine learning technology coupled with large imaging datasets from biobank repositories have facilitated a rapid expansion in our capacity to measure and genotype aortic traits, resulting in the identification of dozens of genetic associations. In this Review, we describe the history of technological advances in genetic discovery and explain how newer technologies such as deep learning can rapidly define aortic traits at scale. Furthermore, we integrate novel genetic observations provided by these advances into our current biological understanding of thoracic aortic disease and describe how these new findings can contribute to strategies to prevent and treat aortic disease.
    MeSH term(s) Humans ; Aortic Aneurysm, Thoracic/genetics ; Aortic Aneurysm, Thoracic/therapy ; Aortic Diseases/genetics ; Aorta ; Aortic Coarctation ; Aortic Aneurysm
    Language English
    Publishing date 2022-09-21
    Publishing country England
    Document type Journal Article ; Review ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 2490375-9
    ISSN 1759-5010 ; 1759-5002
    ISSN (online) 1759-5010
    ISSN 1759-5002
    DOI 10.1038/s41569-022-00763-0
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